# from tvm.script import ir as I # from tvm.script import tir as T # from tvm.script import relax as R @I.ir_module class Module: I.module_attrs({"external_mods": [metadata["runtime.Module"][0], metadata["runtime.Module"][1], metadata["runtime.Module"][2], metadata["runtime.Module"][3], metadata["runtime.Module"][4], metadata["runtime.Module"][5], metadata["runtime.Module"][6], metadata["runtime.Module"][7], metadata["runtime.Module"][8], metadata["runtime.Module"][9], metadata["runtime.Module"][10], metadata["runtime.Module"][11], metadata["runtime.Module"][12], metadata["runtime.Module"][13], metadata["runtime.Module"][14]]}) @T.prim_func(private=True) def NT_matmul(layer_norm356: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), model_decoder_layers_0_self_attn_q_proj_weight5: T.Buffer((T.int64(1280), T.int64(1280)), "float16"), NT_matmul: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): T.func_attr({"tir.noalias": T.bool(True)}) # with T.block("root"): for i0, i1, i2, k in T.grid(T.int64(1), T.int64(1), T.int64(1280), T.int64(1280)): with T.block("NT_matmul"): v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k]) T.reads(layer_norm356[v_i0, v_i1, v_k], model_decoder_layers_0_self_attn_q_proj_weight5[v_i2, v_k]) T.writes(NT_matmul[v_i0, v_i1, v_i2]) with T.init(): NT_matmul[v_i0, v_i1, v_i2] = T.float16(0) NT_matmul[v_i0, v_i1, v_i2] = NT_matmul[v_i0, v_i1, v_i2] + layer_norm356[v_i0, v_i1, v_k] * model_decoder_layers_0_self_attn_q_proj_weight5[v_i2, v_k] @T.prim_func(private=True) def NT_matmul1(layer_norm358: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), model_decoder_layers_0_fc1_weight5: T.Buffer((T.int64(5120), T.int64(1280)), "float16"), NT_matmul: T.Buffer((T.int64(1), T.int64(1), T.int64(5120)), "float16")): T.func_attr({"tir.noalias": T.bool(True)}) # with T.block("root"): for i0, i1, i2, k in T.grid(T.int64(1), T.int64(1), T.int64(5120), T.int64(1280)): with T.block("NT_matmul"): v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k]) T.reads(layer_norm358[v_i0, v_i1, v_k], model_decoder_layers_0_fc1_weight5[v_i2, v_k]) T.writes(NT_matmul[v_i0, v_i1, v_i2]) with T.init(): NT_matmul[v_i0, v_i1, v_i2] = T.float16(0) NT_matmul[v_i0, v_i1, v_i2] = NT_matmul[v_i0, v_i1, v_i2] + layer_norm358[v_i0, v_i1, v_k] * model_decoder_layers_0_fc1_weight5[v_i2, v_k] @T.prim_func(private=True) def NT_matmul2(gelu130: T.Buffer((T.int64(1), T.int64(1), T.int64(5120)), "float16"), model_decoder_layers_0_fc2_weight5: T.Buffer((T.int64(1280), T.int64(5120)), "float16"), NT_matmul: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): T.func_attr({"tir.noalias": T.bool(True)}) # with T.block("root"): for i0, i1, i2, k in T.grid(T.int64(1), T.int64(1), T.int64(1280), T.int64(5120)): with T.block("NT_matmul"): v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k]) T.reads(gelu130[v_i0, v_i1, v_k], model_decoder_layers_0_fc2_weight5[v_i2, v_k]) T.writes(NT_matmul[v_i0, v_i1, v_i2]) with T.init(): NT_matmul[v_i0, v_i1, v_i2] = T.float16(0) NT_matmul[v_i0, v_i1, v_i2] = NT_matmul[v_i0, v_i1, v_i2] + gelu130[v_i0, v_i1, v_k] * model_decoder_layers_0_fc2_weight5[v_i2, v_k] @T.prim_func(private=True) def NT_matmul3(layer_norm452: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16"), model_decoder_embed_tokens_weight5: T.Buffer((T.int64(51866), T.int64(1280)), "float16"), NT_matmul: T.Buffer((T.int64(1), T.int64(1), T.int64(51866)), "float32")): T.func_attr({"tir.noalias": T.bool(True)}) # with T.block("root"): for i0, i1, i2, k in T.grid(T.int64(1), T.int64(1), T.int64(51866), T.int64(1280)): with T.block("NT_matmul"): v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k]) T.reads(layer_norm452[v_i0, v_i1, v_k], model_decoder_embed_tokens_weight5[v_i2, v_k]) T.writes(NT_matmul[v_i0, v_i1, v_i2]) with T.init(): NT_matmul[v_i0, v_i1, v_i2] = T.float32(0) NT_matmul[v_i0, v_i1, v_i2] = NT_matmul[v_i0, v_i1, v_i2] + T.Cast("float32", layer_norm452[v_i0, v_i1, v_k]) * T.Cast("float32", model_decoder_embed_tokens_weight5[v_i2, v_k]) @T.prim_func def apply_bitmask_inplace(var_logits: T.handle, var_seq_ids: T.handle, var_bitmask: T.handle): T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": T.bool(True), "tir.noalias": T.bool(True)}) batch_size, vocab_size = T.int32(is_size_var=True), T.int32(is_size_var=True) logits = T.match_buffer(var_logits, (batch_size, vocab_size)) num_seq = T.int32(is_size_var=True) seq_ids = T.match_buffer(var_seq_ids, (num_seq,), "int32") bitmask = T.match_buffer(var_bitmask, (batch_size, (vocab_size + 31) // 32), "int32") # with T.block("root"): for fused_s_v_0 in T.thread_binding((num_seq * vocab_size + 1023) // 1024, thread="blockIdx.x"): for fused_s_v_1 in T.thread_binding(1024, thread="threadIdx.x"): with T.block("block"): vs = T.axis.spatial(num_seq, (fused_s_v_0 * 1024 + fused_s_v_1) // vocab_size) vv = T.axis.spatial(vocab_size, (fused_s_v_0 * 1024 + fused_s_v_1) % vocab_size) T.where(fused_s_v_0 * 1024 + fused_s_v_1 < num_seq * vocab_size) T.reads(bitmask[seq_ids[vs], vv // 32], seq_ids[vs], logits[seq_ids[vs], vv]) T.writes(logits[seq_ids[vs], vv]) logits[seq_ids[vs], vv] = T.if_then_else(T.bitwise_and(T.shift_right(bitmask[seq_ids[vs], vv // 32], vv % 32), 1) == 1, logits[seq_ids[vs], vv], T.float32(-3.4028234663852886e+38)) @T.prim_func def apply_logit_bias_inplace(var_logits: T.handle, var_pos2seq_id: T.handle, var_token_ids: T.handle, var_logit_bias: T.handle): T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": T.bool(True), "tir.noalias": T.bool(True)}) batch_size, vocab_size = T.int32(is_size_var=True), T.int32(is_size_var=True) logits = T.match_buffer(var_logits, (batch_size, vocab_size)) num_token = T.int32(is_size_var=True) pos2seq_id = T.match_buffer(var_pos2seq_id, (num_token,), "int32") token_ids = T.match_buffer(var_token_ids, (num_token,), "int32") logit_bias = T.match_buffer(var_logit_bias, (num_token,)) # with T.block("root"): for p0 in T.thread_binding((num_token + 1023) // 1024, thread="blockIdx.x"): for p1 in T.thread_binding(1024, thread="threadIdx.x"): with T.block("block"): vp = T.axis.spatial(num_token, p0 * 1024 + p1) T.where(p0 * 1024 + p1 < num_token) T.reads(logits[pos2seq_id[vp], token_ids[vp]], pos2seq_id[vp], token_ids[vp], logit_bias[vp]) T.writes(logits[pos2seq_id[vp], token_ids[vp]]) logits[pos2seq_id[vp], token_ids[vp]] = logits[pos2seq_id[vp], token_ids[vp]] + logit_bias[vp] @T.prim_func def apply_penalty_inplace(var_logits: T.handle, var_seq_ids: T.handle, var_pos2seq_id: T.handle, var_token_ids: T.handle, var_token_cnt: T.handle, var_penalties: T.handle): T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": T.bool(True), "tir.noalias": T.bool(True)}) batch_size, vocab_size = T.int32(is_size_var=True), T.int32(is_size_var=True) logits = T.match_buffer(var_logits, (batch_size, vocab_size)) num_seq = T.int32(is_size_var=True) seq_ids = T.match_buffer(var_seq_ids, (num_seq,), "int32") num_token = T.int32(is_size_var=True) pos2seq_id = T.match_buffer(var_pos2seq_id, (num_token,), "int32") token_ids = T.match_buffer(var_token_ids, (num_token,), "int32") token_cnt = T.match_buffer(var_token_cnt, (num_token,), "int32") penalties = T.match_buffer(var_penalties, (num_seq, 3)) # with T.block("root"): for p0 in T.thread_binding((num_token + 1023) // 1024, thread="blockIdx.x"): for p1 in T.thread_binding(1024, thread="threadIdx.x"): with T.block("block"): vp = T.axis.spatial(num_token, p0 * 1024 + p1) T.where(p0 * 1024 + p1 < num_token) T.reads(logits[seq_ids[pos2seq_id[vp]], token_ids[vp]], seq_ids[pos2seq_id[vp]], pos2seq_id[vp], token_ids[vp], penalties[pos2seq_id[vp], 0:3], token_cnt[vp]) T.writes(logits[seq_ids[pos2seq_id[vp]], token_ids[vp]]) logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] = logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] - (penalties[pos2seq_id[vp], 0] + T.Cast("float32", token_cnt[vp]) * penalties[pos2seq_id[vp], 1]) logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] = T.if_then_else(logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] > T.float32(0), logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] * penalties[pos2seq_id[vp], 2], logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] / penalties[pos2seq_id[vp], 2]) @T.prim_func def batch_decode_paged_kv(_0: T.int32, Q_handle: T.handle, pages_handle: T.handle, page_table_indptr_handle: T.handle, page_table_values_handle: T.handle, var_length_info: T.handle, k_rope_pos_offset_handle: T.handle, q_rope_position_handle: T.handle, output_handle: T.handle, lse_handle: T.handle, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) B = T.int32(is_size_var=True) Q = T.match_buffer(Q_handle, (B, 20, 64), "float16") max_num_pages = T.int32(is_size_var=True) pages = T.match_buffer(pages_handle, (max_num_pages, 2, 20, 16, 64), "float16") page_table_indptr = T.match_buffer(page_table_indptr_handle, (B + 1,), "int32", offset_factor=1) nnz_pages = T.int32(is_size_var=True) page_table_values = T.match_buffer(page_table_values_handle, (nnz_pages,), "int32", offset_factor=1) length_info = T.match_buffer(var_length_info, (B,), "int32", offset_factor=1) k_rope_pos_offset = T.match_buffer(k_rope_pos_offset_handle, (B,), "int32", offset_factor=1) q_rope_position = T.match_buffer(q_rope_position_handle, (B,), "int32", offset_factor=1) output = T.match_buffer(output_handle, (B, 20, 64), "float16") lse = T.match_buffer(lse_handle, (B, 20)) # with T.block("root"): sm_scale: T.float32 = T.float32(0.18033688011112042) for bx in T.thread_binding(B, thread="blockIdx.x"): for fused_by_bz in T.thread_binding(20, thread="blockIdx.y"): for ty in T.thread_binding(1, thread="threadIdx.y"): for tx in T.thread_binding(16, thread="threadIdx.x"): for tz in T.thread_binding(32, thread="threadIdx.z"): with T.block("attn"): T.reads(page_table_indptr[bx:bx + 2], length_info[bx], q_rope_position[bx], Q[bx, fused_by_bz // 20 + ty + fused_by_bz % 20, tx * 4 - 32:tx * 4 - 32 + 68]) T.writes(output[bx, fused_by_bz % 20 + fused_by_bz // 20 + ty, tx * 4:tx * 4 + 4], lse[bx, fused_by_bz % 20 + fused_by_bz // 20 + ty]) Q_local = T.alloc_buffer((4,), "float16", scope="local") kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") K_smem = T.alloc_buffer((64, 64), "float16", scope="shared") V_smem = T.alloc_buffer((64, 64), "float16", scope="shared") O_allreduce = T.alloc_buffer((32, 1, 64), scope="shared") md_allreduce = T.alloc_buffer((32, 1, 2), scope="shared") S_reduce_local = T.alloc_buffer((1,), scope="local") t0 = T.alloc_buffer((1,), scope="local") S_local = T.alloc_buffer((2,), scope="local") QK_local = T.alloc_buffer((4,), scope="local") V_local = T.alloc_buffer((4,), "float16", scope="local") m_prev = T.alloc_buffer((1,), scope="local") d_prev = T.alloc_buffer((1,), scope="local") other_m = T.alloc_buffer((1,), scope="local") other_d = T.alloc_buffer((1,), scope="local") exp_mprev = T.alloc_buffer((1,), scope="local") exp_otherm = T.alloc_buffer((1,), scope="local") other_o = T.alloc_buffer((4,), scope="local") st_m = T.alloc_buffer((1,), scope="local") st_d = T.alloc_buffer((1,), scope="local") O_local = T.alloc_buffer((4,), scope="local") by: T.int32 = fused_by_bz % 20 bz: T.int32 = fused_by_bz // 20 batch_idx: T.int32 = bx cur_page_indptr_begin: T.int32 = page_table_indptr[batch_idx] cur_page_indptr_end: T.int32 = page_table_indptr[batch_idx + 1] kv_chunk_len[0] = T.if_then_else(cur_page_indptr_begin != cur_page_indptr_end, (cur_page_indptr_end - cur_page_indptr_begin - 1) * 16 + length_info[batch_idx], 0) st_m[0] = T.float32(-50000) st_d[0] = T.float32(1) for vec in T.vectorized(4): O_local[vec] = T.float32(0) for vec in T.vectorized(4): Q_local[vec] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[batch_idx]) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", Q[bx, by + bz + ty, tx * 4 + vec]) + T.sin(T.Cast("float32", q_rope_position[batch_idx]) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(tx * 4 + vec < 32, Q[bx, by + bz + ty, tx * 4 + vec + 32] * T.float16(-1), Q[bx, by + bz + ty, tx * 4 + vec - 32]))), Q[bx, by + bz + ty, tx * 4 + vec]) for iterator in range((kv_chunk_len[0] + 63) // 64): tile_start_s: T.int32 = (tz + ty) * 2 tile_start_g: T.int32 = (iterator * 32 + tz + ty) * 2 for j in range(2): with T.block("KV_load"): T.reads() T.writes() row_g: T.int32 = tile_start_g + j if row_g < kv_chunk_len[0]: seq_offset: T.int32 = row_g page_no: T.int32 = page_table_values[cur_page_indptr_begin + seq_offset // 16] page_offset: T.int32 = seq_offset % 16 for vec in T.vectorized(4): K_smem[tile_start_s + j, tx * 4 + vec] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[batch_idx] + row_g) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", pages[page_no, 0, by, page_offset, tx * 4 + vec]) + T.sin(T.Cast("float32", k_rope_pos_offset[batch_idx] + row_g) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(tx * 4 + vec < 32, pages[page_no, 0, by, page_offset, tx * 4 + vec + 32] * T.float16(-1), pages[page_no, 0, by, page_offset, tx * 4 + vec - 32]))), pages[page_no, 0, by, page_offset, tx * 4 + vec]) V_smem[tile_start_s + j, tx * 4 + vec] = pages[page_no, 1, by, page_offset, tx * 4 + vec] else: for vec in T.vectorized(4): K_smem[tile_start_s + j, tx * 4 + vec] = T.float16(0) V_smem[tile_start_s + j, tx * 4 + vec] = T.float16(0) T.tvm_storage_sync("shared") m_prev[0] = st_m[0] for j in range(2): for vec in T.vectorized(4): QK_local[vec] = T.Cast("float32", Q_local[vec]) * T.Cast("float32", K_smem[tz * 2 + j, tx * 4 + vec]) * attn_score_scaling_factor * sm_scale S_reduce_local[0] = T.float32(0) for vec in T.unroll(4): S_reduce_local[0] = S_reduce_local[0] + QK_local[vec] with T.block("block_cross_thread"): T.reads(S_reduce_local[0]) T.writes(t0[0]) T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) T.tvm_thread_allreduce(T.uint32(1), S_reduce_local[0], T.bool(True), t0[0], tx) S_local[j] = T.float32(-50000) if (iterator * 32 + tz) * 2 + j < kv_chunk_len[0]: S_local[j] = t0[0] st_m[0] = T.max(st_m[0], S_local[j]) o_scale: T.float32 = T.exp2(m_prev[0] - st_m[0]) st_d[0] = st_d[0] * o_scale for j in range(2): S_local[j] = T.exp2(S_local[j] - st_m[0]) st_d[0] = st_d[0] + S_local[j] for j in T.vectorized(4): O_local[j] = O_local[j] * o_scale for j in range(2): for vec in T.vectorized(4): V_local[vec] = V_smem[tz * 2 + j, tx * 4 + vec] for vec in T.vectorized(4): O_local[vec] = O_local[vec] + T.Cast("float32", V_local[vec]) * S_local[j] for vec in T.vectorized(4): O_allreduce[tz, ty, tx * 4 + vec] = O_local[vec] md_allreduce[tz, ty, 0] = st_m[0] md_allreduce[tz, ty, 1] = st_d[0] T.tvm_storage_sync("shared") st_m[0] = T.float32(-50000) st_d[0] = T.float32(1) for vec in T.vectorized(4): O_local[vec] = T.float32(0) for j in range(32): m_prev[0] = st_m[0] d_prev[0] = st_d[0] other_m[0] = md_allreduce[j, ty, 0] other_d[0] = md_allreduce[j, ty, 1] for vec in T.vectorized(4): other_o[vec] = O_allreduce[j, ty, tx * 4 + vec] st_m[0] = T.max(st_m[0], other_m[0]) st_d[0] = d_prev[0] * T.exp2(m_prev[0] - st_m[0]) + other_d[0] * T.exp2(other_m[0] - st_m[0]) exp_mprev[0] = T.exp2(m_prev[0] - st_m[0]) exp_otherm[0] = T.exp2(other_m[0] - st_m[0]) for vec in T.vectorized(4): O_local[vec] = O_local[vec] * exp_mprev[0] + other_o[vec] * exp_otherm[0] for vec in T.vectorized(4): O_local[vec] = O_local[vec] / st_d[0] for vec in T.vectorized(4): output[batch_idx, by + bz + ty, tx * 4 + vec] = T.Cast("float16", O_local[vec]) lse[batch_idx, by + bz + ty] = st_m[0] + T.log2(st_d[0]) @T.prim_func def batch_decode_paged_kv_sliding_window(_0: T.int32, Q_handle: T.handle, pages_handle: T.handle, page_table_indptr_handle: T.handle, page_table_values_handle: T.handle, var_length_info: T.handle, k_rope_pos_offset_handle: T.handle, q_rope_position_handle: T.handle, output_handle: T.handle, lse_handle: T.handle, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) B = T.int32(is_size_var=True) Q = T.match_buffer(Q_handle, (B, 20, 64), "float16") max_num_pages = T.int32(is_size_var=True) pages = T.match_buffer(pages_handle, (max_num_pages, 2, 20, 16, 64), "float16") page_table_indptr = T.match_buffer(page_table_indptr_handle, (B + 1,), "int32", offset_factor=1) nnz_pages = T.int32(is_size_var=True) page_table_values = T.match_buffer(page_table_values_handle, (nnz_pages,), "int32", offset_factor=1) length_info = T.match_buffer(var_length_info, (3, B), "int32", offset_factor=1) k_rope_pos_offset = T.match_buffer(k_rope_pos_offset_handle, (B,), "int32", offset_factor=1) q_rope_position = T.match_buffer(q_rope_position_handle, (B,), "int32", offset_factor=1) output = T.match_buffer(output_handle, (B, 20, 64), "float16") lse = T.match_buffer(lse_handle, (B, 20)) # with T.block("root"): sm_scale: T.float32 = T.float32(0.18033688011112042) for bx in T.thread_binding(B, thread="blockIdx.x"): for fused_by_bz in T.thread_binding(20, thread="blockIdx.y"): for ty in T.thread_binding(1, thread="threadIdx.y"): for tx in T.thread_binding(16, thread="threadIdx.x"): for tz in T.thread_binding(32, thread="threadIdx.z"): with T.block("attn"): T.reads(page_table_indptr[bx:bx + 2], length_info[0:3, bx], q_rope_position[bx], Q[bx, fused_by_bz // 20 + ty + fused_by_bz % 20, tx * 4 - 32:tx * 4 - 32 + 68]) T.writes(output[bx, fused_by_bz % 20 + fused_by_bz // 20 + ty, tx * 4:tx * 4 + 4], lse[bx, fused_by_bz % 20 + fused_by_bz // 20 + ty]) Q_local = T.alloc_buffer((4,), "float16", scope="local") kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") K_smem = T.alloc_buffer((64, 64), "float16", scope="shared") V_smem = T.alloc_buffer((64, 64), "float16", scope="shared") O_allreduce = T.alloc_buffer((32, 1, 64), scope="shared") md_allreduce = T.alloc_buffer((32, 1, 2), scope="shared") S_reduce_local = T.alloc_buffer((1,), scope="local") t0 = T.alloc_buffer((1,), scope="local") S_local = T.alloc_buffer((2,), scope="local") QK_local = T.alloc_buffer((4,), scope="local") V_local = T.alloc_buffer((4,), "float16", scope="local") m_prev = T.alloc_buffer((1,), scope="local") d_prev = T.alloc_buffer((1,), scope="local") other_m = T.alloc_buffer((1,), scope="local") other_d = T.alloc_buffer((1,), scope="local") exp_mprev = T.alloc_buffer((1,), scope="local") exp_otherm = T.alloc_buffer((1,), scope="local") other_o = T.alloc_buffer((4,), scope="local") st_m = T.alloc_buffer((1,), scope="local") st_d = T.alloc_buffer((1,), scope="local") O_local = T.alloc_buffer((4,), scope="local") by: T.int32 = fused_by_bz % 20 bz: T.int32 = fused_by_bz // 20 batch_idx: T.int32 = bx cur_page_indptr_begin: T.int32 = page_table_indptr[batch_idx] cur_page_indptr_end: T.int32 = page_table_indptr[batch_idx + 1] kv_chunk_len[0] = T.if_then_else(cur_page_indptr_begin != cur_page_indptr_end, (cur_page_indptr_end - cur_page_indptr_begin - 1) * 16 + length_info[0, batch_idx] - length_info[1, batch_idx] + length_info[2, batch_idx], 0) st_m[0] = T.float32(-50000) st_d[0] = T.float32(1) for vec in T.vectorized(4): O_local[vec] = T.float32(0) for vec in T.vectorized(4): Q_local[vec] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[batch_idx]) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", Q[bx, by + bz + ty, tx * 4 + vec]) + T.sin(T.Cast("float32", q_rope_position[batch_idx]) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(tx * 4 + vec < 32, Q[bx, by + bz + ty, tx * 4 + vec + 32] * T.float16(-1), Q[bx, by + bz + ty, tx * 4 + vec - 32]))), Q[bx, by + bz + ty, tx * 4 + vec]) for iterator in range((kv_chunk_len[0] + 63) // 64): tile_start_s: T.int32 = (tz + ty) * 2 tile_start_g: T.int32 = (iterator * 32 + tz + ty) * 2 for j in range(2): with T.block("KV_load"): T.reads() T.writes() row_g: T.int32 = tile_start_g + j if row_g < kv_chunk_len[0]: seq_offset: T.int32 = T.if_then_else(row_g < length_info[2, batch_idx], row_g, row_g - length_info[2, batch_idx] + length_info[1, batch_idx]) page_no: T.int32 = page_table_values[cur_page_indptr_begin + seq_offset // 16] page_offset: T.int32 = seq_offset % 16 for vec in T.vectorized(4): K_smem[tile_start_s + j, tx * 4 + vec] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[batch_idx] + row_g) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", pages[page_no, 0, by, page_offset, tx * 4 + vec]) + T.sin(T.Cast("float32", k_rope_pos_offset[batch_idx] + row_g) * rope_scale / T.pow(rope_theta, T.Cast("float32", (tx * 4 + vec) * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(tx * 4 + vec < 32, pages[page_no, 0, by, page_offset, tx * 4 + vec + 32] * T.float16(-1), pages[page_no, 0, by, page_offset, tx * 4 + vec - 32]))), pages[page_no, 0, by, page_offset, tx * 4 + vec]) V_smem[tile_start_s + j, tx * 4 + vec] = pages[page_no, 1, by, page_offset, tx * 4 + vec] else: for vec in T.vectorized(4): K_smem[tile_start_s + j, tx * 4 + vec] = T.float16(0) V_smem[tile_start_s + j, tx * 4 + vec] = T.float16(0) T.tvm_storage_sync("shared") m_prev[0] = st_m[0] for j in range(2): for vec in T.vectorized(4): QK_local[vec] = T.Cast("float32", Q_local[vec]) * T.Cast("float32", K_smem[tz * 2 + j, tx * 4 + vec]) * attn_score_scaling_factor * sm_scale S_reduce_local[0] = T.float32(0) for vec in T.unroll(4): S_reduce_local[0] = S_reduce_local[0] + QK_local[vec] with T.block("block_cross_thread"): T.reads(S_reduce_local[0]) T.writes(t0[0]) T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) T.tvm_thread_allreduce(T.uint32(1), S_reduce_local[0], T.bool(True), t0[0], tx) S_local[j] = T.float32(-50000) if (iterator * 32 + tz) * 2 + j < kv_chunk_len[0]: S_local[j] = t0[0] st_m[0] = T.max(st_m[0], S_local[j]) o_scale: T.float32 = T.exp2(m_prev[0] - st_m[0]) st_d[0] = st_d[0] * o_scale for j in range(2): S_local[j] = T.exp2(S_local[j] - st_m[0]) st_d[0] = st_d[0] + S_local[j] for j in T.vectorized(4): O_local[j] = O_local[j] * o_scale for j in range(2): for vec in T.vectorized(4): V_local[vec] = V_smem[tz * 2 + j, tx * 4 + vec] for vec in T.vectorized(4): O_local[vec] = O_local[vec] + T.Cast("float32", V_local[vec]) * S_local[j] for vec in T.vectorized(4): O_allreduce[tz, ty, tx * 4 + vec] = O_local[vec] md_allreduce[tz, ty, 0] = st_m[0] md_allreduce[tz, ty, 1] = st_d[0] T.tvm_storage_sync("shared") st_m[0] = T.float32(-50000) st_d[0] = T.float32(1) for vec in T.vectorized(4): O_local[vec] = T.float32(0) for j in range(32): m_prev[0] = st_m[0] d_prev[0] = st_d[0] other_m[0] = md_allreduce[j, ty, 0] other_d[0] = md_allreduce[j, ty, 1] for vec in T.vectorized(4): other_o[vec] = O_allreduce[j, ty, tx * 4 + vec] st_m[0] = T.max(st_m[0], other_m[0]) st_d[0] = d_prev[0] * T.exp2(m_prev[0] - st_m[0]) + other_d[0] * T.exp2(other_m[0] - st_m[0]) exp_mprev[0] = T.exp2(m_prev[0] - st_m[0]) exp_otherm[0] = T.exp2(other_m[0] - st_m[0]) for vec in T.vectorized(4): O_local[vec] = O_local[vec] * exp_mprev[0] + other_o[vec] * exp_otherm[0] for vec in T.vectorized(4): O_local[vec] = O_local[vec] / st_d[0] for vec in T.vectorized(4): output[batch_idx, by + bz + ty, tx * 4 + vec] = T.Cast("float16", O_local[vec]) lse[batch_idx, by + bz + ty] = st_m[0] + T.log2(st_d[0]) @T.prim_func def batch_prefill_paged_kv(_0: T.int32, var_q: T.handle, var_q_indptr: T.handle, var_pages: T.handle, var_page_indptr: T.handle, var_page_values: T.handle, var_length_info: T.handle, var_k_rope_pos_offset: T.handle, var_q_rope_position: T.handle, var_output: T.handle, var_lse: T.handle, causal: T.int32, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) total_len = T.int32(is_size_var=True) q = T.match_buffer(var_q, (total_len, 20, 64), "float16") batch_size = T.int32(is_size_var=True) q_indptr = T.match_buffer(var_q_indptr, (batch_size + 1,), "int32", offset_factor=1) max_num_pages = T.int32(is_size_var=True) pages = T.match_buffer(var_pages, (max_num_pages, 2, 20, 16, 64), "float16") page_indptr = T.match_buffer(var_page_indptr, (batch_size + 1,), "int32", offset_factor=1) nnz_pages = T.int32(is_size_var=True) page_values = T.match_buffer(var_page_values, (nnz_pages,), "int32", offset_factor=1) length_info = T.match_buffer(var_length_info, (batch_size,), "int32", offset_factor=1) k_rope_pos_offset = T.match_buffer(var_k_rope_pos_offset, (batch_size,), "int32", offset_factor=1) q_rope_position = T.match_buffer(var_q_rope_position, (total_len,), "int32", offset_factor=1) output = T.match_buffer(var_output, (total_len, 20, 64), "float16") lse = T.match_buffer(var_lse, (total_len, 20)) # with T.block("root"): for lbx in T.thread_binding(16, thread="blockIdx.x"): for lby in T.thread_binding(20, thread="blockIdx.y"): for lty in T.thread_binding(4, thread="threadIdx.y"): for ltx in T.thread_binding(32, thread="threadIdx.x"): with T.block("attn"): bx, by, ty, tx = T.axis.remap("SSSS", [lbx, lby, lty, ltx]) T.reads() T.writes() tile_id = T.alloc_buffer((1,), "int32", scope="local") batch_idx = T.alloc_buffer((1,), "int32", scope="local") batch_tiles = T.alloc_buffer((1,), "int32", scope="local") batch_rows = T.alloc_buffer((1,), "int32", scope="local") iterator = T.alloc_buffer((1,), "int32", scope="local") kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") Q_smem = T.alloc_buffer((32, 64), "float16", scope="shared") K_smem = T.alloc_buffer((16, 64), "float16", scope="shared") V_smem = T.alloc_buffer((16, 64), "float16", scope="shared") S_smem = T.alloc_buffer((32, 16), scope="shared") S_local = T.alloc_buffer((32, 16), scope="local") O_local = T.alloc_buffer((32, 64), scope="local") m_smem = T.alloc_buffer((32,), scope="shared") m_prev_smem = T.alloc_buffer((32,), scope="shared") d_smem = T.alloc_buffer((32,), scope="shared") m_new = T.alloc_buffer((1,), scope="local") m_prev = T.alloc_buffer((1,), scope="local") d_new = T.alloc_buffer((1,), scope="local") tile_id[0] = bx batch_idx[0] = 0 batch_rows[0] = q_indptr[1] - q_indptr[0] batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 while T.tvm_thread_invariant(batch_idx[0] < batch_size): while tile_id[0] >= batch_tiles[0] and batch_idx[0] < batch_size: tile_id[0] = tile_id[0] - batch_tiles[0] batch_idx[0] = batch_idx[0] + 1 if batch_idx[0] < batch_size: b_idx: T.int32 = batch_idx[0] batch_rows[0] = q_indptr[b_idx + 1] - q_indptr[b_idx] batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 if T.tvm_thread_invariant(batch_idx[0] < batch_size): b_idx: T.int32 = batch_idx[0] LH_start: T.int32 = tile_id[0] * 32 q_indptr_val: T.int32 = q_indptr[b_idx] cur_page_indptr_begin: T.int32 = page_indptr[b_idx] cur_page_indptr_end: T.int32 = page_indptr[b_idx + 1] kv_chunk_len[0] = T.if_then_else(cur_page_indptr_begin != cur_page_indptr_end, (cur_page_indptr_end - cur_page_indptr_begin - 1) * 16 + length_info[b_idx], 0) T.tvm_storage_sync("shared") for i in range(1): row: T.int32 = i * 32 * 4 + ty * 32 + tx if row < 32: m_smem[row] = T.float32(-50000) d_smem[row] = T.float32(1) for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): for li_1, lj_1 in T.grid(4, 4): with T.block("O_init"): i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) T.reads() T.writes(O_local[i, j]) O_local[i, j] = T.float32(0) T.tvm_storage_sync("shared") for li_lj_fused_0 in range(4): for li_lj_fused_1 in T.thread_binding(4, thread="threadIdx.y"): for li_lj_fused_2 in T.thread_binding(32, thread="threadIdx.x"): for li_lj_fused_3 in T.vectorized(4): with T.block("Q_load"): i = T.axis.spatial(32, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) // 64) j = T.axis.spatial(64, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) % 64) T.reads() T.writes() cur_L: T.int32 = q_indptr_val + (LH_start + i) cur_H_qo: T.int32 = by if cur_L < q_indptr[b_idx + 1]: Q_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", q[cur_L, cur_H_qo, j]) + T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, q[cur_L, cur_H_qo, j + 32] * T.float16(-1), q[cur_L, cur_H_qo, j - 32]))), q[cur_L, cur_H_qo, j]) else: Q_smem[i, j] = T.float16(0) T.tvm_storage_sync("shared") for iterator_1 in range((kv_chunk_len[0] + 15) // 16): L_kv_start: T.int32 = iterator_1 * 16 for lz_ly_fused_0 in range(2): for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): for lz_ly_fused_3 in T.vectorized(4): with T.block("K_load"): i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) T.reads() T.writes() cur_L: T.int32 = L_kv_start + i if cur_L < kv_chunk_len[0]: seq_offset: T.int32 = cur_L page_no: T.int32 = page_values[cur_page_indptr_begin + seq_offset // 16] page_offset: T.int32 = seq_offset % 16 K_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", pages[page_no, 0, by, page_offset, j]) + T.sin(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, pages[page_no, 0, by, page_offset, j + 32] * T.float16(-1), pages[page_no, 0, by, page_offset, j - 32]))), pages[page_no, 0, by, page_offset, j]) else: K_smem[i, j] = T.float16(0) T.tvm_storage_sync("shared") for lz_ly_fused_0 in range(2): for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): for lz_ly_fused_3 in T.vectorized(4): with T.block("V_load"): i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) T.reads() T.writes() cur_L: T.int32 = L_kv_start + i if cur_L < kv_chunk_len[0]: seq_offset: T.int32 = cur_L page_no: T.int32 = page_values[cur_page_indptr_begin + seq_offset // 16] page_offset: T.int32 = seq_offset % 16 V_smem[i, j] = pages[page_no, 1, by, page_offset, j] else: V_smem[i, j] = T.float16(0) T.tvm_storage_sync("shared") with T.block(""): T.reads(Q_smem[0:32, 0:64], K_smem[0:16, 0:64]) T.writes(S_local[0:32, 0:16]) for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): for li_1_init, lj_1_init in T.grid(2, 2): with T.block("S_gemm_init"): i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 8 * 2 + li_1_init) j = T.axis.spatial(16, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 8 * 2 + lj_1_init) T.reads() T.writes(S_local[i, j]) S_local[i, j] = T.float32(0) for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): for lk_0, li_1, lj_1, lk_1 in T.grid(8, 2, 2, 8): with T.block("S_gemm_update"): i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) k = T.axis.reduce(64, lk_0 * 8 + lk_1) T.reads(S_local[i, j], Q_smem[i, k], K_smem[j, k]) T.writes(S_local[i, j]) S_local[i, j] = S_local[i, j] + T.Cast("float32", Q_smem[i, k]) * T.Cast("float32", K_smem[j, k]) * attn_score_scaling_factor * T.float32(0.18033688011112042) T.tvm_storage_sync("shared") for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): for li_1, lj_1 in T.grid(2, 2): with T.block("S_store"): i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) T.reads(S_local[i, j]) T.writes(S_smem[i, j]) S_smem[i, j] = S_local[i, j] T.tvm_storage_sync("shared") for i in range(1): row: T.int32 = i * 32 * 4 + ty * 32 + tx if row < 32: with T.block("update1"): T.reads(m_smem[row], kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], m_new[i], S_smem[row, 0:16], d_smem[row], m_prev[i]) T.writes(m_prev[i], m_new[i], d_new[i]) m_prev[i] = m_smem[row] m_new[i] = m_smem[row] row_: T.int32 = LH_start + row for j in range(16): if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): m_new[i] = T.max(m_new[i], S_smem[row, j]) d_new[i] = d_smem[row] * T.exp2(m_prev[i] - m_new[i]) for i in range(1): row: T.int32 = i * 32 * 4 + ty * 32 + tx with T.block("update"): T.reads(kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], S_smem[row, 0:16], m_new[i]) T.writes(S_smem[row, 0:16]) for j in range(16): if row < 32: row_: T.int32 = LH_start + row if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): S_smem[row, j] = T.exp2(S_smem[row, j] - m_new[i]) else: S_smem[row, j] = T.exp2(T.float32(-50000) - m_new[i]) for i in range(1): row: T.int32 = i * 32 * 4 + ty * 32 + tx if row < 32: with T.block("update"): T.reads(d_new[i], S_smem[row, 0:16], m_new[i], m_prev[i]) T.writes(d_new[i], m_smem[row], d_smem[row], m_prev_smem[row]) for j in range(16): d_new[i] = d_new[i] + S_smem[row, j] m_smem[row] = m_new[i] d_smem[row] = d_new[i] m_prev_smem[row] = m_prev[i] T.tvm_storage_sync("shared") with T.block(""): T.reads(m_prev_smem[0:32], m_smem[0:32], S_smem[0:32, 0:16], V_smem[0:16, 0:64]) T.writes(O_local[0:32, 0:64]) for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): for li_1_init, lj_1_init in T.grid(4, 4): with T.block("O_gemm_init"): i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 16 * 4 + li_1_init) j = T.axis.spatial(64, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 16 * 4 + lj_1_init) T.reads() T.writes(O_local[i, j]) O_local[i, j] = O_local[i, j] * T.exp2(m_prev_smem[i] - m_smem[i]) for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): for lk_0, lk_1, li_1, lj_1 in T.grid(2, 8, 4, 4): with T.block("O_gemm_update"): i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) k = T.axis.reduce(16, lk_0 * 8 + lk_1) T.reads(O_local[i, j], m_prev_smem[i], m_smem[i], S_smem[i, k], V_smem[k, j]) T.writes(O_local[i, j]) O_local[i, j] = O_local[i, j] + S_smem[i, k] * T.Cast("float32", V_smem[k, j]) for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): for li_1, lj_1 in T.grid(4, 4): with T.block("O_store"): i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) T.reads(q_indptr[b_idx:b_idx + 2], O_local[i, j], d_smem[i]) T.writes(output[q_indptr[b_idx] + (LH_start + i), by, j]) cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) cur_H_qo: T.int32 = by if cur_L < q_indptr[b_idx + 1]: output[cur_L, cur_H_qo, j] = T.Cast("float16", O_local[i, j] / d_smem[i]) for li_0 in range(1): for li_1 in T.thread_binding(4, thread="threadIdx.y"): for li_2 in T.thread_binding(32, thread="threadIdx.x"): with T.block("lse_store"): i = T.axis.spatial(32, li_0 * 128 + li_1 * 32 + li_2) T.where((li_0 * 4 + li_1) * 32 + li_2 < 32) T.reads(q_indptr[b_idx:b_idx + 2], m_smem[i], d_smem[i]) T.writes(lse[q_indptr[b_idx] + (LH_start + i), by]) cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) cur_H_qo: T.int32 = by if cur_L < q_indptr[b_idx + 1]: lse[cur_L, cur_H_qo] = m_smem[i] + T.log2(d_smem[i]) tile_id[0] = tile_id[0] + 16 @T.prim_func def batch_prefill_paged_kv_sliding_window(_0: T.int32, var_q: T.handle, var_q_indptr: T.handle, var_pages: T.handle, var_page_indptr: T.handle, var_page_values: T.handle, var_length_info: T.handle, var_k_rope_pos_offset: T.handle, var_q_rope_position: T.handle, var_output: T.handle, var_lse: T.handle, causal: T.int32, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) total_len = T.int32(is_size_var=True) q = T.match_buffer(var_q, (total_len, 20, 64), "float16") batch_size = T.int32(is_size_var=True) q_indptr = T.match_buffer(var_q_indptr, (batch_size + 1,), "int32", offset_factor=1) max_num_pages = T.int32(is_size_var=True) pages = T.match_buffer(var_pages, (max_num_pages, 2, 20, 16, 64), "float16") page_indptr = T.match_buffer(var_page_indptr, (batch_size + 1,), "int32", offset_factor=1) nnz_pages = T.int32(is_size_var=True) page_values = T.match_buffer(var_page_values, (nnz_pages,), "int32", offset_factor=1) length_info = T.match_buffer(var_length_info, (3, batch_size), "int32", offset_factor=1) k_rope_pos_offset = T.match_buffer(var_k_rope_pos_offset, (batch_size,), "int32", offset_factor=1) q_rope_position = T.match_buffer(var_q_rope_position, (total_len,), "int32", offset_factor=1) output = T.match_buffer(var_output, (total_len, 20, 64), "float16") lse = T.match_buffer(var_lse, (total_len, 20)) # with T.block("root"): for lbx in T.thread_binding(16, thread="blockIdx.x"): for lby in T.thread_binding(20, thread="blockIdx.y"): for lty in T.thread_binding(4, thread="threadIdx.y"): for ltx in T.thread_binding(32, thread="threadIdx.x"): with T.block("attn"): bx, by, ty, tx = T.axis.remap("SSSS", [lbx, lby, lty, ltx]) T.reads() T.writes() tile_id = T.alloc_buffer((1,), "int32", scope="local") batch_idx = T.alloc_buffer((1,), "int32", scope="local") batch_tiles = T.alloc_buffer((1,), "int32", scope="local") batch_rows = T.alloc_buffer((1,), "int32", scope="local") iterator = T.alloc_buffer((1,), "int32", scope="local") kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") Q_smem = T.alloc_buffer((32, 64), "float16", scope="shared") K_smem = T.alloc_buffer((16, 64), "float16", scope="shared") V_smem = T.alloc_buffer((16, 64), "float16", scope="shared") S_smem = T.alloc_buffer((32, 16), scope="shared") S_local = T.alloc_buffer((32, 16), scope="local") O_local = T.alloc_buffer((32, 64), scope="local") m_smem = T.alloc_buffer((32,), scope="shared") m_prev_smem = T.alloc_buffer((32,), scope="shared") d_smem = T.alloc_buffer((32,), scope="shared") m_new = T.alloc_buffer((1,), scope="local") m_prev = T.alloc_buffer((1,), scope="local") d_new = T.alloc_buffer((1,), scope="local") tile_id[0] = bx batch_idx[0] = 0 batch_rows[0] = q_indptr[1] - q_indptr[0] batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 while T.tvm_thread_invariant(batch_idx[0] < batch_size): while tile_id[0] >= batch_tiles[0] and batch_idx[0] < batch_size: tile_id[0] = tile_id[0] - batch_tiles[0] batch_idx[0] = batch_idx[0] + 1 if batch_idx[0] < batch_size: b_idx: T.int32 = batch_idx[0] batch_rows[0] = q_indptr[b_idx + 1] - q_indptr[b_idx] batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 if T.tvm_thread_invariant(batch_idx[0] < batch_size): b_idx: T.int32 = batch_idx[0] LH_start: T.int32 = tile_id[0] * 32 q_indptr_val: T.int32 = q_indptr[b_idx] cur_page_indptr_begin: T.int32 = page_indptr[b_idx] cur_page_indptr_end: T.int32 = page_indptr[b_idx + 1] kv_chunk_len[0] = T.if_then_else(cur_page_indptr_begin != cur_page_indptr_end, (cur_page_indptr_end - cur_page_indptr_begin - 1) * 16 + length_info[0, b_idx] - length_info[1, b_idx] + length_info[2, b_idx], 0) T.tvm_storage_sync("shared") for i in range(1): row: T.int32 = i * 32 * 4 + ty * 32 + tx if row < 32: m_smem[row] = T.float32(-50000) d_smem[row] = T.float32(1) for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): for li_1, lj_1 in T.grid(4, 4): with T.block("O_init"): i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) T.reads() T.writes(O_local[i, j]) O_local[i, j] = T.float32(0) T.tvm_storage_sync("shared") for li_lj_fused_0 in range(4): for li_lj_fused_1 in T.thread_binding(4, thread="threadIdx.y"): for li_lj_fused_2 in T.thread_binding(32, thread="threadIdx.x"): for li_lj_fused_3 in T.vectorized(4): with T.block("Q_load"): i = T.axis.spatial(32, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) // 64) j = T.axis.spatial(64, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) % 64) T.reads() T.writes() cur_L: T.int32 = q_indptr_val + (LH_start + i) cur_H_qo: T.int32 = by if cur_L < q_indptr[b_idx + 1]: Q_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", q[cur_L, cur_H_qo, j]) + T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, q[cur_L, cur_H_qo, j + 32] * T.float16(-1), q[cur_L, cur_H_qo, j - 32]))), q[cur_L, cur_H_qo, j]) else: Q_smem[i, j] = T.float16(0) T.tvm_storage_sync("shared") for iterator_1 in range((kv_chunk_len[0] + 15) // 16): L_kv_start: T.int32 = iterator_1 * 16 for lz_ly_fused_0 in range(2): for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): for lz_ly_fused_3 in T.vectorized(4): with T.block("K_load"): i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) T.reads() T.writes() cur_L: T.int32 = L_kv_start + i if cur_L < kv_chunk_len[0]: seq_offset: T.int32 = T.if_then_else(cur_L < length_info[2, b_idx], cur_L, cur_L - length_info[2, b_idx] + length_info[1, b_idx]) page_no: T.int32 = page_values[cur_page_indptr_begin + seq_offset // 16] page_offset: T.int32 = seq_offset % 16 K_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", pages[page_no, 0, by, page_offset, j]) + T.sin(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, pages[page_no, 0, by, page_offset, j + 32] * T.float16(-1), pages[page_no, 0, by, page_offset, j - 32]))), pages[page_no, 0, by, page_offset, j]) else: K_smem[i, j] = T.float16(0) T.tvm_storage_sync("shared") for lz_ly_fused_0 in range(2): for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): for lz_ly_fused_3 in T.vectorized(4): with T.block("V_load"): i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) T.reads() T.writes() cur_L: T.int32 = L_kv_start + i if cur_L < kv_chunk_len[0]: seq_offset: T.int32 = T.if_then_else(cur_L < length_info[2, b_idx], cur_L, cur_L - length_info[2, b_idx] + length_info[1, b_idx]) page_no: T.int32 = page_values[cur_page_indptr_begin + seq_offset // 16] page_offset: T.int32 = seq_offset % 16 V_smem[i, j] = pages[page_no, 1, by, page_offset, j] else: V_smem[i, j] = T.float16(0) T.tvm_storage_sync("shared") with T.block(""): T.reads(Q_smem[0:32, 0:64], K_smem[0:16, 0:64]) T.writes(S_local[0:32, 0:16]) for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): for li_1_init, lj_1_init in T.grid(2, 2): with T.block("S_gemm_init"): i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 8 * 2 + li_1_init) j = T.axis.spatial(16, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 8 * 2 + lj_1_init) T.reads() T.writes(S_local[i, j]) S_local[i, j] = T.float32(0) for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): for lk_0, li_1, lj_1, lk_1 in T.grid(8, 2, 2, 8): with T.block("S_gemm_update"): i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) k = T.axis.reduce(64, lk_0 * 8 + lk_1) T.reads(S_local[i, j], Q_smem[i, k], K_smem[j, k]) T.writes(S_local[i, j]) S_local[i, j] = S_local[i, j] + T.Cast("float32", Q_smem[i, k]) * T.Cast("float32", K_smem[j, k]) * attn_score_scaling_factor * T.float32(0.18033688011112042) T.tvm_storage_sync("shared") for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): for li_1, lj_1 in T.grid(2, 2): with T.block("S_store"): i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) T.reads(S_local[i, j]) T.writes(S_smem[i, j]) S_smem[i, j] = S_local[i, j] T.tvm_storage_sync("shared") for i in range(1): row: T.int32 = i * 32 * 4 + ty * 32 + tx if row < 32: with T.block("update1"): T.reads(m_smem[row], kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], m_new[i], S_smem[row, 0:16], d_smem[row], m_prev[i]) T.writes(m_prev[i], m_new[i], d_new[i]) m_prev[i] = m_smem[row] m_new[i] = m_smem[row] row_: T.int32 = LH_start + row for j in range(16): if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): m_new[i] = T.max(m_new[i], S_smem[row, j]) d_new[i] = d_smem[row] * T.exp2(m_prev[i] - m_new[i]) for i in range(1): row: T.int32 = i * 32 * 4 + ty * 32 + tx with T.block("update"): T.reads(kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], S_smem[row, 0:16], m_new[i]) T.writes(S_smem[row, 0:16]) for j in range(16): if row < 32: row_: T.int32 = LH_start + row if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): S_smem[row, j] = T.exp2(S_smem[row, j] - m_new[i]) else: S_smem[row, j] = T.exp2(T.float32(-50000) - m_new[i]) for i in range(1): row: T.int32 = i * 32 * 4 + ty * 32 + tx if row < 32: with T.block("update"): T.reads(d_new[i], S_smem[row, 0:16], m_new[i], m_prev[i]) T.writes(d_new[i], m_smem[row], d_smem[row], m_prev_smem[row]) for j in range(16): d_new[i] = d_new[i] + S_smem[row, j] m_smem[row] = m_new[i] d_smem[row] = d_new[i] m_prev_smem[row] = m_prev[i] T.tvm_storage_sync("shared") with T.block(""): T.reads(m_prev_smem[0:32], m_smem[0:32], S_smem[0:32, 0:16], V_smem[0:16, 0:64]) T.writes(O_local[0:32, 0:64]) for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): for li_1_init, lj_1_init in T.grid(4, 4): with T.block("O_gemm_init"): i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 16 * 4 + li_1_init) j = T.axis.spatial(64, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 16 * 4 + lj_1_init) T.reads() T.writes(O_local[i, j]) O_local[i, j] = O_local[i, j] * T.exp2(m_prev_smem[i] - m_smem[i]) for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): for lk_0, lk_1, li_1, lj_1 in T.grid(2, 8, 4, 4): with T.block("O_gemm_update"): i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) k = T.axis.reduce(16, lk_0 * 8 + lk_1) T.reads(O_local[i, j], m_prev_smem[i], m_smem[i], S_smem[i, k], V_smem[k, j]) T.writes(O_local[i, j]) O_local[i, j] = O_local[i, j] + S_smem[i, k] * T.Cast("float32", V_smem[k, j]) for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): for li_1, lj_1 in T.grid(4, 4): with T.block("O_store"): i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) T.reads(q_indptr[b_idx:b_idx + 2], O_local[i, j], d_smem[i]) T.writes(output[q_indptr[b_idx] + (LH_start + i), by, j]) cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) cur_H_qo: T.int32 = by if cur_L < q_indptr[b_idx + 1]: output[cur_L, cur_H_qo, j] = T.Cast("float16", O_local[i, j] / d_smem[i]) for li_0 in range(1): for li_1 in T.thread_binding(4, thread="threadIdx.y"): for li_2 in T.thread_binding(32, thread="threadIdx.x"): with T.block("lse_store"): i = T.axis.spatial(32, li_0 * 128 + li_1 * 32 + li_2) T.where((li_0 * 4 + li_1) * 32 + li_2 < 32) T.reads(q_indptr[b_idx:b_idx + 2], m_smem[i], d_smem[i]) T.writes(lse[q_indptr[b_idx] + (LH_start + i), by]) cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) cur_H_qo: T.int32 = by if cur_L < q_indptr[b_idx + 1]: lse[cur_L, cur_H_qo] = m_smem[i] + T.log2(d_smem[i]) tile_id[0] = tile_id[0] + 16 @T.prim_func def batch_prefill_ragged_kv(var_q: T.handle, var_q_indptr: T.handle, var_k: T.handle, var_v: T.handle, var_kv_indptr: T.handle, var_q_rope_position: T.handle, var_k_rope_pos_offset: T.handle, var_output: T.handle, var_lse: T.handle, causal: T.int32, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32): T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) qo_len = T.int32(is_size_var=True) q = T.match_buffer(var_q, (qo_len, 20, 64), "float16") batch_size = T.int32(is_size_var=True) q_indptr = T.match_buffer(var_q_indptr, (batch_size + 1,), "int32", offset_factor=1) kv_len = T.int32(is_size_var=True) k = T.match_buffer(var_k, (kv_len, 20, 64), "float16") v = T.match_buffer(var_v, (kv_len, 20, 64), "float16") kv_indptr = T.match_buffer(var_kv_indptr, (batch_size + 1,), "int32", offset_factor=1) q_rope_position = T.match_buffer(var_q_rope_position, (qo_len,), "int32", offset_factor=1) k_rope_pos_offset = T.match_buffer(var_k_rope_pos_offset, (batch_size,), "int32", offset_factor=1) output = T.match_buffer(var_output, (qo_len, 20, 64), "float16") lse = T.match_buffer(var_lse, (qo_len, 20)) # with T.block("root"): for lbx in T.thread_binding(16, thread="blockIdx.x"): for lby in T.thread_binding(20, thread="blockIdx.y"): for lty in T.thread_binding(4, thread="threadIdx.y"): for ltx in T.thread_binding(32, thread="threadIdx.x"): with T.block("attn"): bx, by, ty, tx = T.axis.remap("SSSS", [lbx, lby, lty, ltx]) T.reads() T.writes() tile_id = T.alloc_buffer((1,), "int32", scope="local") batch_idx = T.alloc_buffer((1,), "int32", scope="local") batch_tiles = T.alloc_buffer((1,), "int32", scope="local") batch_rows = T.alloc_buffer((1,), "int32", scope="local") iterator = T.alloc_buffer((1,), "int32", scope="local") kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") Q_smem = T.alloc_buffer((32, 64), "float16", scope="shared") K_smem = T.alloc_buffer((16, 64), "float16", scope="shared") V_smem = T.alloc_buffer((16, 64), "float16", scope="shared") S_smem = T.alloc_buffer((32, 16), scope="shared") S_local = T.alloc_buffer((32, 16), scope="local") O_local = T.alloc_buffer((32, 64), scope="local") m_smem = T.alloc_buffer((32,), scope="shared") m_prev_smem = T.alloc_buffer((32,), scope="shared") d_smem = T.alloc_buffer((32,), scope="shared") m_new = T.alloc_buffer((1,), scope="local") m_prev = T.alloc_buffer((1,), scope="local") d_new = T.alloc_buffer((1,), scope="local") tile_id[0] = bx batch_idx[0] = 0 batch_rows[0] = q_indptr[1] - q_indptr[0] batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 while T.tvm_thread_invariant(batch_idx[0] < batch_size): while tile_id[0] >= batch_tiles[0] and batch_idx[0] < batch_size: tile_id[0] = tile_id[0] - batch_tiles[0] batch_idx[0] = batch_idx[0] + 1 if batch_idx[0] < batch_size: b_idx: T.int32 = batch_idx[0] batch_rows[0] = q_indptr[b_idx + 1] - q_indptr[b_idx] batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 if T.tvm_thread_invariant(batch_idx[0] < batch_size): b_idx: T.int32 = batch_idx[0] q_indptr_val: T.int32 = q_indptr[b_idx] LH_start: T.int32 = tile_id[0] * 32 kv_chunk_len[0] = kv_indptr[b_idx + 1] - kv_indptr[b_idx] T.tvm_storage_sync("shared") for i in range(1): row: T.int32 = i * 32 * 4 + ty * 32 + tx if row < 32: m_smem[row] = T.float32(-50000) d_smem[row] = T.float32(1) for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): for li_1, lj_1 in T.grid(4, 4): with T.block("O_init"): i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) T.reads() T.writes(O_local[i, j]) O_local[i, j] = T.float32(0) T.tvm_storage_sync("shared") for li_lj_fused_0 in range(4): for li_lj_fused_1 in T.thread_binding(4, thread="threadIdx.y"): for li_lj_fused_2 in T.thread_binding(32, thread="threadIdx.x"): for li_lj_fused_3 in T.vectorized(4): with T.block("Q_load"): i = T.axis.spatial(32, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) // 64) j = T.axis.spatial(64, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) % 64) T.reads() T.writes() cur_L: T.int32 = q_indptr_val + (LH_start + i) cur_H_qo: T.int32 = by if cur_L < q_indptr[b_idx + 1]: Q_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", q[cur_L, cur_H_qo, j]) + T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, q[cur_L, cur_H_qo, j + 32] * T.float16(-1), q[cur_L, cur_H_qo, j - 32]))), q[cur_L, cur_H_qo, j]) else: Q_smem[i, j] = T.float16(0) T.tvm_storage_sync("shared") for iterator_1 in range((kv_chunk_len[0] + 15) // 16): L_kv_start: T.int32 = iterator_1 * 16 L_kv_base: T.int32 = kv_indptr[b_idx] for lz_ly_fused_0 in range(2): for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): for lz_ly_fused_3 in T.vectorized(4): with T.block("K_load"): i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) T.reads() T.writes() cur_L: T.int32 = L_kv_start + i if cur_L < kv_chunk_len[0]: K_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", k[L_kv_base + cur_L, by, j]) + T.sin(T.Cast("float32", k_rope_pos_offset[b_idx] + cur_L) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(j < 32, k[L_kv_base + cur_L, by, j + 32] * T.float16(-1), k[L_kv_base + cur_L, by, j - 32]))), k[L_kv_base + cur_L, by, j]) else: K_smem[i, j] = T.float16(0) T.tvm_storage_sync("shared") for lz_ly_fused_0 in range(2): for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): for lz_ly_fused_3 in T.vectorized(4): with T.block("V_load"): i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) T.reads() T.writes() cur_L: T.int32 = L_kv_start + i if cur_L < kv_chunk_len[0]: V_smem[i, j] = v[L_kv_base + cur_L, by, j] else: V_smem[i, j] = T.float16(0) T.tvm_storage_sync("shared") with T.block(""): T.reads(Q_smem[0:32, 0:64], K_smem[0:16, 0:64]) T.writes(S_local[0:32, 0:16]) for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): for li_1_init, lj_1_init in T.grid(2, 2): with T.block("S_gemm_init"): i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 8 * 2 + li_1_init) j = T.axis.spatial(16, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 8 * 2 + lj_1_init) T.reads() T.writes(S_local[i, j]) S_local[i, j] = T.float32(0) for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): for lk_0, li_1, lj_1, lk_1 in T.grid(8, 2, 2, 8): with T.block("S_gemm_update"): i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) k_1 = T.axis.reduce(64, lk_0 * 8 + lk_1) T.reads(S_local[i, j], Q_smem[i, k_1], K_smem[j, k_1]) T.writes(S_local[i, j]) S_local[i, j] = S_local[i, j] + T.Cast("float32", Q_smem[i, k_1]) * T.Cast("float32", K_smem[j, k_1]) * attn_score_scaling_factor * T.float32(0.18033688011112042) T.tvm_storage_sync("shared") for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): for li_1, lj_1 in T.grid(2, 2): with T.block("S_store"): i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) T.reads(S_local[i, j]) T.writes(S_smem[i, j]) S_smem[i, j] = S_local[i, j] T.tvm_storage_sync("shared") for i in range(1): row: T.int32 = i * 32 * 4 + ty * 32 + tx if row < 32: with T.block("update1"): T.reads(m_smem[row], kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], m_new[i], S_smem[row, 0:16], d_smem[row], m_prev[i]) T.writes(m_prev[i], m_new[i], d_new[i]) m_prev[i] = m_smem[row] m_new[i] = m_smem[row] row_: T.int32 = LH_start + row for j in range(16): if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): m_new[i] = T.max(m_new[i], S_smem[row, j]) d_new[i] = d_smem[row] * T.exp2(m_prev[i] - m_new[i]) for i in range(1): row: T.int32 = i * 32 * 4 + ty * 32 + tx with T.block("update"): T.reads(kv_chunk_len[0], q_indptr[b_idx:b_idx + 2], S_smem[row, 0:16], m_new[i]) T.writes(S_smem[row, 0:16]) for j in range(16): if row < 32: row_: T.int32 = LH_start + row if T.if_then_else(causal > 0, L_kv_start + j < kv_chunk_len[0] - (q_indptr[b_idx + 1] - q_indptr[b_idx]) + row_ + 1, L_kv_start + j < kv_chunk_len[0]): S_smem[row, j] = T.exp2(S_smem[row, j] - m_new[i]) else: S_smem[row, j] = T.exp2(T.float32(-50000) - m_new[i]) for i in range(1): row: T.int32 = i * 32 * 4 + ty * 32 + tx if row < 32: with T.block("update"): T.reads(d_new[i], S_smem[row, 0:16], m_new[i], m_prev[i]) T.writes(d_new[i], m_smem[row], d_smem[row], m_prev_smem[row]) for j in range(16): d_new[i] = d_new[i] + S_smem[row, j] m_smem[row] = m_new[i] d_smem[row] = d_new[i] m_prev_smem[row] = m_prev[i] T.tvm_storage_sync("shared") with T.block(""): T.reads(m_prev_smem[0:32], m_smem[0:32], S_smem[0:32, 0:16], V_smem[0:16, 0:64]) T.writes(O_local[0:32, 0:64]) for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): for li_1_init, lj_1_init in T.grid(4, 4): with T.block("O_gemm_init"): i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 16 * 4 + li_1_init) j = T.axis.spatial(64, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 16 * 4 + lj_1_init) T.reads() T.writes(O_local[i, j]) O_local[i, j] = O_local[i, j] * T.exp2(m_prev_smem[i] - m_smem[i]) for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): for lk_0, lk_1, li_1, lj_1 in T.grid(2, 8, 4, 4): with T.block("O_gemm_update"): i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) k_1 = T.axis.reduce(16, lk_0 * 8 + lk_1) T.reads(O_local[i, j], m_prev_smem[i], m_smem[i], S_smem[i, k_1], V_smem[k_1, j]) T.writes(O_local[i, j]) O_local[i, j] = O_local[i, j] + S_smem[i, k_1] * T.Cast("float32", V_smem[k_1, j]) for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): for li_1, lj_1 in T.grid(4, 4): with T.block("O_store"): i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) T.reads(q_indptr[b_idx:b_idx + 2], O_local[i, j], d_smem[i]) T.writes(output[q_indptr[b_idx] + (LH_start + i), by, j]) cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) cur_H_qo: T.int32 = by if cur_L < q_indptr[b_idx + 1]: output[cur_L, cur_H_qo, j] = T.Cast("float16", O_local[i, j] / d_smem[i]) for li_0 in range(1): for li_1 in T.thread_binding(4, thread="threadIdx.y"): for li_2 in T.thread_binding(32, thread="threadIdx.x"): with T.block("lse_store"): i = T.axis.spatial(32, li_0 * 128 + li_1 * 32 + li_2) T.where((li_0 * 4 + li_1) * 32 + li_2 < 32) T.reads(q_indptr[b_idx:b_idx + 2], m_smem[i], d_smem[i]) T.writes(lse[q_indptr[b_idx] + (LH_start + i), by]) cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) cur_H_qo: T.int32 = by if cur_L < q_indptr[b_idx + 1]: lse[cur_L, cur_H_qo] = m_smem[i] + T.log2(d_smem[i]) tile_id[0] = tile_id[0] + 16 @T.prim_func def batch_tree_attn(var_q: T.handle, var_q_indptr: T.handle, var_k: T.handle, var_v: T.handle, var_kv_indptr: T.handle, var_q_rope_position: T.handle, var_mn_indptr: T.handle, var_mask: T.handle, var_output: T.handle, var_lse: T.handle, rotary_mode: T.int32, rope_scale: T.float32, rope_theta: T.float32, attn_score_scaling_factor: T.float32, batch_size: T.int32): T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) qo_len = T.int32(is_size_var=True) q = T.match_buffer(var_q, (qo_len, 20, 64), "float16") q_indptr = T.match_buffer(var_q_indptr, (batch_size + 1,), "int32", offset_factor=1) kv_len = T.int32(is_size_var=True) k = T.match_buffer(var_k, (kv_len, 20, 64), "float16") v = T.match_buffer(var_v, (kv_len, 20, 64), "float16") kv_indptr = T.match_buffer(var_kv_indptr, (batch_size + 1,), "int32", offset_factor=1) q_rope_position = T.match_buffer(var_q_rope_position, (qo_len,), "int32", offset_factor=1) mn_indptr = T.match_buffer(var_mn_indptr, (batch_size + 1,), "int32", offset_factor=1) tree_size = T.int32(is_size_var=True) mask = T.match_buffer(var_mask, (tree_size,), "int32", offset_factor=1) output = T.match_buffer(var_output, (qo_len, 20, 64), "float16") lse = T.match_buffer(var_lse, (qo_len, 20)) # with T.block("root"): for lbx in T.thread_binding(16, thread="blockIdx.x"): for lby in T.thread_binding(20, thread="blockIdx.y"): for lty in T.thread_binding(4, thread="threadIdx.y"): for ltx in T.thread_binding(32, thread="threadIdx.x"): with T.block("attn"): bx, by, ty, tx = T.axis.remap("SSSS", [lbx, lby, lty, ltx]) T.reads() T.writes() tile_id = T.alloc_buffer((1,), "int32", scope="local") batch_idx = T.alloc_buffer((1,), "int32", scope="local") batch_tiles = T.alloc_buffer((1,), "int32", scope="local") batch_rows = T.alloc_buffer((1,), "int32", scope="local") iterator = T.alloc_buffer((1,), "int32", scope="local") kv_chunk_len = T.alloc_buffer((1,), "int32", scope="local") Q_smem = T.alloc_buffer((32, 64), "float16", scope="shared") K_smem = T.alloc_buffer((16, 64), "float16", scope="shared") V_smem = T.alloc_buffer((16, 64), "float16", scope="shared") S_smem = T.alloc_buffer((32, 16), scope="shared") S_local = T.alloc_buffer((32, 16), scope="local") O_local = T.alloc_buffer((32, 64), scope="local") m_smem = T.alloc_buffer((32,), scope="shared") m_prev_smem = T.alloc_buffer((32,), scope="shared") d_smem = T.alloc_buffer((32,), scope="shared") m_new = T.alloc_buffer((1,), scope="local") m_prev = T.alloc_buffer((1,), scope="local") d_new = T.alloc_buffer((1,), scope="local") tile_id[0] = bx batch_idx[0] = 0 batch_rows[0] = q_indptr[1] - q_indptr[0] batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 while T.tvm_thread_invariant(batch_idx[0] < batch_size): while tile_id[0] >= batch_tiles[0] and batch_idx[0] < batch_size: tile_id[0] = tile_id[0] - batch_tiles[0] batch_idx[0] = batch_idx[0] + 1 if batch_idx[0] < batch_size: b_idx: T.int32 = batch_idx[0] batch_rows[0] = q_indptr[b_idx + 1] - q_indptr[b_idx] batch_tiles[0] = (batch_rows[0] + 32 - 1) // 32 if T.tvm_thread_invariant(batch_idx[0] < batch_size): b_idx: T.int32 = batch_idx[0] LH_start: T.int32 = tile_id[0] * 32 q_indptr_val: T.int32 = q_indptr[b_idx] kv_chunk_len[0] = kv_indptr[b_idx + 1] - kv_indptr[b_idx] T.tvm_storage_sync("shared") for i in range(1): row: T.int32 = i * 32 * 4 + ty * 32 + tx if row < 32: m_smem[row] = T.float32(-50000) d_smem[row] = T.float32(1) for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): for li_1, lj_1 in T.grid(4, 4): with T.block("O_init"): i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) T.reads() T.writes(O_local[i, j]) O_local[i, j] = T.float32(0) T.tvm_storage_sync("shared") for li_lj_fused_0 in range(4): for li_lj_fused_1 in T.thread_binding(4, thread="threadIdx.y"): for li_lj_fused_2 in T.thread_binding(32, thread="threadIdx.x"): for li_lj_fused_3 in T.vectorized(4): with T.block("Q_load"): i = T.axis.spatial(32, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) // 64) j = T.axis.spatial(64, (li_lj_fused_0 * 512 + li_lj_fused_1 * 128 + li_lj_fused_2 * 4 + li_lj_fused_3) % 64) T.reads() T.writes() cur_L: T.int32 = q_indptr_val + (LH_start + i) cur_H_qo: T.int32 = by if cur_L < q_indptr[b_idx + 1]: Q_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64)))) * q[cur_L, cur_H_qo, j] + T.Cast("float16", T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64)))) * T.if_then_else(j < 32, q[cur_L, cur_H_qo, j + 32] * T.float16(-1), q[cur_L, cur_H_qo, j - 32]), q[cur_L, cur_H_qo, j]) else: Q_smem[i, j] = T.float16(0) T.tvm_storage_sync("shared") for iterator_1 in range((kv_chunk_len[0] + 15) // 16): L_kv_start: T.int32 = iterator_1 * 16 L_kv_base: T.int32 = kv_indptr[b_idx] for lz_ly_fused_0 in range(2): for lz_ly_fused_1 in T.thread_binding(4, thread="threadIdx.y"): for lz_ly_fused_2 in T.thread_binding(32, thread="threadIdx.x"): for lz_ly_fused_3 in T.vectorized(4): with T.block("KV_load"): i = T.axis.spatial(16, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) // 64) j = T.axis.spatial(64, (lz_ly_fused_0 * 512 + lz_ly_fused_1 * 128 + lz_ly_fused_2 * 4 + lz_ly_fused_3) % 64) T.reads() T.writes() cur_L: T.int32 = L_kv_base + L_kv_start + i if L_kv_start + i < kv_chunk_len[0]: K_smem[i, j] = T.if_then_else(rotary_mode == 1, T.Cast("float16", T.cos(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64)))) * k[cur_L, by, j] + T.Cast("float16", T.sin(T.Cast("float32", q_rope_position[cur_L]) * rope_scale / T.pow(rope_theta, T.Cast("float32", j * 2 % 64) / T.float32(64)))) * T.if_then_else(j < 32, k[cur_L, by, j + 32] * T.float16(-1), k[cur_L, by, j - 32]), k[cur_L, by, j]) V_smem[i, j] = v[cur_L, by, j] else: K_smem[i, j] = T.float16(0) V_smem[i, j] = T.float16(0) T.tvm_storage_sync("shared") with T.block(""): T.reads(Q_smem[0:32, 0:64], K_smem[0:16, 0:64]) T.writes(S_local[0:32, 0:16]) for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): for li_1_init, lj_1_init in T.grid(2, 2): with T.block("S_gemm_init"): i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 8 * 2 + li_1_init) j = T.axis.spatial(16, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 8 * 2 + lj_1_init) T.reads() T.writes(S_local[i, j]) S_local[i, j] = T.float32(0) for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): for lk_0, li_1, lj_1, lk_1 in T.grid(8, 2, 2, 8): with T.block("S_gemm_update"): i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) k_1 = T.axis.reduce(64, lk_0 * 8 + lk_1) T.reads(S_local[i, j], Q_smem[i, k_1], K_smem[j, k_1]) T.writes(S_local[i, j]) S_local[i, j] = S_local[i, j] + T.Cast("float32", Q_smem[i, k_1]) * T.Cast("float32", K_smem[j, k_1]) * attn_score_scaling_factor * T.float32(0.18033688011112042) T.tvm_storage_sync("shared") for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): for li_1, lj_1 in T.grid(2, 2): with T.block("S_store"): i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 8 * 2 + li_1) j = T.axis.spatial(16, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 8 * 2 + lj_1) T.reads(S_local[i, j]) T.writes(S_smem[i, j]) S_smem[i, j] = S_local[i, j] T.tvm_storage_sync("shared") for i in range(1): row: T.int32 = i * 32 * 4 + ty * 32 + tx if row < 32: with T.block("update1"): T.reads(m_smem[row], kv_chunk_len[0], mask[mn_indptr[b_idx] + (LH_start + row) * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + L_kv_start:mn_indptr[b_idx] + (LH_start + row) * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + L_kv_start + 16], mn_indptr[b_idx], q_indptr[b_idx:b_idx + 2], m_new[i], S_smem[row, 0:16], d_smem[row], m_prev[i]) T.writes(m_prev[i], m_new[i], d_new[i]) m_prev[i] = m_smem[row] m_new[i] = m_smem[row] row_: T.int32 = LH_start + row for j in range(16): if L_kv_start + j < kv_chunk_len[0] and mask[mn_indptr[b_idx] + row_ * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + (L_kv_start + j)] == 1: m_new[i] = T.max(m_new[i], S_smem[row, j]) d_new[i] = d_smem[row] * T.exp2(m_prev[i] - m_new[i]) for i in range(1): row: T.int32 = i * 32 * 4 + ty * 32 + tx with T.block("update"): T.reads(kv_chunk_len[0], mask[mn_indptr[b_idx] + (LH_start + row) * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + L_kv_start:mn_indptr[b_idx] + (LH_start + row) * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + L_kv_start + 16], mn_indptr[b_idx], q_indptr[b_idx:b_idx + 2], S_smem[row, 0:16], m_new[i]) T.writes(S_smem[row, 0:16]) for j in range(16): if row < 32: row_: T.int32 = LH_start + row if L_kv_start + j < kv_chunk_len[0] and mask[mn_indptr[b_idx] + row_ * (q_indptr[b_idx + 1] - q_indptr[b_idx]) + (L_kv_start + j)] == 1: S_smem[row, j] = T.exp2(S_smem[row, j] - m_new[i]) else: S_smem[row, j] = T.exp2(T.float32(-50000) - m_new[i]) for i in range(1): row: T.int32 = i * 32 * 4 + ty * 32 + tx if row < 32: with T.block("update"): T.reads(d_new[i], S_smem[row, 0:16], m_new[i], m_prev[i]) T.writes(d_new[i], m_smem[row], d_smem[row], m_prev_smem[row]) for j in range(16): d_new[i] = d_new[i] + S_smem[row, j] m_smem[row] = m_new[i] d_smem[row] = d_new[i] m_prev_smem[row] = m_prev[i] T.tvm_storage_sync("shared") with T.block(""): T.reads(m_prev_smem[0:32], m_smem[0:32], S_smem[0:32, 0:16], V_smem[0:16, 0:64]) T.writes(O_local[0:32, 0:64]) for li_0_lj_0_fused_0_init in T.thread_binding(4, thread="threadIdx.y"): for li_0_lj_0_fused_1_init in T.thread_binding(32, thread="threadIdx.x"): for li_1_init, lj_1_init in T.grid(4, 4): with T.block("O_gemm_init"): i = T.axis.spatial(32, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) // 16 * 4 + li_1_init) j = T.axis.spatial(64, (li_0_lj_0_fused_0_init * 32 + li_0_lj_0_fused_1_init) % 16 * 4 + lj_1_init) T.reads() T.writes(O_local[i, j]) O_local[i, j] = O_local[i, j] * T.exp2(m_prev_smem[i] - m_smem[i]) for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): for lk_0, lk_1, li_1, lj_1 in T.grid(2, 8, 4, 4): with T.block("O_gemm_update"): i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) k_1 = T.axis.reduce(16, lk_0 * 8 + lk_1) T.reads(O_local[i, j], m_prev_smem[i], m_smem[i], S_smem[i, k_1], V_smem[k_1, j]) T.writes(O_local[i, j]) O_local[i, j] = O_local[i, j] + S_smem[i, k_1] * T.Cast("float32", V_smem[k_1, j]) for li_0_lj_0_fused_0 in T.thread_binding(4, thread="threadIdx.y"): for li_0_lj_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"): for li_1, lj_1 in T.grid(4, 4): with T.block("O_store"): i = T.axis.spatial(32, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) // 16 * 4 + li_1) j = T.axis.spatial(64, (li_0_lj_0_fused_0 * 32 + li_0_lj_0_fused_1) % 16 * 4 + lj_1) T.reads(q_indptr[b_idx:b_idx + 2], O_local[i, j], d_smem[i]) T.writes(output[q_indptr[b_idx] + (LH_start + i), by, j]) cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) cur_H_qo: T.int32 = by if cur_L < q_indptr[b_idx + 1]: output[cur_L, cur_H_qo, j] = T.Cast("float16", O_local[i, j] / d_smem[i]) for li_0 in range(1): for li_1 in T.thread_binding(4, thread="threadIdx.y"): for li_2 in T.thread_binding(32, thread="threadIdx.x"): with T.block("lse_store"): i = T.axis.spatial(32, li_0 * 128 + li_1 * 32 + li_2) T.where((li_0 * 4 + li_1) * 32 + li_2 < 32) T.reads(q_indptr[b_idx:b_idx + 2], m_smem[i], d_smem[i]) T.writes(lse[q_indptr[b_idx] + (LH_start + i), by]) cur_L: T.int32 = q_indptr[b_idx] + (LH_start + i) cur_H_qo: T.int32 = by if cur_L < q_indptr[b_idx + 1]: lse[cur_L, cur_H_qo] = m_smem[i] + T.log2(d_smem[i]) tile_id[0] = tile_id[0] + 16 @T.prim_func(private=True) def batch_verify_on_gpu_single_kernel(var_draft_probs: T.handle, var_draft_tokens: T.handle, var_model_probs: T.handle, var_token_tree_first_child: T.handle, var_token_tree_next_sibling: T.handle, var_uniform_samples: T.handle, var_token_tree_parent_ptr: T.handle): T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) num_nodes, vocab_size = T.int32(is_size_var=True), T.int64() draft_probs = T.match_buffer(var_draft_probs, (num_nodes, vocab_size)) draft_tokens = T.match_buffer(var_draft_tokens, (num_nodes,), "int32") model_probs = T.match_buffer(var_model_probs, (num_nodes, vocab_size)) token_tree_first_child = T.match_buffer(var_token_tree_first_child, (num_nodes,), "int32") token_tree_next_sibling = T.match_buffer(var_token_tree_next_sibling, (num_nodes,), "int32") uniform_samples = T.match_buffer(var_uniform_samples, (num_nodes,)) nbatch = T.int32(is_size_var=True) token_tree_parent_ptr = T.match_buffer(var_token_tree_parent_ptr, (nbatch,), "int32") # with T.block("root"): child_ptr = T.alloc_buffer((1,), "int32", scope="local") parent_ptr = T.alloc_buffer((1,), "int32", scope="local") child_token = T.alloc_buffer((1,), "int32", scope="local") done = T.alloc_buffer((1,), "bool", scope="local") psum = T.alloc_buffer((1,), scope="local") t0 = T.alloc_buffer((1,), scope="local") model_prob_local = T.alloc_buffer((1,), scope="local") draft_prob_local = T.alloc_buffer((1,), scope="local") p_child = T.alloc_buffer((1,), scope="local") q_child = T.alloc_buffer((1,), scope="local") uniform_sample = T.alloc_buffer((1,), scope="local") pred_shared = T.alloc_buffer((1,), "bool", scope="shared") pred_local = T.alloc_buffer((1,), "bool", scope="local") for _bx in T.thread_binding(nbatch, thread="blockIdx.x"): for _tx in T.thread_binding(1024, thread="threadIdx.x"): with T.block("CTA"): b, tx = T.axis.remap("SS", [_bx, _tx]) T.reads(token_tree_parent_ptr[b], token_tree_first_child[T.min(parent_ptr[0], child_ptr[0]):T.min(parent_ptr[0], child_ptr[0]) + (T.max(parent_ptr[0], child_ptr[0]) + 1 - T.min(parent_ptr[0], child_ptr[0]))], parent_ptr[0], done[0], child_ptr[0], draft_tokens[child_ptr[0]], model_probs[parent_ptr[0], T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)):T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)) + (T.max(T.Cast("int64", child_token[0]), (vocab_size + T.int64(1023)) // T.int64(1024) * T.int64(1024) + T.Cast("int64", tx) - T.int64(1024)) + T.int64(1) - T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)))], child_token[0], draft_probs[child_ptr[0], T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)):T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)) + (T.max(T.Cast("int64", child_token[0]), (vocab_size + T.int64(1023)) // T.int64(1024) * T.int64(1024) + T.Cast("int64", tx) - T.int64(1024)) + T.int64(1) - T.min(T.Cast("int64", child_token[0]), T.Cast("int64", tx)))], uniform_samples[child_ptr[0]], p_child[0], uniform_sample[0], q_child[0], pred_shared[0], pred_local[0], model_prob_local[0], draft_prob_local[0], psum[0], t0[0], token_tree_next_sibling[child_ptr[0]]) T.writes(parent_ptr[0], child_ptr[0], done[0], child_token[0], p_child[0], q_child[0], uniform_sample[0], pred_shared[0], pred_local[0], psum[0], model_prob_local[0], draft_prob_local[0], t0[0], model_probs[parent_ptr[0], T.Cast("int64", tx):T.Cast("int64", tx) + ((vocab_size + T.int64(1023)) // T.int64(1024) * T.int64(1024) - T.int64(1023))], token_tree_parent_ptr[b]) parent_ptr[0] = token_tree_parent_ptr[b] child_ptr[0] = token_tree_first_child[parent_ptr[0]] done[0] = T.bool(False) while not done[0]: T.tvm_storage_sync("shared") if child_ptr[0] == -1: done[0] = T.bool(True) T.tvm_storage_sync("shared") else: if tx == 0: child_token[0] = draft_tokens[child_ptr[0]] p_child[0] = model_probs[parent_ptr[0], child_token[0]] q_child[0] = draft_probs[child_ptr[0], child_token[0]] uniform_sample[0] = uniform_samples[child_ptr[0]] pred_shared[0] = p_child[0] >= uniform_sample[0] * q_child[0] T.tvm_storage_sync("shared") pred_local[0] = pred_shared[0] if pred_local[0]: parent_ptr[0] = child_ptr[0] child_ptr[0] = token_tree_first_child[child_ptr[0]] else: psum[0] = T.float32(0) for i in range((vocab_size + T.int64(1023)) // T.int64(1024)): if i * T.int64(1024) + T.Cast("int64", tx) < vocab_size: model_prob_local[0] = model_probs[parent_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] draft_prob_local[0] = draft_probs[child_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] model_prob_local[0] = T.max(model_prob_local[0] - draft_prob_local[0], T.float32(0)) psum[0] = psum[0] + model_prob_local[0] with T.block("block_cross_thread"): T.reads(psum[0]) T.writes(t0[0]) T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) T.tvm_thread_allreduce(T.uint32(1), psum[0], T.bool(True), t0[0], tx) if t0[0] < T.float32(9.9999999999999995e-08): parent_ptr[0] = child_ptr[0] child_ptr[0] = token_tree_first_child[child_ptr[0]] else: for i in range((vocab_size + T.int64(1023)) // T.int64(1024)): if i * T.int64(1024) + T.Cast("int64", tx) < vocab_size: model_prob_local[0] = model_probs[parent_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] draft_prob_local[0] = draft_probs[child_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] model_prob_local[0] = T.max(model_prob_local[0] - draft_prob_local[0], T.float32(0)) model_probs[parent_ptr[0], i * T.int64(1024) + T.Cast("int64", tx)] = model_prob_local[0] / t0[0] child_ptr[0] = token_tree_next_sibling[child_ptr[0]] if tx == 0: token_tree_parent_ptr[b] = parent_ptr[0] @T.prim_func def chunk_lse(var_A: T.handle, var_temperature: T.handle, var_chunked_sum: T.handle, var_chunked_max: T.handle): T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) batch_size, vocab_size = T.int64(is_size_var=True), T.int64(is_size_var=True) A = T.match_buffer(var_A, (batch_size, vocab_size)) temperature = T.match_buffer(var_temperature, (batch_size,)) num_chunks = T.int64(is_size_var=True) chunked_sum = T.match_buffer(var_chunked_sum, (batch_size, num_chunks)) chunked_max = T.match_buffer(var_chunked_max, (batch_size, num_chunks)) # with T.block("root"): A_pad = T.alloc_buffer((batch_size, num_chunks, T.int64(4096))) temp_max = T.alloc_buffer((batch_size, num_chunks)) temp_sum = T.alloc_buffer((batch_size, num_chunks)) for l0, l1, l2 in T.grid(batch_size, num_chunks, T.int64(4096)): with T.block("pad"): v0, v1, v2 = T.axis.remap("SSS", [l0, l1, l2]) T.reads(temperature[v0], A[v0, v1 * T.int64(4096) + v2]) T.writes(A_pad[v0, v1, v2]) A_pad[v0, v1, v2] = T.if_then_else(v1 * T.int64(4096) + v2 < vocab_size, T.if_then_else(temperature[v0] > T.float32(1.0000000000000001e-05), A[v0, v1 * T.int64(4096) + v2] / temperature[v0], A[v0, v1 * T.int64(4096) + v2]), T.float32(-3.4028234663852886e+38)) for l0, l1, l2 in T.grid(batch_size, num_chunks, T.int64(4096)): with T.block("max"): v0, v1, v2 = T.axis.remap("SSR", [l0, l1, l2]) T.reads(A_pad[v0, v1, v2]) T.writes(temp_max[v0, v1]) with T.init(): temp_max[v0, v1] = T.float32(-3.4028234663852886e+38) temp_max[v0, v1] = T.max(temp_max[v0, v1], A_pad[v0, v1, v2]) for l0, l1, l2 in T.grid(batch_size, num_chunks, T.int64(4096)): with T.block("sum_exp"): v0, v1, v2 = T.axis.remap("SSR", [l0, l1, l2]) T.reads(temperature[v0], A_pad[v0, v1, v2], temp_max[v0, v1]) T.writes(temp_sum[v0, v1]) with T.init(): temp_sum[v0, v1] = T.float32(0) temp_sum[v0, v1] = temp_sum[v0, v1] + T.if_then_else(v1 * T.int64(4096) + v2 < vocab_size, T.Select(temperature[v0] > T.float32(1.0000000000000001e-05), T.exp(A_pad[v0, v1, v2] - temp_max[v0, v1]), T.Cast("float32", A_pad[v0, v1, v2] == temp_max[v0, v1])), T.float32(0)) for l0, l1, l2 in T.grid(batch_size, num_chunks, T.int64(1)): with T.block("log"): v0, v1, v2 = T.axis.remap("SSS", [l0, l1, l2]) T.reads(temperature[v0], temp_sum[v0, v1], temp_max[v0, v1]) T.writes(chunked_sum[v0, v1], chunked_max[v0, v1]) chunked_sum[v0, v1] = T.Select(temperature[v0] > T.float32(1.0000000000000001e-05), T.log(temp_sum[v0, v1]), temp_sum[v0, v1]) chunked_max[v0, v1] = temp_max[v0, v1] @T.prim_func def compact_kv_copy(var_pages: T.handle, var_copy_length_indptr: T.handle, var_copy_src_dst_pos: T.handle, batch_size: T.int32): T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) num_pages = T.int32() pages = T.match_buffer(var_pages, (num_pages, 2, 20, 16, 64), "float16") copy_length_indptr = T.match_buffer(var_copy_length_indptr, (batch_size + 1,), "int32", offset_factor=1) total_copy_length = T.int32() copy_src_dst_pos = T.match_buffer(var_copy_src_dst_pos, (2, total_copy_length), "int32", offset_factor=1) with T.block("root"): T.reads() T.writes() for bhd_o in T.thread_binding((batch_size * 1280 + 1023) // 1024, thread="blockIdx.x"): for bhd_i in T.thread_binding(1024, thread="threadIdx.x"): b: T.int32 = (bhd_o * 1024 + bhd_i) // 1280 h: T.int32 = (bhd_o * 1024 + bhd_i) // 64 % 20 d: T.int32 = (bhd_o * 1024 + bhd_i) % 64 if bhd_o * 1024 + bhd_i < batch_size * 20 * 64: for i in range(copy_length_indptr[b + 1] - copy_length_indptr[b]): src_pos: T.int32 = copy_src_dst_pos[0, copy_length_indptr[b] + i] dst_pos: T.int32 = copy_src_dst_pos[1, copy_length_indptr[b] + i] pages[dst_pos // 16, 0, h, dst_pos % 16, d] = pages[src_pos // 16, 0, h, src_pos % 16, d] pages[dst_pos // 16, 1, h, dst_pos % 16, d] = pages[src_pos // 16, 1, h, src_pos % 16, d] @T.prim_func def copy_single_page(var_pages: T.handle, src_page_id: T.int64, tgt_page_id: T.int64, copy_length: T.int64): T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) num_pages, page_size = T.int32(), T.int64() pages = T.match_buffer(var_pages, (num_pages, 2, 20, page_size, 64), "float16") # with T.block("root"): for b in T.thread_binding((copy_length * T.int64(1280) + T.int64(1023)) // T.int64(1024), thread="blockIdx.x"): for t in T.thread_binding(1024, thread="threadIdx.x"): with T.block("copy"): vh = T.axis.spatial(20, T.Cast("int32", (b * T.int64(1024) + T.Cast("int64", t)) // (copy_length * T.int64(64)))) vp = T.axis.spatial(copy_length, (b * T.int64(1024) + T.Cast("int64", t)) % (copy_length * T.int64(64)) // T.int64(64)) vd = T.axis.spatial(64, T.Cast("int32", (b * T.int64(1024) + T.Cast("int64", t)) % T.int64(64))) T.reads(pages[src_page_id, 0:2, vh, vp, vd]) T.writes(pages[tgt_page_id, 0:2, vh, vp, vd]) pages[tgt_page_id, 0, vh, vp, vd] = pages[src_page_id, 0, vh, vp, vd] pages[tgt_page_id, 1, vh, vp, vd] = pages[src_page_id, 1, vh, vp, vd] @T.prim_func def full(var_result: T.handle, value: T.int32): T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32})}) batch_size = T.int32(is_size_var=True) result = T.match_buffer(var_result, (batch_size, 1), "int32") # with T.block("root"): for i in range(batch_size): with T.block("block"): vi = T.axis.spatial(batch_size, i) T.reads() T.writes(result[vi, 0]) result[vi, 0] = value @T.prim_func def fused_rope(var_qkv: T.handle, var_position_map: T.handle, var_q: T.handle, var_k: T.handle, var_v: T.handle, apply_rope: T.int32): T.func_attr({"op_pattern": 8, "target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) seq_len = T.int64() qkv = T.match_buffer(var_qkv, (seq_len, 60, 64), "float16") position_map = T.match_buffer(var_position_map, (seq_len,), "int32", offset_factor=1) q = T.match_buffer(var_q, (seq_len, 20, 64), "float16") k = T.match_buffer(var_k, (seq_len, 20, 64), "float16") v = T.match_buffer(var_v, (seq_len, 20, 64), "float16") # with T.block("root"): for iters_0, iters_1, iters_2 in T.grid(seq_len, 60, 64): with T.block("llama_fused_rope"): s, h, d = T.axis.remap("SSS", [iters_0, iters_1, iters_2]) T.reads(position_map[s], qkv[s, h, d - 32:d - 32 + 65]) T.writes(q[s, h, d], k[s, h - 20, d], v[s, h - 40, d]) if h < 20: q[s, h, d] = T.if_then_else(apply_rope > 0 and d < 64, T.Cast("float16", T.cos(T.Cast("float32", position_map[s]) / T.pow(T.float32(1), T.Cast("float32", d * 2 % 64) / T.float32(64))) * T.Cast("float32", qkv[s, h, d]) + T.sin(T.Cast("float32", position_map[s]) / T.pow(T.float32(1), T.Cast("float32", d * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(d < 32, qkv[s, h, d + 32] * T.float16(-1), qkv[s, h, d - 32]))), qkv[s, h, d]) else: if h < 40: k[s, h - 20, d] = T.if_then_else(apply_rope > 0 and d < 64, T.Cast("float16", T.cos(T.Cast("float32", position_map[s]) / T.pow(T.float32(1), T.Cast("float32", d * 2 % 64) / T.float32(64))) * T.Cast("float32", qkv[s, h, d]) + T.sin(T.Cast("float32", position_map[s]) / T.pow(T.float32(1), T.Cast("float32", d * 2 % 64) / T.float32(64))) * T.Cast("float32", T.if_then_else(d < 32, qkv[s, h, d + 32] * T.float16(-1), qkv[s, h, d - 32]))), qkv[s, h, d]) else: v[s, h - 40, d] = qkv[s, h, d] @T.prim_func def gather_probs(var_src: T.handle, var_indices: T.handle, var_dst: T.handle): T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) m, n = T.int32(is_size_var=True), T.int32(is_size_var=True) src = T.match_buffer(var_src, (m, n)) batch_size = T.int32(is_size_var=True) indices = T.match_buffer(var_indices, (batch_size,), "int32") dst = T.match_buffer(var_dst, (batch_size, n)) # with T.block("root"): for b, j in T.grid(batch_size, n): with T.block("gather_2d"): vb, vj = T.axis.remap("SS", [b, j]) T.reads(src[indices[vb], vj], indices[vb]) T.writes(dst[vb, vj]) dst[vb, vj] = src[indices[vb], vj] @T.prim_func(private=True) def get_index_from_sorted(A: T.handle, B: T.handle, C: T.handle, D: T.handle, E: T.handle, F: T.handle): T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32})}) batch, vocab_size = T.int64(), T.int64() cumsum_sorted = T.match_buffer(A, (batch, vocab_size)) indices = T.match_buffer(B, (batch, vocab_size), "int32") renorm_prob = T.match_buffer(C, (batch, 1)) out_batch = T.int64() usample = T.match_buffer(D, (out_batch, 1)) sample_indices = T.match_buffer(E, (out_batch, 1), "int32") output_index = T.match_buffer(F, (out_batch, 1), "int32") # with T.block("root"): for ax0, ax1 in T.grid(out_batch, vocab_size): with T.block("T_get_index_from_sorted"): v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1]) T.reads(usample[v_ax0, T.int64(0)], cumsum_sorted[sample_indices[v_ax0, T.int64(0)], v_ax1 - T.int64(1):v_ax1 - T.int64(1) + T.int64(2)], sample_indices[v_ax0, T.int64(0)], renorm_prob[sample_indices[v_ax0, T.int64(0)], 0], indices[sample_indices[v_ax0, T.int64(0)], T.min(T.int64(0), v_ax1):T.min(T.int64(0), v_ax1) + (T.max(T.int64(0), v_ax1) + T.int64(1) - T.min(T.int64(0), v_ax1))]) T.writes(output_index[v_ax0, 0]) if usample[v_ax0, T.int64(0)] < cumsum_sorted[sample_indices[v_ax0, T.int64(0)], v_ax1] / renorm_prob[sample_indices[v_ax0, T.int64(0)], 0] or v_ax1 + T.int64(1) == vocab_size: if v_ax1 == T.int64(0): output_index[v_ax0, 0] = indices[sample_indices[v_ax0, T.int64(0)], 0] else: if usample[v_ax0, T.int64(0)] >= cumsum_sorted[sample_indices[v_ax0, T.int64(0)], v_ax1 - T.int64(1)] / renorm_prob[sample_indices[v_ax0, T.int64(0)], 0]: output_index[v_ax0, 0] = indices[sample_indices[v_ax0, T.int64(0)], v_ax1] @T.prim_func(private=True) def get_renorm_prob(A: T.handle, B: T.handle, C: T.handle, D: T.handle): T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32})}) batch, vocab_size = T.int64(), T.int64() cumsum_sorted = T.match_buffer(A, (batch, vocab_size)) top_p = T.match_buffer(B, (batch, 1)) top_k = T.match_buffer(C, (batch, 1), "int32") renorm_prob = T.match_buffer(D, (batch, 1)) # with T.block("root"): for ax0, ax1 in T.grid(batch, vocab_size): with T.block("T_get_renorm_prob"): v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1]) T.reads(cumsum_sorted[v_ax0, T.min(T.min(T.int64(0), v_ax1), v_ax1 + T.int64(1)):T.min(T.min(T.int64(0), v_ax1), v_ax1 + T.int64(1)) + (T.max(T.max(T.int64(0), v_ax1), v_ax1 + T.int64(1)) + T.int64(1) - T.min(T.min(T.int64(0), v_ax1), v_ax1 + T.int64(1)))], top_p[v_ax0, 0], top_k[v_ax0, 0]) T.writes(renorm_prob[v_ax0, 0]) if not (cumsum_sorted[v_ax0, 0] < top_p[v_ax0, 0] and top_k[v_ax0, 0] > 1): renorm_prob[v_ax0, 0] = cumsum_sorted[v_ax0, 0] else: if cumsum_sorted[v_ax0, v_ax1] < top_p[v_ax0, 0] and v_ax1 + T.int64(1) < T.Cast("int64", top_k[v_ax0, 0]): if v_ax1 + T.int64(1) == vocab_size: renorm_prob[v_ax0, 0] = cumsum_sorted[v_ax0, v_ax1] else: if not (cumsum_sorted[v_ax0, v_ax1 + T.int64(1)] < top_p[v_ax0, 0] and v_ax1 + T.int64(1) + T.int64(1) < T.Cast("int64", top_k[v_ax0, 0])): renorm_prob[v_ax0, 0] = cumsum_sorted[v_ax0, v_ax1 + T.int64(1)] @T.prim_func(private=True) def index(var_layer_norm355: T.handle, index: T.Buffer((T.int64(1), T.int64(1), T.int64(1280)), "float16")): T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) seq_len = T.int64() layer_norm355 = T.match_buffer(var_layer_norm355, (T.int64(1), seq_len, T.int64(1280)), "float16") # with T.block("root"): for i, _, k in T.grid(T.int64(1), T.int64(1), T.int64(1280)): with T.block("index"): v_i, v__, v_k = T.axis.remap("SSS", [i, _, k]) T.reads(layer_norm355[v_i, seq_len - T.int64(1), v_k]) T.writes(index[v_i, v__, v_k]) index[v_i, v__, v_k] = layer_norm355[v_i, seq_len - T.int64(1), v_k] @T.prim_func def merge_state_inplace(v: T.handle, s: T.handle, v_other: T.handle, s_other: T.handle): T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1}) N, H, D = T.int32(is_size_var=True), T.int32(is_size_var=True), T.int32(is_size_var=True) V = T.match_buffer(v, (N, H, D), "float16") S = T.match_buffer(s, (N, H)) V_other = T.match_buffer(v_other, (N, H, D), "float16") S_other = T.match_buffer(s_other, (N, H)) # with T.block("root"): for bx in T.thread_binding(N, thread="blockIdx.x"): for by in T.thread_binding(1, thread="blockIdx.y"): for ty in T.thread_binding(20, thread="threadIdx.y"): for tx in T.thread_binding(16, thread="threadIdx.x"): with T.block("merge"): T.reads(S[bx, ty + by * 20], S_other[bx, ty + by * 20], V[bx, ty + by * 20, tx * 4:tx * 4 + 4], V_other[bx, ty + by * 20, tx * 4:tx * 4 + 4]) T.writes(V[bx, ty + by * 20, tx * 4:tx * 4 + 4], S[bx, ty + by * 20]) s_val = T.alloc_buffer((1,), scope="local") s_other_val = T.alloc_buffer((1,), scope="local") s_max = T.alloc_buffer((1,), scope="local") scale = T.alloc_buffer((1,), scope="local") other_scale = T.alloc_buffer((1,), scope="local") v_vec = T.alloc_buffer((4,), "float16", scope="local") v_other_vec = T.alloc_buffer((4,), "float16", scope="local") s_val[0] = S[bx, ty + by * 20] s_other_val[0] = S_other[bx, ty + by * 20] s_max[0] = T.max(s_val[0], s_other_val[0]) s_val[0] = T.exp2(s_val[0] - s_max[0]) s_other_val[0] = T.exp2(s_other_val[0] - s_max[0]) scale[0] = s_val[0] / (s_val[0] + s_other_val[0]) other_scale[0] = s_other_val[0] / (s_val[0] + s_other_val[0]) for vec in T.vectorized(4): v_vec[vec] = V[bx, ty + by * 20, tx * 4 + vec] for vec in T.vectorized(4): v_other_vec[vec] = V_other[bx, ty + by * 20, tx * 4 + vec] for vec in range(4): v_vec[vec] = T.Cast("float16", T.Cast("float32", v_vec[vec]) * scale[0] + T.Cast("float32", v_other_vec[vec]) * other_scale[0]) for vec in T.vectorized(4): V[bx, ty + by * 20, tx * 4 + vec] = v_vec[vec] S[bx, ty + by * 20] = T.log2(s_val[0] + s_other_val[0]) + s_max[0] @T.prim_func def sampler_take_probs_tir(var_unsorted_probs: T.handle, var_sorted_indices: T.handle, var_sample_indices: T.handle, var_sampling_results: T.handle, var_top_prob_offsets: T.handle, var_sampled_values: T.handle, var_top_prob_probs: T.handle, var_top_prob_indices: T.handle): T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32})}) batch_size, vocab_size = T.int32(is_size_var=True), T.int32(is_size_var=True) unsorted_probs = T.match_buffer(var_unsorted_probs, (batch_size, vocab_size)) sorted_indices = T.match_buffer(var_sorted_indices, (batch_size, vocab_size), "int32") num_samples = T.int32(is_size_var=True) sample_indices = T.match_buffer(var_sample_indices, (num_samples,), "int32") sampling_results = T.match_buffer(var_sampling_results, (num_samples,), "int32") num_positions = T.int32(is_size_var=True) top_prob_offsets = T.match_buffer(var_top_prob_offsets, (num_positions,), "int32") sampled_values = T.match_buffer(var_sampled_values, (num_samples,)) top_prob_probs = T.match_buffer(var_top_prob_probs, (num_positions,)) top_prob_indices = T.match_buffer(var_top_prob_indices, (num_positions,), "int32") # with T.block("root"): for i in range(num_positions + num_samples): with T.block("block"): vi = T.axis.spatial(num_positions + num_samples, i) T.reads(top_prob_offsets[vi], sorted_indices[top_prob_offsets[vi] // vocab_size, top_prob_offsets[vi] % vocab_size], unsorted_probs[T.min(top_prob_offsets[vi] // vocab_size, sample_indices[vi - num_positions]):T.min(top_prob_offsets[vi] // vocab_size, sample_indices[vi - num_positions]) + (T.max(top_prob_offsets[vi] // vocab_size, sample_indices[vi - num_positions]) + 1 - T.min(top_prob_offsets[vi] // vocab_size, sample_indices[vi - num_positions])), T.min(sorted_indices[top_prob_offsets[vi] // vocab_size, top_prob_offsets[vi] % vocab_size], sampling_results[vi - num_positions]):T.min(sorted_indices[top_prob_offsets[vi] // vocab_size, top_prob_offsets[vi] % vocab_size], sampling_results[vi - num_positions]) + (T.max(sorted_indices[top_prob_offsets[vi] // vocab_size, top_prob_offsets[vi] % vocab_size], sampling_results[vi - num_positions]) + 1 - T.min(sorted_indices[top_prob_offsets[vi] // vocab_size, top_prob_offsets[vi] % vocab_size], sampling_results[vi - num_positions]))], sample_indices[vi - num_positions], sampling_results[vi - num_positions]) T.writes(top_prob_indices[vi], top_prob_probs[vi], sampled_values[vi - num_positions]) if vi < num_positions: row: T.int32 = top_prob_offsets[vi] // vocab_size col: T.int32 = top_prob_offsets[vi] % vocab_size top_prob_indices[vi] = sorted_indices[row, col] top_prob_probs[vi] = unsorted_probs[row, sorted_indices[row, col]] else: vj: T.int32 = vi - num_positions sampled_values[vj] = unsorted_probs[sample_indices[vj], sampling_results[vj]] @T.prim_func def scatter_probs(var_src: T.handle, var_indices: T.handle, var_dst: T.handle): T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) batch_size, n = T.int32(is_size_var=True), T.int32(is_size_var=True) src = T.match_buffer(var_src, (batch_size, n)) indices = T.match_buffer(var_indices, (batch_size,), "int32") m = T.int32(is_size_var=True) dst = T.match_buffer(var_dst, (m, n)) # with T.block("root"): for b, j in T.grid(batch_size, n): with T.block("scatter_2d"): vb, vj = T.axis.remap("SS", [b, j]) T.reads(src[vb, vj], indices[vb]) T.writes(dst[indices[vb], vj]) dst[indices[vb], vj] = src[vb, vj] @T.prim_func def softmax_with_chunked_sum(var_A: T.handle, var_temperature: T.handle, var_chunked_sum: T.handle, var_chunked_max: T.handle, var_softmax: T.handle): T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) batch_size, vocab_size = T.int64(is_size_var=True), T.int64(is_size_var=True) A = T.match_buffer(var_A, (batch_size, vocab_size)) temperature = T.match_buffer(var_temperature, (batch_size,)) num_chunks = T.int64(is_size_var=True) chunked_sum = T.match_buffer(var_chunked_sum, (batch_size, num_chunks)) chunked_max = T.match_buffer(var_chunked_max, (batch_size, num_chunks)) softmax = T.match_buffer(var_softmax, (batch_size, vocab_size)) # with T.block("root"): temp_max_shared = T.alloc_buffer((batch_size,), scope="shared") temp_sum_shared = T.alloc_buffer((batch_size,), scope="shared") for l0_l1_fused in T.thread_binding(batch_size * num_chunks, thread="blockIdx.x"): for ax0_1 in T.thread_binding(T.int64(32), thread="threadIdx.x"): for ax0_0 in T.serial((num_chunks + T.int64(31)) // T.int64(32), annotations={"pragma_auto_unroll_max_step": 64, "pragma_unroll_explicit": 1}): with T.block("max"): v0 = T.axis.spatial(batch_size, l0_l1_fused % (num_chunks * batch_size) // num_chunks) v1 = T.axis.reduce(num_chunks, ax0_0 * T.int64(32) + ax0_1) T.where(ax0_0 * T.int64(32) + ax0_1 < num_chunks) T.reads(chunked_max[v0, v1]) T.writes(temp_max_shared[v0]) with T.init(): temp_max_shared[v0] = T.float32(-3.4028234663852886e+38) temp_max_shared[v0] = T.max(temp_max_shared[v0], chunked_max[v0, v1]) for ax0_1 in T.thread_binding(T.int64(32), thread="threadIdx.x"): for ax0_0 in T.serial((num_chunks + T.int64(31)) // T.int64(32), annotations={"pragma_auto_unroll_max_step": 64, "pragma_unroll_explicit": 1}): with T.block("sum_exp"): v0 = T.axis.spatial(batch_size, l0_l1_fused % (num_chunks * batch_size) // num_chunks) v1 = T.axis.reduce(num_chunks, ax0_0 * T.int64(32) + ax0_1) T.where(ax0_0 * T.int64(32) + ax0_1 < num_chunks) T.reads(temperature[v0], chunked_sum[v0, v1], chunked_max[v0, v1], temp_max_shared[v0]) T.writes(temp_sum_shared[v0]) with T.init(): temp_sum_shared[v0] = T.float32(0) temp_sum_shared[v0] = temp_sum_shared[v0] + T.Select(temperature[v0] > T.float32(1.0000000000000001e-05), T.exp(chunked_sum[v0, v1] + chunked_max[v0, v1] - temp_max_shared[v0]), T.Cast("float32", chunked_max[v0, v1] == temp_max_shared[v0]) * chunked_sum[v0, v1]) for l2_0 in T.serial(T.int64(4), annotations={"pragma_auto_unroll_max_step": 64, "pragma_unroll_explicit": 1}): for l2_1 in T.thread_binding(T.int64(32), thread="threadIdx.y"): for l2_2 in T.thread_binding(T.int64(32), thread="threadIdx.x"): with T.block("log_pad"): v0 = T.axis.spatial(batch_size, l0_l1_fused % (num_chunks * batch_size) // num_chunks) v1 = T.axis.spatial(num_chunks, l0_l1_fused % num_chunks) v2 = T.axis.spatial(T.int64(4096), l2_0 * T.int64(1024) + l2_1 * T.int64(32) + l2_2) T.reads(temperature[v0], A[v0, v1 * T.int64(4096) + v2], temp_sum_shared[v0], temp_max_shared[v0]) T.writes(softmax[v0, v1 * T.int64(4096) + v2]) if v1 * T.int64(4096) + v2 < vocab_size: softmax[v0, v1 * T.int64(4096) + v2] = T.if_then_else(temperature[v0] > T.float32(1.0000000000000001e-05), T.exp(A[v0, v1 * T.int64(4096) + v2] / temperature[v0] - (T.log(temp_sum_shared[v0]) + temp_max_shared[v0])), T.Cast("float32", A[v0, v1 * T.int64(4096) + v2] == temp_max_shared[v0]) / temp_sum_shared[v0]) @T.prim_func(private=True) def take_sorted_probs(var_probs: T.handle, var_lv1: T.handle, var_take_sorted_probs: T.handle): T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) batch_size, vocab_size = T.int64(), T.int64() probs = T.match_buffer(var_probs, (batch_size, vocab_size)) lv1 = T.match_buffer(var_lv1, (batch_size, vocab_size), "int32") batch_size_1, vocab_size_1 = T.int64(), T.int64() take_sorted_probs = T.match_buffer(var_take_sorted_probs, (batch_size_1, vocab_size_1)) # with T.block("root"): for i, j in T.grid(batch_size_1, vocab_size_1): with T.block("take_sorted_probs"): v_i, v_j = T.axis.remap("SS", [i, j]) T.reads(probs[v_i, lv1[v_i, v_j]], lv1[v_i, v_j]) T.writes(take_sorted_probs[v_i, v_j]) take_sorted_probs[v_i, v_j] = probs[v_i, lv1[v_i, v_j]] @T.prim_func def tir_kv_cache_debug_get_kv(var_pages: T.handle, var_position_map: T.handle, var_k_data: T.handle, var_v_data: T.handle, layer_id: T.int64): T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) num_pages, page_size = T.int64(), T.int64(is_size_var=True) pages = T.match_buffer(var_pages, (num_pages, 2, 20, page_size, 64), "float16") seqlen = T.int64(is_size_var=True) position_map = T.match_buffer(var_position_map, (seqlen,), "int32", offset_factor=1) k_data = T.match_buffer(var_k_data, (32, seqlen, 20, 64), "float16") v_data = T.match_buffer(var_v_data, (32, seqlen, 20, 64), "float16") # with T.block("root"): for p, h, d in T.grid(seqlen, 20, 64): with T.block("copy0"): vp, vh, vd = T.axis.remap("SSS", [p, h, d]) T.reads(position_map[vp], pages[T.Cast("int64", position_map[vp]) // page_size, 0:2, vh, T.Cast("int64", position_map[vp]) % page_size, vd]) T.writes(k_data[layer_id, vp, vh, vd], v_data[layer_id, vp, vh, vd]) position: T.int32 = position_map[vp] k_data[layer_id, vp, vh, vd] = pages[T.Cast("int64", position) // page_size, 0, vh, T.Cast("int64", position) % page_size, vd] v_data[layer_id, vp, vh, vd] = pages[T.Cast("int64", position) // page_size, 1, vh, T.Cast("int64", position) % page_size, vd] @T.prim_func def tir_kv_cache_transpose_append(var_pages: T.handle, var_k_data: T.handle, var_v_data: T.handle, var_position_map: T.handle): T.func_attr({"target": T.target({"arch": "sm_89", "host": {"keys": ["cpu"], "kind": "llvm", "mcpu": "znver3", "mtriple": "x86_64-pc-linux-gnu", "tag": ""}, "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.noalias": T.bool(True)}) num_pages = T.int64() pages = T.match_buffer(var_pages, (num_pages, 2, 20, 16, 64), "float16") ntoken = T.int64(is_size_var=True) k_data = T.match_buffer(var_k_data, (ntoken, 20, 64), "float16") v_data = T.match_buffer(var_v_data, (ntoken, 20, 64), "float16") position_map = T.match_buffer(var_position_map, (ntoken,), "int32", offset_factor=1) # with T.block("root"): for global_pos, h, f in T.grid(ntoken, 20, 64): if position_map[global_pos] != -1: with T.block("k_transpose_append"): vgpos, vh, vf = T.axis.remap("SSS", [global_pos, h, f]) T.reads(position_map[vgpos], k_data[vgpos, vh, vf]) T.writes(pages[position_map[vgpos] // 16, 0, vh, position_map[vgpos] % 16, vf]) position: T.int32 = position_map[vgpos] pages[position // 16, 0, vh, position % 16, vf] = k_data[vgpos, vh, vf] with T.block("v_transpose_append"): vgpos, vh, vf = T.axis.remap("SSS", [global_pos, h, f]) T.reads(position_map[vgpos], v_data[vgpos, vh, vf]) T.writes(pages[position_map[vgpos] // 16, 1, vh, position_map[vgpos] % 16, vf]) position: T.int32 = position_map[vgpos] pages[position // 16, 1, vh, position % 16, vf] = v_data[vgpos, vh, vf] @T.prim_func(private=True) def top_p_pivot_cutoff(var_prob: T.handle, var_top_p_arr: T.handle, var_init_pivots: T.handle, var_final_pivot: T.handle, var_final_lsum: T.handle): T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) B, N = T.int32(), T.int32() prob = T.match_buffer(var_prob, (B, N)) top_p_arr = T.match_buffer(var_top_p_arr, (B,)) init_pivots = T.match_buffer(var_init_pivots, (B, 3)) final_pivot = T.match_buffer(var_final_pivot, (B,)) final_lsum = T.match_buffer(var_final_lsum, (B,)) # with T.block("root"): pivot = T.alloc_buffer((3,), scope="local") top_p = T.alloc_buffer((1,), scope="local") L = T.alloc_buffer((1,), scope="shared") R_1 = T.alloc_buffer((1,), scope="shared") L_local = T.alloc_buffer((1,), scope="local") R_local = T.alloc_buffer((1,), scope="local") q = T.alloc_buffer((1,), scope="local") lsum = T.alloc_buffer((3,), scope="local") lmin_broadcast = T.alloc_buffer((1,), scope="shared") lmin_broadcast_local = T.alloc_buffer((1,), scope="local") lmin = T.alloc_buffer((3,), scope="local") cmin = T.alloc_buffer((3,), "int32", scope="local") total_sum = T.alloc_buffer((1,), scope="local") it = T.alloc_buffer((1,), "int32", scope="local") es_local = T.alloc_buffer((1,), "bool", scope="local") es = T.alloc_buffer((1,), "bool", scope="shared") find_pivot_local = T.alloc_buffer((1,), "bool", scope="local") find_pivot = T.alloc_buffer((1,), "bool", scope="shared") total_sum_reduce = T.alloc_buffer((1,), scope="local") lsum_reduce = T.alloc_buffer((1,), scope="local") lmin_reduce = T.alloc_buffer((1,), scope="local") cmin_reduce = T.alloc_buffer((1,), "int32", scope="local") for _bx in T.thread_binding(B, thread="blockIdx.x"): for _tx in T.thread_binding(1024, thread="threadIdx.x"): with T.block("CTA"): b, tx = T.axis.remap("SS", [_bx, _tx]) T.reads(top_p_arr[b], top_p[0], L[0], R_1[0], init_pivots[b, 0:3], L_local[0], R_local[0], find_pivot_local[0], it[0], es_local[0], prob[b, it[0] * 1024 + tx], total_sum[0], q[0], pivot[T.min(0, it[0]):T.min(0, it[0]) + (T.max(2, it[0]) + 1 - T.min(0, it[0]))], lsum[T.min(0, it[0]):T.min(0, it[0]) + (T.max(2, it[0]) + 1 - T.min(0, it[0]))], lmin[T.min(0, it[0]):T.min(0, it[0]) + (T.max(2, it[0]) + 1 - T.min(0, it[0]))], cmin[T.min(0, it[0]):T.min(0, it[0]) + (T.max(2, it[0]) + 1 - T.min(0, it[0]))], total_sum_reduce[0], es[0], lmin_reduce[0], lmin_broadcast[0], lmin_broadcast_local[0], lsum_reduce[0], cmin_reduce[0], find_pivot[0]) T.writes(top_p[0], L[0], R_1[0], find_pivot[0], L_local[0], R_local[0], pivot[0:3], find_pivot_local[0], final_lsum[b], final_pivot[b], lsum[0:3], lmin[0:3], cmin[0:3], total_sum[0], it[0], es_local[0], q[0], total_sum_reduce[0], es[0], lsum_reduce[0], lmin_reduce[0], lmin_broadcast[0], lmin_broadcast_local[0], cmin_reduce[0]) top_p[0] = top_p_arr[b] if tx == 0: L[0] = T.float32(1) - top_p[0] R_1[0] = T.float32(9.9999999999999995e-08) find_pivot[0] = T.bool(False) T.tvm_storage_sync("shared") L_local[0] = L[0] R_local[0] = R_1[0] for i in T.unroll(3): pivot[i] = init_pivots[b, i] find_pivot_local[0] = T.bool(False) if L_local[0] - R_local[0] <= T.float32(9.9999999999999995e-08): if tx == 0: final_lsum[b] = T.float32(1) final_pivot[b] = T.float32(0) find_pivot_local[0] = T.bool(True) while T.tvm_thread_invariant(L_local[0] - R_local[0] > T.float32(9.9999999999999995e-08) and not find_pivot_local[0]): T.tvm_storage_sync("shared") for pidx in T.unroll(3): lsum[pidx] = T.float32(0) lmin[pidx] = T.float32(3.4028234663852886e+38) cmin[pidx] = 0 total_sum[0] = T.float32(0) it[0] = 0 es_local[0] = T.bool(False) while it[0] < (N + 1024 - 1) // 1024 and not es_local[0]: q[0] = T.if_then_else(it[0] * 1024 + tx < N, prob[b, it[0] * 1024 + tx], T.float32(0)) total_sum[0] = total_sum[0] + q[0] for pidx in T.unroll(3): if q[0] >= pivot[pidx]: lsum[pidx] = lsum[pidx] + q[0] if lmin[pidx] > q[0]: lmin[pidx] = q[0] cmin[pidx] = 1 else: if lmin[pidx] == q[0]: cmin[pidx] = cmin[pidx] + 1 it[0] = it[0] + 1 if it[0] % 32 == 0: with T.block("block_cross_thread"): T.reads(total_sum[0]) T.writes(total_sum_reduce[0]) T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) T.tvm_thread_allreduce(T.uint32(1), total_sum[0], T.bool(True), total_sum_reduce[0], tx) if tx == 0: es[0] = T.float32(1) - total_sum_reduce[0] < pivot[2] T.tvm_storage_sync("shared") es_local[0] = es[0] T.tvm_storage_sync("shared") for pidx in range(3): with T.block("block_cross_thread"): T.reads(lsum[pidx]) T.writes(lsum_reduce[0]) T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) T.tvm_thread_allreduce(T.uint32(1), lsum[pidx], T.bool(True), lsum_reduce[0], tx) with T.block("block_cross_thread"): T.reads(lmin[pidx]) T.writes(lmin_reduce[0]) T.attr(T.comm_reducer(lambda x0, y0: T.min(x0, y0), [T.float32(0)]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) T.tvm_thread_allreduce(T.uint32(1), lmin[pidx], T.bool(True), lmin_reduce[0], tx) if tx == 0: lmin_broadcast[0] = lmin_reduce[0] T.tvm_storage_sync("shared") lmin_broadcast_local[0] = lmin_broadcast[0] if lmin[pidx] > lmin_broadcast_local[0]: cmin[pidx] = 0 if tx == 0: lsum[pidx] = lsum_reduce[0] lmin[pidx] = lmin_reduce[0] with T.block("block_cross_thread"): T.reads(cmin[pidx]) T.writes(cmin_reduce[0]) T.attr(T.comm_reducer(lambda x0, y0: x0 + y0, [0]), "reduce_scope", T.reinterpret("handle", T.uint64(0))) T.tvm_thread_allreduce(T.uint32(1), cmin[pidx], T.bool(True), cmin_reduce[0], tx) if tx == 0: cmin[pidx] = cmin_reduce[0] T.tvm_storage_sync("shared") if tx == 0: it[0] = 0 while it[0] < 3 and not find_pivot_local[0]: if lsum[it[0]] >= top_p[0] and top_p[0] > lsum[it[0]] - T.Cast("float32", cmin[it[0]]) * lmin[it[0]]: find_pivot[0] = T.bool(True) find_pivot_local[0] = T.bool(True) final_pivot[b] = pivot[it[0]] final_lsum[b] = lsum[it[0]] else: if lsum[it[0]] - lmin[it[0]] * T.Cast("float32", cmin[it[0]]) >= top_p[0]: R_1[0] = pivot[it[0]] final_lsum[b] = lsum[it[0]] else: if lsum[it[0]] < top_p[0]: L[0] = pivot[it[0]] it[0] = it[0] + 1 T.tvm_storage_sync("shared") L_local[0] = L[0] R_local[0] = R_1[0] find_pivot_local[0] = find_pivot[0] for pidx in T.unroll(3): pivot[pidx] = L[0] - T.Cast("float32", pidx + 1) * (L_local[0] - R_local[0]) / T.float32(4) if tx == 0: if not find_pivot_local[0]: final_pivot[b] = R_local[0] if R_local[0] == T.float32(9.9999999999999995e-08): final_lsum[b] = lsum[2] @T.prim_func(private=True) def top_p_renorm_after_cutoff(var_prob: T.handle, var_final_pivot: T.handle, var_final_lsum: T.handle, var_renorm_prob: T.handle): T.func_attr({"target": T.target({"arch": "sm_89", "keys": ["cuda", "gpu"], "kind": "cuda", "libs": ["thrust"], "max_num_threads": 1024, "max_shared_memory_per_block": 49152, "max_threads_per_block": 1024, "tag": "", "thread_warp_size": 32}), "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) B, N = T.int32(), T.int32() prob = T.match_buffer(var_prob, (B, N)) final_pivot = T.match_buffer(var_final_pivot, (B,)) final_lsum = T.match_buffer(var_final_lsum, (B,)) renorm_prob = T.match_buffer(var_renorm_prob, (B, N)) # with T.block("root"): pivot = T.alloc_buffer((1,), scope="local") lsum = T.alloc_buffer((1,), scope="local") for _by in T.thread_binding(B, thread="blockIdx.y"): for _bx in T.thread_binding((B + 511) // B, thread="blockIdx.x"): for _tx in T.thread_binding(1024, thread="threadIdx.x"): with T.block("CTA"): by, bx, tx = T.axis.remap("SSS", [_by, _bx, _tx]) T.reads(final_pivot[by], final_lsum[by], prob[by, T.Select(0 <= (B + 511) // B, 0, (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + bx * 1024 + tx:T.Select(0 <= (B + 511) // B, 0, (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + bx * 1024 + tx + (T.Select(0 <= (B + 511) // B, (N - 1) // ((B + 511) // B * 1024) * ((B + 511) // B), 0 - (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + 1)], pivot[0], lsum[0]) T.writes(pivot[0], lsum[0], renorm_prob[by, T.Select(0 <= (B + 511) // B, 0, (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + bx * 1024 + tx:T.Select(0 <= (B + 511) // B, 0, (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + bx * 1024 + tx + (T.Select(0 <= (B + 511) // B, (N - 1) // ((B + 511) // B * 1024) * ((B + 511) // B), 0 - (((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024) - 1) * ((B + 511) // B)) * 1024 + 1)]) pivot[0] = final_pivot[by] lsum[0] = final_lsum[by] for i in range(((B + 511) // B * 1024 + N - 1) // ((B + 511) // B * 1024)): if i * ((512 + B - 1) // B) * 1024 + bx * 1024 + tx < N: renorm_prob[by, i * ((512 + B - 1) // B) * 1024 + bx * 1024 + tx] = T.if_then_else(prob[by, i * ((512 + B - 1) // B) * 1024 + bx * 1024 + tx] >= pivot[0], prob[by, i * ((512 + B - 1) // B) * 1024 + bx * 1024 + tx] / lsum[0], T.float32(0)) @R.function def argsort_probs(probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32")) -> R.Tuple(R.Tensor(("batch_size", "vocab_size"), dtype="float32"), R.Tensor(("batch_size", "vocab_size"), dtype="int32")): batch_size = T.int64() vocab_size = T.int64() R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) cls = Module with R.dataflow(): lv1: R.Tensor((batch_size, vocab_size), dtype="int32") = R.argsort(probs, axis=-1, descending=True, dtype="int32") lv2 = R.call_tir(cls.take_sorted_probs, (probs, lv1), out_sinfo=R.Tensor((batch_size, vocab_size), dtype="float32")) gv1: R.Tuple(R.Tensor((batch_size, vocab_size), dtype="float32"), R.Tensor((batch_size, vocab_size), dtype="int32")) = lv2, lv1 R.output(gv1) return gv1 @R.function def batch_compute_cross_attn_kv(encoder_hidden_states: R.Tensor(("batch_size", 1500, 1280), dtype="float16"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((51866, 1280), dtype="float16"), R.Tensor((448, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Object: batch_size = T.int64() R.func_attr({"num_input": 2, "relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) with R.dataflow(): model_decoder_layers_0_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[498] model_decoder_layers_0_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[499] model_decoder_layers_0_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[500] model_decoder_layers_1_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[522] model_decoder_layers_1_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[523] model_decoder_layers_1_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[524] model_decoder_layers_2_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[546] model_decoder_layers_2_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[547] model_decoder_layers_2_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[548] model_decoder_layers_3_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[570] model_decoder_layers_3_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[571] model_decoder_layers_3_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[572] model_decoder_layers_4_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[594] model_decoder_layers_4_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[595] model_decoder_layers_4_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[596] model_decoder_layers_5_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[618] model_decoder_layers_5_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[619] model_decoder_layers_5_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[620] model_decoder_layers_6_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[642] model_decoder_layers_6_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[643] model_decoder_layers_6_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[644] model_decoder_layers_7_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[666] model_decoder_layers_7_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[667] model_decoder_layers_7_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[668] model_decoder_layers_8_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[690] model_decoder_layers_8_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[691] model_decoder_layers_8_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[692] model_decoder_layers_9_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[714] model_decoder_layers_9_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[715] model_decoder_layers_9_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[716] model_decoder_layers_10_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[738] model_decoder_layers_10_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[739] model_decoder_layers_10_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[740] model_decoder_layers_11_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[762] model_decoder_layers_11_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[763] model_decoder_layers_11_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[764] model_decoder_layers_12_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[786] model_decoder_layers_12_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[787] model_decoder_layers_12_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[788] model_decoder_layers_13_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[810] model_decoder_layers_13_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[811] model_decoder_layers_13_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[812] model_decoder_layers_14_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[834] model_decoder_layers_14_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[835] model_decoder_layers_14_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[836] model_decoder_layers_15_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[858] model_decoder_layers_15_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[859] model_decoder_layers_15_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[860] model_decoder_layers_16_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[882] model_decoder_layers_16_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[883] model_decoder_layers_16_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[884] model_decoder_layers_17_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[906] model_decoder_layers_17_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[907] model_decoder_layers_17_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[908] model_decoder_layers_18_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[930] model_decoder_layers_18_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[931] model_decoder_layers_18_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[932] model_decoder_layers_19_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[954] model_decoder_layers_19_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[955] model_decoder_layers_19_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[956] model_decoder_layers_20_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[978] model_decoder_layers_20_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[979] model_decoder_layers_20_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[980] model_decoder_layers_21_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1002] model_decoder_layers_21_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1003] model_decoder_layers_21_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1004] model_decoder_layers_22_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1026] model_decoder_layers_22_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1027] model_decoder_layers_22_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1028] model_decoder_layers_23_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1050] model_decoder_layers_23_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1051] model_decoder_layers_23_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1052] model_decoder_layers_24_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1074] model_decoder_layers_24_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1075] model_decoder_layers_24_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1076] model_decoder_layers_25_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1098] model_decoder_layers_25_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1099] model_decoder_layers_25_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1100] model_decoder_layers_26_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1122] model_decoder_layers_26_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1123] model_decoder_layers_26_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1124] model_decoder_layers_27_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1146] model_decoder_layers_27_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1147] model_decoder_layers_27_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1148] model_decoder_layers_28_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1170] model_decoder_layers_28_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1171] model_decoder_layers_28_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1172] model_decoder_layers_29_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1194] model_decoder_layers_29_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1195] model_decoder_layers_29_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1196] model_decoder_layers_30_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1218] model_decoder_layers_30_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1219] model_decoder_layers_30_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1220] model_decoder_layers_31_encoder_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1242] model_decoder_layers_31_encoder_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1243] model_decoder_layers_31_encoder_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1244] lv = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_0_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape256: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv, R.shape([batch_size, 1500, 20, 64])) lv_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_0_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_0_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape257: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv_1, R.shape([batch_size, 1500, 20, 64])) reshape258: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape256, R.shape([batch_size * 1500, 20, 64])) reshape259: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape257, R.shape([batch_size * 1500, 20, 64])) lv36: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", paged_kv_cache, R.prim_value(0), reshape258, reshape259, sinfo_args=(R.Object,)) lv1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_1_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape260: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv1, R.shape([batch_size, 1500, 20, 64])) lv1_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_1_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_1_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape261: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv1_1, R.shape([batch_size, 1500, 20, 64])) reshape262: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape260, R.shape([batch_size * 1500, 20, 64])) reshape263: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape261, R.shape([batch_size * 1500, 20, 64])) lv37: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv36, R.prim_value(1), reshape262, reshape263, sinfo_args=(R.Object,)) lv2 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_2_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape264: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv2, R.shape([batch_size, 1500, 20, 64])) lv2_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_2_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_2_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape265: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv2_1, R.shape([batch_size, 1500, 20, 64])) reshape266: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape264, R.shape([batch_size * 1500, 20, 64])) reshape267: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape265, R.shape([batch_size * 1500, 20, 64])) lv38: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv37, R.prim_value(2), reshape266, reshape267, sinfo_args=(R.Object,)) lv3 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_3_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape268: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv3, R.shape([batch_size, 1500, 20, 64])) lv3_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_3_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_3_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape269: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv3_1, R.shape([batch_size, 1500, 20, 64])) reshape270: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape268, R.shape([batch_size * 1500, 20, 64])) reshape271: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape269, R.shape([batch_size * 1500, 20, 64])) lv39: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv38, R.prim_value(3), reshape270, reshape271, sinfo_args=(R.Object,)) lv4 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_4_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape272: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv4, R.shape([batch_size, 1500, 20, 64])) lv4_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_4_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_4_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape273: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv4_1, R.shape([batch_size, 1500, 20, 64])) reshape274: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape272, R.shape([batch_size * 1500, 20, 64])) reshape275: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape273, R.shape([batch_size * 1500, 20, 64])) lv40: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv39, R.prim_value(4), reshape274, reshape275, sinfo_args=(R.Object,)) lv5 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_5_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape276: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv5, R.shape([batch_size, 1500, 20, 64])) lv5_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_5_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_5_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape277: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv5_1, R.shape([batch_size, 1500, 20, 64])) reshape278: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape276, R.shape([batch_size * 1500, 20, 64])) reshape279: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape277, R.shape([batch_size * 1500, 20, 64])) lv41: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv40, R.prim_value(5), reshape278, reshape279, sinfo_args=(R.Object,)) lv6 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_6_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape280: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv6, R.shape([batch_size, 1500, 20, 64])) lv6_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_6_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_6_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape281: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv6_1, R.shape([batch_size, 1500, 20, 64])) reshape282: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape280, R.shape([batch_size * 1500, 20, 64])) reshape283: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape281, R.shape([batch_size * 1500, 20, 64])) lv42: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv41, R.prim_value(6), reshape282, reshape283, sinfo_args=(R.Object,)) lv7 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_7_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape284: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv7, R.shape([batch_size, 1500, 20, 64])) lv7_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_7_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_7_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape285: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv7_1, R.shape([batch_size, 1500, 20, 64])) reshape286: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape284, R.shape([batch_size * 1500, 20, 64])) reshape287: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape285, R.shape([batch_size * 1500, 20, 64])) lv43: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv42, R.prim_value(7), reshape286, reshape287, sinfo_args=(R.Object,)) lv8 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_8_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape288: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv8, R.shape([batch_size, 1500, 20, 64])) lv8_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_8_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_8_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape289: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv8_1, R.shape([batch_size, 1500, 20, 64])) reshape290: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape288, R.shape([batch_size * 1500, 20, 64])) reshape291: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape289, R.shape([batch_size * 1500, 20, 64])) lv44: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv43, R.prim_value(8), reshape290, reshape291, sinfo_args=(R.Object,)) lv9 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_9_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape292: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv9, R.shape([batch_size, 1500, 20, 64])) lv9_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_9_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_9_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape293: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv9_1, R.shape([batch_size, 1500, 20, 64])) reshape294: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape292, R.shape([batch_size * 1500, 20, 64])) reshape295: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape293, R.shape([batch_size * 1500, 20, 64])) lv45: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv44, R.prim_value(9), reshape294, reshape295, sinfo_args=(R.Object,)) lv10 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_10_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape296: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv10, R.shape([batch_size, 1500, 20, 64])) lv10_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_10_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_10_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape297: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv10_1, R.shape([batch_size, 1500, 20, 64])) reshape298: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape296, R.shape([batch_size * 1500, 20, 64])) reshape299: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape297, R.shape([batch_size * 1500, 20, 64])) lv46: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv45, R.prim_value(10), reshape298, reshape299, sinfo_args=(R.Object,)) lv11 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_11_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape300: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv11, R.shape([batch_size, 1500, 20, 64])) lv11_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_11_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_11_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape301: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv11_1, R.shape([batch_size, 1500, 20, 64])) reshape302: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape300, R.shape([batch_size * 1500, 20, 64])) reshape303: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape301, R.shape([batch_size * 1500, 20, 64])) lv47: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv46, R.prim_value(11), reshape302, reshape303, sinfo_args=(R.Object,)) lv12 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_12_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape304: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv12, R.shape([batch_size, 1500, 20, 64])) lv12_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_12_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_12_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape305: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv12_1, R.shape([batch_size, 1500, 20, 64])) reshape306: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape304, R.shape([batch_size * 1500, 20, 64])) reshape307: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape305, R.shape([batch_size * 1500, 20, 64])) lv48: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv47, R.prim_value(12), reshape306, reshape307, sinfo_args=(R.Object,)) lv13 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_13_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape308: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv13, R.shape([batch_size, 1500, 20, 64])) lv13_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_13_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_13_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape309: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv13_1, R.shape([batch_size, 1500, 20, 64])) reshape310: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape308, R.shape([batch_size * 1500, 20, 64])) reshape311: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape309, R.shape([batch_size * 1500, 20, 64])) lv49: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv48, R.prim_value(13), reshape310, reshape311, sinfo_args=(R.Object,)) lv14 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_14_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape312: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv14, R.shape([batch_size, 1500, 20, 64])) lv14_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_14_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_14_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape313: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv14_1, R.shape([batch_size, 1500, 20, 64])) reshape314: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape312, R.shape([batch_size * 1500, 20, 64])) reshape315: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape313, R.shape([batch_size * 1500, 20, 64])) lv50: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv49, R.prim_value(14), reshape314, reshape315, sinfo_args=(R.Object,)) lv15 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_15_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape316: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv15, R.shape([batch_size, 1500, 20, 64])) lv15_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_15_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_15_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape317: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv15_1, R.shape([batch_size, 1500, 20, 64])) reshape318: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape316, R.shape([batch_size * 1500, 20, 64])) reshape319: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape317, R.shape([batch_size * 1500, 20, 64])) lv51: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv50, R.prim_value(15), reshape318, reshape319, sinfo_args=(R.Object,)) lv16 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_16_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape320: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv16, R.shape([batch_size, 1500, 20, 64])) lv16_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_16_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_16_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape321: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv16_1, R.shape([batch_size, 1500, 20, 64])) reshape322: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape320, R.shape([batch_size * 1500, 20, 64])) reshape323: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape321, R.shape([batch_size * 1500, 20, 64])) lv52: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv51, R.prim_value(16), reshape322, reshape323, sinfo_args=(R.Object,)) lv17 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_17_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape324: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv17, R.shape([batch_size, 1500, 20, 64])) lv17_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_17_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_17_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape325: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv17_1, R.shape([batch_size, 1500, 20, 64])) reshape326: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape324, R.shape([batch_size * 1500, 20, 64])) reshape327: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape325, R.shape([batch_size * 1500, 20, 64])) lv53: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv52, R.prim_value(17), reshape326, reshape327, sinfo_args=(R.Object,)) lv18 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_18_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape328: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv18, R.shape([batch_size, 1500, 20, 64])) lv18_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_18_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_18_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape329: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv18_1, R.shape([batch_size, 1500, 20, 64])) reshape330: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape328, R.shape([batch_size * 1500, 20, 64])) reshape331: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape329, R.shape([batch_size * 1500, 20, 64])) lv54: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv53, R.prim_value(18), reshape330, reshape331, sinfo_args=(R.Object,)) lv19 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_19_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape332: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv19, R.shape([batch_size, 1500, 20, 64])) lv19_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_19_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_19_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape333: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv19_1, R.shape([batch_size, 1500, 20, 64])) reshape334: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape332, R.shape([batch_size * 1500, 20, 64])) reshape335: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape333, R.shape([batch_size * 1500, 20, 64])) lv55: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv54, R.prim_value(19), reshape334, reshape335, sinfo_args=(R.Object,)) lv20 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_20_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape336: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv20, R.shape([batch_size, 1500, 20, 64])) lv20_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_20_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_20_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape337: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv20_1, R.shape([batch_size, 1500, 20, 64])) reshape338: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape336, R.shape([batch_size * 1500, 20, 64])) reshape339: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape337, R.shape([batch_size * 1500, 20, 64])) lv56: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv55, R.prim_value(20), reshape338, reshape339, sinfo_args=(R.Object,)) lv21 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_21_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape340: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv21, R.shape([batch_size, 1500, 20, 64])) lv21_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_21_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_21_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape341: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv21_1, R.shape([batch_size, 1500, 20, 64])) reshape342: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape340, R.shape([batch_size * 1500, 20, 64])) reshape343: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape341, R.shape([batch_size * 1500, 20, 64])) lv57: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv56, R.prim_value(21), reshape342, reshape343, sinfo_args=(R.Object,)) lv22 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_22_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape344: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv22, R.shape([batch_size, 1500, 20, 64])) lv22_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_22_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_22_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape345: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv22_1, R.shape([batch_size, 1500, 20, 64])) reshape346: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape344, R.shape([batch_size * 1500, 20, 64])) reshape347: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape345, R.shape([batch_size * 1500, 20, 64])) lv58: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv57, R.prim_value(22), reshape346, reshape347, sinfo_args=(R.Object,)) lv23 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_23_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape348: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv23, R.shape([batch_size, 1500, 20, 64])) lv23_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_23_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_23_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape349: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv23_1, R.shape([batch_size, 1500, 20, 64])) reshape350: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape348, R.shape([batch_size * 1500, 20, 64])) reshape351: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape349, R.shape([batch_size * 1500, 20, 64])) lv59: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv58, R.prim_value(23), reshape350, reshape351, sinfo_args=(R.Object,)) lv24 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_24_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape352: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv24, R.shape([batch_size, 1500, 20, 64])) lv24_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_24_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_24_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape353: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv24_1, R.shape([batch_size, 1500, 20, 64])) reshape354: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape352, R.shape([batch_size * 1500, 20, 64])) reshape355: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape353, R.shape([batch_size * 1500, 20, 64])) lv60: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv59, R.prim_value(24), reshape354, reshape355, sinfo_args=(R.Object,)) lv25 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_25_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape356: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv25, R.shape([batch_size, 1500, 20, 64])) lv25_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_25_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_25_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape357: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv25_1, R.shape([batch_size, 1500, 20, 64])) reshape358: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape356, R.shape([batch_size * 1500, 20, 64])) reshape359: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape357, R.shape([batch_size * 1500, 20, 64])) lv61: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv60, R.prim_value(25), reshape358, reshape359, sinfo_args=(R.Object,)) lv26 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_26_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape360: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv26, R.shape([batch_size, 1500, 20, 64])) lv26_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_26_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_26_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape361: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv26_1, R.shape([batch_size, 1500, 20, 64])) reshape362: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape360, R.shape([batch_size * 1500, 20, 64])) reshape363: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape361, R.shape([batch_size * 1500, 20, 64])) lv62: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv61, R.prim_value(26), reshape362, reshape363, sinfo_args=(R.Object,)) lv27 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_27_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape364: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv27, R.shape([batch_size, 1500, 20, 64])) lv27_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_27_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_27_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape365: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv27_1, R.shape([batch_size, 1500, 20, 64])) reshape366: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape364, R.shape([batch_size * 1500, 20, 64])) reshape367: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape365, R.shape([batch_size * 1500, 20, 64])) lv63: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv62, R.prim_value(27), reshape366, reshape367, sinfo_args=(R.Object,)) lv28 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_28_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape368: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv28, R.shape([batch_size, 1500, 20, 64])) lv28_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_28_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_28_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape369: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv28_1, R.shape([batch_size, 1500, 20, 64])) reshape370: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape368, R.shape([batch_size * 1500, 20, 64])) reshape371: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape369, R.shape([batch_size * 1500, 20, 64])) lv64: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv63, R.prim_value(28), reshape370, reshape371, sinfo_args=(R.Object,)) lv29 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_29_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape372: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv29, R.shape([batch_size, 1500, 20, 64])) lv29_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_29_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_29_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape373: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv29_1, R.shape([batch_size, 1500, 20, 64])) reshape374: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape372, R.shape([batch_size * 1500, 20, 64])) reshape375: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape373, R.shape([batch_size * 1500, 20, 64])) lv65: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv64, R.prim_value(29), reshape374, reshape375, sinfo_args=(R.Object,)) lv30 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_30_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape376: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv30, R.shape([batch_size, 1500, 20, 64])) lv30_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_30_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_30_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape377: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv30_1, R.shape([batch_size, 1500, 20, 64])) reshape378: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape376, R.shape([batch_size * 1500, 20, 64])) reshape379: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape377, R.shape([batch_size * 1500, 20, 64])) lv66: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv65, R.prim_value(30), reshape378, reshape379, sinfo_args=(R.Object,)) lv31 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_decoder_layers_31_encoder_attn_k_proj_weight1, encoder_hidden_states), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape380: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv31, R.shape([batch_size, 1500, 20, 64])) lv31_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_decoder_layers_31_encoder_attn_v_proj_weight1, encoder_hidden_states, model_decoder_layers_31_encoder_attn_v_proj_bias1), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape381: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv31_1, R.shape([batch_size, 1500, 20, 64])) reshape382: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape380, R.shape([batch_size * 1500, 20, 64])) reshape383: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape381, R.shape([batch_size * 1500, 20, 64])) lv67: R.Object = R.call_pure_packed("vm.builtin.attention_kv_cache_push_cross_attention_kv", lv66, R.prim_value(31), reshape382, reshape383, sinfo_args=(R.Object,)) gv1: R.Object = lv67 R.output(gv1) return gv1 @R.function def batch_decode(input_ids: R.Tensor(("batch_size", 1), dtype="int32"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((51866, 1280), dtype="float16"), R.Tensor((448, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor(("batch_size", 1, 51866), dtype="float32"): batch_size = T.int64() R.func_attr({"num_input": 2, "relax.memory_plan_dynamic_func_output": 1, "relax.rewrite_cuda_graph.capture_symbolic_vars": ["batch_size"], "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) with R.dataflow(): model_decoder_embed_tokens_weight3: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] model_decoder_embed_positions_weight3: R.Tensor((448, 1280), dtype="float16") = packed_params[488] model_decoder_layers_0_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] model_decoder_layers_0_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] model_decoder_layers_0_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[491] model_decoder_layers_0_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] model_decoder_layers_0_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[493] model_decoder_layers_0_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] model_decoder_layers_0_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[495] model_decoder_layers_0_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[496] model_decoder_layers_0_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[497] model_decoder_layers_0_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] model_decoder_layers_0_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[502] model_decoder_layers_0_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] model_decoder_layers_0_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[504] model_decoder_layers_0_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[505] model_decoder_layers_0_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[506] model_decoder_layers_0_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] model_decoder_layers_0_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[508] model_decoder_layers_0_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] model_decoder_layers_0_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[510] model_decoder_layers_0_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[511] model_decoder_layers_0_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[512] model_decoder_layers_1_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] model_decoder_layers_1_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] model_decoder_layers_1_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[515] model_decoder_layers_1_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] model_decoder_layers_1_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[517] model_decoder_layers_1_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] model_decoder_layers_1_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[519] model_decoder_layers_1_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[520] model_decoder_layers_1_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[521] model_decoder_layers_1_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] model_decoder_layers_1_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[526] model_decoder_layers_1_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] model_decoder_layers_1_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[528] model_decoder_layers_1_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[529] model_decoder_layers_1_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[530] model_decoder_layers_1_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] model_decoder_layers_1_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[532] model_decoder_layers_1_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] model_decoder_layers_1_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[534] model_decoder_layers_1_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[535] model_decoder_layers_1_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[536] model_decoder_layers_2_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] model_decoder_layers_2_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] model_decoder_layers_2_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[539] model_decoder_layers_2_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] model_decoder_layers_2_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[541] model_decoder_layers_2_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] model_decoder_layers_2_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[543] model_decoder_layers_2_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[544] model_decoder_layers_2_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[545] model_decoder_layers_2_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] model_decoder_layers_2_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[550] model_decoder_layers_2_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] model_decoder_layers_2_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[552] model_decoder_layers_2_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[553] model_decoder_layers_2_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[554] model_decoder_layers_2_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] model_decoder_layers_2_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[556] model_decoder_layers_2_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] model_decoder_layers_2_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[558] model_decoder_layers_2_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[559] model_decoder_layers_2_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[560] model_decoder_layers_3_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] model_decoder_layers_3_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] model_decoder_layers_3_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[563] model_decoder_layers_3_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] model_decoder_layers_3_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[565] model_decoder_layers_3_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] model_decoder_layers_3_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[567] model_decoder_layers_3_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[568] model_decoder_layers_3_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[569] model_decoder_layers_3_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] model_decoder_layers_3_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[574] model_decoder_layers_3_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] model_decoder_layers_3_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[576] model_decoder_layers_3_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[577] model_decoder_layers_3_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[578] model_decoder_layers_3_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] model_decoder_layers_3_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[580] model_decoder_layers_3_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] model_decoder_layers_3_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[582] model_decoder_layers_3_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[583] model_decoder_layers_3_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[584] model_decoder_layers_4_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] model_decoder_layers_4_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] model_decoder_layers_4_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[587] model_decoder_layers_4_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] model_decoder_layers_4_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[589] model_decoder_layers_4_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] model_decoder_layers_4_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[591] model_decoder_layers_4_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[592] model_decoder_layers_4_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[593] model_decoder_layers_4_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] model_decoder_layers_4_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[598] model_decoder_layers_4_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] model_decoder_layers_4_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[600] model_decoder_layers_4_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[601] model_decoder_layers_4_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[602] model_decoder_layers_4_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] model_decoder_layers_4_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[604] model_decoder_layers_4_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] model_decoder_layers_4_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[606] model_decoder_layers_4_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[607] model_decoder_layers_4_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[608] model_decoder_layers_5_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] model_decoder_layers_5_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] model_decoder_layers_5_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[611] model_decoder_layers_5_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] model_decoder_layers_5_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[613] model_decoder_layers_5_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] model_decoder_layers_5_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[615] model_decoder_layers_5_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[616] model_decoder_layers_5_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[617] model_decoder_layers_5_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] model_decoder_layers_5_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[622] model_decoder_layers_5_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] model_decoder_layers_5_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[624] model_decoder_layers_5_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[625] model_decoder_layers_5_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[626] model_decoder_layers_5_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] model_decoder_layers_5_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[628] model_decoder_layers_5_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] model_decoder_layers_5_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[630] model_decoder_layers_5_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[631] model_decoder_layers_5_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[632] model_decoder_layers_6_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] model_decoder_layers_6_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] model_decoder_layers_6_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[635] model_decoder_layers_6_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] model_decoder_layers_6_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[637] model_decoder_layers_6_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] model_decoder_layers_6_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[639] model_decoder_layers_6_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[640] model_decoder_layers_6_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[641] model_decoder_layers_6_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] model_decoder_layers_6_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[646] model_decoder_layers_6_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] model_decoder_layers_6_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[648] model_decoder_layers_6_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[649] model_decoder_layers_6_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[650] model_decoder_layers_6_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] model_decoder_layers_6_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[652] model_decoder_layers_6_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] model_decoder_layers_6_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[654] model_decoder_layers_6_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[655] model_decoder_layers_6_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[656] model_decoder_layers_7_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] model_decoder_layers_7_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] model_decoder_layers_7_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[659] model_decoder_layers_7_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] model_decoder_layers_7_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[661] model_decoder_layers_7_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] model_decoder_layers_7_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[663] model_decoder_layers_7_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[664] model_decoder_layers_7_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[665] model_decoder_layers_7_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] model_decoder_layers_7_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[670] model_decoder_layers_7_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] model_decoder_layers_7_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[672] model_decoder_layers_7_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[673] model_decoder_layers_7_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[674] model_decoder_layers_7_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] model_decoder_layers_7_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[676] model_decoder_layers_7_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] model_decoder_layers_7_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[678] model_decoder_layers_7_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[679] model_decoder_layers_7_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[680] model_decoder_layers_8_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] model_decoder_layers_8_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] model_decoder_layers_8_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[683] model_decoder_layers_8_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] model_decoder_layers_8_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[685] model_decoder_layers_8_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] model_decoder_layers_8_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[687] model_decoder_layers_8_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[688] model_decoder_layers_8_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[689] model_decoder_layers_8_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] model_decoder_layers_8_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[694] model_decoder_layers_8_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] model_decoder_layers_8_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[696] model_decoder_layers_8_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[697] model_decoder_layers_8_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[698] model_decoder_layers_8_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] model_decoder_layers_8_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[700] model_decoder_layers_8_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] model_decoder_layers_8_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[702] model_decoder_layers_8_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[703] model_decoder_layers_8_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[704] model_decoder_layers_9_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] model_decoder_layers_9_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] model_decoder_layers_9_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[707] model_decoder_layers_9_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] model_decoder_layers_9_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[709] model_decoder_layers_9_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] model_decoder_layers_9_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[711] model_decoder_layers_9_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[712] model_decoder_layers_9_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[713] model_decoder_layers_9_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] model_decoder_layers_9_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[718] model_decoder_layers_9_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] model_decoder_layers_9_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[720] model_decoder_layers_9_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[721] model_decoder_layers_9_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[722] model_decoder_layers_9_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] model_decoder_layers_9_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[724] model_decoder_layers_9_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] model_decoder_layers_9_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[726] model_decoder_layers_9_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[727] model_decoder_layers_9_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[728] model_decoder_layers_10_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] model_decoder_layers_10_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] model_decoder_layers_10_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[731] model_decoder_layers_10_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] model_decoder_layers_10_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[733] model_decoder_layers_10_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] model_decoder_layers_10_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[735] model_decoder_layers_10_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[736] model_decoder_layers_10_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[737] model_decoder_layers_10_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] model_decoder_layers_10_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[742] model_decoder_layers_10_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] model_decoder_layers_10_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[744] model_decoder_layers_10_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[745] model_decoder_layers_10_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[746] model_decoder_layers_10_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] model_decoder_layers_10_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[748] model_decoder_layers_10_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] model_decoder_layers_10_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[750] model_decoder_layers_10_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[751] model_decoder_layers_10_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[752] model_decoder_layers_11_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] model_decoder_layers_11_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] model_decoder_layers_11_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[755] model_decoder_layers_11_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] model_decoder_layers_11_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[757] model_decoder_layers_11_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] model_decoder_layers_11_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[759] model_decoder_layers_11_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[760] model_decoder_layers_11_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[761] model_decoder_layers_11_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] model_decoder_layers_11_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[766] model_decoder_layers_11_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] model_decoder_layers_11_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[768] model_decoder_layers_11_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[769] model_decoder_layers_11_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[770] model_decoder_layers_11_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] model_decoder_layers_11_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[772] model_decoder_layers_11_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] model_decoder_layers_11_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[774] model_decoder_layers_11_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[775] model_decoder_layers_11_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[776] model_decoder_layers_12_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] model_decoder_layers_12_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] model_decoder_layers_12_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[779] model_decoder_layers_12_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] model_decoder_layers_12_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[781] model_decoder_layers_12_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] model_decoder_layers_12_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[783] model_decoder_layers_12_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[784] model_decoder_layers_12_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[785] model_decoder_layers_12_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] model_decoder_layers_12_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[790] model_decoder_layers_12_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] model_decoder_layers_12_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[792] model_decoder_layers_12_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[793] model_decoder_layers_12_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[794] model_decoder_layers_12_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] model_decoder_layers_12_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[796] model_decoder_layers_12_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] model_decoder_layers_12_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[798] model_decoder_layers_12_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[799] model_decoder_layers_12_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[800] model_decoder_layers_13_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] model_decoder_layers_13_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] model_decoder_layers_13_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[803] model_decoder_layers_13_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] model_decoder_layers_13_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[805] model_decoder_layers_13_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] model_decoder_layers_13_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[807] model_decoder_layers_13_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[808] model_decoder_layers_13_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[809] model_decoder_layers_13_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] model_decoder_layers_13_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[814] model_decoder_layers_13_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] model_decoder_layers_13_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[816] model_decoder_layers_13_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[817] model_decoder_layers_13_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[818] model_decoder_layers_13_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] model_decoder_layers_13_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[820] model_decoder_layers_13_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] model_decoder_layers_13_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[822] model_decoder_layers_13_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[823] model_decoder_layers_13_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[824] model_decoder_layers_14_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] model_decoder_layers_14_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] model_decoder_layers_14_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[827] model_decoder_layers_14_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] model_decoder_layers_14_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[829] model_decoder_layers_14_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] model_decoder_layers_14_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[831] model_decoder_layers_14_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[832] model_decoder_layers_14_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[833] model_decoder_layers_14_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] model_decoder_layers_14_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[838] model_decoder_layers_14_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] model_decoder_layers_14_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[840] model_decoder_layers_14_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[841] model_decoder_layers_14_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[842] model_decoder_layers_14_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] model_decoder_layers_14_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[844] model_decoder_layers_14_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] model_decoder_layers_14_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[846] model_decoder_layers_14_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[847] model_decoder_layers_14_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[848] model_decoder_layers_15_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] model_decoder_layers_15_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] model_decoder_layers_15_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[851] model_decoder_layers_15_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] model_decoder_layers_15_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[853] model_decoder_layers_15_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] model_decoder_layers_15_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[855] model_decoder_layers_15_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[856] model_decoder_layers_15_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[857] model_decoder_layers_15_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] model_decoder_layers_15_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[862] model_decoder_layers_15_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] model_decoder_layers_15_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[864] model_decoder_layers_15_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[865] model_decoder_layers_15_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[866] model_decoder_layers_15_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] model_decoder_layers_15_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[868] model_decoder_layers_15_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] model_decoder_layers_15_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[870] model_decoder_layers_15_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[871] model_decoder_layers_15_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[872] model_decoder_layers_16_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] model_decoder_layers_16_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] model_decoder_layers_16_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[875] model_decoder_layers_16_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] model_decoder_layers_16_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[877] model_decoder_layers_16_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] model_decoder_layers_16_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[879] model_decoder_layers_16_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[880] model_decoder_layers_16_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[881] model_decoder_layers_16_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] model_decoder_layers_16_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[886] model_decoder_layers_16_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] model_decoder_layers_16_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[888] model_decoder_layers_16_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[889] model_decoder_layers_16_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[890] model_decoder_layers_16_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] model_decoder_layers_16_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[892] model_decoder_layers_16_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] model_decoder_layers_16_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[894] model_decoder_layers_16_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[895] model_decoder_layers_16_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[896] model_decoder_layers_17_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] model_decoder_layers_17_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] model_decoder_layers_17_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[899] model_decoder_layers_17_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] model_decoder_layers_17_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[901] model_decoder_layers_17_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] model_decoder_layers_17_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[903] model_decoder_layers_17_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[904] model_decoder_layers_17_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[905] model_decoder_layers_17_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] model_decoder_layers_17_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[910] model_decoder_layers_17_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] model_decoder_layers_17_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[912] model_decoder_layers_17_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[913] model_decoder_layers_17_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[914] model_decoder_layers_17_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] model_decoder_layers_17_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[916] model_decoder_layers_17_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] model_decoder_layers_17_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[918] model_decoder_layers_17_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[919] model_decoder_layers_17_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[920] model_decoder_layers_18_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] model_decoder_layers_18_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] model_decoder_layers_18_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[923] model_decoder_layers_18_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] model_decoder_layers_18_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[925] model_decoder_layers_18_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] model_decoder_layers_18_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[927] model_decoder_layers_18_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[928] model_decoder_layers_18_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[929] model_decoder_layers_18_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] model_decoder_layers_18_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[934] model_decoder_layers_18_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] model_decoder_layers_18_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[936] model_decoder_layers_18_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[937] model_decoder_layers_18_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[938] model_decoder_layers_18_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] model_decoder_layers_18_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[940] model_decoder_layers_18_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] model_decoder_layers_18_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[942] model_decoder_layers_18_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[943] model_decoder_layers_18_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[944] model_decoder_layers_19_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] model_decoder_layers_19_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] model_decoder_layers_19_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[947] model_decoder_layers_19_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] model_decoder_layers_19_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[949] model_decoder_layers_19_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] model_decoder_layers_19_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[951] model_decoder_layers_19_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[952] model_decoder_layers_19_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[953] model_decoder_layers_19_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] model_decoder_layers_19_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[958] model_decoder_layers_19_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] model_decoder_layers_19_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[960] model_decoder_layers_19_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[961] model_decoder_layers_19_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[962] model_decoder_layers_19_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] model_decoder_layers_19_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[964] model_decoder_layers_19_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] model_decoder_layers_19_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[966] model_decoder_layers_19_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[967] model_decoder_layers_19_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[968] model_decoder_layers_20_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] model_decoder_layers_20_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] model_decoder_layers_20_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[971] model_decoder_layers_20_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] model_decoder_layers_20_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[973] model_decoder_layers_20_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] model_decoder_layers_20_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[975] model_decoder_layers_20_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[976] model_decoder_layers_20_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[977] model_decoder_layers_20_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] model_decoder_layers_20_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[982] model_decoder_layers_20_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] model_decoder_layers_20_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[984] model_decoder_layers_20_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[985] model_decoder_layers_20_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[986] model_decoder_layers_20_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] model_decoder_layers_20_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[988] model_decoder_layers_20_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] model_decoder_layers_20_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[990] model_decoder_layers_20_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[991] model_decoder_layers_20_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[992] model_decoder_layers_21_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] model_decoder_layers_21_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] model_decoder_layers_21_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[995] model_decoder_layers_21_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] model_decoder_layers_21_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[997] model_decoder_layers_21_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] model_decoder_layers_21_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[999] model_decoder_layers_21_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1000] model_decoder_layers_21_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1001] model_decoder_layers_21_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] model_decoder_layers_21_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1006] model_decoder_layers_21_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] model_decoder_layers_21_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1008] model_decoder_layers_21_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1009] model_decoder_layers_21_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1010] model_decoder_layers_21_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] model_decoder_layers_21_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1012] model_decoder_layers_21_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] model_decoder_layers_21_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1014] model_decoder_layers_21_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1015] model_decoder_layers_21_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1016] model_decoder_layers_22_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] model_decoder_layers_22_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] model_decoder_layers_22_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1019] model_decoder_layers_22_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] model_decoder_layers_22_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1021] model_decoder_layers_22_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] model_decoder_layers_22_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1023] model_decoder_layers_22_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1024] model_decoder_layers_22_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1025] model_decoder_layers_22_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] model_decoder_layers_22_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1030] model_decoder_layers_22_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] model_decoder_layers_22_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1032] model_decoder_layers_22_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1033] model_decoder_layers_22_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1034] model_decoder_layers_22_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] model_decoder_layers_22_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1036] model_decoder_layers_22_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] model_decoder_layers_22_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1038] model_decoder_layers_22_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1039] model_decoder_layers_22_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1040] model_decoder_layers_23_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] model_decoder_layers_23_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] model_decoder_layers_23_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1043] model_decoder_layers_23_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] model_decoder_layers_23_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1045] model_decoder_layers_23_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] model_decoder_layers_23_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1047] model_decoder_layers_23_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1048] model_decoder_layers_23_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1049] model_decoder_layers_23_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] model_decoder_layers_23_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1054] model_decoder_layers_23_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] model_decoder_layers_23_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1056] model_decoder_layers_23_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1057] model_decoder_layers_23_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1058] model_decoder_layers_23_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] model_decoder_layers_23_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1060] model_decoder_layers_23_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] model_decoder_layers_23_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1062] model_decoder_layers_23_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1063] model_decoder_layers_23_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1064] model_decoder_layers_24_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] model_decoder_layers_24_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] model_decoder_layers_24_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1067] model_decoder_layers_24_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] model_decoder_layers_24_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1069] model_decoder_layers_24_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] model_decoder_layers_24_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1071] model_decoder_layers_24_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1072] model_decoder_layers_24_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1073] model_decoder_layers_24_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] model_decoder_layers_24_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1078] model_decoder_layers_24_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] model_decoder_layers_24_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1080] model_decoder_layers_24_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1081] model_decoder_layers_24_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1082] model_decoder_layers_24_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] model_decoder_layers_24_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1084] model_decoder_layers_24_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] model_decoder_layers_24_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1086] model_decoder_layers_24_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1087] model_decoder_layers_24_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1088] model_decoder_layers_25_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] model_decoder_layers_25_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] model_decoder_layers_25_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1091] model_decoder_layers_25_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] model_decoder_layers_25_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1093] model_decoder_layers_25_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] model_decoder_layers_25_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1095] model_decoder_layers_25_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1096] model_decoder_layers_25_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1097] model_decoder_layers_25_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] model_decoder_layers_25_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1102] model_decoder_layers_25_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] model_decoder_layers_25_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1104] model_decoder_layers_25_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1105] model_decoder_layers_25_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1106] model_decoder_layers_25_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] model_decoder_layers_25_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1108] model_decoder_layers_25_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] model_decoder_layers_25_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1110] model_decoder_layers_25_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1111] model_decoder_layers_25_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1112] model_decoder_layers_26_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] model_decoder_layers_26_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] model_decoder_layers_26_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1115] model_decoder_layers_26_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] model_decoder_layers_26_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1117] model_decoder_layers_26_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] model_decoder_layers_26_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1119] model_decoder_layers_26_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1120] model_decoder_layers_26_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1121] model_decoder_layers_26_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] model_decoder_layers_26_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1126] model_decoder_layers_26_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] model_decoder_layers_26_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1128] model_decoder_layers_26_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1129] model_decoder_layers_26_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1130] model_decoder_layers_26_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] model_decoder_layers_26_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1132] model_decoder_layers_26_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] model_decoder_layers_26_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1134] model_decoder_layers_26_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1135] model_decoder_layers_26_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1136] model_decoder_layers_27_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] model_decoder_layers_27_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] model_decoder_layers_27_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1139] model_decoder_layers_27_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] model_decoder_layers_27_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1141] model_decoder_layers_27_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] model_decoder_layers_27_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1143] model_decoder_layers_27_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1144] model_decoder_layers_27_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1145] model_decoder_layers_27_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] model_decoder_layers_27_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1150] model_decoder_layers_27_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] model_decoder_layers_27_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1152] model_decoder_layers_27_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1153] model_decoder_layers_27_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1154] model_decoder_layers_27_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] model_decoder_layers_27_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1156] model_decoder_layers_27_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] model_decoder_layers_27_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1158] model_decoder_layers_27_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1159] model_decoder_layers_27_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1160] model_decoder_layers_28_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] model_decoder_layers_28_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] model_decoder_layers_28_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1163] model_decoder_layers_28_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] model_decoder_layers_28_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1165] model_decoder_layers_28_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] model_decoder_layers_28_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1167] model_decoder_layers_28_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1168] model_decoder_layers_28_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1169] model_decoder_layers_28_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] model_decoder_layers_28_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1174] model_decoder_layers_28_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] model_decoder_layers_28_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1176] model_decoder_layers_28_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1177] model_decoder_layers_28_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1178] model_decoder_layers_28_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] model_decoder_layers_28_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1180] model_decoder_layers_28_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] model_decoder_layers_28_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1182] model_decoder_layers_28_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1183] model_decoder_layers_28_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1184] model_decoder_layers_29_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] model_decoder_layers_29_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] model_decoder_layers_29_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1187] model_decoder_layers_29_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] model_decoder_layers_29_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1189] model_decoder_layers_29_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] model_decoder_layers_29_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1191] model_decoder_layers_29_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1192] model_decoder_layers_29_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1193] model_decoder_layers_29_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] model_decoder_layers_29_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1198] model_decoder_layers_29_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] model_decoder_layers_29_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1200] model_decoder_layers_29_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1201] model_decoder_layers_29_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1202] model_decoder_layers_29_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] model_decoder_layers_29_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1204] model_decoder_layers_29_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] model_decoder_layers_29_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1206] model_decoder_layers_29_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1207] model_decoder_layers_29_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1208] model_decoder_layers_30_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] model_decoder_layers_30_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] model_decoder_layers_30_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1211] model_decoder_layers_30_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] model_decoder_layers_30_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1213] model_decoder_layers_30_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] model_decoder_layers_30_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1215] model_decoder_layers_30_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1216] model_decoder_layers_30_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1217] model_decoder_layers_30_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] model_decoder_layers_30_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1222] model_decoder_layers_30_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] model_decoder_layers_30_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1224] model_decoder_layers_30_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1225] model_decoder_layers_30_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1226] model_decoder_layers_30_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] model_decoder_layers_30_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1228] model_decoder_layers_30_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] model_decoder_layers_30_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1230] model_decoder_layers_30_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1231] model_decoder_layers_30_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1232] model_decoder_layers_31_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] model_decoder_layers_31_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] model_decoder_layers_31_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1235] model_decoder_layers_31_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] model_decoder_layers_31_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1237] model_decoder_layers_31_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] model_decoder_layers_31_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1239] model_decoder_layers_31_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1240] model_decoder_layers_31_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1241] model_decoder_layers_31_encoder_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] model_decoder_layers_31_encoder_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1246] model_decoder_layers_31_encoder_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] model_decoder_layers_31_encoder_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1248] model_decoder_layers_31_encoder_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1249] model_decoder_layers_31_encoder_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1250] model_decoder_layers_31_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] model_decoder_layers_31_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[1252] model_decoder_layers_31_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] model_decoder_layers_31_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1254] model_decoder_layers_31_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1255] model_decoder_layers_31_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1256] model_decoder_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[1257] model_decoder_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1258] reshape707: R.Tensor((batch_size,), dtype="int32") = R.reshape(input_ids, R.shape([batch_size])) take3: R.Tensor((batch_size, 1280), dtype="float16") = R.take(model_decoder_embed_tokens_weight3, reshape707, axis=0) reshape708: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(take3, R.shape([batch_size, 1, 1280])) lv133: R.Tensor((batch_size,), dtype="int32") = R.call_pure_packed("vm.builtin.attention_kv_cache_get_query_positions", paged_kv_cache, sinfo_args=(R.Tensor((batch_size,), dtype="int32"),)) take4: R.Tensor((batch_size, 1280), dtype="float16") = R.take(model_decoder_embed_positions_weight3, lv133, axis=0) reshape709: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(take4, R.shape([batch_size, 1, 1280])) add578: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(reshape708, reshape709) layer_norm162: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add578, model_decoder_layers_0_self_attn_layer_norm_weight3, model_decoder_layers_0_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv224 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_0_self_attn_q_proj_weight3, layer_norm162, model_decoder_layers_0_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape710: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv224, R.shape([batch_size, 1, 20, 64])) lv65 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_0_self_attn_k_proj_weight3, layer_norm162), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape711: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv65, R.shape([batch_size, 1, 20, 64])) lv225 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_0_self_attn_v_proj_weight3, layer_norm162, model_decoder_layers_0_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape712: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv225, R.shape([batch_size, 1, 20, 64])) concat32: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape710, reshape711, reshape712), axis=2) reshape713: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat32, R.shape([batch_size, 60, 64])) lv134 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape713), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape714: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv134, R.shape([batch_size, 1, 20, 64])) reshape715: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape714, R.shape([batch_size, 1, 1280])) lv226 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_0_self_attn_out_proj_weight3, reshape715, model_decoder_layers_0_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add582: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add578, lv226) layer_norm163: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add582, model_decoder_layers_0_encoder_attn_layer_norm_weight3, model_decoder_layers_0_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv227 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_0_encoder_attn_q_proj_weight3, layer_norm163, model_decoder_layers_0_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape716: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv227, R.shape([batch_size, 1, 20, 64])) reshape717: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape716, R.shape([batch_size, 20, 64])) lv135 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape717), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape718: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv135, R.shape([batch_size, 1, 20, 64])) reshape719: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape718, R.shape([batch_size, 1, 1280])) lv228 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_0_encoder_attn_out_proj_weight3, reshape719, model_decoder_layers_0_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add585: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add582, lv228) layer_norm164: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add585, model_decoder_layers_0_final_layer_norm_weight3, model_decoder_layers_0_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv32 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_0_fc1_weight3, layer_norm164, model_decoder_layers_0_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv229 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_0_fc2_weight3, lv32, model_decoder_layers_0_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add588: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add585, lv229) layer_norm165: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add588, model_decoder_layers_1_self_attn_layer_norm_weight3, model_decoder_layers_1_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv230 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_1_self_attn_q_proj_weight3, layer_norm165, model_decoder_layers_1_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape720: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv230, R.shape([batch_size, 1, 20, 64])) lv66 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_1_self_attn_k_proj_weight3, layer_norm165), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape721: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv66, R.shape([batch_size, 1, 20, 64])) lv231 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_1_self_attn_v_proj_weight3, layer_norm165, model_decoder_layers_1_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape722: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv231, R.shape([batch_size, 1, 20, 64])) concat33: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape720, reshape721, reshape722), axis=2) reshape723: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat33, R.shape([batch_size, 60, 64])) lv136 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape723), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape724: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv136, R.shape([batch_size, 1, 20, 64])) reshape725: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape724, R.shape([batch_size, 1, 1280])) lv232 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_1_self_attn_out_proj_weight3, reshape725, model_decoder_layers_1_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add592: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add588, lv232) layer_norm166: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add592, model_decoder_layers_1_encoder_attn_layer_norm_weight3, model_decoder_layers_1_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv233 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_1_encoder_attn_q_proj_weight3, layer_norm166, model_decoder_layers_1_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape726: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv233, R.shape([batch_size, 1, 20, 64])) reshape727: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape726, R.shape([batch_size, 20, 64])) lv137 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape727), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape728: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv137, R.shape([batch_size, 1, 20, 64])) reshape729: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape728, R.shape([batch_size, 1, 1280])) lv234 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_1_encoder_attn_out_proj_weight3, reshape729, model_decoder_layers_1_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add595: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add592, lv234) layer_norm167: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add595, model_decoder_layers_1_final_layer_norm_weight3, model_decoder_layers_1_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv33 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_1_fc1_weight3, layer_norm167, model_decoder_layers_1_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv235 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_1_fc2_weight3, lv33, model_decoder_layers_1_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add598: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add595, lv235) layer_norm168: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add598, model_decoder_layers_2_self_attn_layer_norm_weight3, model_decoder_layers_2_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv236 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_2_self_attn_q_proj_weight3, layer_norm168, model_decoder_layers_2_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape730: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv236, R.shape([batch_size, 1, 20, 64])) lv67 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_2_self_attn_k_proj_weight3, layer_norm168), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape731: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv67, R.shape([batch_size, 1, 20, 64])) lv237 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_2_self_attn_v_proj_weight3, layer_norm168, model_decoder_layers_2_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape732: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv237, R.shape([batch_size, 1, 20, 64])) concat34: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape730, reshape731, reshape732), axis=2) reshape733: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat34, R.shape([batch_size, 60, 64])) lv138 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape733), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape734: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv138, R.shape([batch_size, 1, 20, 64])) reshape735: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape734, R.shape([batch_size, 1, 1280])) lv238 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_2_self_attn_out_proj_weight3, reshape735, model_decoder_layers_2_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add602: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add598, lv238) layer_norm169: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add602, model_decoder_layers_2_encoder_attn_layer_norm_weight3, model_decoder_layers_2_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv239 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_2_encoder_attn_q_proj_weight3, layer_norm169, model_decoder_layers_2_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape736: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv239, R.shape([batch_size, 1, 20, 64])) reshape737: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape736, R.shape([batch_size, 20, 64])) lv139 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape737), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape738: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv139, R.shape([batch_size, 1, 20, 64])) reshape739: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape738, R.shape([batch_size, 1, 1280])) lv240 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_2_encoder_attn_out_proj_weight3, reshape739, model_decoder_layers_2_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add605: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add602, lv240) layer_norm170: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add605, model_decoder_layers_2_final_layer_norm_weight3, model_decoder_layers_2_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv34 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_2_fc1_weight3, layer_norm170, model_decoder_layers_2_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv241 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_2_fc2_weight3, lv34, model_decoder_layers_2_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add608: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add605, lv241) layer_norm171: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add608, model_decoder_layers_3_self_attn_layer_norm_weight3, model_decoder_layers_3_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv242 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_3_self_attn_q_proj_weight3, layer_norm171, model_decoder_layers_3_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape740: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv242, R.shape([batch_size, 1, 20, 64])) lv68 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_3_self_attn_k_proj_weight3, layer_norm171), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape741: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv68, R.shape([batch_size, 1, 20, 64])) lv243 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_3_self_attn_v_proj_weight3, layer_norm171, model_decoder_layers_3_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape742: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv243, R.shape([batch_size, 1, 20, 64])) concat35: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape740, reshape741, reshape742), axis=2) reshape743: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat35, R.shape([batch_size, 60, 64])) lv140 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape743), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape744: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv140, R.shape([batch_size, 1, 20, 64])) reshape745: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape744, R.shape([batch_size, 1, 1280])) lv244 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_3_self_attn_out_proj_weight3, reshape745, model_decoder_layers_3_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add612: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add608, lv244) layer_norm172: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add612, model_decoder_layers_3_encoder_attn_layer_norm_weight3, model_decoder_layers_3_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv245 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_3_encoder_attn_q_proj_weight3, layer_norm172, model_decoder_layers_3_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape746: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv245, R.shape([batch_size, 1, 20, 64])) reshape747: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape746, R.shape([batch_size, 20, 64])) lv141 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape747), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape748: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv141, R.shape([batch_size, 1, 20, 64])) reshape749: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape748, R.shape([batch_size, 1, 1280])) lv246 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_3_encoder_attn_out_proj_weight3, reshape749, model_decoder_layers_3_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add615: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add612, lv246) layer_norm173: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add615, model_decoder_layers_3_final_layer_norm_weight3, model_decoder_layers_3_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv35 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_3_fc1_weight3, layer_norm173, model_decoder_layers_3_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv247 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_3_fc2_weight3, lv35, model_decoder_layers_3_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add618: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add615, lv247) layer_norm174: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add618, model_decoder_layers_4_self_attn_layer_norm_weight3, model_decoder_layers_4_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv248 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_4_self_attn_q_proj_weight3, layer_norm174, model_decoder_layers_4_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape750: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv248, R.shape([batch_size, 1, 20, 64])) lv69 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_4_self_attn_k_proj_weight3, layer_norm174), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape751: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv69, R.shape([batch_size, 1, 20, 64])) lv249 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_4_self_attn_v_proj_weight3, layer_norm174, model_decoder_layers_4_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape752: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv249, R.shape([batch_size, 1, 20, 64])) concat36: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape750, reshape751, reshape752), axis=2) reshape753: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat36, R.shape([batch_size, 60, 64])) lv142 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape753), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape754: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv142, R.shape([batch_size, 1, 20, 64])) reshape755: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape754, R.shape([batch_size, 1, 1280])) lv250 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_4_self_attn_out_proj_weight3, reshape755, model_decoder_layers_4_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add622: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add618, lv250) layer_norm175: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add622, model_decoder_layers_4_encoder_attn_layer_norm_weight3, model_decoder_layers_4_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv251 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_4_encoder_attn_q_proj_weight3, layer_norm175, model_decoder_layers_4_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape756: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv251, R.shape([batch_size, 1, 20, 64])) reshape757: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape756, R.shape([batch_size, 20, 64])) lv143 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape757), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape758: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv143, R.shape([batch_size, 1, 20, 64])) reshape759: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape758, R.shape([batch_size, 1, 1280])) lv252 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_4_encoder_attn_out_proj_weight3, reshape759, model_decoder_layers_4_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add625: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add622, lv252) layer_norm176: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add625, model_decoder_layers_4_final_layer_norm_weight3, model_decoder_layers_4_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv36 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_4_fc1_weight3, layer_norm176, model_decoder_layers_4_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv253 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_4_fc2_weight3, lv36, model_decoder_layers_4_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add628: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add625, lv253) layer_norm177: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add628, model_decoder_layers_5_self_attn_layer_norm_weight3, model_decoder_layers_5_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv254 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_5_self_attn_q_proj_weight3, layer_norm177, model_decoder_layers_5_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape760: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv254, R.shape([batch_size, 1, 20, 64])) lv70 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_5_self_attn_k_proj_weight3, layer_norm177), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape761: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv70, R.shape([batch_size, 1, 20, 64])) lv255 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_5_self_attn_v_proj_weight3, layer_norm177, model_decoder_layers_5_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape762: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv255, R.shape([batch_size, 1, 20, 64])) concat37: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape760, reshape761, reshape762), axis=2) reshape763: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat37, R.shape([batch_size, 60, 64])) lv144 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape763), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape764: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv144, R.shape([batch_size, 1, 20, 64])) reshape765: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape764, R.shape([batch_size, 1, 1280])) lv256 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_5_self_attn_out_proj_weight3, reshape765, model_decoder_layers_5_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add632: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add628, lv256) layer_norm178: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add632, model_decoder_layers_5_encoder_attn_layer_norm_weight3, model_decoder_layers_5_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv257 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_5_encoder_attn_q_proj_weight3, layer_norm178, model_decoder_layers_5_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape766: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv257, R.shape([batch_size, 1, 20, 64])) reshape767: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape766, R.shape([batch_size, 20, 64])) lv145 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape767), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape768: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv145, R.shape([batch_size, 1, 20, 64])) reshape769: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape768, R.shape([batch_size, 1, 1280])) lv258 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_5_encoder_attn_out_proj_weight3, reshape769, model_decoder_layers_5_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add635: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add632, lv258) layer_norm179: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add635, model_decoder_layers_5_final_layer_norm_weight3, model_decoder_layers_5_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv37 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_5_fc1_weight3, layer_norm179, model_decoder_layers_5_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv259 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_5_fc2_weight3, lv37, model_decoder_layers_5_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add638: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add635, lv259) layer_norm180: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add638, model_decoder_layers_6_self_attn_layer_norm_weight3, model_decoder_layers_6_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv260 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_6_self_attn_q_proj_weight3, layer_norm180, model_decoder_layers_6_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape770: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv260, R.shape([batch_size, 1, 20, 64])) lv71 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_6_self_attn_k_proj_weight3, layer_norm180), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape771: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv71, R.shape([batch_size, 1, 20, 64])) lv261 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_6_self_attn_v_proj_weight3, layer_norm180, model_decoder_layers_6_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape772: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv261, R.shape([batch_size, 1, 20, 64])) concat38: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape770, reshape771, reshape772), axis=2) reshape773: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat38, R.shape([batch_size, 60, 64])) lv146 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape773), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape774: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv146, R.shape([batch_size, 1, 20, 64])) reshape775: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape774, R.shape([batch_size, 1, 1280])) lv262 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_6_self_attn_out_proj_weight3, reshape775, model_decoder_layers_6_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add642: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add638, lv262) layer_norm181: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add642, model_decoder_layers_6_encoder_attn_layer_norm_weight3, model_decoder_layers_6_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv263 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_6_encoder_attn_q_proj_weight3, layer_norm181, model_decoder_layers_6_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape776: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv263, R.shape([batch_size, 1, 20, 64])) reshape777: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape776, R.shape([batch_size, 20, 64])) lv147 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape777), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape778: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv147, R.shape([batch_size, 1, 20, 64])) reshape779: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape778, R.shape([batch_size, 1, 1280])) lv264 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_6_encoder_attn_out_proj_weight3, reshape779, model_decoder_layers_6_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add645: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add642, lv264) layer_norm182: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add645, model_decoder_layers_6_final_layer_norm_weight3, model_decoder_layers_6_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv38 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_6_fc1_weight3, layer_norm182, model_decoder_layers_6_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv265 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_6_fc2_weight3, lv38, model_decoder_layers_6_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add648: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add645, lv265) layer_norm183: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add648, model_decoder_layers_7_self_attn_layer_norm_weight3, model_decoder_layers_7_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv266 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_7_self_attn_q_proj_weight3, layer_norm183, model_decoder_layers_7_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape780: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv266, R.shape([batch_size, 1, 20, 64])) lv72 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_7_self_attn_k_proj_weight3, layer_norm183), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape781: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv72, R.shape([batch_size, 1, 20, 64])) lv267 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_7_self_attn_v_proj_weight3, layer_norm183, model_decoder_layers_7_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape782: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv267, R.shape([batch_size, 1, 20, 64])) concat39: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape780, reshape781, reshape782), axis=2) reshape783: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat39, R.shape([batch_size, 60, 64])) lv148 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape783), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape784: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv148, R.shape([batch_size, 1, 20, 64])) reshape785: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape784, R.shape([batch_size, 1, 1280])) lv268 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_7_self_attn_out_proj_weight3, reshape785, model_decoder_layers_7_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add652: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add648, lv268) layer_norm184: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add652, model_decoder_layers_7_encoder_attn_layer_norm_weight3, model_decoder_layers_7_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv269 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_7_encoder_attn_q_proj_weight3, layer_norm184, model_decoder_layers_7_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape786: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv269, R.shape([batch_size, 1, 20, 64])) reshape787: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape786, R.shape([batch_size, 20, 64])) lv149 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape787), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape788: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv149, R.shape([batch_size, 1, 20, 64])) reshape789: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape788, R.shape([batch_size, 1, 1280])) lv270 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_7_encoder_attn_out_proj_weight3, reshape789, model_decoder_layers_7_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add655: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add652, lv270) layer_norm185: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add655, model_decoder_layers_7_final_layer_norm_weight3, model_decoder_layers_7_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv39 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_7_fc1_weight3, layer_norm185, model_decoder_layers_7_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv271 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_7_fc2_weight3, lv39, model_decoder_layers_7_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add658: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add655, lv271) layer_norm186: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add658, model_decoder_layers_8_self_attn_layer_norm_weight3, model_decoder_layers_8_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv272 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_8_self_attn_q_proj_weight3, layer_norm186, model_decoder_layers_8_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape790: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv272, R.shape([batch_size, 1, 20, 64])) lv73 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_8_self_attn_k_proj_weight3, layer_norm186), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape791: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv73, R.shape([batch_size, 1, 20, 64])) lv273 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_8_self_attn_v_proj_weight3, layer_norm186, model_decoder_layers_8_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape792: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv273, R.shape([batch_size, 1, 20, 64])) concat40: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape790, reshape791, reshape792), axis=2) reshape793: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat40, R.shape([batch_size, 60, 64])) lv150 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape793), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape794: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv150, R.shape([batch_size, 1, 20, 64])) reshape795: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape794, R.shape([batch_size, 1, 1280])) lv274 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_8_self_attn_out_proj_weight3, reshape795, model_decoder_layers_8_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add662: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add658, lv274) layer_norm187: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add662, model_decoder_layers_8_encoder_attn_layer_norm_weight3, model_decoder_layers_8_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv275 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_8_encoder_attn_q_proj_weight3, layer_norm187, model_decoder_layers_8_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape796: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv275, R.shape([batch_size, 1, 20, 64])) reshape797: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape796, R.shape([batch_size, 20, 64])) lv151 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape797), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape798: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv151, R.shape([batch_size, 1, 20, 64])) reshape799: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape798, R.shape([batch_size, 1, 1280])) lv276 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_8_encoder_attn_out_proj_weight3, reshape799, model_decoder_layers_8_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add665: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add662, lv276) layer_norm188: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add665, model_decoder_layers_8_final_layer_norm_weight3, model_decoder_layers_8_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv40 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_8_fc1_weight3, layer_norm188, model_decoder_layers_8_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv277 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_8_fc2_weight3, lv40, model_decoder_layers_8_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add668: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add665, lv277) layer_norm189: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add668, model_decoder_layers_9_self_attn_layer_norm_weight3, model_decoder_layers_9_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv278 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_9_self_attn_q_proj_weight3, layer_norm189, model_decoder_layers_9_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape800: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv278, R.shape([batch_size, 1, 20, 64])) lv74 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_9_self_attn_k_proj_weight3, layer_norm189), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape801: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv74, R.shape([batch_size, 1, 20, 64])) lv279 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_9_self_attn_v_proj_weight3, layer_norm189, model_decoder_layers_9_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape802: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv279, R.shape([batch_size, 1, 20, 64])) concat41: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape800, reshape801, reshape802), axis=2) reshape803: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat41, R.shape([batch_size, 60, 64])) lv152 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape803), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape804: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv152, R.shape([batch_size, 1, 20, 64])) reshape805: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape804, R.shape([batch_size, 1, 1280])) lv280 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_9_self_attn_out_proj_weight3, reshape805, model_decoder_layers_9_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add672: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add668, lv280) layer_norm190: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add672, model_decoder_layers_9_encoder_attn_layer_norm_weight3, model_decoder_layers_9_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv281 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_9_encoder_attn_q_proj_weight3, layer_norm190, model_decoder_layers_9_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape806: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv281, R.shape([batch_size, 1, 20, 64])) reshape807: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape806, R.shape([batch_size, 20, 64])) lv153 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape807), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape808: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv153, R.shape([batch_size, 1, 20, 64])) reshape809: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape808, R.shape([batch_size, 1, 1280])) lv282 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_9_encoder_attn_out_proj_weight3, reshape809, model_decoder_layers_9_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add675: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add672, lv282) layer_norm191: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add675, model_decoder_layers_9_final_layer_norm_weight3, model_decoder_layers_9_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv41 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_9_fc1_weight3, layer_norm191, model_decoder_layers_9_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv283 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_9_fc2_weight3, lv41, model_decoder_layers_9_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add678: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add675, lv283) layer_norm192: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add678, model_decoder_layers_10_self_attn_layer_norm_weight3, model_decoder_layers_10_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv284 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_10_self_attn_q_proj_weight3, layer_norm192, model_decoder_layers_10_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape810: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv284, R.shape([batch_size, 1, 20, 64])) lv75 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_10_self_attn_k_proj_weight3, layer_norm192), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape811: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv75, R.shape([batch_size, 1, 20, 64])) lv285 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_10_self_attn_v_proj_weight3, layer_norm192, model_decoder_layers_10_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape812: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv285, R.shape([batch_size, 1, 20, 64])) concat42: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape810, reshape811, reshape812), axis=2) reshape813: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat42, R.shape([batch_size, 60, 64])) lv154 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape813), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape814: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv154, R.shape([batch_size, 1, 20, 64])) reshape815: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape814, R.shape([batch_size, 1, 1280])) lv286 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_10_self_attn_out_proj_weight3, reshape815, model_decoder_layers_10_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add682: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add678, lv286) layer_norm193: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add682, model_decoder_layers_10_encoder_attn_layer_norm_weight3, model_decoder_layers_10_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv287 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_10_encoder_attn_q_proj_weight3, layer_norm193, model_decoder_layers_10_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape816: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv287, R.shape([batch_size, 1, 20, 64])) reshape817: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape816, R.shape([batch_size, 20, 64])) lv155 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape817), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape818: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv155, R.shape([batch_size, 1, 20, 64])) reshape819: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape818, R.shape([batch_size, 1, 1280])) lv288 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_10_encoder_attn_out_proj_weight3, reshape819, model_decoder_layers_10_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add685: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add682, lv288) layer_norm194: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add685, model_decoder_layers_10_final_layer_norm_weight3, model_decoder_layers_10_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv42 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_10_fc1_weight3, layer_norm194, model_decoder_layers_10_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv289 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_10_fc2_weight3, lv42, model_decoder_layers_10_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add688: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add685, lv289) layer_norm195: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add688, model_decoder_layers_11_self_attn_layer_norm_weight3, model_decoder_layers_11_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv290 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_11_self_attn_q_proj_weight3, layer_norm195, model_decoder_layers_11_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape820: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv290, R.shape([batch_size, 1, 20, 64])) lv76 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_11_self_attn_k_proj_weight3, layer_norm195), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape821: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv76, R.shape([batch_size, 1, 20, 64])) lv291 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_11_self_attn_v_proj_weight3, layer_norm195, model_decoder_layers_11_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape822: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv291, R.shape([batch_size, 1, 20, 64])) concat43: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape820, reshape821, reshape822), axis=2) reshape823: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat43, R.shape([batch_size, 60, 64])) lv156 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape823), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape824: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv156, R.shape([batch_size, 1, 20, 64])) reshape825: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape824, R.shape([batch_size, 1, 1280])) lv292 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_11_self_attn_out_proj_weight3, reshape825, model_decoder_layers_11_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add692: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add688, lv292) layer_norm196: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add692, model_decoder_layers_11_encoder_attn_layer_norm_weight3, model_decoder_layers_11_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv293 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_11_encoder_attn_q_proj_weight3, layer_norm196, model_decoder_layers_11_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape826: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv293, R.shape([batch_size, 1, 20, 64])) reshape827: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape826, R.shape([batch_size, 20, 64])) lv157 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape827), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape828: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv157, R.shape([batch_size, 1, 20, 64])) reshape829: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape828, R.shape([batch_size, 1, 1280])) lv294 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_11_encoder_attn_out_proj_weight3, reshape829, model_decoder_layers_11_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add695: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add692, lv294) layer_norm197: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add695, model_decoder_layers_11_final_layer_norm_weight3, model_decoder_layers_11_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv43 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_11_fc1_weight3, layer_norm197, model_decoder_layers_11_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv295 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_11_fc2_weight3, lv43, model_decoder_layers_11_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add698: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add695, lv295) layer_norm198: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add698, model_decoder_layers_12_self_attn_layer_norm_weight3, model_decoder_layers_12_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv296 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_12_self_attn_q_proj_weight3, layer_norm198, model_decoder_layers_12_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape830: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv296, R.shape([batch_size, 1, 20, 64])) lv77 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_12_self_attn_k_proj_weight3, layer_norm198), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape831: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv77, R.shape([batch_size, 1, 20, 64])) lv297 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_12_self_attn_v_proj_weight3, layer_norm198, model_decoder_layers_12_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape832: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv297, R.shape([batch_size, 1, 20, 64])) concat44: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape830, reshape831, reshape832), axis=2) reshape833: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat44, R.shape([batch_size, 60, 64])) lv158 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape833), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape834: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv158, R.shape([batch_size, 1, 20, 64])) reshape835: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape834, R.shape([batch_size, 1, 1280])) lv298 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_12_self_attn_out_proj_weight3, reshape835, model_decoder_layers_12_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add702: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add698, lv298) layer_norm199: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add702, model_decoder_layers_12_encoder_attn_layer_norm_weight3, model_decoder_layers_12_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv299 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_12_encoder_attn_q_proj_weight3, layer_norm199, model_decoder_layers_12_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape836: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv299, R.shape([batch_size, 1, 20, 64])) reshape837: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape836, R.shape([batch_size, 20, 64])) lv159 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape837), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape838: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv159, R.shape([batch_size, 1, 20, 64])) reshape839: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape838, R.shape([batch_size, 1, 1280])) lv300 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_12_encoder_attn_out_proj_weight3, reshape839, model_decoder_layers_12_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add705: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add702, lv300) layer_norm200: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add705, model_decoder_layers_12_final_layer_norm_weight3, model_decoder_layers_12_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv44 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_12_fc1_weight3, layer_norm200, model_decoder_layers_12_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv301 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_12_fc2_weight3, lv44, model_decoder_layers_12_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add708: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add705, lv301) layer_norm201: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add708, model_decoder_layers_13_self_attn_layer_norm_weight3, model_decoder_layers_13_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv302 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_13_self_attn_q_proj_weight3, layer_norm201, model_decoder_layers_13_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape840: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv302, R.shape([batch_size, 1, 20, 64])) lv78 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_13_self_attn_k_proj_weight3, layer_norm201), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape841: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv78, R.shape([batch_size, 1, 20, 64])) lv303 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_13_self_attn_v_proj_weight3, layer_norm201, model_decoder_layers_13_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape842: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv303, R.shape([batch_size, 1, 20, 64])) concat45: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape840, reshape841, reshape842), axis=2) reshape843: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat45, R.shape([batch_size, 60, 64])) lv160 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape843), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape844: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv160, R.shape([batch_size, 1, 20, 64])) reshape845: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape844, R.shape([batch_size, 1, 1280])) lv304 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_13_self_attn_out_proj_weight3, reshape845, model_decoder_layers_13_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add712: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add708, lv304) layer_norm202: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add712, model_decoder_layers_13_encoder_attn_layer_norm_weight3, model_decoder_layers_13_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv305 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_13_encoder_attn_q_proj_weight3, layer_norm202, model_decoder_layers_13_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape846: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv305, R.shape([batch_size, 1, 20, 64])) reshape847: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape846, R.shape([batch_size, 20, 64])) lv161 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape847), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape848: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv161, R.shape([batch_size, 1, 20, 64])) reshape849: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape848, R.shape([batch_size, 1, 1280])) lv306 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_13_encoder_attn_out_proj_weight3, reshape849, model_decoder_layers_13_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add715: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add712, lv306) layer_norm203: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add715, model_decoder_layers_13_final_layer_norm_weight3, model_decoder_layers_13_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv45 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_13_fc1_weight3, layer_norm203, model_decoder_layers_13_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv307 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_13_fc2_weight3, lv45, model_decoder_layers_13_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add718: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add715, lv307) layer_norm204: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add718, model_decoder_layers_14_self_attn_layer_norm_weight3, model_decoder_layers_14_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv308 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_14_self_attn_q_proj_weight3, layer_norm204, model_decoder_layers_14_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape850: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv308, R.shape([batch_size, 1, 20, 64])) lv79 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_14_self_attn_k_proj_weight3, layer_norm204), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape851: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv79, R.shape([batch_size, 1, 20, 64])) lv309 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_14_self_attn_v_proj_weight3, layer_norm204, model_decoder_layers_14_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape852: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv309, R.shape([batch_size, 1, 20, 64])) concat46: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape850, reshape851, reshape852), axis=2) reshape853: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat46, R.shape([batch_size, 60, 64])) lv162 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape853), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape854: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv162, R.shape([batch_size, 1, 20, 64])) reshape855: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape854, R.shape([batch_size, 1, 1280])) lv310 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_14_self_attn_out_proj_weight3, reshape855, model_decoder_layers_14_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add722: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add718, lv310) layer_norm205: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add722, model_decoder_layers_14_encoder_attn_layer_norm_weight3, model_decoder_layers_14_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv311 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_14_encoder_attn_q_proj_weight3, layer_norm205, model_decoder_layers_14_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape856: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv311, R.shape([batch_size, 1, 20, 64])) reshape857: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape856, R.shape([batch_size, 20, 64])) lv163 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape857), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape858: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv163, R.shape([batch_size, 1, 20, 64])) reshape859: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape858, R.shape([batch_size, 1, 1280])) lv312 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_14_encoder_attn_out_proj_weight3, reshape859, model_decoder_layers_14_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add725: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add722, lv312) layer_norm206: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add725, model_decoder_layers_14_final_layer_norm_weight3, model_decoder_layers_14_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv46 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_14_fc1_weight3, layer_norm206, model_decoder_layers_14_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv313 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_14_fc2_weight3, lv46, model_decoder_layers_14_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add728: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add725, lv313) layer_norm207: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add728, model_decoder_layers_15_self_attn_layer_norm_weight3, model_decoder_layers_15_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv314 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_15_self_attn_q_proj_weight3, layer_norm207, model_decoder_layers_15_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape860: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv314, R.shape([batch_size, 1, 20, 64])) lv80 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_15_self_attn_k_proj_weight3, layer_norm207), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape861: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv80, R.shape([batch_size, 1, 20, 64])) lv315 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_15_self_attn_v_proj_weight3, layer_norm207, model_decoder_layers_15_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape862: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv315, R.shape([batch_size, 1, 20, 64])) concat47: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape860, reshape861, reshape862), axis=2) reshape863: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat47, R.shape([batch_size, 60, 64])) lv164 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape863), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape864: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv164, R.shape([batch_size, 1, 20, 64])) reshape865: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape864, R.shape([batch_size, 1, 1280])) lv316 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_15_self_attn_out_proj_weight3, reshape865, model_decoder_layers_15_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add732: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add728, lv316) layer_norm208: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add732, model_decoder_layers_15_encoder_attn_layer_norm_weight3, model_decoder_layers_15_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv317 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_15_encoder_attn_q_proj_weight3, layer_norm208, model_decoder_layers_15_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape866: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv317, R.shape([batch_size, 1, 20, 64])) reshape867: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape866, R.shape([batch_size, 20, 64])) lv165 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape867), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape868: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv165, R.shape([batch_size, 1, 20, 64])) reshape869: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape868, R.shape([batch_size, 1, 1280])) lv318 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_15_encoder_attn_out_proj_weight3, reshape869, model_decoder_layers_15_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add735: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add732, lv318) layer_norm209: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add735, model_decoder_layers_15_final_layer_norm_weight3, model_decoder_layers_15_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv47 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_15_fc1_weight3, layer_norm209, model_decoder_layers_15_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv319 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_15_fc2_weight3, lv47, model_decoder_layers_15_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add738: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add735, lv319) layer_norm210: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add738, model_decoder_layers_16_self_attn_layer_norm_weight3, model_decoder_layers_16_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv320 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_16_self_attn_q_proj_weight3, layer_norm210, model_decoder_layers_16_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape870: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv320, R.shape([batch_size, 1, 20, 64])) lv81 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_16_self_attn_k_proj_weight3, layer_norm210), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape871: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv81, R.shape([batch_size, 1, 20, 64])) lv321 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_16_self_attn_v_proj_weight3, layer_norm210, model_decoder_layers_16_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape872: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv321, R.shape([batch_size, 1, 20, 64])) concat48: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape870, reshape871, reshape872), axis=2) reshape873: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat48, R.shape([batch_size, 60, 64])) lv166 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape873), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape874: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv166, R.shape([batch_size, 1, 20, 64])) reshape875: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape874, R.shape([batch_size, 1, 1280])) lv322 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_16_self_attn_out_proj_weight3, reshape875, model_decoder_layers_16_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add742: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add738, lv322) layer_norm211: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add742, model_decoder_layers_16_encoder_attn_layer_norm_weight3, model_decoder_layers_16_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv323 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_16_encoder_attn_q_proj_weight3, layer_norm211, model_decoder_layers_16_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape876: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv323, R.shape([batch_size, 1, 20, 64])) reshape877: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape876, R.shape([batch_size, 20, 64])) lv167 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape877), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape878: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv167, R.shape([batch_size, 1, 20, 64])) reshape879: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape878, R.shape([batch_size, 1, 1280])) lv324 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_16_encoder_attn_out_proj_weight3, reshape879, model_decoder_layers_16_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add745: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add742, lv324) layer_norm212: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add745, model_decoder_layers_16_final_layer_norm_weight3, model_decoder_layers_16_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv48 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_16_fc1_weight3, layer_norm212, model_decoder_layers_16_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv325 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_16_fc2_weight3, lv48, model_decoder_layers_16_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add748: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add745, lv325) layer_norm213: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add748, model_decoder_layers_17_self_attn_layer_norm_weight3, model_decoder_layers_17_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv326 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_17_self_attn_q_proj_weight3, layer_norm213, model_decoder_layers_17_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape880: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv326, R.shape([batch_size, 1, 20, 64])) lv82 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_17_self_attn_k_proj_weight3, layer_norm213), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape881: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv82, R.shape([batch_size, 1, 20, 64])) lv327 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_17_self_attn_v_proj_weight3, layer_norm213, model_decoder_layers_17_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape882: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv327, R.shape([batch_size, 1, 20, 64])) concat49: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape880, reshape881, reshape882), axis=2) reshape883: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat49, R.shape([batch_size, 60, 64])) lv168 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape883), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape884: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv168, R.shape([batch_size, 1, 20, 64])) reshape885: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape884, R.shape([batch_size, 1, 1280])) lv328 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_17_self_attn_out_proj_weight3, reshape885, model_decoder_layers_17_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add752: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add748, lv328) layer_norm214: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add752, model_decoder_layers_17_encoder_attn_layer_norm_weight3, model_decoder_layers_17_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv329 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_17_encoder_attn_q_proj_weight3, layer_norm214, model_decoder_layers_17_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape886: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv329, R.shape([batch_size, 1, 20, 64])) reshape887: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape886, R.shape([batch_size, 20, 64])) lv169 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape887), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape888: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv169, R.shape([batch_size, 1, 20, 64])) reshape889: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape888, R.shape([batch_size, 1, 1280])) lv330 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_17_encoder_attn_out_proj_weight3, reshape889, model_decoder_layers_17_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add755: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add752, lv330) layer_norm215: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add755, model_decoder_layers_17_final_layer_norm_weight3, model_decoder_layers_17_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv49 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_17_fc1_weight3, layer_norm215, model_decoder_layers_17_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv331 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_17_fc2_weight3, lv49, model_decoder_layers_17_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add758: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add755, lv331) layer_norm216: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add758, model_decoder_layers_18_self_attn_layer_norm_weight3, model_decoder_layers_18_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv332 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_18_self_attn_q_proj_weight3, layer_norm216, model_decoder_layers_18_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape890: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv332, R.shape([batch_size, 1, 20, 64])) lv83 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_18_self_attn_k_proj_weight3, layer_norm216), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape891: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv83, R.shape([batch_size, 1, 20, 64])) lv333 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_18_self_attn_v_proj_weight3, layer_norm216, model_decoder_layers_18_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape892: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv333, R.shape([batch_size, 1, 20, 64])) concat50: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape890, reshape891, reshape892), axis=2) reshape893: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat50, R.shape([batch_size, 60, 64])) lv170 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape893), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape894: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv170, R.shape([batch_size, 1, 20, 64])) reshape895: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape894, R.shape([batch_size, 1, 1280])) lv334 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_18_self_attn_out_proj_weight3, reshape895, model_decoder_layers_18_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add762: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add758, lv334) layer_norm217: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add762, model_decoder_layers_18_encoder_attn_layer_norm_weight3, model_decoder_layers_18_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv335 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_18_encoder_attn_q_proj_weight3, layer_norm217, model_decoder_layers_18_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape896: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv335, R.shape([batch_size, 1, 20, 64])) reshape897: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape896, R.shape([batch_size, 20, 64])) lv171 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape897), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape898: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv171, R.shape([batch_size, 1, 20, 64])) reshape899: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape898, R.shape([batch_size, 1, 1280])) lv336 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_18_encoder_attn_out_proj_weight3, reshape899, model_decoder_layers_18_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add765: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add762, lv336) layer_norm218: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add765, model_decoder_layers_18_final_layer_norm_weight3, model_decoder_layers_18_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv50 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_18_fc1_weight3, layer_norm218, model_decoder_layers_18_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv337 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_18_fc2_weight3, lv50, model_decoder_layers_18_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add768: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add765, lv337) layer_norm219: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add768, model_decoder_layers_19_self_attn_layer_norm_weight3, model_decoder_layers_19_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv338 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_19_self_attn_q_proj_weight3, layer_norm219, model_decoder_layers_19_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape900: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv338, R.shape([batch_size, 1, 20, 64])) lv84 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_19_self_attn_k_proj_weight3, layer_norm219), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape901: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv84, R.shape([batch_size, 1, 20, 64])) lv339 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_19_self_attn_v_proj_weight3, layer_norm219, model_decoder_layers_19_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape902: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv339, R.shape([batch_size, 1, 20, 64])) concat51: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape900, reshape901, reshape902), axis=2) reshape903: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat51, R.shape([batch_size, 60, 64])) lv172 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape903), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape904: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv172, R.shape([batch_size, 1, 20, 64])) reshape905: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape904, R.shape([batch_size, 1, 1280])) lv340 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_19_self_attn_out_proj_weight3, reshape905, model_decoder_layers_19_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add772: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add768, lv340) layer_norm220: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add772, model_decoder_layers_19_encoder_attn_layer_norm_weight3, model_decoder_layers_19_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv341 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_19_encoder_attn_q_proj_weight3, layer_norm220, model_decoder_layers_19_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape906: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv341, R.shape([batch_size, 1, 20, 64])) reshape907: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape906, R.shape([batch_size, 20, 64])) lv173 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape907), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape908: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv173, R.shape([batch_size, 1, 20, 64])) reshape909: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape908, R.shape([batch_size, 1, 1280])) lv342 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_19_encoder_attn_out_proj_weight3, reshape909, model_decoder_layers_19_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add775: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add772, lv342) layer_norm221: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add775, model_decoder_layers_19_final_layer_norm_weight3, model_decoder_layers_19_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv51 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_19_fc1_weight3, layer_norm221, model_decoder_layers_19_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv343 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_19_fc2_weight3, lv51, model_decoder_layers_19_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add778: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add775, lv343) layer_norm222: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add778, model_decoder_layers_20_self_attn_layer_norm_weight3, model_decoder_layers_20_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv344 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_20_self_attn_q_proj_weight3, layer_norm222, model_decoder_layers_20_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape910: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv344, R.shape([batch_size, 1, 20, 64])) lv85 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_20_self_attn_k_proj_weight3, layer_norm222), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape911: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv85, R.shape([batch_size, 1, 20, 64])) lv345 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_20_self_attn_v_proj_weight3, layer_norm222, model_decoder_layers_20_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape912: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv345, R.shape([batch_size, 1, 20, 64])) concat52: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape910, reshape911, reshape912), axis=2) reshape913: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat52, R.shape([batch_size, 60, 64])) lv174 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape913), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape914: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv174, R.shape([batch_size, 1, 20, 64])) reshape915: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape914, R.shape([batch_size, 1, 1280])) lv346 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_20_self_attn_out_proj_weight3, reshape915, model_decoder_layers_20_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add782: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add778, lv346) layer_norm223: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add782, model_decoder_layers_20_encoder_attn_layer_norm_weight3, model_decoder_layers_20_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv347 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_20_encoder_attn_q_proj_weight3, layer_norm223, model_decoder_layers_20_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape916: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv347, R.shape([batch_size, 1, 20, 64])) reshape917: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape916, R.shape([batch_size, 20, 64])) lv175 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape917), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape918: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv175, R.shape([batch_size, 1, 20, 64])) reshape919: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape918, R.shape([batch_size, 1, 1280])) lv348 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_20_encoder_attn_out_proj_weight3, reshape919, model_decoder_layers_20_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add785: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add782, lv348) layer_norm224: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add785, model_decoder_layers_20_final_layer_norm_weight3, model_decoder_layers_20_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv52 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_20_fc1_weight3, layer_norm224, model_decoder_layers_20_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv349 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_20_fc2_weight3, lv52, model_decoder_layers_20_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add788: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add785, lv349) layer_norm225: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add788, model_decoder_layers_21_self_attn_layer_norm_weight3, model_decoder_layers_21_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv350 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_21_self_attn_q_proj_weight3, layer_norm225, model_decoder_layers_21_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape920: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv350, R.shape([batch_size, 1, 20, 64])) lv86 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_21_self_attn_k_proj_weight3, layer_norm225), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape921: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv86, R.shape([batch_size, 1, 20, 64])) lv351 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_21_self_attn_v_proj_weight3, layer_norm225, model_decoder_layers_21_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape922: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv351, R.shape([batch_size, 1, 20, 64])) concat53: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape920, reshape921, reshape922), axis=2) reshape923: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat53, R.shape([batch_size, 60, 64])) lv176 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape923), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape924: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv176, R.shape([batch_size, 1, 20, 64])) reshape925: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape924, R.shape([batch_size, 1, 1280])) lv352 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_21_self_attn_out_proj_weight3, reshape925, model_decoder_layers_21_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add792: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add788, lv352) layer_norm226: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add792, model_decoder_layers_21_encoder_attn_layer_norm_weight3, model_decoder_layers_21_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv353 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_21_encoder_attn_q_proj_weight3, layer_norm226, model_decoder_layers_21_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape926: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv353, R.shape([batch_size, 1, 20, 64])) reshape927: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape926, R.shape([batch_size, 20, 64])) lv177 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape927), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape928: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv177, R.shape([batch_size, 1, 20, 64])) reshape929: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape928, R.shape([batch_size, 1, 1280])) lv354 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_21_encoder_attn_out_proj_weight3, reshape929, model_decoder_layers_21_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add795: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add792, lv354) layer_norm227: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add795, model_decoder_layers_21_final_layer_norm_weight3, model_decoder_layers_21_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv53 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_21_fc1_weight3, layer_norm227, model_decoder_layers_21_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv355 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_21_fc2_weight3, lv53, model_decoder_layers_21_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add798: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add795, lv355) layer_norm228: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add798, model_decoder_layers_22_self_attn_layer_norm_weight3, model_decoder_layers_22_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv356 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_22_self_attn_q_proj_weight3, layer_norm228, model_decoder_layers_22_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape930: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv356, R.shape([batch_size, 1, 20, 64])) lv87 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_22_self_attn_k_proj_weight3, layer_norm228), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape931: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv87, R.shape([batch_size, 1, 20, 64])) lv357 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_22_self_attn_v_proj_weight3, layer_norm228, model_decoder_layers_22_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape932: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv357, R.shape([batch_size, 1, 20, 64])) concat54: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape930, reshape931, reshape932), axis=2) reshape933: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat54, R.shape([batch_size, 60, 64])) lv178 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape933), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape934: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv178, R.shape([batch_size, 1, 20, 64])) reshape935: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape934, R.shape([batch_size, 1, 1280])) lv358 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_22_self_attn_out_proj_weight3, reshape935, model_decoder_layers_22_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add802: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add798, lv358) layer_norm229: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add802, model_decoder_layers_22_encoder_attn_layer_norm_weight3, model_decoder_layers_22_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv359 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_22_encoder_attn_q_proj_weight3, layer_norm229, model_decoder_layers_22_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape936: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv359, R.shape([batch_size, 1, 20, 64])) reshape937: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape936, R.shape([batch_size, 20, 64])) lv179 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape937), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape938: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv179, R.shape([batch_size, 1, 20, 64])) reshape939: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape938, R.shape([batch_size, 1, 1280])) lv360 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_22_encoder_attn_out_proj_weight3, reshape939, model_decoder_layers_22_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add805: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add802, lv360) layer_norm230: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add805, model_decoder_layers_22_final_layer_norm_weight3, model_decoder_layers_22_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv54 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_22_fc1_weight3, layer_norm230, model_decoder_layers_22_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv361 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_22_fc2_weight3, lv54, model_decoder_layers_22_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add808: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add805, lv361) layer_norm231: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add808, model_decoder_layers_23_self_attn_layer_norm_weight3, model_decoder_layers_23_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv362 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_23_self_attn_q_proj_weight3, layer_norm231, model_decoder_layers_23_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape940: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv362, R.shape([batch_size, 1, 20, 64])) lv88 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_23_self_attn_k_proj_weight3, layer_norm231), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape941: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv88, R.shape([batch_size, 1, 20, 64])) lv363 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_23_self_attn_v_proj_weight3, layer_norm231, model_decoder_layers_23_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape942: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv363, R.shape([batch_size, 1, 20, 64])) concat55: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape940, reshape941, reshape942), axis=2) reshape943: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat55, R.shape([batch_size, 60, 64])) lv180 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape943), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape944: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv180, R.shape([batch_size, 1, 20, 64])) reshape945: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape944, R.shape([batch_size, 1, 1280])) lv364 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_23_self_attn_out_proj_weight3, reshape945, model_decoder_layers_23_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add812: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add808, lv364) layer_norm232: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add812, model_decoder_layers_23_encoder_attn_layer_norm_weight3, model_decoder_layers_23_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv365 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_23_encoder_attn_q_proj_weight3, layer_norm232, model_decoder_layers_23_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape946: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv365, R.shape([batch_size, 1, 20, 64])) reshape947: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape946, R.shape([batch_size, 20, 64])) lv181 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape947), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape948: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv181, R.shape([batch_size, 1, 20, 64])) reshape949: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape948, R.shape([batch_size, 1, 1280])) lv366 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_23_encoder_attn_out_proj_weight3, reshape949, model_decoder_layers_23_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add815: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add812, lv366) layer_norm233: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add815, model_decoder_layers_23_final_layer_norm_weight3, model_decoder_layers_23_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv55 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_23_fc1_weight3, layer_norm233, model_decoder_layers_23_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv367 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_23_fc2_weight3, lv55, model_decoder_layers_23_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add818: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add815, lv367) layer_norm234: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add818, model_decoder_layers_24_self_attn_layer_norm_weight3, model_decoder_layers_24_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv368 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_24_self_attn_q_proj_weight3, layer_norm234, model_decoder_layers_24_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape950: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv368, R.shape([batch_size, 1, 20, 64])) lv89 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_24_self_attn_k_proj_weight3, layer_norm234), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape951: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv89, R.shape([batch_size, 1, 20, 64])) lv369 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_24_self_attn_v_proj_weight3, layer_norm234, model_decoder_layers_24_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape952: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv369, R.shape([batch_size, 1, 20, 64])) concat56: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape950, reshape951, reshape952), axis=2) reshape953: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat56, R.shape([batch_size, 60, 64])) lv182 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape953), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape954: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv182, R.shape([batch_size, 1, 20, 64])) reshape955: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape954, R.shape([batch_size, 1, 1280])) lv370 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_24_self_attn_out_proj_weight3, reshape955, model_decoder_layers_24_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add822: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add818, lv370) layer_norm235: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add822, model_decoder_layers_24_encoder_attn_layer_norm_weight3, model_decoder_layers_24_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv371 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_24_encoder_attn_q_proj_weight3, layer_norm235, model_decoder_layers_24_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape956: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv371, R.shape([batch_size, 1, 20, 64])) reshape957: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape956, R.shape([batch_size, 20, 64])) lv183 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape957), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape958: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv183, R.shape([batch_size, 1, 20, 64])) reshape959: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape958, R.shape([batch_size, 1, 1280])) lv372 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_24_encoder_attn_out_proj_weight3, reshape959, model_decoder_layers_24_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add825: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add822, lv372) layer_norm236: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add825, model_decoder_layers_24_final_layer_norm_weight3, model_decoder_layers_24_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv56 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_24_fc1_weight3, layer_norm236, model_decoder_layers_24_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv373 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_24_fc2_weight3, lv56, model_decoder_layers_24_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add828: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add825, lv373) layer_norm237: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add828, model_decoder_layers_25_self_attn_layer_norm_weight3, model_decoder_layers_25_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv374 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_25_self_attn_q_proj_weight3, layer_norm237, model_decoder_layers_25_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape960: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv374, R.shape([batch_size, 1, 20, 64])) lv90 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_25_self_attn_k_proj_weight3, layer_norm237), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape961: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv90, R.shape([batch_size, 1, 20, 64])) lv375 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_25_self_attn_v_proj_weight3, layer_norm237, model_decoder_layers_25_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape962: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv375, R.shape([batch_size, 1, 20, 64])) concat57: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape960, reshape961, reshape962), axis=2) reshape963: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat57, R.shape([batch_size, 60, 64])) lv184 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape963), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape964: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv184, R.shape([batch_size, 1, 20, 64])) reshape965: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape964, R.shape([batch_size, 1, 1280])) lv376 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_25_self_attn_out_proj_weight3, reshape965, model_decoder_layers_25_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add832: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add828, lv376) layer_norm238: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add832, model_decoder_layers_25_encoder_attn_layer_norm_weight3, model_decoder_layers_25_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv377 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_25_encoder_attn_q_proj_weight3, layer_norm238, model_decoder_layers_25_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape966: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv377, R.shape([batch_size, 1, 20, 64])) reshape967: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape966, R.shape([batch_size, 20, 64])) lv185 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape967), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape968: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv185, R.shape([batch_size, 1, 20, 64])) reshape969: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape968, R.shape([batch_size, 1, 1280])) lv378 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_25_encoder_attn_out_proj_weight3, reshape969, model_decoder_layers_25_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add835: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add832, lv378) layer_norm239: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add835, model_decoder_layers_25_final_layer_norm_weight3, model_decoder_layers_25_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv57 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_25_fc1_weight3, layer_norm239, model_decoder_layers_25_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv379 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_25_fc2_weight3, lv57, model_decoder_layers_25_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add838: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add835, lv379) layer_norm240: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add838, model_decoder_layers_26_self_attn_layer_norm_weight3, model_decoder_layers_26_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv380 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_26_self_attn_q_proj_weight3, layer_norm240, model_decoder_layers_26_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape970: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv380, R.shape([batch_size, 1, 20, 64])) lv91 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_26_self_attn_k_proj_weight3, layer_norm240), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape971: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv91, R.shape([batch_size, 1, 20, 64])) lv381 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_26_self_attn_v_proj_weight3, layer_norm240, model_decoder_layers_26_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape972: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv381, R.shape([batch_size, 1, 20, 64])) concat58: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape970, reshape971, reshape972), axis=2) reshape973: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat58, R.shape([batch_size, 60, 64])) lv186 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape973), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape974: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv186, R.shape([batch_size, 1, 20, 64])) reshape975: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape974, R.shape([batch_size, 1, 1280])) lv382 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_26_self_attn_out_proj_weight3, reshape975, model_decoder_layers_26_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add842: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add838, lv382) layer_norm241: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add842, model_decoder_layers_26_encoder_attn_layer_norm_weight3, model_decoder_layers_26_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv383 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_26_encoder_attn_q_proj_weight3, layer_norm241, model_decoder_layers_26_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape976: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv383, R.shape([batch_size, 1, 20, 64])) reshape977: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape976, R.shape([batch_size, 20, 64])) lv187 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape977), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape978: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv187, R.shape([batch_size, 1, 20, 64])) reshape979: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape978, R.shape([batch_size, 1, 1280])) lv384 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_26_encoder_attn_out_proj_weight3, reshape979, model_decoder_layers_26_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add845: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add842, lv384) layer_norm242: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add845, model_decoder_layers_26_final_layer_norm_weight3, model_decoder_layers_26_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv58 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_26_fc1_weight3, layer_norm242, model_decoder_layers_26_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv385 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_26_fc2_weight3, lv58, model_decoder_layers_26_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add848: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add845, lv385) layer_norm243: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add848, model_decoder_layers_27_self_attn_layer_norm_weight3, model_decoder_layers_27_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv386 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_27_self_attn_q_proj_weight3, layer_norm243, model_decoder_layers_27_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape980: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv386, R.shape([batch_size, 1, 20, 64])) lv92 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_27_self_attn_k_proj_weight3, layer_norm243), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape981: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv92, R.shape([batch_size, 1, 20, 64])) lv387 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_27_self_attn_v_proj_weight3, layer_norm243, model_decoder_layers_27_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape982: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv387, R.shape([batch_size, 1, 20, 64])) concat59: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape980, reshape981, reshape982), axis=2) reshape983: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat59, R.shape([batch_size, 60, 64])) lv188 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape983), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape984: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv188, R.shape([batch_size, 1, 20, 64])) reshape985: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape984, R.shape([batch_size, 1, 1280])) lv388 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_27_self_attn_out_proj_weight3, reshape985, model_decoder_layers_27_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add852: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add848, lv388) layer_norm244: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add852, model_decoder_layers_27_encoder_attn_layer_norm_weight3, model_decoder_layers_27_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv389 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_27_encoder_attn_q_proj_weight3, layer_norm244, model_decoder_layers_27_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape986: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv389, R.shape([batch_size, 1, 20, 64])) reshape987: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape986, R.shape([batch_size, 20, 64])) lv189 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape987), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape988: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv189, R.shape([batch_size, 1, 20, 64])) reshape989: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape988, R.shape([batch_size, 1, 1280])) lv390 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_27_encoder_attn_out_proj_weight3, reshape989, model_decoder_layers_27_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add855: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add852, lv390) layer_norm245: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add855, model_decoder_layers_27_final_layer_norm_weight3, model_decoder_layers_27_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv59 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_27_fc1_weight3, layer_norm245, model_decoder_layers_27_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv391 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_27_fc2_weight3, lv59, model_decoder_layers_27_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add858: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add855, lv391) layer_norm246: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add858, model_decoder_layers_28_self_attn_layer_norm_weight3, model_decoder_layers_28_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv392 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_28_self_attn_q_proj_weight3, layer_norm246, model_decoder_layers_28_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape990: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv392, R.shape([batch_size, 1, 20, 64])) lv93 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_28_self_attn_k_proj_weight3, layer_norm246), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape991: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv93, R.shape([batch_size, 1, 20, 64])) lv393 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_28_self_attn_v_proj_weight3, layer_norm246, model_decoder_layers_28_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape992: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv393, R.shape([batch_size, 1, 20, 64])) concat60: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape990, reshape991, reshape992), axis=2) reshape993: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat60, R.shape([batch_size, 60, 64])) lv190 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape993), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape994: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv190, R.shape([batch_size, 1, 20, 64])) reshape995: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape994, R.shape([batch_size, 1, 1280])) lv394 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_28_self_attn_out_proj_weight3, reshape995, model_decoder_layers_28_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add862: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add858, lv394) layer_norm247: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add862, model_decoder_layers_28_encoder_attn_layer_norm_weight3, model_decoder_layers_28_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv395 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_28_encoder_attn_q_proj_weight3, layer_norm247, model_decoder_layers_28_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape996: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv395, R.shape([batch_size, 1, 20, 64])) reshape997: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape996, R.shape([batch_size, 20, 64])) lv191 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape997), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape998: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv191, R.shape([batch_size, 1, 20, 64])) reshape999: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape998, R.shape([batch_size, 1, 1280])) lv396 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_28_encoder_attn_out_proj_weight3, reshape999, model_decoder_layers_28_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add865: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add862, lv396) layer_norm248: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add865, model_decoder_layers_28_final_layer_norm_weight3, model_decoder_layers_28_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv60 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_28_fc1_weight3, layer_norm248, model_decoder_layers_28_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv397 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_28_fc2_weight3, lv60, model_decoder_layers_28_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add868: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add865, lv397) layer_norm249: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add868, model_decoder_layers_29_self_attn_layer_norm_weight3, model_decoder_layers_29_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv398 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_29_self_attn_q_proj_weight3, layer_norm249, model_decoder_layers_29_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape1000: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv398, R.shape([batch_size, 1, 20, 64])) lv94 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_29_self_attn_k_proj_weight3, layer_norm249), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape1001: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv94, R.shape([batch_size, 1, 20, 64])) lv399 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_29_self_attn_v_proj_weight3, layer_norm249, model_decoder_layers_29_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape1002: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv399, R.shape([batch_size, 1, 20, 64])) concat61: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape1000, reshape1001, reshape1002), axis=2) reshape1003: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat61, R.shape([batch_size, 60, 64])) lv192 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1003), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape1004: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv192, R.shape([batch_size, 1, 20, 64])) reshape1005: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape1004, R.shape([batch_size, 1, 1280])) lv400 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_29_self_attn_out_proj_weight3, reshape1005, model_decoder_layers_29_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add872: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add868, lv400) layer_norm250: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add872, model_decoder_layers_29_encoder_attn_layer_norm_weight3, model_decoder_layers_29_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv401 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_29_encoder_attn_q_proj_weight3, layer_norm250, model_decoder_layers_29_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape1006: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv401, R.shape([batch_size, 1, 20, 64])) reshape1007: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape1006, R.shape([batch_size, 20, 64])) lv193 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1007), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape1008: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv193, R.shape([batch_size, 1, 20, 64])) reshape1009: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape1008, R.shape([batch_size, 1, 1280])) lv402 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_29_encoder_attn_out_proj_weight3, reshape1009, model_decoder_layers_29_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add875: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add872, lv402) layer_norm251: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add875, model_decoder_layers_29_final_layer_norm_weight3, model_decoder_layers_29_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv61 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_29_fc1_weight3, layer_norm251, model_decoder_layers_29_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv403 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_29_fc2_weight3, lv61, model_decoder_layers_29_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add878: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add875, lv403) layer_norm252: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add878, model_decoder_layers_30_self_attn_layer_norm_weight3, model_decoder_layers_30_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv404 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_30_self_attn_q_proj_weight3, layer_norm252, model_decoder_layers_30_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape1010: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv404, R.shape([batch_size, 1, 20, 64])) lv95 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_30_self_attn_k_proj_weight3, layer_norm252), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape1011: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv95, R.shape([batch_size, 1, 20, 64])) lv405 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_30_self_attn_v_proj_weight3, layer_norm252, model_decoder_layers_30_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape1012: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv405, R.shape([batch_size, 1, 20, 64])) concat62: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape1010, reshape1011, reshape1012), axis=2) reshape1013: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat62, R.shape([batch_size, 60, 64])) lv194 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1013), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape1014: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv194, R.shape([batch_size, 1, 20, 64])) reshape1015: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape1014, R.shape([batch_size, 1, 1280])) lv406 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_30_self_attn_out_proj_weight3, reshape1015, model_decoder_layers_30_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add882: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add878, lv406) layer_norm253: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add882, model_decoder_layers_30_encoder_attn_layer_norm_weight3, model_decoder_layers_30_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv407 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_30_encoder_attn_q_proj_weight3, layer_norm253, model_decoder_layers_30_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape1016: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv407, R.shape([batch_size, 1, 20, 64])) reshape1017: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape1016, R.shape([batch_size, 20, 64])) lv195 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1017), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape1018: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv195, R.shape([batch_size, 1, 20, 64])) reshape1019: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape1018, R.shape([batch_size, 1, 1280])) lv408 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_30_encoder_attn_out_proj_weight3, reshape1019, model_decoder_layers_30_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add885: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add882, lv408) layer_norm254: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add885, model_decoder_layers_30_final_layer_norm_weight3, model_decoder_layers_30_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv62 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_30_fc1_weight3, layer_norm254, model_decoder_layers_30_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv409 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_30_fc2_weight3, lv62, model_decoder_layers_30_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add888: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add885, lv409) layer_norm255: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add888, model_decoder_layers_31_self_attn_layer_norm_weight3, model_decoder_layers_31_self_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv410 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_31_self_attn_q_proj_weight3, layer_norm255, model_decoder_layers_31_self_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape1020: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv410, R.shape([batch_size, 1, 20, 64])) lv96 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul3_cublas", (model_decoder_layers_31_self_attn_k_proj_weight3, layer_norm255), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape1021: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv96, R.shape([batch_size, 1, 20, 64])) lv411 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_31_self_attn_v_proj_weight3, layer_norm255, model_decoder_layers_31_self_attn_v_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape1022: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv411, R.shape([batch_size, 1, 20, 64])) concat63: R.Tensor((batch_size, 1, 60, 64), dtype="float16") = R.concat((reshape1020, reshape1021, reshape1022), axis=2) reshape1023: R.Tensor((batch_size, 60, 64), dtype="float16") = R.reshape(concat63, R.shape([batch_size, 60, 64])) lv196 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1023), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape1024: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv196, R.shape([batch_size, 1, 20, 64])) reshape1025: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape1024, R.shape([batch_size, 1, 1280])) lv412 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_31_self_attn_out_proj_weight3, reshape1025, model_decoder_layers_31_self_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add892: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add888, lv412) layer_norm256: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add892, model_decoder_layers_31_encoder_attn_layer_norm_weight3, model_decoder_layers_31_encoder_attn_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv413 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_31_encoder_attn_q_proj_weight3, layer_norm256, model_decoder_layers_31_encoder_attn_q_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) reshape1026: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv413, R.shape([batch_size, 1, 20, 64])) reshape1027: R.Tensor((batch_size, 20, 64), dtype="float16") = R.reshape(reshape1026, R.shape([batch_size, 20, 64])) lv197 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1027), out_sinfo=R.Tensor((batch_size, 20, 64), dtype="float16")) reshape1028: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(lv197, R.shape([batch_size, 1, 20, 64])) reshape1029: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.reshape(reshape1028, R.shape([batch_size, 1, 1280])) lv414 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add3_cublas", (model_decoder_layers_31_encoder_attn_out_proj_weight3, reshape1029, model_decoder_layers_31_encoder_attn_out_proj_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add895: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add892, lv414) layer_norm257: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add895, model_decoder_layers_31_final_layer_norm_weight3, model_decoder_layers_31_final_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv63 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu1_cublas", (model_decoder_layers_31_fc1_weight3, layer_norm257, model_decoder_layers_31_fc1_bias3), out_sinfo=R.Tensor((batch_size, 1, 5120), dtype="float16")) lv415 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add4_cublas", (model_decoder_layers_31_fc2_weight3, lv63, model_decoder_layers_31_fc2_bias3), out_sinfo=R.Tensor((batch_size, 1, 1280), dtype="float16")) add898: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add895, lv415) layer_norm258: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.nn.layer_norm(add898, model_decoder_layer_norm_weight3, model_decoder_layer_norm_bias3, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv97 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul4_cublas", (model_decoder_embed_tokens_weight3, layer_norm258), out_sinfo=R.Tensor((batch_size, 1, 51866), dtype="float32")) gv3: R.Tensor((batch_size, 1, 51866), dtype="float32") = lv97 R.output(gv3) return gv3 @R.function def batch_encode(input_features: R.Tensor(("batch_size", 128, 3000), dtype="float16"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((51866, 1280), dtype="float16"), R.Tensor((448, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor(("batch_size", 1500, 1280), dtype="float16"): batch_size = T.int64() R.func_attr({"num_input": 2, "relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) with R.dataflow(): model_encoder_conv1_weight: R.Tensor((1280, 128, 3), dtype="float16") = packed_params[0] model_encoder_conv1_bias: R.Tensor((1280,), dtype="float16") = packed_params[1] model_encoder_conv2_weight: R.Tensor((1280, 1280, 3), dtype="float16") = packed_params[2] model_encoder_conv2_bias: R.Tensor((1280,), dtype="float16") = packed_params[3] model_encoder_embed_positions_weight: R.Tensor((1500, 1280), dtype="float16") = packed_params[4] model_encoder_layers_0_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[5] model_encoder_layers_0_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[6] model_encoder_layers_0_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[7] model_encoder_layers_0_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[8] model_encoder_layers_0_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[9] model_encoder_layers_0_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[10] model_encoder_layers_0_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[11] model_encoder_layers_0_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[12] model_encoder_layers_0_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[13] model_encoder_layers_0_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[14] model_encoder_layers_0_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[15] model_encoder_layers_0_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[16] model_encoder_layers_0_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[17] model_encoder_layers_0_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[18] model_encoder_layers_0_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[19] model_encoder_layers_1_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[20] model_encoder_layers_1_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[21] model_encoder_layers_1_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[22] model_encoder_layers_1_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[23] model_encoder_layers_1_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[24] model_encoder_layers_1_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[25] model_encoder_layers_1_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[26] model_encoder_layers_1_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[27] model_encoder_layers_1_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[28] model_encoder_layers_1_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[29] model_encoder_layers_1_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[30] model_encoder_layers_1_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[31] model_encoder_layers_1_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[32] model_encoder_layers_1_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[33] model_encoder_layers_1_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[34] model_encoder_layers_2_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[35] model_encoder_layers_2_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[36] model_encoder_layers_2_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[37] model_encoder_layers_2_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[38] model_encoder_layers_2_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[39] model_encoder_layers_2_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[40] model_encoder_layers_2_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[41] model_encoder_layers_2_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[42] model_encoder_layers_2_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[43] model_encoder_layers_2_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[44] model_encoder_layers_2_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[45] model_encoder_layers_2_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[46] model_encoder_layers_2_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[47] model_encoder_layers_2_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[48] model_encoder_layers_2_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[49] model_encoder_layers_3_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[50] model_encoder_layers_3_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[51] model_encoder_layers_3_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[52] model_encoder_layers_3_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[53] model_encoder_layers_3_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[54] model_encoder_layers_3_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[55] model_encoder_layers_3_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[56] model_encoder_layers_3_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[57] model_encoder_layers_3_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[58] model_encoder_layers_3_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[59] model_encoder_layers_3_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[60] model_encoder_layers_3_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[61] model_encoder_layers_3_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[62] model_encoder_layers_3_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[63] model_encoder_layers_3_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[64] model_encoder_layers_4_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[65] model_encoder_layers_4_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[66] model_encoder_layers_4_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[67] model_encoder_layers_4_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[68] model_encoder_layers_4_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[69] model_encoder_layers_4_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[70] model_encoder_layers_4_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[71] model_encoder_layers_4_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[72] model_encoder_layers_4_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[73] model_encoder_layers_4_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[74] model_encoder_layers_4_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[75] model_encoder_layers_4_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[76] model_encoder_layers_4_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[77] model_encoder_layers_4_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[78] model_encoder_layers_4_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[79] model_encoder_layers_5_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[80] model_encoder_layers_5_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[81] model_encoder_layers_5_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[82] model_encoder_layers_5_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[83] model_encoder_layers_5_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[84] model_encoder_layers_5_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[85] model_encoder_layers_5_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[86] model_encoder_layers_5_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[87] model_encoder_layers_5_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[88] model_encoder_layers_5_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[89] model_encoder_layers_5_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[90] model_encoder_layers_5_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[91] model_encoder_layers_5_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[92] model_encoder_layers_5_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[93] model_encoder_layers_5_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[94] model_encoder_layers_6_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[95] model_encoder_layers_6_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[96] model_encoder_layers_6_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[97] model_encoder_layers_6_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[98] model_encoder_layers_6_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[99] model_encoder_layers_6_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[100] model_encoder_layers_6_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[101] model_encoder_layers_6_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[102] model_encoder_layers_6_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[103] model_encoder_layers_6_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[104] model_encoder_layers_6_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[105] model_encoder_layers_6_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[106] model_encoder_layers_6_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[107] model_encoder_layers_6_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[108] model_encoder_layers_6_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[109] model_encoder_layers_7_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[110] model_encoder_layers_7_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[111] model_encoder_layers_7_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[112] model_encoder_layers_7_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[113] model_encoder_layers_7_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[114] model_encoder_layers_7_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[115] model_encoder_layers_7_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[116] model_encoder_layers_7_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[117] model_encoder_layers_7_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[118] model_encoder_layers_7_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[119] model_encoder_layers_7_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[120] model_encoder_layers_7_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[121] model_encoder_layers_7_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[122] model_encoder_layers_7_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[123] model_encoder_layers_7_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[124] model_encoder_layers_8_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[125] model_encoder_layers_8_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[126] model_encoder_layers_8_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[127] model_encoder_layers_8_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[128] model_encoder_layers_8_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[129] model_encoder_layers_8_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[130] model_encoder_layers_8_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[131] model_encoder_layers_8_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[132] model_encoder_layers_8_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[133] model_encoder_layers_8_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[134] model_encoder_layers_8_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[135] model_encoder_layers_8_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[136] model_encoder_layers_8_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[137] model_encoder_layers_8_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[138] model_encoder_layers_8_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[139] model_encoder_layers_9_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[140] model_encoder_layers_9_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[141] model_encoder_layers_9_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[142] model_encoder_layers_9_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[143] model_encoder_layers_9_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[144] model_encoder_layers_9_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[145] model_encoder_layers_9_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[146] model_encoder_layers_9_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[147] model_encoder_layers_9_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[148] model_encoder_layers_9_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[149] model_encoder_layers_9_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[150] model_encoder_layers_9_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[151] model_encoder_layers_9_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[152] model_encoder_layers_9_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[153] model_encoder_layers_9_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[154] model_encoder_layers_10_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[155] model_encoder_layers_10_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[156] model_encoder_layers_10_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[157] model_encoder_layers_10_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[158] model_encoder_layers_10_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[159] model_encoder_layers_10_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[160] model_encoder_layers_10_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[161] model_encoder_layers_10_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[162] model_encoder_layers_10_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[163] model_encoder_layers_10_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[164] model_encoder_layers_10_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[165] model_encoder_layers_10_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[166] model_encoder_layers_10_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[167] model_encoder_layers_10_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[168] model_encoder_layers_10_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[169] model_encoder_layers_11_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[170] model_encoder_layers_11_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[171] model_encoder_layers_11_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[172] model_encoder_layers_11_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[173] model_encoder_layers_11_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[174] model_encoder_layers_11_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[175] model_encoder_layers_11_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[176] model_encoder_layers_11_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[177] model_encoder_layers_11_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[178] model_encoder_layers_11_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[179] model_encoder_layers_11_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[180] model_encoder_layers_11_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[181] model_encoder_layers_11_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[182] model_encoder_layers_11_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[183] model_encoder_layers_11_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[184] model_encoder_layers_12_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[185] model_encoder_layers_12_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[186] model_encoder_layers_12_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[187] model_encoder_layers_12_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[188] model_encoder_layers_12_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[189] model_encoder_layers_12_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[190] model_encoder_layers_12_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[191] model_encoder_layers_12_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[192] model_encoder_layers_12_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[193] model_encoder_layers_12_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[194] model_encoder_layers_12_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[195] model_encoder_layers_12_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[196] model_encoder_layers_12_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[197] model_encoder_layers_12_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[198] model_encoder_layers_12_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[199] model_encoder_layers_13_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[200] model_encoder_layers_13_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[201] model_encoder_layers_13_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[202] model_encoder_layers_13_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[203] model_encoder_layers_13_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[204] model_encoder_layers_13_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[205] model_encoder_layers_13_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[206] model_encoder_layers_13_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[207] model_encoder_layers_13_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[208] model_encoder_layers_13_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[209] model_encoder_layers_13_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[210] model_encoder_layers_13_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[211] model_encoder_layers_13_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[212] model_encoder_layers_13_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[213] model_encoder_layers_13_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[214] model_encoder_layers_14_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[215] model_encoder_layers_14_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[216] model_encoder_layers_14_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[217] model_encoder_layers_14_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[218] model_encoder_layers_14_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[219] model_encoder_layers_14_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[220] model_encoder_layers_14_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[221] model_encoder_layers_14_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[222] model_encoder_layers_14_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[223] model_encoder_layers_14_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[224] model_encoder_layers_14_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[225] model_encoder_layers_14_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[226] model_encoder_layers_14_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[227] model_encoder_layers_14_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[228] model_encoder_layers_14_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[229] model_encoder_layers_15_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[230] model_encoder_layers_15_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[231] model_encoder_layers_15_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[232] model_encoder_layers_15_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[233] model_encoder_layers_15_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[234] model_encoder_layers_15_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[235] model_encoder_layers_15_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[236] model_encoder_layers_15_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[237] model_encoder_layers_15_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[238] model_encoder_layers_15_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[239] model_encoder_layers_15_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[240] model_encoder_layers_15_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[241] model_encoder_layers_15_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[242] model_encoder_layers_15_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[243] model_encoder_layers_15_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[244] model_encoder_layers_16_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[245] model_encoder_layers_16_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[246] model_encoder_layers_16_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[247] model_encoder_layers_16_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[248] model_encoder_layers_16_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[249] model_encoder_layers_16_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[250] model_encoder_layers_16_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[251] model_encoder_layers_16_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[252] model_encoder_layers_16_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[253] model_encoder_layers_16_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[254] model_encoder_layers_16_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[255] model_encoder_layers_16_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[256] model_encoder_layers_16_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[257] model_encoder_layers_16_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[258] model_encoder_layers_16_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[259] model_encoder_layers_17_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[260] model_encoder_layers_17_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[261] model_encoder_layers_17_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[262] model_encoder_layers_17_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[263] model_encoder_layers_17_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[264] model_encoder_layers_17_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[265] model_encoder_layers_17_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[266] model_encoder_layers_17_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[267] model_encoder_layers_17_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[268] model_encoder_layers_17_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[269] model_encoder_layers_17_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[270] model_encoder_layers_17_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[271] model_encoder_layers_17_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[272] model_encoder_layers_17_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[273] model_encoder_layers_17_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[274] model_encoder_layers_18_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[275] model_encoder_layers_18_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[276] model_encoder_layers_18_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[277] model_encoder_layers_18_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[278] model_encoder_layers_18_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[279] model_encoder_layers_18_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[280] model_encoder_layers_18_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[281] model_encoder_layers_18_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[282] model_encoder_layers_18_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[283] model_encoder_layers_18_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[284] model_encoder_layers_18_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[285] model_encoder_layers_18_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[286] model_encoder_layers_18_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[287] model_encoder_layers_18_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[288] model_encoder_layers_18_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[289] model_encoder_layers_19_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[290] model_encoder_layers_19_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[291] model_encoder_layers_19_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[292] model_encoder_layers_19_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[293] model_encoder_layers_19_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[294] model_encoder_layers_19_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[295] model_encoder_layers_19_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[296] model_encoder_layers_19_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[297] model_encoder_layers_19_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[298] model_encoder_layers_19_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[299] model_encoder_layers_19_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[300] model_encoder_layers_19_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[301] model_encoder_layers_19_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[302] model_encoder_layers_19_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[303] model_encoder_layers_19_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[304] model_encoder_layers_20_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[305] model_encoder_layers_20_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[306] model_encoder_layers_20_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[307] model_encoder_layers_20_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[308] model_encoder_layers_20_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[309] model_encoder_layers_20_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[310] model_encoder_layers_20_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[311] model_encoder_layers_20_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[312] model_encoder_layers_20_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[313] model_encoder_layers_20_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[314] model_encoder_layers_20_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[315] model_encoder_layers_20_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[316] model_encoder_layers_20_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[317] model_encoder_layers_20_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[318] model_encoder_layers_20_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[319] model_encoder_layers_21_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[320] model_encoder_layers_21_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[321] model_encoder_layers_21_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[322] model_encoder_layers_21_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[323] model_encoder_layers_21_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[324] model_encoder_layers_21_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[325] model_encoder_layers_21_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[326] model_encoder_layers_21_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[327] model_encoder_layers_21_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[328] model_encoder_layers_21_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[329] model_encoder_layers_21_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[330] model_encoder_layers_21_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[331] model_encoder_layers_21_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[332] model_encoder_layers_21_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[333] model_encoder_layers_21_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[334] model_encoder_layers_22_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[335] model_encoder_layers_22_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[336] model_encoder_layers_22_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[337] model_encoder_layers_22_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[338] model_encoder_layers_22_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[339] model_encoder_layers_22_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[340] model_encoder_layers_22_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[341] model_encoder_layers_22_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[342] model_encoder_layers_22_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[343] model_encoder_layers_22_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[344] model_encoder_layers_22_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[345] model_encoder_layers_22_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[346] model_encoder_layers_22_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[347] model_encoder_layers_22_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[348] model_encoder_layers_22_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[349] model_encoder_layers_23_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[350] model_encoder_layers_23_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[351] model_encoder_layers_23_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[352] model_encoder_layers_23_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[353] model_encoder_layers_23_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[354] model_encoder_layers_23_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[355] model_encoder_layers_23_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[356] model_encoder_layers_23_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[357] model_encoder_layers_23_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[358] model_encoder_layers_23_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[359] model_encoder_layers_23_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[360] model_encoder_layers_23_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[361] model_encoder_layers_23_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[362] model_encoder_layers_23_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[363] model_encoder_layers_23_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[364] model_encoder_layers_24_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[365] model_encoder_layers_24_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[366] model_encoder_layers_24_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[367] model_encoder_layers_24_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[368] model_encoder_layers_24_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[369] model_encoder_layers_24_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[370] model_encoder_layers_24_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[371] model_encoder_layers_24_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[372] model_encoder_layers_24_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[373] model_encoder_layers_24_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[374] model_encoder_layers_24_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[375] model_encoder_layers_24_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[376] model_encoder_layers_24_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[377] model_encoder_layers_24_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[378] model_encoder_layers_24_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[379] model_encoder_layers_25_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[380] model_encoder_layers_25_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[381] model_encoder_layers_25_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[382] model_encoder_layers_25_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[383] model_encoder_layers_25_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[384] model_encoder_layers_25_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[385] model_encoder_layers_25_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[386] model_encoder_layers_25_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[387] model_encoder_layers_25_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[388] model_encoder_layers_25_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[389] model_encoder_layers_25_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[390] model_encoder_layers_25_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[391] model_encoder_layers_25_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[392] model_encoder_layers_25_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[393] model_encoder_layers_25_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[394] model_encoder_layers_26_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[395] model_encoder_layers_26_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[396] model_encoder_layers_26_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[397] model_encoder_layers_26_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[398] model_encoder_layers_26_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[399] model_encoder_layers_26_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[400] model_encoder_layers_26_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[401] model_encoder_layers_26_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[402] model_encoder_layers_26_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[403] model_encoder_layers_26_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[404] model_encoder_layers_26_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[405] model_encoder_layers_26_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[406] model_encoder_layers_26_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[407] model_encoder_layers_26_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[408] model_encoder_layers_26_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[409] model_encoder_layers_27_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[410] model_encoder_layers_27_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[411] model_encoder_layers_27_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[412] model_encoder_layers_27_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[413] model_encoder_layers_27_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[414] model_encoder_layers_27_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[415] model_encoder_layers_27_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[416] model_encoder_layers_27_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[417] model_encoder_layers_27_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[418] model_encoder_layers_27_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[419] model_encoder_layers_27_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[420] model_encoder_layers_27_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[421] model_encoder_layers_27_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[422] model_encoder_layers_27_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[423] model_encoder_layers_27_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[424] model_encoder_layers_28_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[425] model_encoder_layers_28_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[426] model_encoder_layers_28_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[427] model_encoder_layers_28_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[428] model_encoder_layers_28_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[429] model_encoder_layers_28_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[430] model_encoder_layers_28_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[431] model_encoder_layers_28_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[432] model_encoder_layers_28_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[433] model_encoder_layers_28_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[434] model_encoder_layers_28_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[435] model_encoder_layers_28_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[436] model_encoder_layers_28_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[437] model_encoder_layers_28_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[438] model_encoder_layers_28_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[439] model_encoder_layers_29_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[440] model_encoder_layers_29_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[441] model_encoder_layers_29_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[442] model_encoder_layers_29_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[443] model_encoder_layers_29_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[444] model_encoder_layers_29_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[445] model_encoder_layers_29_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[446] model_encoder_layers_29_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[447] model_encoder_layers_29_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[448] model_encoder_layers_29_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[449] model_encoder_layers_29_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[450] model_encoder_layers_29_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[451] model_encoder_layers_29_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[452] model_encoder_layers_29_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[453] model_encoder_layers_29_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[454] model_encoder_layers_30_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[455] model_encoder_layers_30_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[456] model_encoder_layers_30_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[457] model_encoder_layers_30_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[458] model_encoder_layers_30_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[459] model_encoder_layers_30_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[460] model_encoder_layers_30_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[461] model_encoder_layers_30_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[462] model_encoder_layers_30_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[463] model_encoder_layers_30_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[464] model_encoder_layers_30_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[465] model_encoder_layers_30_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[466] model_encoder_layers_30_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[467] model_encoder_layers_30_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[468] model_encoder_layers_30_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[469] model_encoder_layers_31_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[470] model_encoder_layers_31_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[471] model_encoder_layers_31_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[472] model_encoder_layers_31_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[473] model_encoder_layers_31_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[474] model_encoder_layers_31_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[475] model_encoder_layers_31_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[476] model_encoder_layers_31_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[477] model_encoder_layers_31_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[478] model_encoder_layers_31_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[479] model_encoder_layers_31_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[480] model_encoder_layers_31_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[481] model_encoder_layers_31_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[482] model_encoder_layers_31_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[483] model_encoder_layers_31_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[484] model_encoder_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[485] model_encoder_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[486] lv: R.Tensor((batch_size, 1280, 3000), dtype="float16") = R.nn.conv1d(input_features, model_encoder_conv1_weight, strides=[1], padding=[1, 1], dilation=[1], groups=1, data_layout="NCW", kernel_layout="OIW", out_layout="NCW", out_dtype="void") lv1: R.Tensor((1, 1280, 1), dtype="float16") = R.reshape(model_encoder_conv1_bias, R.shape([1, 1280, 1])) conv1d: R.Tensor((batch_size, 1280, 3000), dtype="float16") = R.add(lv, lv1) gelu: R.Tensor((batch_size, 1280, 3000), dtype="float16") = R.nn.gelu(conv1d) lv2: R.Tensor((batch_size, 1280, 1500), dtype="float16") = R.nn.conv1d(gelu, model_encoder_conv2_weight, strides=[2], padding=[1, 1], dilation=[1], groups=1, data_layout="NCW", kernel_layout="OIW", out_layout="NCW", out_dtype="void") lv3: R.Tensor((1, 1280, 1), dtype="float16") = R.reshape(model_encoder_conv2_bias, R.shape([1, 1280, 1])) conv1d1: R.Tensor((batch_size, 1280, 1500), dtype="float16") = R.add(lv2, lv3) gelu1: R.Tensor((batch_size, 1280, 1500), dtype="float16") = R.nn.gelu(conv1d1) permute_dims: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.permute_dims(gelu1, axes=[0, 2, 1]) add: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(permute_dims, model_encoder_embed_positions_weight) layer_norm: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add, model_encoder_layers_0_self_attn_layer_norm_weight, model_encoder_layers_0_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv608 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_0_self_attn_q_proj_weight, layer_norm, model_encoder_layers_0_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv608, R.shape([batch_size, 1500, 20, 64])) lv131 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_0_self_attn_k_proj_weight, layer_norm), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape1: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv131, R.shape([batch_size, 1500, 20, 64])) lv609 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_0_self_attn_v_proj_weight, layer_norm, model_encoder_layers_0_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape2: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv609, R.shape([batch_size, 1500, 20, 64])) reshape3: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape, R.shape([batch_size * 1500, 20, 64])) reshape4: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape1, R.shape([batch_size * 1500, 20, 64])) reshape5: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape2, R.shape([batch_size * 1500, 20, 64])) lv4 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape3, reshape4, reshape5), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape6: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv4, R.shape([batch_size, 1500, 20, 64])) reshape7: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape6, R.shape([batch_size, 1500, 1280])) lv610 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_0_self_attn_out_proj_weight, reshape7, model_encoder_layers_0_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add4: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add, lv610) layer_norm1: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add4, model_encoder_layers_0_final_layer_norm_weight, model_encoder_layers_0_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv96 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_0_fc1_weight, layer_norm1, model_encoder_layers_0_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv611 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_0_fc2_weight, lv96, model_encoder_layers_0_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add7: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add4, lv611) maximum: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add7, R.const(-65504, "float16")) minimum: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum, R.const(65504, "float16")) layer_norm2: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum, model_encoder_layers_1_self_attn_layer_norm_weight, model_encoder_layers_1_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv612 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_1_self_attn_q_proj_weight, layer_norm2, model_encoder_layers_1_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape8: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv612, R.shape([batch_size, 1500, 20, 64])) lv132 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_1_self_attn_k_proj_weight, layer_norm2), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape9: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv132, R.shape([batch_size, 1500, 20, 64])) lv613 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_1_self_attn_v_proj_weight, layer_norm2, model_encoder_layers_1_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape10: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv613, R.shape([batch_size, 1500, 20, 64])) reshape11: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape8, R.shape([batch_size * 1500, 20, 64])) reshape12: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape9, R.shape([batch_size * 1500, 20, 64])) reshape13: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape10, R.shape([batch_size * 1500, 20, 64])) lv5 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape11, reshape12, reshape13), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape14: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv5, R.shape([batch_size, 1500, 20, 64])) reshape15: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape14, R.shape([batch_size, 1500, 1280])) lv614 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_1_self_attn_out_proj_weight, reshape15, model_encoder_layers_1_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add11: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum, lv614) layer_norm3: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add11, model_encoder_layers_1_final_layer_norm_weight, model_encoder_layers_1_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv97 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_1_fc1_weight, layer_norm3, model_encoder_layers_1_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv615 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_1_fc2_weight, lv97, model_encoder_layers_1_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add14: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add11, lv615) maximum1: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add14, R.const(-65504, "float16")) minimum1: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum1, R.const(65504, "float16")) layer_norm4: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum1, model_encoder_layers_2_self_attn_layer_norm_weight, model_encoder_layers_2_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv616 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_2_self_attn_q_proj_weight, layer_norm4, model_encoder_layers_2_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape16: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv616, R.shape([batch_size, 1500, 20, 64])) lv133 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_2_self_attn_k_proj_weight, layer_norm4), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape17: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv133, R.shape([batch_size, 1500, 20, 64])) lv617 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_2_self_attn_v_proj_weight, layer_norm4, model_encoder_layers_2_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape18: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv617, R.shape([batch_size, 1500, 20, 64])) reshape19: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape16, R.shape([batch_size * 1500, 20, 64])) reshape20: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape17, R.shape([batch_size * 1500, 20, 64])) reshape21: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape18, R.shape([batch_size * 1500, 20, 64])) lv6 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape19, reshape20, reshape21), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape22: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv6, R.shape([batch_size, 1500, 20, 64])) reshape23: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape22, R.shape([batch_size, 1500, 1280])) lv618 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_2_self_attn_out_proj_weight, reshape23, model_encoder_layers_2_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add18: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum1, lv618) layer_norm5: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add18, model_encoder_layers_2_final_layer_norm_weight, model_encoder_layers_2_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv98 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_2_fc1_weight, layer_norm5, model_encoder_layers_2_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv619 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_2_fc2_weight, lv98, model_encoder_layers_2_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add21: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add18, lv619) maximum2: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add21, R.const(-65504, "float16")) minimum2: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum2, R.const(65504, "float16")) layer_norm6: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum2, model_encoder_layers_3_self_attn_layer_norm_weight, model_encoder_layers_3_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv620 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_3_self_attn_q_proj_weight, layer_norm6, model_encoder_layers_3_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape24: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv620, R.shape([batch_size, 1500, 20, 64])) lv134 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_3_self_attn_k_proj_weight, layer_norm6), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape25: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv134, R.shape([batch_size, 1500, 20, 64])) lv621 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_3_self_attn_v_proj_weight, layer_norm6, model_encoder_layers_3_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape26: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv621, R.shape([batch_size, 1500, 20, 64])) reshape27: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape24, R.shape([batch_size * 1500, 20, 64])) reshape28: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape25, R.shape([batch_size * 1500, 20, 64])) reshape29: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape26, R.shape([batch_size * 1500, 20, 64])) lv7 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape27, reshape28, reshape29), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape30: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv7, R.shape([batch_size, 1500, 20, 64])) reshape31: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape30, R.shape([batch_size, 1500, 1280])) lv622 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_3_self_attn_out_proj_weight, reshape31, model_encoder_layers_3_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add25: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum2, lv622) layer_norm7: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add25, model_encoder_layers_3_final_layer_norm_weight, model_encoder_layers_3_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv99 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_3_fc1_weight, layer_norm7, model_encoder_layers_3_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv623 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_3_fc2_weight, lv99, model_encoder_layers_3_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add28: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add25, lv623) maximum3: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add28, R.const(-65504, "float16")) minimum3: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum3, R.const(65504, "float16")) layer_norm8: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum3, model_encoder_layers_4_self_attn_layer_norm_weight, model_encoder_layers_4_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv624 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_4_self_attn_q_proj_weight, layer_norm8, model_encoder_layers_4_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape32: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv624, R.shape([batch_size, 1500, 20, 64])) lv135 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_4_self_attn_k_proj_weight, layer_norm8), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape33: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv135, R.shape([batch_size, 1500, 20, 64])) lv625 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_4_self_attn_v_proj_weight, layer_norm8, model_encoder_layers_4_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape34: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv625, R.shape([batch_size, 1500, 20, 64])) reshape35: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape32, R.shape([batch_size * 1500, 20, 64])) reshape36: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape33, R.shape([batch_size * 1500, 20, 64])) reshape37: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape34, R.shape([batch_size * 1500, 20, 64])) lv8 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape35, reshape36, reshape37), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape38: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv8, R.shape([batch_size, 1500, 20, 64])) reshape39: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape38, R.shape([batch_size, 1500, 1280])) lv626 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_4_self_attn_out_proj_weight, reshape39, model_encoder_layers_4_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add32: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum3, lv626) layer_norm9: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add32, model_encoder_layers_4_final_layer_norm_weight, model_encoder_layers_4_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv100 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_4_fc1_weight, layer_norm9, model_encoder_layers_4_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv627 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_4_fc2_weight, lv100, model_encoder_layers_4_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add35: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add32, lv627) maximum4: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add35, R.const(-65504, "float16")) minimum4: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum4, R.const(65504, "float16")) layer_norm10: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum4, model_encoder_layers_5_self_attn_layer_norm_weight, model_encoder_layers_5_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv628 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_5_self_attn_q_proj_weight, layer_norm10, model_encoder_layers_5_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape40: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv628, R.shape([batch_size, 1500, 20, 64])) lv136 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_5_self_attn_k_proj_weight, layer_norm10), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape41: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv136, R.shape([batch_size, 1500, 20, 64])) lv629 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_5_self_attn_v_proj_weight, layer_norm10, model_encoder_layers_5_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape42: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv629, R.shape([batch_size, 1500, 20, 64])) reshape43: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape40, R.shape([batch_size * 1500, 20, 64])) reshape44: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape41, R.shape([batch_size * 1500, 20, 64])) reshape45: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape42, R.shape([batch_size * 1500, 20, 64])) lv9 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape43, reshape44, reshape45), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape46: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv9, R.shape([batch_size, 1500, 20, 64])) reshape47: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape46, R.shape([batch_size, 1500, 1280])) lv630 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_5_self_attn_out_proj_weight, reshape47, model_encoder_layers_5_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add39: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum4, lv630) layer_norm11: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add39, model_encoder_layers_5_final_layer_norm_weight, model_encoder_layers_5_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv101 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_5_fc1_weight, layer_norm11, model_encoder_layers_5_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv631 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_5_fc2_weight, lv101, model_encoder_layers_5_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add42: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add39, lv631) maximum5: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add42, R.const(-65504, "float16")) minimum5: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum5, R.const(65504, "float16")) layer_norm12: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum5, model_encoder_layers_6_self_attn_layer_norm_weight, model_encoder_layers_6_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv632 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_6_self_attn_q_proj_weight, layer_norm12, model_encoder_layers_6_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape48: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv632, R.shape([batch_size, 1500, 20, 64])) lv137 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_6_self_attn_k_proj_weight, layer_norm12), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape49: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv137, R.shape([batch_size, 1500, 20, 64])) lv633 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_6_self_attn_v_proj_weight, layer_norm12, model_encoder_layers_6_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape50: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv633, R.shape([batch_size, 1500, 20, 64])) reshape51: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape48, R.shape([batch_size * 1500, 20, 64])) reshape52: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape49, R.shape([batch_size * 1500, 20, 64])) reshape53: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape50, R.shape([batch_size * 1500, 20, 64])) lv10 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape51, reshape52, reshape53), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape54: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv10, R.shape([batch_size, 1500, 20, 64])) reshape55: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape54, R.shape([batch_size, 1500, 1280])) lv634 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_6_self_attn_out_proj_weight, reshape55, model_encoder_layers_6_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add46: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum5, lv634) layer_norm13: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add46, model_encoder_layers_6_final_layer_norm_weight, model_encoder_layers_6_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv102 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_6_fc1_weight, layer_norm13, model_encoder_layers_6_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv635 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_6_fc2_weight, lv102, model_encoder_layers_6_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add49: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add46, lv635) maximum6: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add49, R.const(-65504, "float16")) minimum6: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum6, R.const(65504, "float16")) layer_norm14: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum6, model_encoder_layers_7_self_attn_layer_norm_weight, model_encoder_layers_7_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv636 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_7_self_attn_q_proj_weight, layer_norm14, model_encoder_layers_7_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape56: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv636, R.shape([batch_size, 1500, 20, 64])) lv138 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_7_self_attn_k_proj_weight, layer_norm14), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape57: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv138, R.shape([batch_size, 1500, 20, 64])) lv637 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_7_self_attn_v_proj_weight, layer_norm14, model_encoder_layers_7_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape58: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv637, R.shape([batch_size, 1500, 20, 64])) reshape59: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape56, R.shape([batch_size * 1500, 20, 64])) reshape60: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape57, R.shape([batch_size * 1500, 20, 64])) reshape61: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape58, R.shape([batch_size * 1500, 20, 64])) lv11 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape59, reshape60, reshape61), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape62: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv11, R.shape([batch_size, 1500, 20, 64])) reshape63: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape62, R.shape([batch_size, 1500, 1280])) lv638 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_7_self_attn_out_proj_weight, reshape63, model_encoder_layers_7_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add53: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum6, lv638) layer_norm15: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add53, model_encoder_layers_7_final_layer_norm_weight, model_encoder_layers_7_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv103 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_7_fc1_weight, layer_norm15, model_encoder_layers_7_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv639 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_7_fc2_weight, lv103, model_encoder_layers_7_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add56: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add53, lv639) maximum7: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add56, R.const(-65504, "float16")) minimum7: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum7, R.const(65504, "float16")) layer_norm16: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum7, model_encoder_layers_8_self_attn_layer_norm_weight, model_encoder_layers_8_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv640 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_8_self_attn_q_proj_weight, layer_norm16, model_encoder_layers_8_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape64: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv640, R.shape([batch_size, 1500, 20, 64])) lv139 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_8_self_attn_k_proj_weight, layer_norm16), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape65: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv139, R.shape([batch_size, 1500, 20, 64])) lv641 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_8_self_attn_v_proj_weight, layer_norm16, model_encoder_layers_8_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape66: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv641, R.shape([batch_size, 1500, 20, 64])) reshape67: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape64, R.shape([batch_size * 1500, 20, 64])) reshape68: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape65, R.shape([batch_size * 1500, 20, 64])) reshape69: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape66, R.shape([batch_size * 1500, 20, 64])) lv12 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape67, reshape68, reshape69), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape70: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv12, R.shape([batch_size, 1500, 20, 64])) reshape71: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape70, R.shape([batch_size, 1500, 1280])) lv642 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_8_self_attn_out_proj_weight, reshape71, model_encoder_layers_8_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add60: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum7, lv642) layer_norm17: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add60, model_encoder_layers_8_final_layer_norm_weight, model_encoder_layers_8_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv104 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_8_fc1_weight, layer_norm17, model_encoder_layers_8_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv643 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_8_fc2_weight, lv104, model_encoder_layers_8_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add63: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add60, lv643) maximum8: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add63, R.const(-65504, "float16")) minimum8: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum8, R.const(65504, "float16")) layer_norm18: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum8, model_encoder_layers_9_self_attn_layer_norm_weight, model_encoder_layers_9_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv644 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_9_self_attn_q_proj_weight, layer_norm18, model_encoder_layers_9_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape72: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv644, R.shape([batch_size, 1500, 20, 64])) lv140 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_9_self_attn_k_proj_weight, layer_norm18), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape73: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv140, R.shape([batch_size, 1500, 20, 64])) lv645 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_9_self_attn_v_proj_weight, layer_norm18, model_encoder_layers_9_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape74: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv645, R.shape([batch_size, 1500, 20, 64])) reshape75: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape72, R.shape([batch_size * 1500, 20, 64])) reshape76: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape73, R.shape([batch_size * 1500, 20, 64])) reshape77: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape74, R.shape([batch_size * 1500, 20, 64])) lv13 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape75, reshape76, reshape77), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape78: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv13, R.shape([batch_size, 1500, 20, 64])) reshape79: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape78, R.shape([batch_size, 1500, 1280])) lv646 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_9_self_attn_out_proj_weight, reshape79, model_encoder_layers_9_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add67: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum8, lv646) layer_norm19: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add67, model_encoder_layers_9_final_layer_norm_weight, model_encoder_layers_9_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv105 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_9_fc1_weight, layer_norm19, model_encoder_layers_9_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv647 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_9_fc2_weight, lv105, model_encoder_layers_9_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add70: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add67, lv647) maximum9: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add70, R.const(-65504, "float16")) minimum9: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum9, R.const(65504, "float16")) layer_norm20: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum9, model_encoder_layers_10_self_attn_layer_norm_weight, model_encoder_layers_10_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv648 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_10_self_attn_q_proj_weight, layer_norm20, model_encoder_layers_10_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape80: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv648, R.shape([batch_size, 1500, 20, 64])) lv141 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_10_self_attn_k_proj_weight, layer_norm20), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape81: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv141, R.shape([batch_size, 1500, 20, 64])) lv649 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_10_self_attn_v_proj_weight, layer_norm20, model_encoder_layers_10_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape82: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv649, R.shape([batch_size, 1500, 20, 64])) reshape83: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape80, R.shape([batch_size * 1500, 20, 64])) reshape84: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape81, R.shape([batch_size * 1500, 20, 64])) reshape85: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape82, R.shape([batch_size * 1500, 20, 64])) lv14 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape83, reshape84, reshape85), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape86: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv14, R.shape([batch_size, 1500, 20, 64])) reshape87: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape86, R.shape([batch_size, 1500, 1280])) lv650 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_10_self_attn_out_proj_weight, reshape87, model_encoder_layers_10_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add74: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum9, lv650) layer_norm21: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add74, model_encoder_layers_10_final_layer_norm_weight, model_encoder_layers_10_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv106 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_10_fc1_weight, layer_norm21, model_encoder_layers_10_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv651 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_10_fc2_weight, lv106, model_encoder_layers_10_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add77: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add74, lv651) maximum10: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add77, R.const(-65504, "float16")) minimum10: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum10, R.const(65504, "float16")) layer_norm22: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum10, model_encoder_layers_11_self_attn_layer_norm_weight, model_encoder_layers_11_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv652 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_11_self_attn_q_proj_weight, layer_norm22, model_encoder_layers_11_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape88: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv652, R.shape([batch_size, 1500, 20, 64])) lv142 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_11_self_attn_k_proj_weight, layer_norm22), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape89: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv142, R.shape([batch_size, 1500, 20, 64])) lv653 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_11_self_attn_v_proj_weight, layer_norm22, model_encoder_layers_11_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape90: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv653, R.shape([batch_size, 1500, 20, 64])) reshape91: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape88, R.shape([batch_size * 1500, 20, 64])) reshape92: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape89, R.shape([batch_size * 1500, 20, 64])) reshape93: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape90, R.shape([batch_size * 1500, 20, 64])) lv15 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape91, reshape92, reshape93), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape94: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv15, R.shape([batch_size, 1500, 20, 64])) reshape95: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape94, R.shape([batch_size, 1500, 1280])) lv654 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_11_self_attn_out_proj_weight, reshape95, model_encoder_layers_11_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add81: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum10, lv654) layer_norm23: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add81, model_encoder_layers_11_final_layer_norm_weight, model_encoder_layers_11_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv107 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_11_fc1_weight, layer_norm23, model_encoder_layers_11_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv655 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_11_fc2_weight, lv107, model_encoder_layers_11_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add84: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add81, lv655) maximum11: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add84, R.const(-65504, "float16")) minimum11: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum11, R.const(65504, "float16")) layer_norm24: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum11, model_encoder_layers_12_self_attn_layer_norm_weight, model_encoder_layers_12_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv656 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_12_self_attn_q_proj_weight, layer_norm24, model_encoder_layers_12_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape96: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv656, R.shape([batch_size, 1500, 20, 64])) lv143 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_12_self_attn_k_proj_weight, layer_norm24), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape97: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv143, R.shape([batch_size, 1500, 20, 64])) lv657 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_12_self_attn_v_proj_weight, layer_norm24, model_encoder_layers_12_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape98: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv657, R.shape([batch_size, 1500, 20, 64])) reshape99: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape96, R.shape([batch_size * 1500, 20, 64])) reshape100: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape97, R.shape([batch_size * 1500, 20, 64])) reshape101: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape98, R.shape([batch_size * 1500, 20, 64])) lv16 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape99, reshape100, reshape101), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape102: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv16, R.shape([batch_size, 1500, 20, 64])) reshape103: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape102, R.shape([batch_size, 1500, 1280])) lv658 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_12_self_attn_out_proj_weight, reshape103, model_encoder_layers_12_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add88: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum11, lv658) layer_norm25: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add88, model_encoder_layers_12_final_layer_norm_weight, model_encoder_layers_12_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv108 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_12_fc1_weight, layer_norm25, model_encoder_layers_12_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv659 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_12_fc2_weight, lv108, model_encoder_layers_12_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add91: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add88, lv659) maximum12: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add91, R.const(-65504, "float16")) minimum12: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum12, R.const(65504, "float16")) layer_norm26: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum12, model_encoder_layers_13_self_attn_layer_norm_weight, model_encoder_layers_13_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv660 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_13_self_attn_q_proj_weight, layer_norm26, model_encoder_layers_13_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape104: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv660, R.shape([batch_size, 1500, 20, 64])) lv144 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_13_self_attn_k_proj_weight, layer_norm26), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape105: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv144, R.shape([batch_size, 1500, 20, 64])) lv661 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_13_self_attn_v_proj_weight, layer_norm26, model_encoder_layers_13_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape106: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv661, R.shape([batch_size, 1500, 20, 64])) reshape107: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape104, R.shape([batch_size * 1500, 20, 64])) reshape108: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape105, R.shape([batch_size * 1500, 20, 64])) reshape109: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape106, R.shape([batch_size * 1500, 20, 64])) lv17 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape107, reshape108, reshape109), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape110: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv17, R.shape([batch_size, 1500, 20, 64])) reshape111: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape110, R.shape([batch_size, 1500, 1280])) lv662 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_13_self_attn_out_proj_weight, reshape111, model_encoder_layers_13_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add95: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum12, lv662) layer_norm27: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add95, model_encoder_layers_13_final_layer_norm_weight, model_encoder_layers_13_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv109 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_13_fc1_weight, layer_norm27, model_encoder_layers_13_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv663 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_13_fc2_weight, lv109, model_encoder_layers_13_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add98: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add95, lv663) maximum13: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add98, R.const(-65504, "float16")) minimum13: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum13, R.const(65504, "float16")) layer_norm28: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum13, model_encoder_layers_14_self_attn_layer_norm_weight, model_encoder_layers_14_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv664 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_14_self_attn_q_proj_weight, layer_norm28, model_encoder_layers_14_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape112: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv664, R.shape([batch_size, 1500, 20, 64])) lv145 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_14_self_attn_k_proj_weight, layer_norm28), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape113: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv145, R.shape([batch_size, 1500, 20, 64])) lv665 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_14_self_attn_v_proj_weight, layer_norm28, model_encoder_layers_14_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape114: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv665, R.shape([batch_size, 1500, 20, 64])) reshape115: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape112, R.shape([batch_size * 1500, 20, 64])) reshape116: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape113, R.shape([batch_size * 1500, 20, 64])) reshape117: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape114, R.shape([batch_size * 1500, 20, 64])) lv18 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape115, reshape116, reshape117), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape118: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv18, R.shape([batch_size, 1500, 20, 64])) reshape119: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape118, R.shape([batch_size, 1500, 1280])) lv666 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_14_self_attn_out_proj_weight, reshape119, model_encoder_layers_14_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add102: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum13, lv666) layer_norm29: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add102, model_encoder_layers_14_final_layer_norm_weight, model_encoder_layers_14_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv110 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_14_fc1_weight, layer_norm29, model_encoder_layers_14_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv667 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_14_fc2_weight, lv110, model_encoder_layers_14_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add105: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add102, lv667) maximum14: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add105, R.const(-65504, "float16")) minimum14: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum14, R.const(65504, "float16")) layer_norm30: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum14, model_encoder_layers_15_self_attn_layer_norm_weight, model_encoder_layers_15_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv668 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_15_self_attn_q_proj_weight, layer_norm30, model_encoder_layers_15_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape120: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv668, R.shape([batch_size, 1500, 20, 64])) lv146 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_15_self_attn_k_proj_weight, layer_norm30), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape121: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv146, R.shape([batch_size, 1500, 20, 64])) lv669 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_15_self_attn_v_proj_weight, layer_norm30, model_encoder_layers_15_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape122: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv669, R.shape([batch_size, 1500, 20, 64])) reshape123: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape120, R.shape([batch_size * 1500, 20, 64])) reshape124: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape121, R.shape([batch_size * 1500, 20, 64])) reshape125: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape122, R.shape([batch_size * 1500, 20, 64])) lv19 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape123, reshape124, reshape125), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape126: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv19, R.shape([batch_size, 1500, 20, 64])) reshape127: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape126, R.shape([batch_size, 1500, 1280])) lv670 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_15_self_attn_out_proj_weight, reshape127, model_encoder_layers_15_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add109: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum14, lv670) layer_norm31: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add109, model_encoder_layers_15_final_layer_norm_weight, model_encoder_layers_15_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv111 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_15_fc1_weight, layer_norm31, model_encoder_layers_15_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv671 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_15_fc2_weight, lv111, model_encoder_layers_15_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add112: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add109, lv671) maximum15: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add112, R.const(-65504, "float16")) minimum15: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum15, R.const(65504, "float16")) layer_norm32: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum15, model_encoder_layers_16_self_attn_layer_norm_weight, model_encoder_layers_16_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv672 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_16_self_attn_q_proj_weight, layer_norm32, model_encoder_layers_16_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape128: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv672, R.shape([batch_size, 1500, 20, 64])) lv147 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_16_self_attn_k_proj_weight, layer_norm32), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape129: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv147, R.shape([batch_size, 1500, 20, 64])) lv673 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_16_self_attn_v_proj_weight, layer_norm32, model_encoder_layers_16_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape130: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv673, R.shape([batch_size, 1500, 20, 64])) reshape131: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape128, R.shape([batch_size * 1500, 20, 64])) reshape132: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape129, R.shape([batch_size * 1500, 20, 64])) reshape133: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape130, R.shape([batch_size * 1500, 20, 64])) lv20 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape131, reshape132, reshape133), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape134: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv20, R.shape([batch_size, 1500, 20, 64])) reshape135: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape134, R.shape([batch_size, 1500, 1280])) lv674 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_16_self_attn_out_proj_weight, reshape135, model_encoder_layers_16_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add116: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum15, lv674) layer_norm33: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add116, model_encoder_layers_16_final_layer_norm_weight, model_encoder_layers_16_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv112 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_16_fc1_weight, layer_norm33, model_encoder_layers_16_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv675 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_16_fc2_weight, lv112, model_encoder_layers_16_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add119: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add116, lv675) maximum16: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add119, R.const(-65504, "float16")) minimum16: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum16, R.const(65504, "float16")) layer_norm34: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum16, model_encoder_layers_17_self_attn_layer_norm_weight, model_encoder_layers_17_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv676 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_17_self_attn_q_proj_weight, layer_norm34, model_encoder_layers_17_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape136: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv676, R.shape([batch_size, 1500, 20, 64])) lv148 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_17_self_attn_k_proj_weight, layer_norm34), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape137: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv148, R.shape([batch_size, 1500, 20, 64])) lv677 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_17_self_attn_v_proj_weight, layer_norm34, model_encoder_layers_17_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape138: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv677, R.shape([batch_size, 1500, 20, 64])) reshape139: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape136, R.shape([batch_size * 1500, 20, 64])) reshape140: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape137, R.shape([batch_size * 1500, 20, 64])) reshape141: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape138, R.shape([batch_size * 1500, 20, 64])) lv21 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape139, reshape140, reshape141), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape142: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv21, R.shape([batch_size, 1500, 20, 64])) reshape143: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape142, R.shape([batch_size, 1500, 1280])) lv678 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_17_self_attn_out_proj_weight, reshape143, model_encoder_layers_17_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add123: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum16, lv678) layer_norm35: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add123, model_encoder_layers_17_final_layer_norm_weight, model_encoder_layers_17_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv113 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_17_fc1_weight, layer_norm35, model_encoder_layers_17_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv679 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_17_fc2_weight, lv113, model_encoder_layers_17_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add126: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add123, lv679) maximum17: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add126, R.const(-65504, "float16")) minimum17: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum17, R.const(65504, "float16")) layer_norm36: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum17, model_encoder_layers_18_self_attn_layer_norm_weight, model_encoder_layers_18_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv680 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_18_self_attn_q_proj_weight, layer_norm36, model_encoder_layers_18_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape144: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv680, R.shape([batch_size, 1500, 20, 64])) lv149 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_18_self_attn_k_proj_weight, layer_norm36), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape145: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv149, R.shape([batch_size, 1500, 20, 64])) lv681 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_18_self_attn_v_proj_weight, layer_norm36, model_encoder_layers_18_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape146: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv681, R.shape([batch_size, 1500, 20, 64])) reshape147: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape144, R.shape([batch_size * 1500, 20, 64])) reshape148: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape145, R.shape([batch_size * 1500, 20, 64])) reshape149: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape146, R.shape([batch_size * 1500, 20, 64])) lv22 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape147, reshape148, reshape149), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape150: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv22, R.shape([batch_size, 1500, 20, 64])) reshape151: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape150, R.shape([batch_size, 1500, 1280])) lv682 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_18_self_attn_out_proj_weight, reshape151, model_encoder_layers_18_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add130: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum17, lv682) layer_norm37: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add130, model_encoder_layers_18_final_layer_norm_weight, model_encoder_layers_18_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv114 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_18_fc1_weight, layer_norm37, model_encoder_layers_18_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv683 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_18_fc2_weight, lv114, model_encoder_layers_18_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add133: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add130, lv683) maximum18: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add133, R.const(-65504, "float16")) minimum18: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum18, R.const(65504, "float16")) layer_norm38: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum18, model_encoder_layers_19_self_attn_layer_norm_weight, model_encoder_layers_19_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv684 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_19_self_attn_q_proj_weight, layer_norm38, model_encoder_layers_19_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape152: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv684, R.shape([batch_size, 1500, 20, 64])) lv150 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_19_self_attn_k_proj_weight, layer_norm38), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape153: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv150, R.shape([batch_size, 1500, 20, 64])) lv685 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_19_self_attn_v_proj_weight, layer_norm38, model_encoder_layers_19_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape154: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv685, R.shape([batch_size, 1500, 20, 64])) reshape155: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape152, R.shape([batch_size * 1500, 20, 64])) reshape156: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape153, R.shape([batch_size * 1500, 20, 64])) reshape157: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape154, R.shape([batch_size * 1500, 20, 64])) lv23 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape155, reshape156, reshape157), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape158: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv23, R.shape([batch_size, 1500, 20, 64])) reshape159: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape158, R.shape([batch_size, 1500, 1280])) lv686 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_19_self_attn_out_proj_weight, reshape159, model_encoder_layers_19_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add137: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum18, lv686) layer_norm39: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add137, model_encoder_layers_19_final_layer_norm_weight, model_encoder_layers_19_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv115 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_19_fc1_weight, layer_norm39, model_encoder_layers_19_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv687 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_19_fc2_weight, lv115, model_encoder_layers_19_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add140: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add137, lv687) maximum19: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add140, R.const(-65504, "float16")) minimum19: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum19, R.const(65504, "float16")) layer_norm40: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum19, model_encoder_layers_20_self_attn_layer_norm_weight, model_encoder_layers_20_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv688 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_20_self_attn_q_proj_weight, layer_norm40, model_encoder_layers_20_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape160: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv688, R.shape([batch_size, 1500, 20, 64])) lv151 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_20_self_attn_k_proj_weight, layer_norm40), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape161: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv151, R.shape([batch_size, 1500, 20, 64])) lv689 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_20_self_attn_v_proj_weight, layer_norm40, model_encoder_layers_20_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape162: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv689, R.shape([batch_size, 1500, 20, 64])) reshape163: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape160, R.shape([batch_size * 1500, 20, 64])) reshape164: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape161, R.shape([batch_size * 1500, 20, 64])) reshape165: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape162, R.shape([batch_size * 1500, 20, 64])) lv24 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape163, reshape164, reshape165), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape166: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv24, R.shape([batch_size, 1500, 20, 64])) reshape167: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape166, R.shape([batch_size, 1500, 1280])) lv690 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_20_self_attn_out_proj_weight, reshape167, model_encoder_layers_20_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add144: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum19, lv690) layer_norm41: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add144, model_encoder_layers_20_final_layer_norm_weight, model_encoder_layers_20_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv116 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_20_fc1_weight, layer_norm41, model_encoder_layers_20_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv691 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_20_fc2_weight, lv116, model_encoder_layers_20_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add147: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add144, lv691) maximum20: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add147, R.const(-65504, "float16")) minimum20: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum20, R.const(65504, "float16")) layer_norm42: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum20, model_encoder_layers_21_self_attn_layer_norm_weight, model_encoder_layers_21_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv692 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_21_self_attn_q_proj_weight, layer_norm42, model_encoder_layers_21_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape168: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv692, R.shape([batch_size, 1500, 20, 64])) lv152 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_21_self_attn_k_proj_weight, layer_norm42), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape169: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv152, R.shape([batch_size, 1500, 20, 64])) lv693 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_21_self_attn_v_proj_weight, layer_norm42, model_encoder_layers_21_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape170: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv693, R.shape([batch_size, 1500, 20, 64])) reshape171: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape168, R.shape([batch_size * 1500, 20, 64])) reshape172: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape169, R.shape([batch_size * 1500, 20, 64])) reshape173: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape170, R.shape([batch_size * 1500, 20, 64])) lv25 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape171, reshape172, reshape173), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape174: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv25, R.shape([batch_size, 1500, 20, 64])) reshape175: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape174, R.shape([batch_size, 1500, 1280])) lv694 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_21_self_attn_out_proj_weight, reshape175, model_encoder_layers_21_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add151: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum20, lv694) layer_norm43: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add151, model_encoder_layers_21_final_layer_norm_weight, model_encoder_layers_21_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv117 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_21_fc1_weight, layer_norm43, model_encoder_layers_21_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv695 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_21_fc2_weight, lv117, model_encoder_layers_21_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add154: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add151, lv695) maximum21: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add154, R.const(-65504, "float16")) minimum21: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum21, R.const(65504, "float16")) layer_norm44: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum21, model_encoder_layers_22_self_attn_layer_norm_weight, model_encoder_layers_22_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv696 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_22_self_attn_q_proj_weight, layer_norm44, model_encoder_layers_22_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape176: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv696, R.shape([batch_size, 1500, 20, 64])) lv153 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_22_self_attn_k_proj_weight, layer_norm44), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape177: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv153, R.shape([batch_size, 1500, 20, 64])) lv697 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_22_self_attn_v_proj_weight, layer_norm44, model_encoder_layers_22_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape178: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv697, R.shape([batch_size, 1500, 20, 64])) reshape179: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape176, R.shape([batch_size * 1500, 20, 64])) reshape180: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape177, R.shape([batch_size * 1500, 20, 64])) reshape181: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape178, R.shape([batch_size * 1500, 20, 64])) lv26 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape179, reshape180, reshape181), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape182: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv26, R.shape([batch_size, 1500, 20, 64])) reshape183: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape182, R.shape([batch_size, 1500, 1280])) lv698 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_22_self_attn_out_proj_weight, reshape183, model_encoder_layers_22_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add158: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum21, lv698) layer_norm45: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add158, model_encoder_layers_22_final_layer_norm_weight, model_encoder_layers_22_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv118 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_22_fc1_weight, layer_norm45, model_encoder_layers_22_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv699 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_22_fc2_weight, lv118, model_encoder_layers_22_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add161: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add158, lv699) maximum22: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add161, R.const(-65504, "float16")) minimum22: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum22, R.const(65504, "float16")) layer_norm46: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum22, model_encoder_layers_23_self_attn_layer_norm_weight, model_encoder_layers_23_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv700 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_23_self_attn_q_proj_weight, layer_norm46, model_encoder_layers_23_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape184: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv700, R.shape([batch_size, 1500, 20, 64])) lv154 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_23_self_attn_k_proj_weight, layer_norm46), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape185: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv154, R.shape([batch_size, 1500, 20, 64])) lv701 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_23_self_attn_v_proj_weight, layer_norm46, model_encoder_layers_23_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape186: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv701, R.shape([batch_size, 1500, 20, 64])) reshape187: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape184, R.shape([batch_size * 1500, 20, 64])) reshape188: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape185, R.shape([batch_size * 1500, 20, 64])) reshape189: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape186, R.shape([batch_size * 1500, 20, 64])) lv27 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape187, reshape188, reshape189), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape190: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv27, R.shape([batch_size, 1500, 20, 64])) reshape191: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape190, R.shape([batch_size, 1500, 1280])) lv702 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_23_self_attn_out_proj_weight, reshape191, model_encoder_layers_23_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add165: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum22, lv702) layer_norm47: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add165, model_encoder_layers_23_final_layer_norm_weight, model_encoder_layers_23_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv119 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_23_fc1_weight, layer_norm47, model_encoder_layers_23_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv703 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_23_fc2_weight, lv119, model_encoder_layers_23_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add168: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add165, lv703) maximum23: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add168, R.const(-65504, "float16")) minimum23: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum23, R.const(65504, "float16")) layer_norm48: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum23, model_encoder_layers_24_self_attn_layer_norm_weight, model_encoder_layers_24_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv704 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_24_self_attn_q_proj_weight, layer_norm48, model_encoder_layers_24_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape192: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv704, R.shape([batch_size, 1500, 20, 64])) lv155 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_24_self_attn_k_proj_weight, layer_norm48), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape193: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv155, R.shape([batch_size, 1500, 20, 64])) lv705 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_24_self_attn_v_proj_weight, layer_norm48, model_encoder_layers_24_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape194: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv705, R.shape([batch_size, 1500, 20, 64])) reshape195: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape192, R.shape([batch_size * 1500, 20, 64])) reshape196: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape193, R.shape([batch_size * 1500, 20, 64])) reshape197: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape194, R.shape([batch_size * 1500, 20, 64])) lv28 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape195, reshape196, reshape197), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape198: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv28, R.shape([batch_size, 1500, 20, 64])) reshape199: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape198, R.shape([batch_size, 1500, 1280])) lv706 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_24_self_attn_out_proj_weight, reshape199, model_encoder_layers_24_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add172: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum23, lv706) layer_norm49: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add172, model_encoder_layers_24_final_layer_norm_weight, model_encoder_layers_24_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv120 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_24_fc1_weight, layer_norm49, model_encoder_layers_24_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv707 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_24_fc2_weight, lv120, model_encoder_layers_24_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add175: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add172, lv707) maximum24: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add175, R.const(-65504, "float16")) minimum24: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum24, R.const(65504, "float16")) layer_norm50: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum24, model_encoder_layers_25_self_attn_layer_norm_weight, model_encoder_layers_25_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv708 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_25_self_attn_q_proj_weight, layer_norm50, model_encoder_layers_25_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape200: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv708, R.shape([batch_size, 1500, 20, 64])) lv156 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_25_self_attn_k_proj_weight, layer_norm50), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape201: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv156, R.shape([batch_size, 1500, 20, 64])) lv709 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_25_self_attn_v_proj_weight, layer_norm50, model_encoder_layers_25_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape202: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv709, R.shape([batch_size, 1500, 20, 64])) reshape203: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape200, R.shape([batch_size * 1500, 20, 64])) reshape204: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape201, R.shape([batch_size * 1500, 20, 64])) reshape205: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape202, R.shape([batch_size * 1500, 20, 64])) lv29 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape203, reshape204, reshape205), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape206: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv29, R.shape([batch_size, 1500, 20, 64])) reshape207: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape206, R.shape([batch_size, 1500, 1280])) lv710 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_25_self_attn_out_proj_weight, reshape207, model_encoder_layers_25_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add179: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum24, lv710) layer_norm51: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add179, model_encoder_layers_25_final_layer_norm_weight, model_encoder_layers_25_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv121 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_25_fc1_weight, layer_norm51, model_encoder_layers_25_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv711 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_25_fc2_weight, lv121, model_encoder_layers_25_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add182: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add179, lv711) maximum25: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add182, R.const(-65504, "float16")) minimum25: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum25, R.const(65504, "float16")) layer_norm52: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum25, model_encoder_layers_26_self_attn_layer_norm_weight, model_encoder_layers_26_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv712 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_26_self_attn_q_proj_weight, layer_norm52, model_encoder_layers_26_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape208: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv712, R.shape([batch_size, 1500, 20, 64])) lv157 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_26_self_attn_k_proj_weight, layer_norm52), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape209: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv157, R.shape([batch_size, 1500, 20, 64])) lv713 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_26_self_attn_v_proj_weight, layer_norm52, model_encoder_layers_26_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape210: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv713, R.shape([batch_size, 1500, 20, 64])) reshape211: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape208, R.shape([batch_size * 1500, 20, 64])) reshape212: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape209, R.shape([batch_size * 1500, 20, 64])) reshape213: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape210, R.shape([batch_size * 1500, 20, 64])) lv30 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape211, reshape212, reshape213), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape214: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv30, R.shape([batch_size, 1500, 20, 64])) reshape215: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape214, R.shape([batch_size, 1500, 1280])) lv714 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_26_self_attn_out_proj_weight, reshape215, model_encoder_layers_26_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add186: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum25, lv714) layer_norm53: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add186, model_encoder_layers_26_final_layer_norm_weight, model_encoder_layers_26_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv122 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_26_fc1_weight, layer_norm53, model_encoder_layers_26_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv715 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_26_fc2_weight, lv122, model_encoder_layers_26_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add189: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add186, lv715) maximum26: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add189, R.const(-65504, "float16")) minimum26: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum26, R.const(65504, "float16")) layer_norm54: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum26, model_encoder_layers_27_self_attn_layer_norm_weight, model_encoder_layers_27_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv716 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_27_self_attn_q_proj_weight, layer_norm54, model_encoder_layers_27_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape216: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv716, R.shape([batch_size, 1500, 20, 64])) lv158 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_27_self_attn_k_proj_weight, layer_norm54), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape217: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv158, R.shape([batch_size, 1500, 20, 64])) lv717 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_27_self_attn_v_proj_weight, layer_norm54, model_encoder_layers_27_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape218: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv717, R.shape([batch_size, 1500, 20, 64])) reshape219: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape216, R.shape([batch_size * 1500, 20, 64])) reshape220: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape217, R.shape([batch_size * 1500, 20, 64])) reshape221: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape218, R.shape([batch_size * 1500, 20, 64])) lv31 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape219, reshape220, reshape221), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape222: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv31, R.shape([batch_size, 1500, 20, 64])) reshape223: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape222, R.shape([batch_size, 1500, 1280])) lv718 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_27_self_attn_out_proj_weight, reshape223, model_encoder_layers_27_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add193: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum26, lv718) layer_norm55: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add193, model_encoder_layers_27_final_layer_norm_weight, model_encoder_layers_27_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv123 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_27_fc1_weight, layer_norm55, model_encoder_layers_27_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv719 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_27_fc2_weight, lv123, model_encoder_layers_27_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add196: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add193, lv719) maximum27: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add196, R.const(-65504, "float16")) minimum27: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum27, R.const(65504, "float16")) layer_norm56: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum27, model_encoder_layers_28_self_attn_layer_norm_weight, model_encoder_layers_28_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv720 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_28_self_attn_q_proj_weight, layer_norm56, model_encoder_layers_28_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape224: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv720, R.shape([batch_size, 1500, 20, 64])) lv159 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_28_self_attn_k_proj_weight, layer_norm56), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape225: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv159, R.shape([batch_size, 1500, 20, 64])) lv721 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_28_self_attn_v_proj_weight, layer_norm56, model_encoder_layers_28_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape226: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv721, R.shape([batch_size, 1500, 20, 64])) reshape227: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape224, R.shape([batch_size * 1500, 20, 64])) reshape228: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape225, R.shape([batch_size * 1500, 20, 64])) reshape229: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape226, R.shape([batch_size * 1500, 20, 64])) lv32 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape227, reshape228, reshape229), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape230: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv32, R.shape([batch_size, 1500, 20, 64])) reshape231: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape230, R.shape([batch_size, 1500, 1280])) lv722 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_28_self_attn_out_proj_weight, reshape231, model_encoder_layers_28_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add200: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum27, lv722) layer_norm57: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add200, model_encoder_layers_28_final_layer_norm_weight, model_encoder_layers_28_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv124 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_28_fc1_weight, layer_norm57, model_encoder_layers_28_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv723 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_28_fc2_weight, lv124, model_encoder_layers_28_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add203: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add200, lv723) maximum28: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add203, R.const(-65504, "float16")) minimum28: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum28, R.const(65504, "float16")) layer_norm58: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum28, model_encoder_layers_29_self_attn_layer_norm_weight, model_encoder_layers_29_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv724 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_29_self_attn_q_proj_weight, layer_norm58, model_encoder_layers_29_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape232: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv724, R.shape([batch_size, 1500, 20, 64])) lv160 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_29_self_attn_k_proj_weight, layer_norm58), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape233: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv160, R.shape([batch_size, 1500, 20, 64])) lv725 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_29_self_attn_v_proj_weight, layer_norm58, model_encoder_layers_29_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape234: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv725, R.shape([batch_size, 1500, 20, 64])) reshape235: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape232, R.shape([batch_size * 1500, 20, 64])) reshape236: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape233, R.shape([batch_size * 1500, 20, 64])) reshape237: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape234, R.shape([batch_size * 1500, 20, 64])) lv33 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape235, reshape236, reshape237), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape238: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv33, R.shape([batch_size, 1500, 20, 64])) reshape239: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape238, R.shape([batch_size, 1500, 1280])) lv726 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_29_self_attn_out_proj_weight, reshape239, model_encoder_layers_29_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add207: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum28, lv726) layer_norm59: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add207, model_encoder_layers_29_final_layer_norm_weight, model_encoder_layers_29_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv125 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_29_fc1_weight, layer_norm59, model_encoder_layers_29_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv727 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_29_fc2_weight, lv125, model_encoder_layers_29_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add210: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add207, lv727) maximum29: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add210, R.const(-65504, "float16")) minimum29: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum29, R.const(65504, "float16")) layer_norm60: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum29, model_encoder_layers_30_self_attn_layer_norm_weight, model_encoder_layers_30_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv728 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_30_self_attn_q_proj_weight, layer_norm60, model_encoder_layers_30_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape240: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv728, R.shape([batch_size, 1500, 20, 64])) lv161 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_30_self_attn_k_proj_weight, layer_norm60), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape241: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv161, R.shape([batch_size, 1500, 20, 64])) lv729 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_30_self_attn_v_proj_weight, layer_norm60, model_encoder_layers_30_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape242: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv729, R.shape([batch_size, 1500, 20, 64])) reshape243: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape240, R.shape([batch_size * 1500, 20, 64])) reshape244: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape241, R.shape([batch_size * 1500, 20, 64])) reshape245: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape242, R.shape([batch_size * 1500, 20, 64])) lv34 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape243, reshape244, reshape245), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape246: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv34, R.shape([batch_size, 1500, 20, 64])) reshape247: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape246, R.shape([batch_size, 1500, 1280])) lv730 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_30_self_attn_out_proj_weight, reshape247, model_encoder_layers_30_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add214: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum29, lv730) layer_norm61: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add214, model_encoder_layers_30_final_layer_norm_weight, model_encoder_layers_30_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv126 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_30_fc1_weight, layer_norm61, model_encoder_layers_30_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv731 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_30_fc2_weight, lv126, model_encoder_layers_30_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add217: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add214, lv731) maximum30: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add217, R.const(-65504, "float16")) minimum30: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum30, R.const(65504, "float16")) layer_norm62: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum30, model_encoder_layers_31_self_attn_layer_norm_weight, model_encoder_layers_31_self_attn_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv732 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_31_self_attn_q_proj_weight, layer_norm62, model_encoder_layers_31_self_attn_q_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape248: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv732, R.shape([batch_size, 1500, 20, 64])) lv162 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_cublas", (model_encoder_layers_31_self_attn_k_proj_weight, layer_norm62), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape249: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv162, R.shape([batch_size, 1500, 20, 64])) lv733 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_31_self_attn_v_proj_weight, layer_norm62, model_encoder_layers_31_self_attn_v_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) reshape250: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv733, R.shape([batch_size, 1500, 20, 64])) reshape251: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape248, R.shape([batch_size * 1500, 20, 64])) reshape252: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape249, R.shape([batch_size * 1500, 20, 64])) reshape253: R.Tensor((batch_size * 1500, 20, 64), dtype="float16") = R.reshape(reshape250, R.shape([batch_size * 1500, 20, 64])) lv35 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_no_append", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape251, reshape252, reshape253), out_sinfo=R.Tensor((batch_size * 1500, 20, 64), dtype="float16")) reshape254: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(lv35, R.shape([batch_size, 1500, 20, 64])) reshape255: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.reshape(reshape254, R.shape([batch_size, 1500, 1280])) lv734 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_cublas", (model_encoder_layers_31_self_attn_out_proj_weight, reshape255, model_encoder_layers_31_self_attn_out_proj_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add221: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum30, lv734) layer_norm63: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(add221, model_encoder_layers_31_final_layer_norm_weight, model_encoder_layers_31_final_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv127 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu2_cublas", (model_encoder_layers_31_fc1_weight, layer_norm63, model_encoder_layers_31_fc1_bias), out_sinfo=R.Tensor((batch_size, 1500, 5120), dtype="float16")) lv735 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add5_cublas", (model_encoder_layers_31_fc2_weight, lv127, model_encoder_layers_31_fc2_bias), out_sinfo=R.Tensor((batch_size, 1500, 1280), dtype="float16")) add224: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add221, lv735) maximum31: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.maximum(add224, R.const(-65504, "float16")) minimum31: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.minimum(maximum31, R.const(65504, "float16")) layer_norm64: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.nn.layer_norm(minimum31, model_encoder_layer_norm_weight, model_encoder_layer_norm_bias, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) gv: R.Tensor((batch_size, 1500, 1280), dtype="float16") = layer_norm64 R.output(gv) return gv @R.function def batch_prefill(input_ids: R.Tensor((1, "seq_len"), dtype="int32"), logit_positions: R.Tensor(("batch_size",), dtype="int32"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((51866, 1280), dtype="float16"), R.Tensor((448, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor((1, "batch_size", 51866), dtype="float32"): batch_size = T.int64() seq_len = T.int64() R.func_attr({"num_input": 3, "relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) with R.dataflow(): model_decoder_embed_tokens_weight2: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] model_decoder_embed_positions_weight2: R.Tensor((448, 1280), dtype="float16") = packed_params[488] model_decoder_layers_0_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] model_decoder_layers_0_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] model_decoder_layers_0_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[491] model_decoder_layers_0_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] model_decoder_layers_0_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[493] model_decoder_layers_0_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] model_decoder_layers_0_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[495] model_decoder_layers_0_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[496] model_decoder_layers_0_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[497] model_decoder_layers_0_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] model_decoder_layers_0_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[502] model_decoder_layers_0_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] model_decoder_layers_0_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[504] model_decoder_layers_0_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[505] model_decoder_layers_0_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[506] model_decoder_layers_0_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] model_decoder_layers_0_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[508] model_decoder_layers_0_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] model_decoder_layers_0_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[510] model_decoder_layers_0_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[511] model_decoder_layers_0_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[512] model_decoder_layers_1_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] model_decoder_layers_1_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] model_decoder_layers_1_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[515] model_decoder_layers_1_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] model_decoder_layers_1_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[517] model_decoder_layers_1_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] model_decoder_layers_1_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[519] model_decoder_layers_1_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[520] model_decoder_layers_1_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[521] model_decoder_layers_1_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] model_decoder_layers_1_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[526] model_decoder_layers_1_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] model_decoder_layers_1_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[528] model_decoder_layers_1_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[529] model_decoder_layers_1_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[530] model_decoder_layers_1_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] model_decoder_layers_1_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[532] model_decoder_layers_1_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] model_decoder_layers_1_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[534] model_decoder_layers_1_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[535] model_decoder_layers_1_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[536] model_decoder_layers_2_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] model_decoder_layers_2_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] model_decoder_layers_2_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[539] model_decoder_layers_2_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] model_decoder_layers_2_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[541] model_decoder_layers_2_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] model_decoder_layers_2_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[543] model_decoder_layers_2_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[544] model_decoder_layers_2_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[545] model_decoder_layers_2_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] model_decoder_layers_2_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[550] model_decoder_layers_2_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] model_decoder_layers_2_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[552] model_decoder_layers_2_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[553] model_decoder_layers_2_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[554] model_decoder_layers_2_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] model_decoder_layers_2_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[556] model_decoder_layers_2_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] model_decoder_layers_2_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[558] model_decoder_layers_2_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[559] model_decoder_layers_2_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[560] model_decoder_layers_3_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] model_decoder_layers_3_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] model_decoder_layers_3_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[563] model_decoder_layers_3_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] model_decoder_layers_3_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[565] model_decoder_layers_3_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] model_decoder_layers_3_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[567] model_decoder_layers_3_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[568] model_decoder_layers_3_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[569] model_decoder_layers_3_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] model_decoder_layers_3_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[574] model_decoder_layers_3_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] model_decoder_layers_3_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[576] model_decoder_layers_3_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[577] model_decoder_layers_3_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[578] model_decoder_layers_3_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] model_decoder_layers_3_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[580] model_decoder_layers_3_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] model_decoder_layers_3_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[582] model_decoder_layers_3_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[583] model_decoder_layers_3_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[584] model_decoder_layers_4_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] model_decoder_layers_4_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] model_decoder_layers_4_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[587] model_decoder_layers_4_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] model_decoder_layers_4_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[589] model_decoder_layers_4_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] model_decoder_layers_4_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[591] model_decoder_layers_4_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[592] model_decoder_layers_4_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[593] model_decoder_layers_4_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] model_decoder_layers_4_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[598] model_decoder_layers_4_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] model_decoder_layers_4_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[600] model_decoder_layers_4_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[601] model_decoder_layers_4_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[602] model_decoder_layers_4_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] model_decoder_layers_4_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[604] model_decoder_layers_4_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] model_decoder_layers_4_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[606] model_decoder_layers_4_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[607] model_decoder_layers_4_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[608] model_decoder_layers_5_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] model_decoder_layers_5_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] model_decoder_layers_5_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[611] model_decoder_layers_5_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] model_decoder_layers_5_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[613] model_decoder_layers_5_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] model_decoder_layers_5_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[615] model_decoder_layers_5_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[616] model_decoder_layers_5_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[617] model_decoder_layers_5_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] model_decoder_layers_5_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[622] model_decoder_layers_5_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] model_decoder_layers_5_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[624] model_decoder_layers_5_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[625] model_decoder_layers_5_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[626] model_decoder_layers_5_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] model_decoder_layers_5_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[628] model_decoder_layers_5_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] model_decoder_layers_5_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[630] model_decoder_layers_5_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[631] model_decoder_layers_5_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[632] model_decoder_layers_6_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] model_decoder_layers_6_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] model_decoder_layers_6_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[635] model_decoder_layers_6_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] model_decoder_layers_6_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[637] model_decoder_layers_6_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] model_decoder_layers_6_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[639] model_decoder_layers_6_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[640] model_decoder_layers_6_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[641] model_decoder_layers_6_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] model_decoder_layers_6_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[646] model_decoder_layers_6_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] model_decoder_layers_6_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[648] model_decoder_layers_6_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[649] model_decoder_layers_6_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[650] model_decoder_layers_6_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] model_decoder_layers_6_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[652] model_decoder_layers_6_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] model_decoder_layers_6_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[654] model_decoder_layers_6_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[655] model_decoder_layers_6_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[656] model_decoder_layers_7_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] model_decoder_layers_7_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] model_decoder_layers_7_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[659] model_decoder_layers_7_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] model_decoder_layers_7_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[661] model_decoder_layers_7_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] model_decoder_layers_7_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[663] model_decoder_layers_7_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[664] model_decoder_layers_7_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[665] model_decoder_layers_7_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] model_decoder_layers_7_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[670] model_decoder_layers_7_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] model_decoder_layers_7_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[672] model_decoder_layers_7_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[673] model_decoder_layers_7_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[674] model_decoder_layers_7_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] model_decoder_layers_7_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[676] model_decoder_layers_7_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] model_decoder_layers_7_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[678] model_decoder_layers_7_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[679] model_decoder_layers_7_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[680] model_decoder_layers_8_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] model_decoder_layers_8_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] model_decoder_layers_8_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[683] model_decoder_layers_8_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] model_decoder_layers_8_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[685] model_decoder_layers_8_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] model_decoder_layers_8_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[687] model_decoder_layers_8_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[688] model_decoder_layers_8_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[689] model_decoder_layers_8_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] model_decoder_layers_8_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[694] model_decoder_layers_8_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] model_decoder_layers_8_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[696] model_decoder_layers_8_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[697] model_decoder_layers_8_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[698] model_decoder_layers_8_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] model_decoder_layers_8_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[700] model_decoder_layers_8_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] model_decoder_layers_8_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[702] model_decoder_layers_8_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[703] model_decoder_layers_8_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[704] model_decoder_layers_9_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] model_decoder_layers_9_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] model_decoder_layers_9_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[707] model_decoder_layers_9_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] model_decoder_layers_9_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[709] model_decoder_layers_9_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] model_decoder_layers_9_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[711] model_decoder_layers_9_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[712] model_decoder_layers_9_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[713] model_decoder_layers_9_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] model_decoder_layers_9_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[718] model_decoder_layers_9_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] model_decoder_layers_9_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[720] model_decoder_layers_9_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[721] model_decoder_layers_9_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[722] model_decoder_layers_9_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] model_decoder_layers_9_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[724] model_decoder_layers_9_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] model_decoder_layers_9_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[726] model_decoder_layers_9_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[727] model_decoder_layers_9_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[728] model_decoder_layers_10_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] model_decoder_layers_10_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] model_decoder_layers_10_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[731] model_decoder_layers_10_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] model_decoder_layers_10_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[733] model_decoder_layers_10_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] model_decoder_layers_10_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[735] model_decoder_layers_10_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[736] model_decoder_layers_10_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[737] model_decoder_layers_10_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] model_decoder_layers_10_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[742] model_decoder_layers_10_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] model_decoder_layers_10_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[744] model_decoder_layers_10_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[745] model_decoder_layers_10_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[746] model_decoder_layers_10_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] model_decoder_layers_10_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[748] model_decoder_layers_10_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] model_decoder_layers_10_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[750] model_decoder_layers_10_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[751] model_decoder_layers_10_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[752] model_decoder_layers_11_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] model_decoder_layers_11_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] model_decoder_layers_11_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[755] model_decoder_layers_11_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] model_decoder_layers_11_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[757] model_decoder_layers_11_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] model_decoder_layers_11_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[759] model_decoder_layers_11_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[760] model_decoder_layers_11_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[761] model_decoder_layers_11_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] model_decoder_layers_11_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[766] model_decoder_layers_11_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] model_decoder_layers_11_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[768] model_decoder_layers_11_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[769] model_decoder_layers_11_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[770] model_decoder_layers_11_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] model_decoder_layers_11_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[772] model_decoder_layers_11_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] model_decoder_layers_11_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[774] model_decoder_layers_11_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[775] model_decoder_layers_11_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[776] model_decoder_layers_12_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] model_decoder_layers_12_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] model_decoder_layers_12_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[779] model_decoder_layers_12_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] model_decoder_layers_12_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[781] model_decoder_layers_12_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] model_decoder_layers_12_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[783] model_decoder_layers_12_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[784] model_decoder_layers_12_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[785] model_decoder_layers_12_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] model_decoder_layers_12_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[790] model_decoder_layers_12_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] model_decoder_layers_12_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[792] model_decoder_layers_12_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[793] model_decoder_layers_12_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[794] model_decoder_layers_12_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] model_decoder_layers_12_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[796] model_decoder_layers_12_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] model_decoder_layers_12_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[798] model_decoder_layers_12_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[799] model_decoder_layers_12_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[800] model_decoder_layers_13_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] model_decoder_layers_13_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] model_decoder_layers_13_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[803] model_decoder_layers_13_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] model_decoder_layers_13_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[805] model_decoder_layers_13_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] model_decoder_layers_13_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[807] model_decoder_layers_13_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[808] model_decoder_layers_13_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[809] model_decoder_layers_13_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] model_decoder_layers_13_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[814] model_decoder_layers_13_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] model_decoder_layers_13_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[816] model_decoder_layers_13_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[817] model_decoder_layers_13_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[818] model_decoder_layers_13_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] model_decoder_layers_13_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[820] model_decoder_layers_13_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] model_decoder_layers_13_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[822] model_decoder_layers_13_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[823] model_decoder_layers_13_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[824] model_decoder_layers_14_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] model_decoder_layers_14_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] model_decoder_layers_14_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[827] model_decoder_layers_14_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] model_decoder_layers_14_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[829] model_decoder_layers_14_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] model_decoder_layers_14_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[831] model_decoder_layers_14_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[832] model_decoder_layers_14_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[833] model_decoder_layers_14_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] model_decoder_layers_14_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[838] model_decoder_layers_14_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] model_decoder_layers_14_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[840] model_decoder_layers_14_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[841] model_decoder_layers_14_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[842] model_decoder_layers_14_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] model_decoder_layers_14_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[844] model_decoder_layers_14_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] model_decoder_layers_14_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[846] model_decoder_layers_14_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[847] model_decoder_layers_14_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[848] model_decoder_layers_15_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] model_decoder_layers_15_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] model_decoder_layers_15_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[851] model_decoder_layers_15_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] model_decoder_layers_15_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[853] model_decoder_layers_15_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] model_decoder_layers_15_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[855] model_decoder_layers_15_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[856] model_decoder_layers_15_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[857] model_decoder_layers_15_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] model_decoder_layers_15_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[862] model_decoder_layers_15_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] model_decoder_layers_15_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[864] model_decoder_layers_15_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[865] model_decoder_layers_15_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[866] model_decoder_layers_15_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] model_decoder_layers_15_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[868] model_decoder_layers_15_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] model_decoder_layers_15_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[870] model_decoder_layers_15_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[871] model_decoder_layers_15_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[872] model_decoder_layers_16_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] model_decoder_layers_16_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] model_decoder_layers_16_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[875] model_decoder_layers_16_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] model_decoder_layers_16_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[877] model_decoder_layers_16_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] model_decoder_layers_16_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[879] model_decoder_layers_16_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[880] model_decoder_layers_16_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[881] model_decoder_layers_16_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] model_decoder_layers_16_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[886] model_decoder_layers_16_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] model_decoder_layers_16_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[888] model_decoder_layers_16_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[889] model_decoder_layers_16_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[890] model_decoder_layers_16_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] model_decoder_layers_16_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[892] model_decoder_layers_16_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] model_decoder_layers_16_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[894] model_decoder_layers_16_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[895] model_decoder_layers_16_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[896] model_decoder_layers_17_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] model_decoder_layers_17_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] model_decoder_layers_17_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[899] model_decoder_layers_17_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] model_decoder_layers_17_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[901] model_decoder_layers_17_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] model_decoder_layers_17_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[903] model_decoder_layers_17_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[904] model_decoder_layers_17_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[905] model_decoder_layers_17_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] model_decoder_layers_17_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[910] model_decoder_layers_17_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] model_decoder_layers_17_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[912] model_decoder_layers_17_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[913] model_decoder_layers_17_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[914] model_decoder_layers_17_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] model_decoder_layers_17_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[916] model_decoder_layers_17_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] model_decoder_layers_17_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[918] model_decoder_layers_17_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[919] model_decoder_layers_17_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[920] model_decoder_layers_18_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] model_decoder_layers_18_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] model_decoder_layers_18_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[923] model_decoder_layers_18_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] model_decoder_layers_18_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[925] model_decoder_layers_18_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] model_decoder_layers_18_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[927] model_decoder_layers_18_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[928] model_decoder_layers_18_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[929] model_decoder_layers_18_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] model_decoder_layers_18_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[934] model_decoder_layers_18_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] model_decoder_layers_18_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[936] model_decoder_layers_18_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[937] model_decoder_layers_18_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[938] model_decoder_layers_18_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] model_decoder_layers_18_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[940] model_decoder_layers_18_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] model_decoder_layers_18_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[942] model_decoder_layers_18_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[943] model_decoder_layers_18_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[944] model_decoder_layers_19_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] model_decoder_layers_19_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] model_decoder_layers_19_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[947] model_decoder_layers_19_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] model_decoder_layers_19_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[949] model_decoder_layers_19_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] model_decoder_layers_19_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[951] model_decoder_layers_19_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[952] model_decoder_layers_19_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[953] model_decoder_layers_19_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] model_decoder_layers_19_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[958] model_decoder_layers_19_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] model_decoder_layers_19_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[960] model_decoder_layers_19_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[961] model_decoder_layers_19_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[962] model_decoder_layers_19_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] model_decoder_layers_19_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[964] model_decoder_layers_19_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] model_decoder_layers_19_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[966] model_decoder_layers_19_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[967] model_decoder_layers_19_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[968] model_decoder_layers_20_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] model_decoder_layers_20_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] model_decoder_layers_20_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[971] model_decoder_layers_20_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] model_decoder_layers_20_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[973] model_decoder_layers_20_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] model_decoder_layers_20_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[975] model_decoder_layers_20_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[976] model_decoder_layers_20_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[977] model_decoder_layers_20_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] model_decoder_layers_20_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[982] model_decoder_layers_20_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] model_decoder_layers_20_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[984] model_decoder_layers_20_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[985] model_decoder_layers_20_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[986] model_decoder_layers_20_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] model_decoder_layers_20_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[988] model_decoder_layers_20_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] model_decoder_layers_20_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[990] model_decoder_layers_20_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[991] model_decoder_layers_20_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[992] model_decoder_layers_21_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] model_decoder_layers_21_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] model_decoder_layers_21_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[995] model_decoder_layers_21_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] model_decoder_layers_21_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[997] model_decoder_layers_21_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] model_decoder_layers_21_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[999] model_decoder_layers_21_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1000] model_decoder_layers_21_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1001] model_decoder_layers_21_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] model_decoder_layers_21_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1006] model_decoder_layers_21_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] model_decoder_layers_21_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1008] model_decoder_layers_21_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1009] model_decoder_layers_21_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1010] model_decoder_layers_21_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] model_decoder_layers_21_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1012] model_decoder_layers_21_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] model_decoder_layers_21_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1014] model_decoder_layers_21_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1015] model_decoder_layers_21_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1016] model_decoder_layers_22_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] model_decoder_layers_22_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] model_decoder_layers_22_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1019] model_decoder_layers_22_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] model_decoder_layers_22_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1021] model_decoder_layers_22_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] model_decoder_layers_22_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1023] model_decoder_layers_22_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1024] model_decoder_layers_22_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1025] model_decoder_layers_22_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] model_decoder_layers_22_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1030] model_decoder_layers_22_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] model_decoder_layers_22_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1032] model_decoder_layers_22_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1033] model_decoder_layers_22_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1034] model_decoder_layers_22_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] model_decoder_layers_22_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1036] model_decoder_layers_22_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] model_decoder_layers_22_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1038] model_decoder_layers_22_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1039] model_decoder_layers_22_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1040] model_decoder_layers_23_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] model_decoder_layers_23_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] model_decoder_layers_23_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1043] model_decoder_layers_23_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] model_decoder_layers_23_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1045] model_decoder_layers_23_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] model_decoder_layers_23_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1047] model_decoder_layers_23_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1048] model_decoder_layers_23_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1049] model_decoder_layers_23_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] model_decoder_layers_23_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1054] model_decoder_layers_23_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] model_decoder_layers_23_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1056] model_decoder_layers_23_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1057] model_decoder_layers_23_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1058] model_decoder_layers_23_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] model_decoder_layers_23_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1060] model_decoder_layers_23_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] model_decoder_layers_23_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1062] model_decoder_layers_23_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1063] model_decoder_layers_23_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1064] model_decoder_layers_24_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] model_decoder_layers_24_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] model_decoder_layers_24_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1067] model_decoder_layers_24_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] model_decoder_layers_24_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1069] model_decoder_layers_24_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] model_decoder_layers_24_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1071] model_decoder_layers_24_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1072] model_decoder_layers_24_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1073] model_decoder_layers_24_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] model_decoder_layers_24_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1078] model_decoder_layers_24_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] model_decoder_layers_24_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1080] model_decoder_layers_24_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1081] model_decoder_layers_24_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1082] model_decoder_layers_24_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] model_decoder_layers_24_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1084] model_decoder_layers_24_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] model_decoder_layers_24_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1086] model_decoder_layers_24_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1087] model_decoder_layers_24_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1088] model_decoder_layers_25_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] model_decoder_layers_25_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] model_decoder_layers_25_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1091] model_decoder_layers_25_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] model_decoder_layers_25_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1093] model_decoder_layers_25_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] model_decoder_layers_25_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1095] model_decoder_layers_25_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1096] model_decoder_layers_25_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1097] model_decoder_layers_25_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] model_decoder_layers_25_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1102] model_decoder_layers_25_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] model_decoder_layers_25_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1104] model_decoder_layers_25_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1105] model_decoder_layers_25_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1106] model_decoder_layers_25_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] model_decoder_layers_25_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1108] model_decoder_layers_25_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] model_decoder_layers_25_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1110] model_decoder_layers_25_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1111] model_decoder_layers_25_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1112] model_decoder_layers_26_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] model_decoder_layers_26_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] model_decoder_layers_26_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1115] model_decoder_layers_26_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] model_decoder_layers_26_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1117] model_decoder_layers_26_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] model_decoder_layers_26_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1119] model_decoder_layers_26_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1120] model_decoder_layers_26_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1121] model_decoder_layers_26_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] model_decoder_layers_26_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1126] model_decoder_layers_26_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] model_decoder_layers_26_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1128] model_decoder_layers_26_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1129] model_decoder_layers_26_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1130] model_decoder_layers_26_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] model_decoder_layers_26_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1132] model_decoder_layers_26_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] model_decoder_layers_26_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1134] model_decoder_layers_26_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1135] model_decoder_layers_26_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1136] model_decoder_layers_27_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] model_decoder_layers_27_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] model_decoder_layers_27_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1139] model_decoder_layers_27_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] model_decoder_layers_27_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1141] model_decoder_layers_27_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] model_decoder_layers_27_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1143] model_decoder_layers_27_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1144] model_decoder_layers_27_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1145] model_decoder_layers_27_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] model_decoder_layers_27_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1150] model_decoder_layers_27_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] model_decoder_layers_27_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1152] model_decoder_layers_27_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1153] model_decoder_layers_27_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1154] model_decoder_layers_27_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] model_decoder_layers_27_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1156] model_decoder_layers_27_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] model_decoder_layers_27_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1158] model_decoder_layers_27_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1159] model_decoder_layers_27_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1160] model_decoder_layers_28_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] model_decoder_layers_28_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] model_decoder_layers_28_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1163] model_decoder_layers_28_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] model_decoder_layers_28_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1165] model_decoder_layers_28_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] model_decoder_layers_28_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1167] model_decoder_layers_28_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1168] model_decoder_layers_28_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1169] model_decoder_layers_28_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] model_decoder_layers_28_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1174] model_decoder_layers_28_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] model_decoder_layers_28_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1176] model_decoder_layers_28_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1177] model_decoder_layers_28_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1178] model_decoder_layers_28_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] model_decoder_layers_28_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1180] model_decoder_layers_28_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] model_decoder_layers_28_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1182] model_decoder_layers_28_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1183] model_decoder_layers_28_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1184] model_decoder_layers_29_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] model_decoder_layers_29_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] model_decoder_layers_29_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1187] model_decoder_layers_29_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] model_decoder_layers_29_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1189] model_decoder_layers_29_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] model_decoder_layers_29_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1191] model_decoder_layers_29_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1192] model_decoder_layers_29_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1193] model_decoder_layers_29_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] model_decoder_layers_29_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1198] model_decoder_layers_29_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] model_decoder_layers_29_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1200] model_decoder_layers_29_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1201] model_decoder_layers_29_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1202] model_decoder_layers_29_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] model_decoder_layers_29_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1204] model_decoder_layers_29_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] model_decoder_layers_29_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1206] model_decoder_layers_29_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1207] model_decoder_layers_29_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1208] model_decoder_layers_30_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] model_decoder_layers_30_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] model_decoder_layers_30_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1211] model_decoder_layers_30_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] model_decoder_layers_30_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1213] model_decoder_layers_30_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] model_decoder_layers_30_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1215] model_decoder_layers_30_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1216] model_decoder_layers_30_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1217] model_decoder_layers_30_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] model_decoder_layers_30_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1222] model_decoder_layers_30_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] model_decoder_layers_30_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1224] model_decoder_layers_30_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1225] model_decoder_layers_30_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1226] model_decoder_layers_30_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] model_decoder_layers_30_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1228] model_decoder_layers_30_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] model_decoder_layers_30_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1230] model_decoder_layers_30_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1231] model_decoder_layers_30_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1232] model_decoder_layers_31_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] model_decoder_layers_31_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] model_decoder_layers_31_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1235] model_decoder_layers_31_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] model_decoder_layers_31_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1237] model_decoder_layers_31_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] model_decoder_layers_31_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1239] model_decoder_layers_31_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1240] model_decoder_layers_31_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1241] model_decoder_layers_31_encoder_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] model_decoder_layers_31_encoder_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1246] model_decoder_layers_31_encoder_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] model_decoder_layers_31_encoder_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1248] model_decoder_layers_31_encoder_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1249] model_decoder_layers_31_encoder_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1250] model_decoder_layers_31_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] model_decoder_layers_31_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[1252] model_decoder_layers_31_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] model_decoder_layers_31_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1254] model_decoder_layers_31_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1255] model_decoder_layers_31_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1256] model_decoder_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[1257] model_decoder_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1258] reshape384: R.Tensor((seq_len,), dtype="int32") = R.reshape(input_ids, R.shape([seq_len])) take: R.Tensor((seq_len, 1280), dtype="float16") = R.take(model_decoder_embed_tokens_weight2, reshape384, axis=0) reshape385: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(take, R.shape([1, seq_len, 1280])) lv68: R.Tensor((seq_len,), dtype="int32") = R.call_pure_packed("vm.builtin.attention_kv_cache_get_query_positions", paged_kv_cache, sinfo_args=(R.Tensor((seq_len,), dtype="int32"),)) take1: R.Tensor((seq_len, 1280), dtype="float16") = R.take(model_decoder_embed_positions_weight2, lv68, axis=0) reshape386: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(take1, R.shape([1, seq_len, 1280])) add257: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(reshape385, reshape386) layer_norm65: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add257, model_decoder_layers_0_self_attn_layer_norm_weight2, model_decoder_layers_0_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv416 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_self_attn_q_proj_weight2, layer_norm65, model_decoder_layers_0_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape387: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv416, R.shape([1, seq_len, 20, 64])) lv98 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_0_self_attn_k_proj_weight2, layer_norm65), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape388: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv98, R.shape([1, seq_len, 20, 64])) lv417 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_self_attn_v_proj_weight2, layer_norm65, model_decoder_layers_0_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape389: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv417, R.shape([1, seq_len, 20, 64])) concat: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape387, reshape388, reshape389), axis=2) reshape390: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat, R.shape([seq_len, 60, 64])) lv69 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape390), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape391: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv69, R.shape([1, seq_len, 20, 64])) reshape392: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape391, R.shape([1, seq_len, 1280])) lv418 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_self_attn_out_proj_weight2, reshape392, model_decoder_layers_0_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add261: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add257, lv418) layer_norm66: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add261, model_decoder_layers_0_encoder_attn_layer_norm_weight2, model_decoder_layers_0_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv419 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_encoder_attn_q_proj_weight2, layer_norm66, model_decoder_layers_0_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape393: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv419, R.shape([1, seq_len, 20, 64])) reshape394: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape393, R.shape([seq_len, 20, 64])) lv70 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape394), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape395: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv70, R.shape([1, seq_len, 20, 64])) reshape396: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape395, R.shape([1, seq_len, 1280])) lv420 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_encoder_attn_out_proj_weight2, reshape396, model_decoder_layers_0_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add264: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add261, lv420) layer_norm67: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add264, model_decoder_layers_0_final_layer_norm_weight2, model_decoder_layers_0_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv64 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_0_fc1_weight2, layer_norm67, model_decoder_layers_0_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv421 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_0_fc2_weight2, lv64, model_decoder_layers_0_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add267: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add264, lv421) layer_norm68: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add267, model_decoder_layers_1_self_attn_layer_norm_weight2, model_decoder_layers_1_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv422 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_self_attn_q_proj_weight2, layer_norm68, model_decoder_layers_1_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape397: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv422, R.shape([1, seq_len, 20, 64])) lv99 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_1_self_attn_k_proj_weight2, layer_norm68), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape398: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv99, R.shape([1, seq_len, 20, 64])) lv423 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_self_attn_v_proj_weight2, layer_norm68, model_decoder_layers_1_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape399: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv423, R.shape([1, seq_len, 20, 64])) concat1: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape397, reshape398, reshape399), axis=2) reshape400: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat1, R.shape([seq_len, 60, 64])) lv71 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape400), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape401: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv71, R.shape([1, seq_len, 20, 64])) reshape402: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape401, R.shape([1, seq_len, 1280])) lv424 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_self_attn_out_proj_weight2, reshape402, model_decoder_layers_1_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add271: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add267, lv424) layer_norm69: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add271, model_decoder_layers_1_encoder_attn_layer_norm_weight2, model_decoder_layers_1_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv425 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_encoder_attn_q_proj_weight2, layer_norm69, model_decoder_layers_1_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape403: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv425, R.shape([1, seq_len, 20, 64])) reshape404: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape403, R.shape([seq_len, 20, 64])) lv72 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape404), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape405: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv72, R.shape([1, seq_len, 20, 64])) reshape406: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape405, R.shape([1, seq_len, 1280])) lv426 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_encoder_attn_out_proj_weight2, reshape406, model_decoder_layers_1_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add274: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add271, lv426) layer_norm70: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add274, model_decoder_layers_1_final_layer_norm_weight2, model_decoder_layers_1_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv65 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_1_fc1_weight2, layer_norm70, model_decoder_layers_1_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv427 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_1_fc2_weight2, lv65, model_decoder_layers_1_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add277: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add274, lv427) layer_norm71: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add277, model_decoder_layers_2_self_attn_layer_norm_weight2, model_decoder_layers_2_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv428 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_self_attn_q_proj_weight2, layer_norm71, model_decoder_layers_2_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape407: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv428, R.shape([1, seq_len, 20, 64])) lv100 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_2_self_attn_k_proj_weight2, layer_norm71), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape408: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv100, R.shape([1, seq_len, 20, 64])) lv429 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_self_attn_v_proj_weight2, layer_norm71, model_decoder_layers_2_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape409: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv429, R.shape([1, seq_len, 20, 64])) concat2: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape407, reshape408, reshape409), axis=2) reshape410: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat2, R.shape([seq_len, 60, 64])) lv73 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape410), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape411: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv73, R.shape([1, seq_len, 20, 64])) reshape412: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape411, R.shape([1, seq_len, 1280])) lv430 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_self_attn_out_proj_weight2, reshape412, model_decoder_layers_2_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add281: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add277, lv430) layer_norm72: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add281, model_decoder_layers_2_encoder_attn_layer_norm_weight2, model_decoder_layers_2_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv431 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_encoder_attn_q_proj_weight2, layer_norm72, model_decoder_layers_2_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape413: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv431, R.shape([1, seq_len, 20, 64])) reshape414: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape413, R.shape([seq_len, 20, 64])) lv74 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape414), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape415: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv74, R.shape([1, seq_len, 20, 64])) reshape416: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape415, R.shape([1, seq_len, 1280])) lv432 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_encoder_attn_out_proj_weight2, reshape416, model_decoder_layers_2_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add284: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add281, lv432) layer_norm73: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add284, model_decoder_layers_2_final_layer_norm_weight2, model_decoder_layers_2_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv66 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_2_fc1_weight2, layer_norm73, model_decoder_layers_2_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv433 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_2_fc2_weight2, lv66, model_decoder_layers_2_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add287: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add284, lv433) layer_norm74: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add287, model_decoder_layers_3_self_attn_layer_norm_weight2, model_decoder_layers_3_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv434 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_self_attn_q_proj_weight2, layer_norm74, model_decoder_layers_3_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape417: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv434, R.shape([1, seq_len, 20, 64])) lv101 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_3_self_attn_k_proj_weight2, layer_norm74), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape418: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv101, R.shape([1, seq_len, 20, 64])) lv435 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_self_attn_v_proj_weight2, layer_norm74, model_decoder_layers_3_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape419: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv435, R.shape([1, seq_len, 20, 64])) concat3: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape417, reshape418, reshape419), axis=2) reshape420: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat3, R.shape([seq_len, 60, 64])) lv75 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape420), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape421: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv75, R.shape([1, seq_len, 20, 64])) reshape422: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape421, R.shape([1, seq_len, 1280])) lv436 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_self_attn_out_proj_weight2, reshape422, model_decoder_layers_3_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add291: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add287, lv436) layer_norm75: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add291, model_decoder_layers_3_encoder_attn_layer_norm_weight2, model_decoder_layers_3_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv437 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_encoder_attn_q_proj_weight2, layer_norm75, model_decoder_layers_3_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape423: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv437, R.shape([1, seq_len, 20, 64])) reshape424: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape423, R.shape([seq_len, 20, 64])) lv76 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape424), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape425: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv76, R.shape([1, seq_len, 20, 64])) reshape426: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape425, R.shape([1, seq_len, 1280])) lv438 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_encoder_attn_out_proj_weight2, reshape426, model_decoder_layers_3_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add294: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add291, lv438) layer_norm76: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add294, model_decoder_layers_3_final_layer_norm_weight2, model_decoder_layers_3_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv67 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_3_fc1_weight2, layer_norm76, model_decoder_layers_3_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv439 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_3_fc2_weight2, lv67, model_decoder_layers_3_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add297: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add294, lv439) layer_norm77: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add297, model_decoder_layers_4_self_attn_layer_norm_weight2, model_decoder_layers_4_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv440 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_self_attn_q_proj_weight2, layer_norm77, model_decoder_layers_4_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape427: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv440, R.shape([1, seq_len, 20, 64])) lv102 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_4_self_attn_k_proj_weight2, layer_norm77), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape428: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv102, R.shape([1, seq_len, 20, 64])) lv441 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_self_attn_v_proj_weight2, layer_norm77, model_decoder_layers_4_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape429: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv441, R.shape([1, seq_len, 20, 64])) concat4: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape427, reshape428, reshape429), axis=2) reshape430: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat4, R.shape([seq_len, 60, 64])) lv77 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape430), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape431: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv77, R.shape([1, seq_len, 20, 64])) reshape432: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape431, R.shape([1, seq_len, 1280])) lv442 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_self_attn_out_proj_weight2, reshape432, model_decoder_layers_4_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add301: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add297, lv442) layer_norm78: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add301, model_decoder_layers_4_encoder_attn_layer_norm_weight2, model_decoder_layers_4_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv443 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_encoder_attn_q_proj_weight2, layer_norm78, model_decoder_layers_4_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape433: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv443, R.shape([1, seq_len, 20, 64])) reshape434: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape433, R.shape([seq_len, 20, 64])) lv78 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape434), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape435: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv78, R.shape([1, seq_len, 20, 64])) reshape436: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape435, R.shape([1, seq_len, 1280])) lv444 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_encoder_attn_out_proj_weight2, reshape436, model_decoder_layers_4_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add304: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add301, lv444) layer_norm79: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add304, model_decoder_layers_4_final_layer_norm_weight2, model_decoder_layers_4_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv68_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_4_fc1_weight2, layer_norm79, model_decoder_layers_4_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv445 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_4_fc2_weight2, lv68_1, model_decoder_layers_4_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add307: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add304, lv445) layer_norm80: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add307, model_decoder_layers_5_self_attn_layer_norm_weight2, model_decoder_layers_5_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv446 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_self_attn_q_proj_weight2, layer_norm80, model_decoder_layers_5_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape437: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv446, R.shape([1, seq_len, 20, 64])) lv103 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_5_self_attn_k_proj_weight2, layer_norm80), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape438: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv103, R.shape([1, seq_len, 20, 64])) lv447 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_self_attn_v_proj_weight2, layer_norm80, model_decoder_layers_5_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape439: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv447, R.shape([1, seq_len, 20, 64])) concat5: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape437, reshape438, reshape439), axis=2) reshape440: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat5, R.shape([seq_len, 60, 64])) lv79 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape440), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape441: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv79, R.shape([1, seq_len, 20, 64])) reshape442: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape441, R.shape([1, seq_len, 1280])) lv448 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_self_attn_out_proj_weight2, reshape442, model_decoder_layers_5_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add311: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add307, lv448) layer_norm81: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add311, model_decoder_layers_5_encoder_attn_layer_norm_weight2, model_decoder_layers_5_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv449 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_encoder_attn_q_proj_weight2, layer_norm81, model_decoder_layers_5_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape443: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv449, R.shape([1, seq_len, 20, 64])) reshape444: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape443, R.shape([seq_len, 20, 64])) lv80 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape444), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape445: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv80, R.shape([1, seq_len, 20, 64])) reshape446: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape445, R.shape([1, seq_len, 1280])) lv450 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_encoder_attn_out_proj_weight2, reshape446, model_decoder_layers_5_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add314: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add311, lv450) layer_norm82: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add314, model_decoder_layers_5_final_layer_norm_weight2, model_decoder_layers_5_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv69_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_5_fc1_weight2, layer_norm82, model_decoder_layers_5_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv451 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_5_fc2_weight2, lv69_1, model_decoder_layers_5_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add317: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add314, lv451) layer_norm83: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add317, model_decoder_layers_6_self_attn_layer_norm_weight2, model_decoder_layers_6_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv452 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_self_attn_q_proj_weight2, layer_norm83, model_decoder_layers_6_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape447: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv452, R.shape([1, seq_len, 20, 64])) lv104 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_6_self_attn_k_proj_weight2, layer_norm83), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape448: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv104, R.shape([1, seq_len, 20, 64])) lv453 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_self_attn_v_proj_weight2, layer_norm83, model_decoder_layers_6_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape449: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv453, R.shape([1, seq_len, 20, 64])) concat6: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape447, reshape448, reshape449), axis=2) reshape450: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat6, R.shape([seq_len, 60, 64])) lv81 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape450), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape451: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv81, R.shape([1, seq_len, 20, 64])) reshape452: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape451, R.shape([1, seq_len, 1280])) lv454 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_self_attn_out_proj_weight2, reshape452, model_decoder_layers_6_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add321: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add317, lv454) layer_norm84: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add321, model_decoder_layers_6_encoder_attn_layer_norm_weight2, model_decoder_layers_6_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv455 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_encoder_attn_q_proj_weight2, layer_norm84, model_decoder_layers_6_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape453: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv455, R.shape([1, seq_len, 20, 64])) reshape454: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape453, R.shape([seq_len, 20, 64])) lv82 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape454), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape455: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv82, R.shape([1, seq_len, 20, 64])) reshape456: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape455, R.shape([1, seq_len, 1280])) lv456 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_encoder_attn_out_proj_weight2, reshape456, model_decoder_layers_6_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add324: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add321, lv456) layer_norm85: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add324, model_decoder_layers_6_final_layer_norm_weight2, model_decoder_layers_6_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv70_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_6_fc1_weight2, layer_norm85, model_decoder_layers_6_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv457 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_6_fc2_weight2, lv70_1, model_decoder_layers_6_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add327: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add324, lv457) layer_norm86: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add327, model_decoder_layers_7_self_attn_layer_norm_weight2, model_decoder_layers_7_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv458 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_self_attn_q_proj_weight2, layer_norm86, model_decoder_layers_7_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape457: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv458, R.shape([1, seq_len, 20, 64])) lv105 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_7_self_attn_k_proj_weight2, layer_norm86), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape458: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv105, R.shape([1, seq_len, 20, 64])) lv459 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_self_attn_v_proj_weight2, layer_norm86, model_decoder_layers_7_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape459: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv459, R.shape([1, seq_len, 20, 64])) concat7: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape457, reshape458, reshape459), axis=2) reshape460: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat7, R.shape([seq_len, 60, 64])) lv83 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape460), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape461: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv83, R.shape([1, seq_len, 20, 64])) reshape462: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape461, R.shape([1, seq_len, 1280])) lv460 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_self_attn_out_proj_weight2, reshape462, model_decoder_layers_7_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add331: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add327, lv460) layer_norm87: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add331, model_decoder_layers_7_encoder_attn_layer_norm_weight2, model_decoder_layers_7_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv461 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_encoder_attn_q_proj_weight2, layer_norm87, model_decoder_layers_7_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape463: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv461, R.shape([1, seq_len, 20, 64])) reshape464: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape463, R.shape([seq_len, 20, 64])) lv84 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape464), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape465: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv84, R.shape([1, seq_len, 20, 64])) reshape466: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape465, R.shape([1, seq_len, 1280])) lv462 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_encoder_attn_out_proj_weight2, reshape466, model_decoder_layers_7_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add334: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add331, lv462) layer_norm88: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add334, model_decoder_layers_7_final_layer_norm_weight2, model_decoder_layers_7_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv71_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_7_fc1_weight2, layer_norm88, model_decoder_layers_7_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv463 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_7_fc2_weight2, lv71_1, model_decoder_layers_7_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add337: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add334, lv463) layer_norm89: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add337, model_decoder_layers_8_self_attn_layer_norm_weight2, model_decoder_layers_8_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv464 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_self_attn_q_proj_weight2, layer_norm89, model_decoder_layers_8_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape467: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv464, R.shape([1, seq_len, 20, 64])) lv106 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_8_self_attn_k_proj_weight2, layer_norm89), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape468: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv106, R.shape([1, seq_len, 20, 64])) lv465 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_self_attn_v_proj_weight2, layer_norm89, model_decoder_layers_8_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape469: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv465, R.shape([1, seq_len, 20, 64])) concat8: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape467, reshape468, reshape469), axis=2) reshape470: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat8, R.shape([seq_len, 60, 64])) lv85 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape470), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape471: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv85, R.shape([1, seq_len, 20, 64])) reshape472: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape471, R.shape([1, seq_len, 1280])) lv466 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_self_attn_out_proj_weight2, reshape472, model_decoder_layers_8_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add341: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add337, lv466) layer_norm90: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add341, model_decoder_layers_8_encoder_attn_layer_norm_weight2, model_decoder_layers_8_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv467 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_encoder_attn_q_proj_weight2, layer_norm90, model_decoder_layers_8_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape473: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv467, R.shape([1, seq_len, 20, 64])) reshape474: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape473, R.shape([seq_len, 20, 64])) lv86 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape474), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape475: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv86, R.shape([1, seq_len, 20, 64])) reshape476: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape475, R.shape([1, seq_len, 1280])) lv468 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_encoder_attn_out_proj_weight2, reshape476, model_decoder_layers_8_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add344: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add341, lv468) layer_norm91: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add344, model_decoder_layers_8_final_layer_norm_weight2, model_decoder_layers_8_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv72_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_8_fc1_weight2, layer_norm91, model_decoder_layers_8_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv469 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_8_fc2_weight2, lv72_1, model_decoder_layers_8_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add347: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add344, lv469) layer_norm92: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add347, model_decoder_layers_9_self_attn_layer_norm_weight2, model_decoder_layers_9_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv470 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_self_attn_q_proj_weight2, layer_norm92, model_decoder_layers_9_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape477: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv470, R.shape([1, seq_len, 20, 64])) lv107 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_9_self_attn_k_proj_weight2, layer_norm92), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape478: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv107, R.shape([1, seq_len, 20, 64])) lv471 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_self_attn_v_proj_weight2, layer_norm92, model_decoder_layers_9_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape479: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv471, R.shape([1, seq_len, 20, 64])) concat9: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape477, reshape478, reshape479), axis=2) reshape480: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat9, R.shape([seq_len, 60, 64])) lv87 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape480), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape481: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv87, R.shape([1, seq_len, 20, 64])) reshape482: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape481, R.shape([1, seq_len, 1280])) lv472 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_self_attn_out_proj_weight2, reshape482, model_decoder_layers_9_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add351: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add347, lv472) layer_norm93: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add351, model_decoder_layers_9_encoder_attn_layer_norm_weight2, model_decoder_layers_9_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv473 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_encoder_attn_q_proj_weight2, layer_norm93, model_decoder_layers_9_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape483: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv473, R.shape([1, seq_len, 20, 64])) reshape484: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape483, R.shape([seq_len, 20, 64])) lv88 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape484), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape485: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv88, R.shape([1, seq_len, 20, 64])) reshape486: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape485, R.shape([1, seq_len, 1280])) lv474 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_encoder_attn_out_proj_weight2, reshape486, model_decoder_layers_9_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add354: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add351, lv474) layer_norm94: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add354, model_decoder_layers_9_final_layer_norm_weight2, model_decoder_layers_9_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv73_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_9_fc1_weight2, layer_norm94, model_decoder_layers_9_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv475 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_9_fc2_weight2, lv73_1, model_decoder_layers_9_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add357: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add354, lv475) layer_norm95: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add357, model_decoder_layers_10_self_attn_layer_norm_weight2, model_decoder_layers_10_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv476 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_self_attn_q_proj_weight2, layer_norm95, model_decoder_layers_10_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape487: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv476, R.shape([1, seq_len, 20, 64])) lv108 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_10_self_attn_k_proj_weight2, layer_norm95), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape488: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv108, R.shape([1, seq_len, 20, 64])) lv477 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_self_attn_v_proj_weight2, layer_norm95, model_decoder_layers_10_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape489: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv477, R.shape([1, seq_len, 20, 64])) concat10: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape487, reshape488, reshape489), axis=2) reshape490: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat10, R.shape([seq_len, 60, 64])) lv89 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape490), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape491: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv89, R.shape([1, seq_len, 20, 64])) reshape492: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape491, R.shape([1, seq_len, 1280])) lv478 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_self_attn_out_proj_weight2, reshape492, model_decoder_layers_10_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add361: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add357, lv478) layer_norm96: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add361, model_decoder_layers_10_encoder_attn_layer_norm_weight2, model_decoder_layers_10_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv479 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_encoder_attn_q_proj_weight2, layer_norm96, model_decoder_layers_10_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape493: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv479, R.shape([1, seq_len, 20, 64])) reshape494: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape493, R.shape([seq_len, 20, 64])) lv90 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape494), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape495: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv90, R.shape([1, seq_len, 20, 64])) reshape496: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape495, R.shape([1, seq_len, 1280])) lv480 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_encoder_attn_out_proj_weight2, reshape496, model_decoder_layers_10_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add364: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add361, lv480) layer_norm97: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add364, model_decoder_layers_10_final_layer_norm_weight2, model_decoder_layers_10_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv74_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_10_fc1_weight2, layer_norm97, model_decoder_layers_10_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv481 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_10_fc2_weight2, lv74_1, model_decoder_layers_10_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add367: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add364, lv481) layer_norm98: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add367, model_decoder_layers_11_self_attn_layer_norm_weight2, model_decoder_layers_11_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv482 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_self_attn_q_proj_weight2, layer_norm98, model_decoder_layers_11_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape497: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv482, R.shape([1, seq_len, 20, 64])) lv109 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_11_self_attn_k_proj_weight2, layer_norm98), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape498: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv109, R.shape([1, seq_len, 20, 64])) lv483 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_self_attn_v_proj_weight2, layer_norm98, model_decoder_layers_11_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape499: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv483, R.shape([1, seq_len, 20, 64])) concat11: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape497, reshape498, reshape499), axis=2) reshape500: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat11, R.shape([seq_len, 60, 64])) lv91 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape500), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape501: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv91, R.shape([1, seq_len, 20, 64])) reshape502: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape501, R.shape([1, seq_len, 1280])) lv484 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_self_attn_out_proj_weight2, reshape502, model_decoder_layers_11_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add371: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add367, lv484) layer_norm99: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add371, model_decoder_layers_11_encoder_attn_layer_norm_weight2, model_decoder_layers_11_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv485 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_encoder_attn_q_proj_weight2, layer_norm99, model_decoder_layers_11_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape503: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv485, R.shape([1, seq_len, 20, 64])) reshape504: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape503, R.shape([seq_len, 20, 64])) lv92 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape504), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape505: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv92, R.shape([1, seq_len, 20, 64])) reshape506: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape505, R.shape([1, seq_len, 1280])) lv486 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_encoder_attn_out_proj_weight2, reshape506, model_decoder_layers_11_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add374: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add371, lv486) layer_norm100: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add374, model_decoder_layers_11_final_layer_norm_weight2, model_decoder_layers_11_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv75_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_11_fc1_weight2, layer_norm100, model_decoder_layers_11_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv487 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_11_fc2_weight2, lv75_1, model_decoder_layers_11_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add377: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add374, lv487) layer_norm101: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add377, model_decoder_layers_12_self_attn_layer_norm_weight2, model_decoder_layers_12_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv488 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_self_attn_q_proj_weight2, layer_norm101, model_decoder_layers_12_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape507: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv488, R.shape([1, seq_len, 20, 64])) lv110 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_12_self_attn_k_proj_weight2, layer_norm101), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape508: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv110, R.shape([1, seq_len, 20, 64])) lv489 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_self_attn_v_proj_weight2, layer_norm101, model_decoder_layers_12_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape509: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv489, R.shape([1, seq_len, 20, 64])) concat12: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape507, reshape508, reshape509), axis=2) reshape510: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat12, R.shape([seq_len, 60, 64])) lv93 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape510), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape511: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv93, R.shape([1, seq_len, 20, 64])) reshape512: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape511, R.shape([1, seq_len, 1280])) lv490 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_self_attn_out_proj_weight2, reshape512, model_decoder_layers_12_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add381: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add377, lv490) layer_norm102: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add381, model_decoder_layers_12_encoder_attn_layer_norm_weight2, model_decoder_layers_12_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv491 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_encoder_attn_q_proj_weight2, layer_norm102, model_decoder_layers_12_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape513: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv491, R.shape([1, seq_len, 20, 64])) reshape514: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape513, R.shape([seq_len, 20, 64])) lv94 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape514), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape515: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv94, R.shape([1, seq_len, 20, 64])) reshape516: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape515, R.shape([1, seq_len, 1280])) lv492 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_encoder_attn_out_proj_weight2, reshape516, model_decoder_layers_12_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add384: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add381, lv492) layer_norm103: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add384, model_decoder_layers_12_final_layer_norm_weight2, model_decoder_layers_12_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv76_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_12_fc1_weight2, layer_norm103, model_decoder_layers_12_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv493 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_12_fc2_weight2, lv76_1, model_decoder_layers_12_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add387: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add384, lv493) layer_norm104: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add387, model_decoder_layers_13_self_attn_layer_norm_weight2, model_decoder_layers_13_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv494 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_self_attn_q_proj_weight2, layer_norm104, model_decoder_layers_13_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape517: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv494, R.shape([1, seq_len, 20, 64])) lv111 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_13_self_attn_k_proj_weight2, layer_norm104), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape518: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv111, R.shape([1, seq_len, 20, 64])) lv495 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_self_attn_v_proj_weight2, layer_norm104, model_decoder_layers_13_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape519: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv495, R.shape([1, seq_len, 20, 64])) concat13: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape517, reshape518, reshape519), axis=2) reshape520: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat13, R.shape([seq_len, 60, 64])) lv95 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape520), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape521: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv95, R.shape([1, seq_len, 20, 64])) reshape522: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape521, R.shape([1, seq_len, 1280])) lv496 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_self_attn_out_proj_weight2, reshape522, model_decoder_layers_13_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add391: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add387, lv496) layer_norm105: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add391, model_decoder_layers_13_encoder_attn_layer_norm_weight2, model_decoder_layers_13_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv497 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_encoder_attn_q_proj_weight2, layer_norm105, model_decoder_layers_13_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape523: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv497, R.shape([1, seq_len, 20, 64])) reshape524: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape523, R.shape([seq_len, 20, 64])) lv96 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape524), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape525: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv96, R.shape([1, seq_len, 20, 64])) reshape526: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape525, R.shape([1, seq_len, 1280])) lv498 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_encoder_attn_out_proj_weight2, reshape526, model_decoder_layers_13_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add394: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add391, lv498) layer_norm106: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add394, model_decoder_layers_13_final_layer_norm_weight2, model_decoder_layers_13_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv77_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_13_fc1_weight2, layer_norm106, model_decoder_layers_13_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv499 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_13_fc2_weight2, lv77_1, model_decoder_layers_13_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add397: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add394, lv499) layer_norm107: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add397, model_decoder_layers_14_self_attn_layer_norm_weight2, model_decoder_layers_14_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv500 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_self_attn_q_proj_weight2, layer_norm107, model_decoder_layers_14_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape527: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv500, R.shape([1, seq_len, 20, 64])) lv112 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_14_self_attn_k_proj_weight2, layer_norm107), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape528: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv112, R.shape([1, seq_len, 20, 64])) lv501 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_self_attn_v_proj_weight2, layer_norm107, model_decoder_layers_14_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape529: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv501, R.shape([1, seq_len, 20, 64])) concat14: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape527, reshape528, reshape529), axis=2) reshape530: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat14, R.shape([seq_len, 60, 64])) lv97 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape530), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape531: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv97, R.shape([1, seq_len, 20, 64])) reshape532: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape531, R.shape([1, seq_len, 1280])) lv502 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_self_attn_out_proj_weight2, reshape532, model_decoder_layers_14_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add401: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add397, lv502) layer_norm108: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add401, model_decoder_layers_14_encoder_attn_layer_norm_weight2, model_decoder_layers_14_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv503 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_encoder_attn_q_proj_weight2, layer_norm108, model_decoder_layers_14_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape533: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv503, R.shape([1, seq_len, 20, 64])) reshape534: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape533, R.shape([seq_len, 20, 64])) lv98_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape534), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape535: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv98_1, R.shape([1, seq_len, 20, 64])) reshape536: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape535, R.shape([1, seq_len, 1280])) lv504 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_encoder_attn_out_proj_weight2, reshape536, model_decoder_layers_14_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add404: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add401, lv504) layer_norm109: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add404, model_decoder_layers_14_final_layer_norm_weight2, model_decoder_layers_14_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv78_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_14_fc1_weight2, layer_norm109, model_decoder_layers_14_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv505 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_14_fc2_weight2, lv78_1, model_decoder_layers_14_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add407: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add404, lv505) layer_norm110: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add407, model_decoder_layers_15_self_attn_layer_norm_weight2, model_decoder_layers_15_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv506 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_self_attn_q_proj_weight2, layer_norm110, model_decoder_layers_15_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape537: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv506, R.shape([1, seq_len, 20, 64])) lv113 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_15_self_attn_k_proj_weight2, layer_norm110), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape538: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv113, R.shape([1, seq_len, 20, 64])) lv507 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_self_attn_v_proj_weight2, layer_norm110, model_decoder_layers_15_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape539: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv507, R.shape([1, seq_len, 20, 64])) concat15: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape537, reshape538, reshape539), axis=2) reshape540: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat15, R.shape([seq_len, 60, 64])) lv99_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape540), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape541: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv99_1, R.shape([1, seq_len, 20, 64])) reshape542: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape541, R.shape([1, seq_len, 1280])) lv508 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_self_attn_out_proj_weight2, reshape542, model_decoder_layers_15_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add411: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add407, lv508) layer_norm111: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add411, model_decoder_layers_15_encoder_attn_layer_norm_weight2, model_decoder_layers_15_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv509 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_encoder_attn_q_proj_weight2, layer_norm111, model_decoder_layers_15_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape543: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv509, R.shape([1, seq_len, 20, 64])) reshape544: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape543, R.shape([seq_len, 20, 64])) lv100_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape544), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape545: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv100_1, R.shape([1, seq_len, 20, 64])) reshape546: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape545, R.shape([1, seq_len, 1280])) lv510 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_encoder_attn_out_proj_weight2, reshape546, model_decoder_layers_15_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add414: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add411, lv510) layer_norm112: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add414, model_decoder_layers_15_final_layer_norm_weight2, model_decoder_layers_15_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv79_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_15_fc1_weight2, layer_norm112, model_decoder_layers_15_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv511 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_15_fc2_weight2, lv79_1, model_decoder_layers_15_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add417: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add414, lv511) layer_norm113: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add417, model_decoder_layers_16_self_attn_layer_norm_weight2, model_decoder_layers_16_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv512 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_self_attn_q_proj_weight2, layer_norm113, model_decoder_layers_16_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape547: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv512, R.shape([1, seq_len, 20, 64])) lv114 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_16_self_attn_k_proj_weight2, layer_norm113), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape548: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv114, R.shape([1, seq_len, 20, 64])) lv513 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_self_attn_v_proj_weight2, layer_norm113, model_decoder_layers_16_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape549: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv513, R.shape([1, seq_len, 20, 64])) concat16: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape547, reshape548, reshape549), axis=2) reshape550: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat16, R.shape([seq_len, 60, 64])) lv101_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape550), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape551: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv101_1, R.shape([1, seq_len, 20, 64])) reshape552: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape551, R.shape([1, seq_len, 1280])) lv514 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_self_attn_out_proj_weight2, reshape552, model_decoder_layers_16_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add421: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add417, lv514) layer_norm114: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add421, model_decoder_layers_16_encoder_attn_layer_norm_weight2, model_decoder_layers_16_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv515 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_encoder_attn_q_proj_weight2, layer_norm114, model_decoder_layers_16_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape553: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv515, R.shape([1, seq_len, 20, 64])) reshape554: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape553, R.shape([seq_len, 20, 64])) lv102_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape554), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape555: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv102_1, R.shape([1, seq_len, 20, 64])) reshape556: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape555, R.shape([1, seq_len, 1280])) lv516 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_encoder_attn_out_proj_weight2, reshape556, model_decoder_layers_16_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add424: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add421, lv516) layer_norm115: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add424, model_decoder_layers_16_final_layer_norm_weight2, model_decoder_layers_16_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv80_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_16_fc1_weight2, layer_norm115, model_decoder_layers_16_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv517 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_16_fc2_weight2, lv80_1, model_decoder_layers_16_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add427: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add424, lv517) layer_norm116: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add427, model_decoder_layers_17_self_attn_layer_norm_weight2, model_decoder_layers_17_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv518 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_self_attn_q_proj_weight2, layer_norm116, model_decoder_layers_17_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape557: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv518, R.shape([1, seq_len, 20, 64])) lv115 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_17_self_attn_k_proj_weight2, layer_norm116), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape558: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv115, R.shape([1, seq_len, 20, 64])) lv519 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_self_attn_v_proj_weight2, layer_norm116, model_decoder_layers_17_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape559: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv519, R.shape([1, seq_len, 20, 64])) concat17: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape557, reshape558, reshape559), axis=2) reshape560: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat17, R.shape([seq_len, 60, 64])) lv103_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape560), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape561: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv103_1, R.shape([1, seq_len, 20, 64])) reshape562: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape561, R.shape([1, seq_len, 1280])) lv520 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_self_attn_out_proj_weight2, reshape562, model_decoder_layers_17_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add431: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add427, lv520) layer_norm117: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add431, model_decoder_layers_17_encoder_attn_layer_norm_weight2, model_decoder_layers_17_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv521 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_encoder_attn_q_proj_weight2, layer_norm117, model_decoder_layers_17_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape563: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv521, R.shape([1, seq_len, 20, 64])) reshape564: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape563, R.shape([seq_len, 20, 64])) lv104_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape564), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape565: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv104_1, R.shape([1, seq_len, 20, 64])) reshape566: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape565, R.shape([1, seq_len, 1280])) lv522 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_encoder_attn_out_proj_weight2, reshape566, model_decoder_layers_17_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add434: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add431, lv522) layer_norm118: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add434, model_decoder_layers_17_final_layer_norm_weight2, model_decoder_layers_17_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv81_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_17_fc1_weight2, layer_norm118, model_decoder_layers_17_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv523 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_17_fc2_weight2, lv81_1, model_decoder_layers_17_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add437: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add434, lv523) layer_norm119: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add437, model_decoder_layers_18_self_attn_layer_norm_weight2, model_decoder_layers_18_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv524 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_self_attn_q_proj_weight2, layer_norm119, model_decoder_layers_18_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape567: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv524, R.shape([1, seq_len, 20, 64])) lv116 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_18_self_attn_k_proj_weight2, layer_norm119), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape568: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv116, R.shape([1, seq_len, 20, 64])) lv525 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_self_attn_v_proj_weight2, layer_norm119, model_decoder_layers_18_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape569: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv525, R.shape([1, seq_len, 20, 64])) concat18: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape567, reshape568, reshape569), axis=2) reshape570: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat18, R.shape([seq_len, 60, 64])) lv105_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape570), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape571: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv105_1, R.shape([1, seq_len, 20, 64])) reshape572: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape571, R.shape([1, seq_len, 1280])) lv526 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_self_attn_out_proj_weight2, reshape572, model_decoder_layers_18_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add441: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add437, lv526) layer_norm120: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add441, model_decoder_layers_18_encoder_attn_layer_norm_weight2, model_decoder_layers_18_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv527 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_encoder_attn_q_proj_weight2, layer_norm120, model_decoder_layers_18_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape573: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv527, R.shape([1, seq_len, 20, 64])) reshape574: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape573, R.shape([seq_len, 20, 64])) lv106_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape574), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape575: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv106_1, R.shape([1, seq_len, 20, 64])) reshape576: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape575, R.shape([1, seq_len, 1280])) lv528 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_encoder_attn_out_proj_weight2, reshape576, model_decoder_layers_18_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add444: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add441, lv528) layer_norm121: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add444, model_decoder_layers_18_final_layer_norm_weight2, model_decoder_layers_18_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv82_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_18_fc1_weight2, layer_norm121, model_decoder_layers_18_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv529 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_18_fc2_weight2, lv82_1, model_decoder_layers_18_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add447: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add444, lv529) layer_norm122: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add447, model_decoder_layers_19_self_attn_layer_norm_weight2, model_decoder_layers_19_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv530 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_self_attn_q_proj_weight2, layer_norm122, model_decoder_layers_19_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape577: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv530, R.shape([1, seq_len, 20, 64])) lv117 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_19_self_attn_k_proj_weight2, layer_norm122), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape578: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv117, R.shape([1, seq_len, 20, 64])) lv531 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_self_attn_v_proj_weight2, layer_norm122, model_decoder_layers_19_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape579: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv531, R.shape([1, seq_len, 20, 64])) concat19: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape577, reshape578, reshape579), axis=2) reshape580: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat19, R.shape([seq_len, 60, 64])) lv107_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape580), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape581: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv107_1, R.shape([1, seq_len, 20, 64])) reshape582: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape581, R.shape([1, seq_len, 1280])) lv532 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_self_attn_out_proj_weight2, reshape582, model_decoder_layers_19_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add451: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add447, lv532) layer_norm123: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add451, model_decoder_layers_19_encoder_attn_layer_norm_weight2, model_decoder_layers_19_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv533 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_encoder_attn_q_proj_weight2, layer_norm123, model_decoder_layers_19_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape583: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv533, R.shape([1, seq_len, 20, 64])) reshape584: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape583, R.shape([seq_len, 20, 64])) lv108_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape584), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape585: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv108_1, R.shape([1, seq_len, 20, 64])) reshape586: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape585, R.shape([1, seq_len, 1280])) lv534 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_encoder_attn_out_proj_weight2, reshape586, model_decoder_layers_19_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add454: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add451, lv534) layer_norm124: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add454, model_decoder_layers_19_final_layer_norm_weight2, model_decoder_layers_19_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv83_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_19_fc1_weight2, layer_norm124, model_decoder_layers_19_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv535 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_19_fc2_weight2, lv83_1, model_decoder_layers_19_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add457: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add454, lv535) layer_norm125: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add457, model_decoder_layers_20_self_attn_layer_norm_weight2, model_decoder_layers_20_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv536 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_self_attn_q_proj_weight2, layer_norm125, model_decoder_layers_20_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape587: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv536, R.shape([1, seq_len, 20, 64])) lv118 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_20_self_attn_k_proj_weight2, layer_norm125), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape588: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv118, R.shape([1, seq_len, 20, 64])) lv537 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_self_attn_v_proj_weight2, layer_norm125, model_decoder_layers_20_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape589: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv537, R.shape([1, seq_len, 20, 64])) concat20: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape587, reshape588, reshape589), axis=2) reshape590: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat20, R.shape([seq_len, 60, 64])) lv109_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape590), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape591: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv109_1, R.shape([1, seq_len, 20, 64])) reshape592: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape591, R.shape([1, seq_len, 1280])) lv538 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_self_attn_out_proj_weight2, reshape592, model_decoder_layers_20_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add461: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add457, lv538) layer_norm126: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add461, model_decoder_layers_20_encoder_attn_layer_norm_weight2, model_decoder_layers_20_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv539 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_encoder_attn_q_proj_weight2, layer_norm126, model_decoder_layers_20_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape593: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv539, R.shape([1, seq_len, 20, 64])) reshape594: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape593, R.shape([seq_len, 20, 64])) lv110_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape594), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape595: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv110_1, R.shape([1, seq_len, 20, 64])) reshape596: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape595, R.shape([1, seq_len, 1280])) lv540 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_encoder_attn_out_proj_weight2, reshape596, model_decoder_layers_20_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add464: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add461, lv540) layer_norm127: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add464, model_decoder_layers_20_final_layer_norm_weight2, model_decoder_layers_20_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv84_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_20_fc1_weight2, layer_norm127, model_decoder_layers_20_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv541 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_20_fc2_weight2, lv84_1, model_decoder_layers_20_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add467: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add464, lv541) layer_norm128: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add467, model_decoder_layers_21_self_attn_layer_norm_weight2, model_decoder_layers_21_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv542 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_self_attn_q_proj_weight2, layer_norm128, model_decoder_layers_21_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape597: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv542, R.shape([1, seq_len, 20, 64])) lv119 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_21_self_attn_k_proj_weight2, layer_norm128), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape598: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv119, R.shape([1, seq_len, 20, 64])) lv543 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_self_attn_v_proj_weight2, layer_norm128, model_decoder_layers_21_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape599: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv543, R.shape([1, seq_len, 20, 64])) concat21: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape597, reshape598, reshape599), axis=2) reshape600: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat21, R.shape([seq_len, 60, 64])) lv111_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape600), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape601: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv111_1, R.shape([1, seq_len, 20, 64])) reshape602: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape601, R.shape([1, seq_len, 1280])) lv544 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_self_attn_out_proj_weight2, reshape602, model_decoder_layers_21_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add471: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add467, lv544) layer_norm129: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add471, model_decoder_layers_21_encoder_attn_layer_norm_weight2, model_decoder_layers_21_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv545 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_encoder_attn_q_proj_weight2, layer_norm129, model_decoder_layers_21_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape603: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv545, R.shape([1, seq_len, 20, 64])) reshape604: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape603, R.shape([seq_len, 20, 64])) lv112_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape604), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape605: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv112_1, R.shape([1, seq_len, 20, 64])) reshape606: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape605, R.shape([1, seq_len, 1280])) lv546 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_encoder_attn_out_proj_weight2, reshape606, model_decoder_layers_21_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add474: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add471, lv546) layer_norm130: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add474, model_decoder_layers_21_final_layer_norm_weight2, model_decoder_layers_21_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv85_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_21_fc1_weight2, layer_norm130, model_decoder_layers_21_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv547 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_21_fc2_weight2, lv85_1, model_decoder_layers_21_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add477: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add474, lv547) layer_norm131: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add477, model_decoder_layers_22_self_attn_layer_norm_weight2, model_decoder_layers_22_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv548 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_self_attn_q_proj_weight2, layer_norm131, model_decoder_layers_22_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape607: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv548, R.shape([1, seq_len, 20, 64])) lv120 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_22_self_attn_k_proj_weight2, layer_norm131), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape608: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv120, R.shape([1, seq_len, 20, 64])) lv549 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_self_attn_v_proj_weight2, layer_norm131, model_decoder_layers_22_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape609: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv549, R.shape([1, seq_len, 20, 64])) concat22: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape607, reshape608, reshape609), axis=2) reshape610: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat22, R.shape([seq_len, 60, 64])) lv113_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape610), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape611: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv113_1, R.shape([1, seq_len, 20, 64])) reshape612: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape611, R.shape([1, seq_len, 1280])) lv550 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_self_attn_out_proj_weight2, reshape612, model_decoder_layers_22_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add481: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add477, lv550) layer_norm132: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add481, model_decoder_layers_22_encoder_attn_layer_norm_weight2, model_decoder_layers_22_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv551 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_encoder_attn_q_proj_weight2, layer_norm132, model_decoder_layers_22_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape613: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv551, R.shape([1, seq_len, 20, 64])) reshape614: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape613, R.shape([seq_len, 20, 64])) lv114_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape614), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape615: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv114_1, R.shape([1, seq_len, 20, 64])) reshape616: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape615, R.shape([1, seq_len, 1280])) lv552 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_encoder_attn_out_proj_weight2, reshape616, model_decoder_layers_22_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add484: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add481, lv552) layer_norm133: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add484, model_decoder_layers_22_final_layer_norm_weight2, model_decoder_layers_22_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv86_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_22_fc1_weight2, layer_norm133, model_decoder_layers_22_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv553 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_22_fc2_weight2, lv86_1, model_decoder_layers_22_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add487: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add484, lv553) layer_norm134: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add487, model_decoder_layers_23_self_attn_layer_norm_weight2, model_decoder_layers_23_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv554 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_self_attn_q_proj_weight2, layer_norm134, model_decoder_layers_23_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape617: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv554, R.shape([1, seq_len, 20, 64])) lv121 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_23_self_attn_k_proj_weight2, layer_norm134), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape618: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv121, R.shape([1, seq_len, 20, 64])) lv555 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_self_attn_v_proj_weight2, layer_norm134, model_decoder_layers_23_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape619: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv555, R.shape([1, seq_len, 20, 64])) concat23: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape617, reshape618, reshape619), axis=2) reshape620: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat23, R.shape([seq_len, 60, 64])) lv115_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape620), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape621: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv115_1, R.shape([1, seq_len, 20, 64])) reshape622: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape621, R.shape([1, seq_len, 1280])) lv556 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_self_attn_out_proj_weight2, reshape622, model_decoder_layers_23_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add491: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add487, lv556) layer_norm135: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add491, model_decoder_layers_23_encoder_attn_layer_norm_weight2, model_decoder_layers_23_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv557 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_encoder_attn_q_proj_weight2, layer_norm135, model_decoder_layers_23_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape623: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv557, R.shape([1, seq_len, 20, 64])) reshape624: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape623, R.shape([seq_len, 20, 64])) lv116_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape624), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape625: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv116_1, R.shape([1, seq_len, 20, 64])) reshape626: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape625, R.shape([1, seq_len, 1280])) lv558 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_encoder_attn_out_proj_weight2, reshape626, model_decoder_layers_23_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add494: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add491, lv558) layer_norm136: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add494, model_decoder_layers_23_final_layer_norm_weight2, model_decoder_layers_23_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv87_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_23_fc1_weight2, layer_norm136, model_decoder_layers_23_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv559 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_23_fc2_weight2, lv87_1, model_decoder_layers_23_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add497: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add494, lv559) layer_norm137: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add497, model_decoder_layers_24_self_attn_layer_norm_weight2, model_decoder_layers_24_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv560 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_self_attn_q_proj_weight2, layer_norm137, model_decoder_layers_24_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape627: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv560, R.shape([1, seq_len, 20, 64])) lv122 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_24_self_attn_k_proj_weight2, layer_norm137), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape628: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv122, R.shape([1, seq_len, 20, 64])) lv561 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_self_attn_v_proj_weight2, layer_norm137, model_decoder_layers_24_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape629: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv561, R.shape([1, seq_len, 20, 64])) concat24: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape627, reshape628, reshape629), axis=2) reshape630: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat24, R.shape([seq_len, 60, 64])) lv117_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape630), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape631: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv117_1, R.shape([1, seq_len, 20, 64])) reshape632: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape631, R.shape([1, seq_len, 1280])) lv562 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_self_attn_out_proj_weight2, reshape632, model_decoder_layers_24_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add501: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add497, lv562) layer_norm138: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add501, model_decoder_layers_24_encoder_attn_layer_norm_weight2, model_decoder_layers_24_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv563 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_encoder_attn_q_proj_weight2, layer_norm138, model_decoder_layers_24_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape633: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv563, R.shape([1, seq_len, 20, 64])) reshape634: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape633, R.shape([seq_len, 20, 64])) lv118_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape634), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape635: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv118_1, R.shape([1, seq_len, 20, 64])) reshape636: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape635, R.shape([1, seq_len, 1280])) lv564 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_encoder_attn_out_proj_weight2, reshape636, model_decoder_layers_24_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add504: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add501, lv564) layer_norm139: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add504, model_decoder_layers_24_final_layer_norm_weight2, model_decoder_layers_24_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv88_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_24_fc1_weight2, layer_norm139, model_decoder_layers_24_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv565 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_24_fc2_weight2, lv88_1, model_decoder_layers_24_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add507: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add504, lv565) layer_norm140: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add507, model_decoder_layers_25_self_attn_layer_norm_weight2, model_decoder_layers_25_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv566 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_self_attn_q_proj_weight2, layer_norm140, model_decoder_layers_25_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape637: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv566, R.shape([1, seq_len, 20, 64])) lv123 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_25_self_attn_k_proj_weight2, layer_norm140), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape638: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv123, R.shape([1, seq_len, 20, 64])) lv567 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_self_attn_v_proj_weight2, layer_norm140, model_decoder_layers_25_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape639: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv567, R.shape([1, seq_len, 20, 64])) concat25: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape637, reshape638, reshape639), axis=2) reshape640: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat25, R.shape([seq_len, 60, 64])) lv119_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape640), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape641: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv119_1, R.shape([1, seq_len, 20, 64])) reshape642: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape641, R.shape([1, seq_len, 1280])) lv568 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_self_attn_out_proj_weight2, reshape642, model_decoder_layers_25_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add511: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add507, lv568) layer_norm141: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add511, model_decoder_layers_25_encoder_attn_layer_norm_weight2, model_decoder_layers_25_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv569 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_encoder_attn_q_proj_weight2, layer_norm141, model_decoder_layers_25_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape643: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv569, R.shape([1, seq_len, 20, 64])) reshape644: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape643, R.shape([seq_len, 20, 64])) lv120_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape644), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape645: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv120_1, R.shape([1, seq_len, 20, 64])) reshape646: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape645, R.shape([1, seq_len, 1280])) lv570 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_encoder_attn_out_proj_weight2, reshape646, model_decoder_layers_25_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add514: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add511, lv570) layer_norm142: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add514, model_decoder_layers_25_final_layer_norm_weight2, model_decoder_layers_25_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv89_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_25_fc1_weight2, layer_norm142, model_decoder_layers_25_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv571 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_25_fc2_weight2, lv89_1, model_decoder_layers_25_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add517: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add514, lv571) layer_norm143: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add517, model_decoder_layers_26_self_attn_layer_norm_weight2, model_decoder_layers_26_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv572 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_self_attn_q_proj_weight2, layer_norm143, model_decoder_layers_26_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape647: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv572, R.shape([1, seq_len, 20, 64])) lv124 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_26_self_attn_k_proj_weight2, layer_norm143), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape648: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv124, R.shape([1, seq_len, 20, 64])) lv573 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_self_attn_v_proj_weight2, layer_norm143, model_decoder_layers_26_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape649: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv573, R.shape([1, seq_len, 20, 64])) concat26: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape647, reshape648, reshape649), axis=2) reshape650: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat26, R.shape([seq_len, 60, 64])) lv121_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape650), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape651: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv121_1, R.shape([1, seq_len, 20, 64])) reshape652: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape651, R.shape([1, seq_len, 1280])) lv574 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_self_attn_out_proj_weight2, reshape652, model_decoder_layers_26_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add521: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add517, lv574) layer_norm144: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add521, model_decoder_layers_26_encoder_attn_layer_norm_weight2, model_decoder_layers_26_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv575 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_encoder_attn_q_proj_weight2, layer_norm144, model_decoder_layers_26_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape653: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv575, R.shape([1, seq_len, 20, 64])) reshape654: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape653, R.shape([seq_len, 20, 64])) lv122_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape654), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape655: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv122_1, R.shape([1, seq_len, 20, 64])) reshape656: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape655, R.shape([1, seq_len, 1280])) lv576 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_encoder_attn_out_proj_weight2, reshape656, model_decoder_layers_26_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add524: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add521, lv576) layer_norm145: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add524, model_decoder_layers_26_final_layer_norm_weight2, model_decoder_layers_26_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv90_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_26_fc1_weight2, layer_norm145, model_decoder_layers_26_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv577 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_26_fc2_weight2, lv90_1, model_decoder_layers_26_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add527: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add524, lv577) layer_norm146: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add527, model_decoder_layers_27_self_attn_layer_norm_weight2, model_decoder_layers_27_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv578 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_self_attn_q_proj_weight2, layer_norm146, model_decoder_layers_27_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape657: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv578, R.shape([1, seq_len, 20, 64])) lv125 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_27_self_attn_k_proj_weight2, layer_norm146), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape658: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv125, R.shape([1, seq_len, 20, 64])) lv579 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_self_attn_v_proj_weight2, layer_norm146, model_decoder_layers_27_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape659: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv579, R.shape([1, seq_len, 20, 64])) concat27: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape657, reshape658, reshape659), axis=2) reshape660: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat27, R.shape([seq_len, 60, 64])) lv123_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape660), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape661: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv123_1, R.shape([1, seq_len, 20, 64])) reshape662: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape661, R.shape([1, seq_len, 1280])) lv580 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_self_attn_out_proj_weight2, reshape662, model_decoder_layers_27_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add531: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add527, lv580) layer_norm147: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add531, model_decoder_layers_27_encoder_attn_layer_norm_weight2, model_decoder_layers_27_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv581 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_encoder_attn_q_proj_weight2, layer_norm147, model_decoder_layers_27_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape663: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv581, R.shape([1, seq_len, 20, 64])) reshape664: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape663, R.shape([seq_len, 20, 64])) lv124_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape664), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape665: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv124_1, R.shape([1, seq_len, 20, 64])) reshape666: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape665, R.shape([1, seq_len, 1280])) lv582 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_encoder_attn_out_proj_weight2, reshape666, model_decoder_layers_27_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add534: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add531, lv582) layer_norm148: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add534, model_decoder_layers_27_final_layer_norm_weight2, model_decoder_layers_27_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv91_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_27_fc1_weight2, layer_norm148, model_decoder_layers_27_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv583 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_27_fc2_weight2, lv91_1, model_decoder_layers_27_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add537: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add534, lv583) layer_norm149: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add537, model_decoder_layers_28_self_attn_layer_norm_weight2, model_decoder_layers_28_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv584 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_self_attn_q_proj_weight2, layer_norm149, model_decoder_layers_28_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape667: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv584, R.shape([1, seq_len, 20, 64])) lv126 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_28_self_attn_k_proj_weight2, layer_norm149), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape668: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv126, R.shape([1, seq_len, 20, 64])) lv585 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_self_attn_v_proj_weight2, layer_norm149, model_decoder_layers_28_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape669: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv585, R.shape([1, seq_len, 20, 64])) concat28: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape667, reshape668, reshape669), axis=2) reshape670: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat28, R.shape([seq_len, 60, 64])) lv125_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape670), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape671: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv125_1, R.shape([1, seq_len, 20, 64])) reshape672: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape671, R.shape([1, seq_len, 1280])) lv586 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_self_attn_out_proj_weight2, reshape672, model_decoder_layers_28_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add541: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add537, lv586) layer_norm150: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add541, model_decoder_layers_28_encoder_attn_layer_norm_weight2, model_decoder_layers_28_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv587 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_encoder_attn_q_proj_weight2, layer_norm150, model_decoder_layers_28_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape673: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv587, R.shape([1, seq_len, 20, 64])) reshape674: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape673, R.shape([seq_len, 20, 64])) lv126_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape674), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape675: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv126_1, R.shape([1, seq_len, 20, 64])) reshape676: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape675, R.shape([1, seq_len, 1280])) lv588 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_encoder_attn_out_proj_weight2, reshape676, model_decoder_layers_28_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add544: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add541, lv588) layer_norm151: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add544, model_decoder_layers_28_final_layer_norm_weight2, model_decoder_layers_28_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv92_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_28_fc1_weight2, layer_norm151, model_decoder_layers_28_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv589 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_28_fc2_weight2, lv92_1, model_decoder_layers_28_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add547: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add544, lv589) layer_norm152: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add547, model_decoder_layers_29_self_attn_layer_norm_weight2, model_decoder_layers_29_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv590 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_self_attn_q_proj_weight2, layer_norm152, model_decoder_layers_29_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape677: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv590, R.shape([1, seq_len, 20, 64])) lv127 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_29_self_attn_k_proj_weight2, layer_norm152), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape678: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv127, R.shape([1, seq_len, 20, 64])) lv591 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_self_attn_v_proj_weight2, layer_norm152, model_decoder_layers_29_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape679: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv591, R.shape([1, seq_len, 20, 64])) concat29: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape677, reshape678, reshape679), axis=2) reshape680: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat29, R.shape([seq_len, 60, 64])) lv127_1 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape680), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape681: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv127_1, R.shape([1, seq_len, 20, 64])) reshape682: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape681, R.shape([1, seq_len, 1280])) lv592 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_self_attn_out_proj_weight2, reshape682, model_decoder_layers_29_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add551: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add547, lv592) layer_norm153: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add551, model_decoder_layers_29_encoder_attn_layer_norm_weight2, model_decoder_layers_29_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv593 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_encoder_attn_q_proj_weight2, layer_norm153, model_decoder_layers_29_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape683: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv593, R.shape([1, seq_len, 20, 64])) reshape684: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape683, R.shape([seq_len, 20, 64])) lv128 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape684), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape685: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv128, R.shape([1, seq_len, 20, 64])) reshape686: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape685, R.shape([1, seq_len, 1280])) lv594 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_encoder_attn_out_proj_weight2, reshape686, model_decoder_layers_29_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add554: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add551, lv594) layer_norm154: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add554, model_decoder_layers_29_final_layer_norm_weight2, model_decoder_layers_29_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv93_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_29_fc1_weight2, layer_norm154, model_decoder_layers_29_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv595 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_29_fc2_weight2, lv93_1, model_decoder_layers_29_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add557: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add554, lv595) layer_norm155: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add557, model_decoder_layers_30_self_attn_layer_norm_weight2, model_decoder_layers_30_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv596 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_self_attn_q_proj_weight2, layer_norm155, model_decoder_layers_30_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape687: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv596, R.shape([1, seq_len, 20, 64])) lv128_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_30_self_attn_k_proj_weight2, layer_norm155), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape688: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv128_1, R.shape([1, seq_len, 20, 64])) lv597 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_self_attn_v_proj_weight2, layer_norm155, model_decoder_layers_30_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape689: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv597, R.shape([1, seq_len, 20, 64])) concat30: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape687, reshape688, reshape689), axis=2) reshape690: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat30, R.shape([seq_len, 60, 64])) lv129 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape690), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape691: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv129, R.shape([1, seq_len, 20, 64])) reshape692: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape691, R.shape([1, seq_len, 1280])) lv598 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_self_attn_out_proj_weight2, reshape692, model_decoder_layers_30_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add561: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add557, lv598) layer_norm156: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add561, model_decoder_layers_30_encoder_attn_layer_norm_weight2, model_decoder_layers_30_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv599 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_encoder_attn_q_proj_weight2, layer_norm156, model_decoder_layers_30_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape693: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv599, R.shape([1, seq_len, 20, 64])) reshape694: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape693, R.shape([seq_len, 20, 64])) lv130 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape694), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape695: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv130, R.shape([1, seq_len, 20, 64])) reshape696: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape695, R.shape([1, seq_len, 1280])) lv600 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_encoder_attn_out_proj_weight2, reshape696, model_decoder_layers_30_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add564: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add561, lv600) layer_norm157: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add564, model_decoder_layers_30_final_layer_norm_weight2, model_decoder_layers_30_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv94_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_30_fc1_weight2, layer_norm157, model_decoder_layers_30_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv601 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_30_fc2_weight2, lv94_1, model_decoder_layers_30_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add567: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add564, lv601) layer_norm158: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add567, model_decoder_layers_31_self_attn_layer_norm_weight2, model_decoder_layers_31_self_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv602 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_self_attn_q_proj_weight2, layer_norm158, model_decoder_layers_31_self_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape697: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv602, R.shape([1, seq_len, 20, 64])) lv129_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_31_self_attn_k_proj_weight2, layer_norm158), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape698: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv129_1, R.shape([1, seq_len, 20, 64])) lv603 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_self_attn_v_proj_weight2, layer_norm158, model_decoder_layers_31_self_attn_v_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape699: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv603, R.shape([1, seq_len, 20, 64])) concat31: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape697, reshape698, reshape699), axis=2) reshape700: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat31, R.shape([seq_len, 60, 64])) lv131 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape700), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape701: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv131, R.shape([1, seq_len, 20, 64])) reshape702: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape701, R.shape([1, seq_len, 1280])) lv604 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_self_attn_out_proj_weight2, reshape702, model_decoder_layers_31_self_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add571: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add567, lv604) layer_norm159: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add571, model_decoder_layers_31_encoder_attn_layer_norm_weight2, model_decoder_layers_31_encoder_attn_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv605 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_encoder_attn_q_proj_weight2, layer_norm159, model_decoder_layers_31_encoder_attn_q_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape703: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv605, R.shape([1, seq_len, 20, 64])) reshape704: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape703, R.shape([seq_len, 20, 64])) lv132 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape704), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape705: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv132, R.shape([1, seq_len, 20, 64])) reshape706: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape705, R.shape([1, seq_len, 1280])) lv606 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_encoder_attn_out_proj_weight2, reshape706, model_decoder_layers_31_encoder_attn_out_proj_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add574: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add571, lv606) layer_norm160: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add574, model_decoder_layers_31_final_layer_norm_weight2, model_decoder_layers_31_final_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv95_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_31_fc1_weight2, layer_norm160, model_decoder_layers_31_fc1_bias2), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv607 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_31_fc2_weight2, lv95_1, model_decoder_layers_31_fc2_bias2), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add577: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add574, lv607) layer_norm161: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add577, model_decoder_layer_norm_weight2, model_decoder_layer_norm_bias2, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) take2: R.Tensor((1, batch_size, 1280), dtype="float16") = R.take(layer_norm161, logit_positions, axis=1) lv130_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul5_cublas", (model_decoder_embed_tokens_weight2, take2), out_sinfo=R.Tensor((1, batch_size, 51866), dtype="float32")) gv2: R.Tensor((1, batch_size, 51866), dtype="float32") = lv130_1 R.output(gv2) return gv2 @R.function def create_tir_paged_kv_cache(max_batch_size_: R.Shape(["max_batch_size"]), max_total_seq_len_: R.Shape(["max_total_seq_len"]), prefill_chunk_size_: R.Shape(["prefill_chunk_size"]), page_size_: R.Shape(["page_size"]), support_sliding_window_: R.Shape(["support_sliding_window"])) -> R.Object: max_batch_size = T.int64() max_total_seq_len = T.int64() prefill_chunk_size = T.int64() page_size = T.int64() support_sliding_window = T.int64() R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) cls = Module gv: R.Tensor((), dtype="float16") = R.zeros(R.shape([]), dtype="float16") paged_kv_cache: R.Object = R.call_pure_packed("vm.builtin.paged_attention_kv_cache_create_reduced", R.shape([max_batch_size, max_total_seq_len, prefill_chunk_size, page_size, support_sliding_window]), R.prim_value(32), R.prim_value(20), R.prim_value(20), R.prim_value(64), R.prim_value(0), R.prim_value(1), R.prim_value(1), gv, cls.tir_kv_cache_transpose_append, cls.batch_prefill_paged_kv, cls.batch_decode_paged_kv, cls.batch_prefill_paged_kv_sliding_window, cls.batch_decode_paged_kv_sliding_window, cls.batch_prefill_ragged_kv, cls.merge_state_inplace, cls.fused_rope, cls.copy_single_page, cls.tir_kv_cache_debug_get_kv, cls.compact_kv_copy, cls.batch_tree_attn, sinfo_args=(R.Object,)) return paged_kv_cache @R.function def decode(input_ids: R.Tensor((1, 1), dtype="int32"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((51866, 1280), dtype="float16"), R.Tensor((448, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor((1, 1, 51866), dtype="float32"): R.func_attr({"num_input": 2, "relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) cls = Module with R.dataflow(): model_decoder_embed_tokens_weight5: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] model_decoder_embed_positions_weight5: R.Tensor((448, 1280), dtype="float16") = packed_params[488] model_decoder_layers_0_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] model_decoder_layers_0_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] model_decoder_layers_0_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[491] model_decoder_layers_0_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] model_decoder_layers_0_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[493] model_decoder_layers_0_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] model_decoder_layers_0_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[495] model_decoder_layers_0_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[496] model_decoder_layers_0_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[497] model_decoder_layers_0_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] model_decoder_layers_0_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[502] model_decoder_layers_0_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] model_decoder_layers_0_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[504] model_decoder_layers_0_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[505] model_decoder_layers_0_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[506] model_decoder_layers_0_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] model_decoder_layers_0_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[508] model_decoder_layers_0_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] model_decoder_layers_0_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[510] model_decoder_layers_0_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[511] model_decoder_layers_0_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[512] model_decoder_layers_1_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] model_decoder_layers_1_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] model_decoder_layers_1_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[515] model_decoder_layers_1_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] model_decoder_layers_1_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[517] model_decoder_layers_1_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] model_decoder_layers_1_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[519] model_decoder_layers_1_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[520] model_decoder_layers_1_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[521] model_decoder_layers_1_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] model_decoder_layers_1_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[526] model_decoder_layers_1_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] model_decoder_layers_1_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[528] model_decoder_layers_1_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[529] model_decoder_layers_1_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[530] model_decoder_layers_1_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] model_decoder_layers_1_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[532] model_decoder_layers_1_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] model_decoder_layers_1_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[534] model_decoder_layers_1_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[535] model_decoder_layers_1_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[536] model_decoder_layers_2_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] model_decoder_layers_2_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] model_decoder_layers_2_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[539] model_decoder_layers_2_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] model_decoder_layers_2_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[541] model_decoder_layers_2_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] model_decoder_layers_2_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[543] model_decoder_layers_2_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[544] model_decoder_layers_2_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[545] model_decoder_layers_2_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] model_decoder_layers_2_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[550] model_decoder_layers_2_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] model_decoder_layers_2_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[552] model_decoder_layers_2_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[553] model_decoder_layers_2_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[554] model_decoder_layers_2_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] model_decoder_layers_2_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[556] model_decoder_layers_2_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] model_decoder_layers_2_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[558] model_decoder_layers_2_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[559] model_decoder_layers_2_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[560] model_decoder_layers_3_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] model_decoder_layers_3_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] model_decoder_layers_3_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[563] model_decoder_layers_3_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] model_decoder_layers_3_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[565] model_decoder_layers_3_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] model_decoder_layers_3_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[567] model_decoder_layers_3_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[568] model_decoder_layers_3_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[569] model_decoder_layers_3_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] model_decoder_layers_3_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[574] model_decoder_layers_3_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] model_decoder_layers_3_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[576] model_decoder_layers_3_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[577] model_decoder_layers_3_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[578] model_decoder_layers_3_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] model_decoder_layers_3_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[580] model_decoder_layers_3_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] model_decoder_layers_3_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[582] model_decoder_layers_3_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[583] model_decoder_layers_3_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[584] model_decoder_layers_4_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] model_decoder_layers_4_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] model_decoder_layers_4_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[587] model_decoder_layers_4_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] model_decoder_layers_4_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[589] model_decoder_layers_4_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] model_decoder_layers_4_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[591] model_decoder_layers_4_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[592] model_decoder_layers_4_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[593] model_decoder_layers_4_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] model_decoder_layers_4_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[598] model_decoder_layers_4_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] model_decoder_layers_4_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[600] model_decoder_layers_4_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[601] model_decoder_layers_4_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[602] model_decoder_layers_4_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] model_decoder_layers_4_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[604] model_decoder_layers_4_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] model_decoder_layers_4_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[606] model_decoder_layers_4_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[607] model_decoder_layers_4_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[608] model_decoder_layers_5_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] model_decoder_layers_5_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] model_decoder_layers_5_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[611] model_decoder_layers_5_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] model_decoder_layers_5_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[613] model_decoder_layers_5_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] model_decoder_layers_5_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[615] model_decoder_layers_5_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[616] model_decoder_layers_5_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[617] model_decoder_layers_5_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] model_decoder_layers_5_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[622] model_decoder_layers_5_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] model_decoder_layers_5_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[624] model_decoder_layers_5_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[625] model_decoder_layers_5_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[626] model_decoder_layers_5_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] model_decoder_layers_5_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[628] model_decoder_layers_5_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] model_decoder_layers_5_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[630] model_decoder_layers_5_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[631] model_decoder_layers_5_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[632] model_decoder_layers_6_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] model_decoder_layers_6_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] model_decoder_layers_6_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[635] model_decoder_layers_6_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] model_decoder_layers_6_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[637] model_decoder_layers_6_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] model_decoder_layers_6_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[639] model_decoder_layers_6_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[640] model_decoder_layers_6_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[641] model_decoder_layers_6_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] model_decoder_layers_6_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[646] model_decoder_layers_6_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] model_decoder_layers_6_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[648] model_decoder_layers_6_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[649] model_decoder_layers_6_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[650] model_decoder_layers_6_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] model_decoder_layers_6_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[652] model_decoder_layers_6_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] model_decoder_layers_6_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[654] model_decoder_layers_6_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[655] model_decoder_layers_6_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[656] model_decoder_layers_7_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] model_decoder_layers_7_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] model_decoder_layers_7_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[659] model_decoder_layers_7_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] model_decoder_layers_7_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[661] model_decoder_layers_7_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] model_decoder_layers_7_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[663] model_decoder_layers_7_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[664] model_decoder_layers_7_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[665] model_decoder_layers_7_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] model_decoder_layers_7_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[670] model_decoder_layers_7_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] model_decoder_layers_7_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[672] model_decoder_layers_7_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[673] model_decoder_layers_7_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[674] model_decoder_layers_7_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] model_decoder_layers_7_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[676] model_decoder_layers_7_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] model_decoder_layers_7_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[678] model_decoder_layers_7_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[679] model_decoder_layers_7_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[680] model_decoder_layers_8_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] model_decoder_layers_8_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] model_decoder_layers_8_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[683] model_decoder_layers_8_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] model_decoder_layers_8_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[685] model_decoder_layers_8_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] model_decoder_layers_8_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[687] model_decoder_layers_8_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[688] model_decoder_layers_8_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[689] model_decoder_layers_8_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] model_decoder_layers_8_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[694] model_decoder_layers_8_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] model_decoder_layers_8_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[696] model_decoder_layers_8_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[697] model_decoder_layers_8_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[698] model_decoder_layers_8_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] model_decoder_layers_8_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[700] model_decoder_layers_8_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] model_decoder_layers_8_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[702] model_decoder_layers_8_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[703] model_decoder_layers_8_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[704] model_decoder_layers_9_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] model_decoder_layers_9_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] model_decoder_layers_9_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[707] model_decoder_layers_9_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] model_decoder_layers_9_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[709] model_decoder_layers_9_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] model_decoder_layers_9_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[711] model_decoder_layers_9_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[712] model_decoder_layers_9_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[713] model_decoder_layers_9_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] model_decoder_layers_9_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[718] model_decoder_layers_9_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] model_decoder_layers_9_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[720] model_decoder_layers_9_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[721] model_decoder_layers_9_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[722] model_decoder_layers_9_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] model_decoder_layers_9_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[724] model_decoder_layers_9_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] model_decoder_layers_9_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[726] model_decoder_layers_9_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[727] model_decoder_layers_9_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[728] model_decoder_layers_10_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] model_decoder_layers_10_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] model_decoder_layers_10_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[731] model_decoder_layers_10_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] model_decoder_layers_10_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[733] model_decoder_layers_10_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] model_decoder_layers_10_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[735] model_decoder_layers_10_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[736] model_decoder_layers_10_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[737] model_decoder_layers_10_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] model_decoder_layers_10_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[742] model_decoder_layers_10_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] model_decoder_layers_10_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[744] model_decoder_layers_10_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[745] model_decoder_layers_10_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[746] model_decoder_layers_10_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] model_decoder_layers_10_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[748] model_decoder_layers_10_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] model_decoder_layers_10_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[750] model_decoder_layers_10_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[751] model_decoder_layers_10_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[752] model_decoder_layers_11_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] model_decoder_layers_11_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] model_decoder_layers_11_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[755] model_decoder_layers_11_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] model_decoder_layers_11_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[757] model_decoder_layers_11_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] model_decoder_layers_11_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[759] model_decoder_layers_11_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[760] model_decoder_layers_11_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[761] model_decoder_layers_11_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] model_decoder_layers_11_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[766] model_decoder_layers_11_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] model_decoder_layers_11_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[768] model_decoder_layers_11_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[769] model_decoder_layers_11_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[770] model_decoder_layers_11_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] model_decoder_layers_11_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[772] model_decoder_layers_11_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] model_decoder_layers_11_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[774] model_decoder_layers_11_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[775] model_decoder_layers_11_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[776] model_decoder_layers_12_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] model_decoder_layers_12_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] model_decoder_layers_12_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[779] model_decoder_layers_12_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] model_decoder_layers_12_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[781] model_decoder_layers_12_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] model_decoder_layers_12_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[783] model_decoder_layers_12_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[784] model_decoder_layers_12_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[785] model_decoder_layers_12_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] model_decoder_layers_12_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[790] model_decoder_layers_12_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] model_decoder_layers_12_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[792] model_decoder_layers_12_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[793] model_decoder_layers_12_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[794] model_decoder_layers_12_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] model_decoder_layers_12_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[796] model_decoder_layers_12_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] model_decoder_layers_12_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[798] model_decoder_layers_12_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[799] model_decoder_layers_12_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[800] model_decoder_layers_13_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] model_decoder_layers_13_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] model_decoder_layers_13_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[803] model_decoder_layers_13_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] model_decoder_layers_13_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[805] model_decoder_layers_13_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] model_decoder_layers_13_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[807] model_decoder_layers_13_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[808] model_decoder_layers_13_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[809] model_decoder_layers_13_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] model_decoder_layers_13_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[814] model_decoder_layers_13_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] model_decoder_layers_13_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[816] model_decoder_layers_13_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[817] model_decoder_layers_13_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[818] model_decoder_layers_13_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] model_decoder_layers_13_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[820] model_decoder_layers_13_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] model_decoder_layers_13_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[822] model_decoder_layers_13_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[823] model_decoder_layers_13_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[824] model_decoder_layers_14_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] model_decoder_layers_14_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] model_decoder_layers_14_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[827] model_decoder_layers_14_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] model_decoder_layers_14_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[829] model_decoder_layers_14_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] model_decoder_layers_14_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[831] model_decoder_layers_14_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[832] model_decoder_layers_14_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[833] model_decoder_layers_14_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] model_decoder_layers_14_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[838] model_decoder_layers_14_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] model_decoder_layers_14_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[840] model_decoder_layers_14_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[841] model_decoder_layers_14_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[842] model_decoder_layers_14_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] model_decoder_layers_14_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[844] model_decoder_layers_14_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] model_decoder_layers_14_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[846] model_decoder_layers_14_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[847] model_decoder_layers_14_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[848] model_decoder_layers_15_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] model_decoder_layers_15_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] model_decoder_layers_15_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[851] model_decoder_layers_15_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] model_decoder_layers_15_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[853] model_decoder_layers_15_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] model_decoder_layers_15_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[855] model_decoder_layers_15_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[856] model_decoder_layers_15_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[857] model_decoder_layers_15_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] model_decoder_layers_15_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[862] model_decoder_layers_15_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] model_decoder_layers_15_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[864] model_decoder_layers_15_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[865] model_decoder_layers_15_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[866] model_decoder_layers_15_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] model_decoder_layers_15_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[868] model_decoder_layers_15_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] model_decoder_layers_15_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[870] model_decoder_layers_15_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[871] model_decoder_layers_15_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[872] model_decoder_layers_16_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] model_decoder_layers_16_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] model_decoder_layers_16_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[875] model_decoder_layers_16_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] model_decoder_layers_16_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[877] model_decoder_layers_16_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] model_decoder_layers_16_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[879] model_decoder_layers_16_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[880] model_decoder_layers_16_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[881] model_decoder_layers_16_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] model_decoder_layers_16_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[886] model_decoder_layers_16_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] model_decoder_layers_16_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[888] model_decoder_layers_16_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[889] model_decoder_layers_16_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[890] model_decoder_layers_16_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] model_decoder_layers_16_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[892] model_decoder_layers_16_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] model_decoder_layers_16_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[894] model_decoder_layers_16_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[895] model_decoder_layers_16_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[896] model_decoder_layers_17_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] model_decoder_layers_17_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] model_decoder_layers_17_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[899] model_decoder_layers_17_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] model_decoder_layers_17_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[901] model_decoder_layers_17_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] model_decoder_layers_17_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[903] model_decoder_layers_17_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[904] model_decoder_layers_17_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[905] model_decoder_layers_17_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] model_decoder_layers_17_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[910] model_decoder_layers_17_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] model_decoder_layers_17_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[912] model_decoder_layers_17_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[913] model_decoder_layers_17_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[914] model_decoder_layers_17_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] model_decoder_layers_17_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[916] model_decoder_layers_17_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] model_decoder_layers_17_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[918] model_decoder_layers_17_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[919] model_decoder_layers_17_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[920] model_decoder_layers_18_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] model_decoder_layers_18_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] model_decoder_layers_18_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[923] model_decoder_layers_18_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] model_decoder_layers_18_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[925] model_decoder_layers_18_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] model_decoder_layers_18_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[927] model_decoder_layers_18_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[928] model_decoder_layers_18_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[929] model_decoder_layers_18_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] model_decoder_layers_18_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[934] model_decoder_layers_18_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] model_decoder_layers_18_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[936] model_decoder_layers_18_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[937] model_decoder_layers_18_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[938] model_decoder_layers_18_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] model_decoder_layers_18_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[940] model_decoder_layers_18_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] model_decoder_layers_18_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[942] model_decoder_layers_18_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[943] model_decoder_layers_18_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[944] model_decoder_layers_19_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] model_decoder_layers_19_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] model_decoder_layers_19_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[947] model_decoder_layers_19_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] model_decoder_layers_19_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[949] model_decoder_layers_19_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] model_decoder_layers_19_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[951] model_decoder_layers_19_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[952] model_decoder_layers_19_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[953] model_decoder_layers_19_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] model_decoder_layers_19_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[958] model_decoder_layers_19_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] model_decoder_layers_19_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[960] model_decoder_layers_19_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[961] model_decoder_layers_19_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[962] model_decoder_layers_19_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] model_decoder_layers_19_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[964] model_decoder_layers_19_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] model_decoder_layers_19_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[966] model_decoder_layers_19_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[967] model_decoder_layers_19_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[968] model_decoder_layers_20_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] model_decoder_layers_20_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] model_decoder_layers_20_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[971] model_decoder_layers_20_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] model_decoder_layers_20_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[973] model_decoder_layers_20_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] model_decoder_layers_20_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[975] model_decoder_layers_20_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[976] model_decoder_layers_20_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[977] model_decoder_layers_20_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] model_decoder_layers_20_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[982] model_decoder_layers_20_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] model_decoder_layers_20_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[984] model_decoder_layers_20_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[985] model_decoder_layers_20_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[986] model_decoder_layers_20_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] model_decoder_layers_20_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[988] model_decoder_layers_20_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] model_decoder_layers_20_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[990] model_decoder_layers_20_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[991] model_decoder_layers_20_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[992] model_decoder_layers_21_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] model_decoder_layers_21_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] model_decoder_layers_21_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[995] model_decoder_layers_21_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] model_decoder_layers_21_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[997] model_decoder_layers_21_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] model_decoder_layers_21_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[999] model_decoder_layers_21_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1000] model_decoder_layers_21_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1001] model_decoder_layers_21_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] model_decoder_layers_21_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1006] model_decoder_layers_21_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] model_decoder_layers_21_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1008] model_decoder_layers_21_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1009] model_decoder_layers_21_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1010] model_decoder_layers_21_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] model_decoder_layers_21_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1012] model_decoder_layers_21_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] model_decoder_layers_21_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1014] model_decoder_layers_21_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1015] model_decoder_layers_21_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1016] model_decoder_layers_22_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] model_decoder_layers_22_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] model_decoder_layers_22_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1019] model_decoder_layers_22_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] model_decoder_layers_22_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1021] model_decoder_layers_22_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] model_decoder_layers_22_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1023] model_decoder_layers_22_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1024] model_decoder_layers_22_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1025] model_decoder_layers_22_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] model_decoder_layers_22_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1030] model_decoder_layers_22_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] model_decoder_layers_22_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1032] model_decoder_layers_22_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1033] model_decoder_layers_22_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1034] model_decoder_layers_22_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] model_decoder_layers_22_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1036] model_decoder_layers_22_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] model_decoder_layers_22_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1038] model_decoder_layers_22_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1039] model_decoder_layers_22_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1040] model_decoder_layers_23_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] model_decoder_layers_23_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] model_decoder_layers_23_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1043] model_decoder_layers_23_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] model_decoder_layers_23_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1045] model_decoder_layers_23_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] model_decoder_layers_23_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1047] model_decoder_layers_23_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1048] model_decoder_layers_23_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1049] model_decoder_layers_23_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] model_decoder_layers_23_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1054] model_decoder_layers_23_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] model_decoder_layers_23_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1056] model_decoder_layers_23_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1057] model_decoder_layers_23_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1058] model_decoder_layers_23_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] model_decoder_layers_23_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1060] model_decoder_layers_23_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] model_decoder_layers_23_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1062] model_decoder_layers_23_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1063] model_decoder_layers_23_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1064] model_decoder_layers_24_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] model_decoder_layers_24_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] model_decoder_layers_24_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1067] model_decoder_layers_24_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] model_decoder_layers_24_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1069] model_decoder_layers_24_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] model_decoder_layers_24_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1071] model_decoder_layers_24_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1072] model_decoder_layers_24_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1073] model_decoder_layers_24_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] model_decoder_layers_24_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1078] model_decoder_layers_24_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] model_decoder_layers_24_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1080] model_decoder_layers_24_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1081] model_decoder_layers_24_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1082] model_decoder_layers_24_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] model_decoder_layers_24_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1084] model_decoder_layers_24_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] model_decoder_layers_24_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1086] model_decoder_layers_24_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1087] model_decoder_layers_24_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1088] model_decoder_layers_25_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] model_decoder_layers_25_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] model_decoder_layers_25_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1091] model_decoder_layers_25_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] model_decoder_layers_25_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1093] model_decoder_layers_25_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] model_decoder_layers_25_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1095] model_decoder_layers_25_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1096] model_decoder_layers_25_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1097] model_decoder_layers_25_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] model_decoder_layers_25_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1102] model_decoder_layers_25_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] model_decoder_layers_25_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1104] model_decoder_layers_25_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1105] model_decoder_layers_25_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1106] model_decoder_layers_25_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] model_decoder_layers_25_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1108] model_decoder_layers_25_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] model_decoder_layers_25_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1110] model_decoder_layers_25_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1111] model_decoder_layers_25_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1112] model_decoder_layers_26_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] model_decoder_layers_26_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] model_decoder_layers_26_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1115] model_decoder_layers_26_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] model_decoder_layers_26_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1117] model_decoder_layers_26_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] model_decoder_layers_26_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1119] model_decoder_layers_26_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1120] model_decoder_layers_26_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1121] model_decoder_layers_26_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] model_decoder_layers_26_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1126] model_decoder_layers_26_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] model_decoder_layers_26_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1128] model_decoder_layers_26_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1129] model_decoder_layers_26_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1130] model_decoder_layers_26_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] model_decoder_layers_26_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1132] model_decoder_layers_26_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] model_decoder_layers_26_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1134] model_decoder_layers_26_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1135] model_decoder_layers_26_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1136] model_decoder_layers_27_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] model_decoder_layers_27_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] model_decoder_layers_27_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1139] model_decoder_layers_27_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] model_decoder_layers_27_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1141] model_decoder_layers_27_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] model_decoder_layers_27_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1143] model_decoder_layers_27_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1144] model_decoder_layers_27_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1145] model_decoder_layers_27_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] model_decoder_layers_27_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1150] model_decoder_layers_27_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] model_decoder_layers_27_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1152] model_decoder_layers_27_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1153] model_decoder_layers_27_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1154] model_decoder_layers_27_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] model_decoder_layers_27_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1156] model_decoder_layers_27_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] model_decoder_layers_27_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1158] model_decoder_layers_27_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1159] model_decoder_layers_27_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1160] model_decoder_layers_28_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] model_decoder_layers_28_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] model_decoder_layers_28_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1163] model_decoder_layers_28_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] model_decoder_layers_28_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1165] model_decoder_layers_28_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] model_decoder_layers_28_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1167] model_decoder_layers_28_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1168] model_decoder_layers_28_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1169] model_decoder_layers_28_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] model_decoder_layers_28_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1174] model_decoder_layers_28_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] model_decoder_layers_28_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1176] model_decoder_layers_28_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1177] model_decoder_layers_28_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1178] model_decoder_layers_28_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] model_decoder_layers_28_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1180] model_decoder_layers_28_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] model_decoder_layers_28_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1182] model_decoder_layers_28_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1183] model_decoder_layers_28_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1184] model_decoder_layers_29_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] model_decoder_layers_29_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] model_decoder_layers_29_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1187] model_decoder_layers_29_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] model_decoder_layers_29_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1189] model_decoder_layers_29_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] model_decoder_layers_29_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1191] model_decoder_layers_29_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1192] model_decoder_layers_29_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1193] model_decoder_layers_29_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] model_decoder_layers_29_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1198] model_decoder_layers_29_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] model_decoder_layers_29_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1200] model_decoder_layers_29_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1201] model_decoder_layers_29_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1202] model_decoder_layers_29_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] model_decoder_layers_29_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1204] model_decoder_layers_29_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] model_decoder_layers_29_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1206] model_decoder_layers_29_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1207] model_decoder_layers_29_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1208] model_decoder_layers_30_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] model_decoder_layers_30_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] model_decoder_layers_30_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1211] model_decoder_layers_30_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] model_decoder_layers_30_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1213] model_decoder_layers_30_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] model_decoder_layers_30_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1215] model_decoder_layers_30_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1216] model_decoder_layers_30_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1217] model_decoder_layers_30_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] model_decoder_layers_30_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1222] model_decoder_layers_30_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] model_decoder_layers_30_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1224] model_decoder_layers_30_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1225] model_decoder_layers_30_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1226] model_decoder_layers_30_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] model_decoder_layers_30_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1228] model_decoder_layers_30_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] model_decoder_layers_30_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1230] model_decoder_layers_30_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1231] model_decoder_layers_30_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1232] model_decoder_layers_31_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] model_decoder_layers_31_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] model_decoder_layers_31_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1235] model_decoder_layers_31_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] model_decoder_layers_31_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1237] model_decoder_layers_31_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] model_decoder_layers_31_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1239] model_decoder_layers_31_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1240] model_decoder_layers_31_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1241] model_decoder_layers_31_encoder_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] model_decoder_layers_31_encoder_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1246] model_decoder_layers_31_encoder_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] model_decoder_layers_31_encoder_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1248] model_decoder_layers_31_encoder_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1249] model_decoder_layers_31_encoder_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1250] model_decoder_layers_31_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] model_decoder_layers_31_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[1252] model_decoder_layers_31_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] model_decoder_layers_31_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1254] model_decoder_layers_31_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1255] model_decoder_layers_31_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1256] model_decoder_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[1257] model_decoder_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1258] reshape1353: R.Tensor((1,), dtype="int32") = R.reshape(input_ids, R.shape([1])) take7: R.Tensor((1, 1280), dtype="float16") = R.take(model_decoder_embed_tokens_weight5, reshape1353, axis=0) reshape1354: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(take7, R.shape([1, 1, 1280])) lv264: R.Tensor((1,), dtype="int32") = R.call_pure_packed("vm.builtin.attention_kv_cache_get_query_positions", paged_kv_cache, sinfo_args=(R.Tensor((1,), dtype="int32"),)) take8: R.Tensor((1, 1280), dtype="float16") = R.take(model_decoder_embed_positions_weight5, lv264, axis=0) reshape1355: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(take8, R.shape([1, 1, 1280])) add1220: R.Tensor((1, 1, 1280), dtype="float16") = R.add(reshape1354, reshape1355) layer_norm356: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1220, model_decoder_layers_0_self_attn_layer_norm_weight5, model_decoder_layers_0_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv = R.call_tir(cls.NT_matmul, (layer_norm356, model_decoder_layers_0_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1221: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv, model_decoder_layers_0_self_attn_q_proj_bias5) reshape1356: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1221, R.shape([1, 1, 20, 64])) lv1 = R.call_tir(cls.NT_matmul, (layer_norm356, model_decoder_layers_0_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1357: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv1, R.shape([1, 1, 20, 64])) lv2 = R.call_tir(cls.NT_matmul, (layer_norm356, model_decoder_layers_0_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1222: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv2, model_decoder_layers_0_self_attn_v_proj_bias5) reshape1358: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1222, R.shape([1, 1, 20, 64])) concat96: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1356, reshape1357, reshape1358), axis=2) reshape1359: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat96, R.shape([1, 60, 64])) lv265 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape1359), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1360: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv265, R.shape([1, 1, 20, 64])) reshape1361: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1360, R.shape([1, 1, 1280])) lv3 = R.call_tir(cls.NT_matmul, (reshape1361, model_decoder_layers_0_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1223: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv3, model_decoder_layers_0_self_attn_out_proj_bias5) add1224: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1220, add1223) layer_norm357: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1224, model_decoder_layers_0_encoder_attn_layer_norm_weight5, model_decoder_layers_0_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv4 = R.call_tir(cls.NT_matmul, (layer_norm357, model_decoder_layers_0_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1225: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv4, model_decoder_layers_0_encoder_attn_q_proj_bias5) reshape1362: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1225, R.shape([1, 1, 20, 64])) reshape1363: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1362, R.shape([1, 20, 64])) lv266 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape1363), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1364: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv266, R.shape([1, 1, 20, 64])) reshape1365: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1364, R.shape([1, 1, 1280])) lv5 = R.call_tir(cls.NT_matmul, (reshape1365, model_decoder_layers_0_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1226: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv5, model_decoder_layers_0_encoder_attn_out_proj_bias5) add1227: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1224, add1226) layer_norm358: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1227, model_decoder_layers_0_final_layer_norm_weight5, model_decoder_layers_0_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv6 = R.call_tir(cls.NT_matmul1, (layer_norm358, model_decoder_layers_0_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1228: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv6, model_decoder_layers_0_fc1_bias5) gelu130: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1228) lv7 = R.call_tir(cls.NT_matmul2, (gelu130, model_decoder_layers_0_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1229: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv7, model_decoder_layers_0_fc2_bias5) add1230: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1227, add1229) layer_norm359: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1230, model_decoder_layers_1_self_attn_layer_norm_weight5, model_decoder_layers_1_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv8 = R.call_tir(cls.NT_matmul, (layer_norm359, model_decoder_layers_1_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1231: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv8, model_decoder_layers_1_self_attn_q_proj_bias5) reshape1366: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1231, R.shape([1, 1, 20, 64])) lv9 = R.call_tir(cls.NT_matmul, (layer_norm359, model_decoder_layers_1_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1367: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv9, R.shape([1, 1, 20, 64])) lv10 = R.call_tir(cls.NT_matmul, (layer_norm359, model_decoder_layers_1_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1232: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv10, model_decoder_layers_1_self_attn_v_proj_bias5) reshape1368: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1232, R.shape([1, 1, 20, 64])) concat97: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1366, reshape1367, reshape1368), axis=2) reshape1369: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat97, R.shape([1, 60, 64])) lv267 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape1369), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1370: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv267, R.shape([1, 1, 20, 64])) reshape1371: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1370, R.shape([1, 1, 1280])) lv11 = R.call_tir(cls.NT_matmul, (reshape1371, model_decoder_layers_1_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1233: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv11, model_decoder_layers_1_self_attn_out_proj_bias5) add1234: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1230, add1233) layer_norm360: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1234, model_decoder_layers_1_encoder_attn_layer_norm_weight5, model_decoder_layers_1_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv12 = R.call_tir(cls.NT_matmul, (layer_norm360, model_decoder_layers_1_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1235: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv12, model_decoder_layers_1_encoder_attn_q_proj_bias5) reshape1372: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1235, R.shape([1, 1, 20, 64])) reshape1373: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1372, R.shape([1, 20, 64])) lv268 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape1373), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1374: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv268, R.shape([1, 1, 20, 64])) reshape1375: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1374, R.shape([1, 1, 1280])) lv13 = R.call_tir(cls.NT_matmul, (reshape1375, model_decoder_layers_1_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1236: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv13, model_decoder_layers_1_encoder_attn_out_proj_bias5) add1237: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1234, add1236) layer_norm361: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1237, model_decoder_layers_1_final_layer_norm_weight5, model_decoder_layers_1_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv14 = R.call_tir(cls.NT_matmul1, (layer_norm361, model_decoder_layers_1_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1238: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv14, model_decoder_layers_1_fc1_bias5) gelu131: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1238) lv15 = R.call_tir(cls.NT_matmul2, (gelu131, model_decoder_layers_1_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1239: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv15, model_decoder_layers_1_fc2_bias5) add1240: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1237, add1239) layer_norm362: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1240, model_decoder_layers_2_self_attn_layer_norm_weight5, model_decoder_layers_2_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv16 = R.call_tir(cls.NT_matmul, (layer_norm362, model_decoder_layers_2_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1241: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv16, model_decoder_layers_2_self_attn_q_proj_bias5) reshape1376: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1241, R.shape([1, 1, 20, 64])) lv17 = R.call_tir(cls.NT_matmul, (layer_norm362, model_decoder_layers_2_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1377: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv17, R.shape([1, 1, 20, 64])) lv18 = R.call_tir(cls.NT_matmul, (layer_norm362, model_decoder_layers_2_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1242: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv18, model_decoder_layers_2_self_attn_v_proj_bias5) reshape1378: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1242, R.shape([1, 1, 20, 64])) concat98: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1376, reshape1377, reshape1378), axis=2) reshape1379: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat98, R.shape([1, 60, 64])) lv269 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape1379), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1380: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv269, R.shape([1, 1, 20, 64])) reshape1381: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1380, R.shape([1, 1, 1280])) lv19 = R.call_tir(cls.NT_matmul, (reshape1381, model_decoder_layers_2_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1243: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv19, model_decoder_layers_2_self_attn_out_proj_bias5) add1244: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1240, add1243) layer_norm363: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1244, model_decoder_layers_2_encoder_attn_layer_norm_weight5, model_decoder_layers_2_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv20 = R.call_tir(cls.NT_matmul, (layer_norm363, model_decoder_layers_2_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1245: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv20, model_decoder_layers_2_encoder_attn_q_proj_bias5) reshape1382: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1245, R.shape([1, 1, 20, 64])) reshape1383: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1382, R.shape([1, 20, 64])) lv270 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape1383), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1384: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv270, R.shape([1, 1, 20, 64])) reshape1385: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1384, R.shape([1, 1, 1280])) lv21 = R.call_tir(cls.NT_matmul, (reshape1385, model_decoder_layers_2_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1246: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv21, model_decoder_layers_2_encoder_attn_out_proj_bias5) add1247: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1244, add1246) layer_norm364: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1247, model_decoder_layers_2_final_layer_norm_weight5, model_decoder_layers_2_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv22 = R.call_tir(cls.NT_matmul1, (layer_norm364, model_decoder_layers_2_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1248: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv22, model_decoder_layers_2_fc1_bias5) gelu132: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1248) lv23 = R.call_tir(cls.NT_matmul2, (gelu132, model_decoder_layers_2_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1249: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv23, model_decoder_layers_2_fc2_bias5) add1250: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1247, add1249) layer_norm365: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1250, model_decoder_layers_3_self_attn_layer_norm_weight5, model_decoder_layers_3_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv24 = R.call_tir(cls.NT_matmul, (layer_norm365, model_decoder_layers_3_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1251: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv24, model_decoder_layers_3_self_attn_q_proj_bias5) reshape1386: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1251, R.shape([1, 1, 20, 64])) lv25 = R.call_tir(cls.NT_matmul, (layer_norm365, model_decoder_layers_3_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1387: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv25, R.shape([1, 1, 20, 64])) lv26 = R.call_tir(cls.NT_matmul, (layer_norm365, model_decoder_layers_3_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1252: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv26, model_decoder_layers_3_self_attn_v_proj_bias5) reshape1388: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1252, R.shape([1, 1, 20, 64])) concat99: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1386, reshape1387, reshape1388), axis=2) reshape1389: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat99, R.shape([1, 60, 64])) lv271 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape1389), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1390: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv271, R.shape([1, 1, 20, 64])) reshape1391: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1390, R.shape([1, 1, 1280])) lv27 = R.call_tir(cls.NT_matmul, (reshape1391, model_decoder_layers_3_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1253: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv27, model_decoder_layers_3_self_attn_out_proj_bias5) add1254: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1250, add1253) layer_norm366: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1254, model_decoder_layers_3_encoder_attn_layer_norm_weight5, model_decoder_layers_3_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv28 = R.call_tir(cls.NT_matmul, (layer_norm366, model_decoder_layers_3_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1255: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv28, model_decoder_layers_3_encoder_attn_q_proj_bias5) reshape1392: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1255, R.shape([1, 1, 20, 64])) reshape1393: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1392, R.shape([1, 20, 64])) lv272 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape1393), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1394: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv272, R.shape([1, 1, 20, 64])) reshape1395: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1394, R.shape([1, 1, 1280])) lv29 = R.call_tir(cls.NT_matmul, (reshape1395, model_decoder_layers_3_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1256: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv29, model_decoder_layers_3_encoder_attn_out_proj_bias5) add1257: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1254, add1256) layer_norm367: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1257, model_decoder_layers_3_final_layer_norm_weight5, model_decoder_layers_3_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv30 = R.call_tir(cls.NT_matmul1, (layer_norm367, model_decoder_layers_3_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1258: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv30, model_decoder_layers_3_fc1_bias5) gelu133: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1258) lv31 = R.call_tir(cls.NT_matmul2, (gelu133, model_decoder_layers_3_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1259: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv31, model_decoder_layers_3_fc2_bias5) add1260: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1257, add1259) layer_norm368: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1260, model_decoder_layers_4_self_attn_layer_norm_weight5, model_decoder_layers_4_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv32 = R.call_tir(cls.NT_matmul, (layer_norm368, model_decoder_layers_4_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1261: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv32, model_decoder_layers_4_self_attn_q_proj_bias5) reshape1396: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1261, R.shape([1, 1, 20, 64])) lv33 = R.call_tir(cls.NT_matmul, (layer_norm368, model_decoder_layers_4_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1397: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv33, R.shape([1, 1, 20, 64])) lv34 = R.call_tir(cls.NT_matmul, (layer_norm368, model_decoder_layers_4_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1262: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv34, model_decoder_layers_4_self_attn_v_proj_bias5) reshape1398: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1262, R.shape([1, 1, 20, 64])) concat100: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1396, reshape1397, reshape1398), axis=2) reshape1399: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat100, R.shape([1, 60, 64])) lv273 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape1399), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1400: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv273, R.shape([1, 1, 20, 64])) reshape1401: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1400, R.shape([1, 1, 1280])) lv35 = R.call_tir(cls.NT_matmul, (reshape1401, model_decoder_layers_4_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1263: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv35, model_decoder_layers_4_self_attn_out_proj_bias5) add1264: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1260, add1263) layer_norm369: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1264, model_decoder_layers_4_encoder_attn_layer_norm_weight5, model_decoder_layers_4_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv36 = R.call_tir(cls.NT_matmul, (layer_norm369, model_decoder_layers_4_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1265: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv36, model_decoder_layers_4_encoder_attn_q_proj_bias5) reshape1402: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1265, R.shape([1, 1, 20, 64])) reshape1403: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1402, R.shape([1, 20, 64])) lv274 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape1403), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1404: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv274, R.shape([1, 1, 20, 64])) reshape1405: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1404, R.shape([1, 1, 1280])) lv37 = R.call_tir(cls.NT_matmul, (reshape1405, model_decoder_layers_4_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1266: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv37, model_decoder_layers_4_encoder_attn_out_proj_bias5) add1267: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1264, add1266) layer_norm370: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1267, model_decoder_layers_4_final_layer_norm_weight5, model_decoder_layers_4_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv38 = R.call_tir(cls.NT_matmul1, (layer_norm370, model_decoder_layers_4_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1268: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv38, model_decoder_layers_4_fc1_bias5) gelu134: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1268) lv39 = R.call_tir(cls.NT_matmul2, (gelu134, model_decoder_layers_4_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1269: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv39, model_decoder_layers_4_fc2_bias5) add1270: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1267, add1269) layer_norm371: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1270, model_decoder_layers_5_self_attn_layer_norm_weight5, model_decoder_layers_5_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv40 = R.call_tir(cls.NT_matmul, (layer_norm371, model_decoder_layers_5_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1271: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv40, model_decoder_layers_5_self_attn_q_proj_bias5) reshape1406: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1271, R.shape([1, 1, 20, 64])) lv41 = R.call_tir(cls.NT_matmul, (layer_norm371, model_decoder_layers_5_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1407: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv41, R.shape([1, 1, 20, 64])) lv42 = R.call_tir(cls.NT_matmul, (layer_norm371, model_decoder_layers_5_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1272: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv42, model_decoder_layers_5_self_attn_v_proj_bias5) reshape1408: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1272, R.shape([1, 1, 20, 64])) concat101: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1406, reshape1407, reshape1408), axis=2) reshape1409: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat101, R.shape([1, 60, 64])) lv275 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape1409), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1410: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv275, R.shape([1, 1, 20, 64])) reshape1411: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1410, R.shape([1, 1, 1280])) lv43 = R.call_tir(cls.NT_matmul, (reshape1411, model_decoder_layers_5_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1273: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv43, model_decoder_layers_5_self_attn_out_proj_bias5) add1274: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1270, add1273) layer_norm372: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1274, model_decoder_layers_5_encoder_attn_layer_norm_weight5, model_decoder_layers_5_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv44 = R.call_tir(cls.NT_matmul, (layer_norm372, model_decoder_layers_5_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1275: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv44, model_decoder_layers_5_encoder_attn_q_proj_bias5) reshape1412: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1275, R.shape([1, 1, 20, 64])) reshape1413: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1412, R.shape([1, 20, 64])) lv276 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape1413), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1414: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv276, R.shape([1, 1, 20, 64])) reshape1415: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1414, R.shape([1, 1, 1280])) lv45 = R.call_tir(cls.NT_matmul, (reshape1415, model_decoder_layers_5_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1276: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv45, model_decoder_layers_5_encoder_attn_out_proj_bias5) add1277: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1274, add1276) layer_norm373: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1277, model_decoder_layers_5_final_layer_norm_weight5, model_decoder_layers_5_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv46 = R.call_tir(cls.NT_matmul1, (layer_norm373, model_decoder_layers_5_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1278: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv46, model_decoder_layers_5_fc1_bias5) gelu135: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1278) lv47 = R.call_tir(cls.NT_matmul2, (gelu135, model_decoder_layers_5_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1279: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv47, model_decoder_layers_5_fc2_bias5) add1280: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1277, add1279) layer_norm374: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1280, model_decoder_layers_6_self_attn_layer_norm_weight5, model_decoder_layers_6_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv48 = R.call_tir(cls.NT_matmul, (layer_norm374, model_decoder_layers_6_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1281: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv48, model_decoder_layers_6_self_attn_q_proj_bias5) reshape1416: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1281, R.shape([1, 1, 20, 64])) lv49 = R.call_tir(cls.NT_matmul, (layer_norm374, model_decoder_layers_6_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1417: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv49, R.shape([1, 1, 20, 64])) lv50 = R.call_tir(cls.NT_matmul, (layer_norm374, model_decoder_layers_6_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1282: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv50, model_decoder_layers_6_self_attn_v_proj_bias5) reshape1418: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1282, R.shape([1, 1, 20, 64])) concat102: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1416, reshape1417, reshape1418), axis=2) reshape1419: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat102, R.shape([1, 60, 64])) lv277 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape1419), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1420: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv277, R.shape([1, 1, 20, 64])) reshape1421: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1420, R.shape([1, 1, 1280])) lv51 = R.call_tir(cls.NT_matmul, (reshape1421, model_decoder_layers_6_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1283: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv51, model_decoder_layers_6_self_attn_out_proj_bias5) add1284: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1280, add1283) layer_norm375: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1284, model_decoder_layers_6_encoder_attn_layer_norm_weight5, model_decoder_layers_6_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv52 = R.call_tir(cls.NT_matmul, (layer_norm375, model_decoder_layers_6_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1285: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv52, model_decoder_layers_6_encoder_attn_q_proj_bias5) reshape1422: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1285, R.shape([1, 1, 20, 64])) reshape1423: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1422, R.shape([1, 20, 64])) lv278 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape1423), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1424: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv278, R.shape([1, 1, 20, 64])) reshape1425: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1424, R.shape([1, 1, 1280])) lv53 = R.call_tir(cls.NT_matmul, (reshape1425, model_decoder_layers_6_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1286: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv53, model_decoder_layers_6_encoder_attn_out_proj_bias5) add1287: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1284, add1286) layer_norm376: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1287, model_decoder_layers_6_final_layer_norm_weight5, model_decoder_layers_6_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv54 = R.call_tir(cls.NT_matmul1, (layer_norm376, model_decoder_layers_6_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1288: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv54, model_decoder_layers_6_fc1_bias5) gelu136: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1288) lv55 = R.call_tir(cls.NT_matmul2, (gelu136, model_decoder_layers_6_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1289: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv55, model_decoder_layers_6_fc2_bias5) add1290: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1287, add1289) layer_norm377: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1290, model_decoder_layers_7_self_attn_layer_norm_weight5, model_decoder_layers_7_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv56 = R.call_tir(cls.NT_matmul, (layer_norm377, model_decoder_layers_7_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1291: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv56, model_decoder_layers_7_self_attn_q_proj_bias5) reshape1426: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1291, R.shape([1, 1, 20, 64])) lv57 = R.call_tir(cls.NT_matmul, (layer_norm377, model_decoder_layers_7_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1427: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv57, R.shape([1, 1, 20, 64])) lv58 = R.call_tir(cls.NT_matmul, (layer_norm377, model_decoder_layers_7_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1292: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv58, model_decoder_layers_7_self_attn_v_proj_bias5) reshape1428: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1292, R.shape([1, 1, 20, 64])) concat103: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1426, reshape1427, reshape1428), axis=2) reshape1429: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat103, R.shape([1, 60, 64])) lv279 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape1429), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1430: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv279, R.shape([1, 1, 20, 64])) reshape1431: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1430, R.shape([1, 1, 1280])) lv59 = R.call_tir(cls.NT_matmul, (reshape1431, model_decoder_layers_7_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1293: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv59, model_decoder_layers_7_self_attn_out_proj_bias5) add1294: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1290, add1293) layer_norm378: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1294, model_decoder_layers_7_encoder_attn_layer_norm_weight5, model_decoder_layers_7_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv60 = R.call_tir(cls.NT_matmul, (layer_norm378, model_decoder_layers_7_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1295: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv60, model_decoder_layers_7_encoder_attn_q_proj_bias5) reshape1432: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1295, R.shape([1, 1, 20, 64])) reshape1433: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1432, R.shape([1, 20, 64])) lv280 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape1433), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1434: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv280, R.shape([1, 1, 20, 64])) reshape1435: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1434, R.shape([1, 1, 1280])) lv61 = R.call_tir(cls.NT_matmul, (reshape1435, model_decoder_layers_7_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1296: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv61, model_decoder_layers_7_encoder_attn_out_proj_bias5) add1297: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1294, add1296) layer_norm379: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1297, model_decoder_layers_7_final_layer_norm_weight5, model_decoder_layers_7_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv62 = R.call_tir(cls.NT_matmul1, (layer_norm379, model_decoder_layers_7_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1298: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv62, model_decoder_layers_7_fc1_bias5) gelu137: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1298) lv63 = R.call_tir(cls.NT_matmul2, (gelu137, model_decoder_layers_7_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1299: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv63, model_decoder_layers_7_fc2_bias5) add1300: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1297, add1299) layer_norm380: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1300, model_decoder_layers_8_self_attn_layer_norm_weight5, model_decoder_layers_8_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv64 = R.call_tir(cls.NT_matmul, (layer_norm380, model_decoder_layers_8_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1301: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv64, model_decoder_layers_8_self_attn_q_proj_bias5) reshape1436: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1301, R.shape([1, 1, 20, 64])) lv65 = R.call_tir(cls.NT_matmul, (layer_norm380, model_decoder_layers_8_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1437: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv65, R.shape([1, 1, 20, 64])) lv66 = R.call_tir(cls.NT_matmul, (layer_norm380, model_decoder_layers_8_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1302: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv66, model_decoder_layers_8_self_attn_v_proj_bias5) reshape1438: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1302, R.shape([1, 1, 20, 64])) concat104: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1436, reshape1437, reshape1438), axis=2) reshape1439: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat104, R.shape([1, 60, 64])) lv281 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape1439), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1440: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv281, R.shape([1, 1, 20, 64])) reshape1441: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1440, R.shape([1, 1, 1280])) lv67 = R.call_tir(cls.NT_matmul, (reshape1441, model_decoder_layers_8_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1303: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv67, model_decoder_layers_8_self_attn_out_proj_bias5) add1304: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1300, add1303) layer_norm381: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1304, model_decoder_layers_8_encoder_attn_layer_norm_weight5, model_decoder_layers_8_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv68 = R.call_tir(cls.NT_matmul, (layer_norm381, model_decoder_layers_8_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1305: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv68, model_decoder_layers_8_encoder_attn_q_proj_bias5) reshape1442: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1305, R.shape([1, 1, 20, 64])) reshape1443: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1442, R.shape([1, 20, 64])) lv282 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape1443), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1444: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv282, R.shape([1, 1, 20, 64])) reshape1445: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1444, R.shape([1, 1, 1280])) lv69 = R.call_tir(cls.NT_matmul, (reshape1445, model_decoder_layers_8_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1306: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv69, model_decoder_layers_8_encoder_attn_out_proj_bias5) add1307: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1304, add1306) layer_norm382: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1307, model_decoder_layers_8_final_layer_norm_weight5, model_decoder_layers_8_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv70 = R.call_tir(cls.NT_matmul1, (layer_norm382, model_decoder_layers_8_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1308: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv70, model_decoder_layers_8_fc1_bias5) gelu138: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1308) lv71 = R.call_tir(cls.NT_matmul2, (gelu138, model_decoder_layers_8_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1309: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv71, model_decoder_layers_8_fc2_bias5) add1310: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1307, add1309) layer_norm383: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1310, model_decoder_layers_9_self_attn_layer_norm_weight5, model_decoder_layers_9_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv72 = R.call_tir(cls.NT_matmul, (layer_norm383, model_decoder_layers_9_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1311: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv72, model_decoder_layers_9_self_attn_q_proj_bias5) reshape1446: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1311, R.shape([1, 1, 20, 64])) lv73 = R.call_tir(cls.NT_matmul, (layer_norm383, model_decoder_layers_9_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1447: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv73, R.shape([1, 1, 20, 64])) lv74 = R.call_tir(cls.NT_matmul, (layer_norm383, model_decoder_layers_9_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1312: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv74, model_decoder_layers_9_self_attn_v_proj_bias5) reshape1448: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1312, R.shape([1, 1, 20, 64])) concat105: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1446, reshape1447, reshape1448), axis=2) reshape1449: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat105, R.shape([1, 60, 64])) lv283 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape1449), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1450: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv283, R.shape([1, 1, 20, 64])) reshape1451: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1450, R.shape([1, 1, 1280])) lv75 = R.call_tir(cls.NT_matmul, (reshape1451, model_decoder_layers_9_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1313: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv75, model_decoder_layers_9_self_attn_out_proj_bias5) add1314: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1310, add1313) layer_norm384: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1314, model_decoder_layers_9_encoder_attn_layer_norm_weight5, model_decoder_layers_9_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv76 = R.call_tir(cls.NT_matmul, (layer_norm384, model_decoder_layers_9_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1315: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv76, model_decoder_layers_9_encoder_attn_q_proj_bias5) reshape1452: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1315, R.shape([1, 1, 20, 64])) reshape1453: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1452, R.shape([1, 20, 64])) lv284 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape1453), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1454: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv284, R.shape([1, 1, 20, 64])) reshape1455: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1454, R.shape([1, 1, 1280])) lv77 = R.call_tir(cls.NT_matmul, (reshape1455, model_decoder_layers_9_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1316: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv77, model_decoder_layers_9_encoder_attn_out_proj_bias5) add1317: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1314, add1316) layer_norm385: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1317, model_decoder_layers_9_final_layer_norm_weight5, model_decoder_layers_9_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv78 = R.call_tir(cls.NT_matmul1, (layer_norm385, model_decoder_layers_9_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1318: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv78, model_decoder_layers_9_fc1_bias5) gelu139: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1318) lv79 = R.call_tir(cls.NT_matmul2, (gelu139, model_decoder_layers_9_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1319: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv79, model_decoder_layers_9_fc2_bias5) add1320: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1317, add1319) layer_norm386: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1320, model_decoder_layers_10_self_attn_layer_norm_weight5, model_decoder_layers_10_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv80 = R.call_tir(cls.NT_matmul, (layer_norm386, model_decoder_layers_10_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1321: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv80, model_decoder_layers_10_self_attn_q_proj_bias5) reshape1456: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1321, R.shape([1, 1, 20, 64])) lv81 = R.call_tir(cls.NT_matmul, (layer_norm386, model_decoder_layers_10_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1457: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv81, R.shape([1, 1, 20, 64])) lv82 = R.call_tir(cls.NT_matmul, (layer_norm386, model_decoder_layers_10_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1322: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv82, model_decoder_layers_10_self_attn_v_proj_bias5) reshape1458: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1322, R.shape([1, 1, 20, 64])) concat106: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1456, reshape1457, reshape1458), axis=2) reshape1459: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat106, R.shape([1, 60, 64])) lv285 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape1459), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1460: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv285, R.shape([1, 1, 20, 64])) reshape1461: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1460, R.shape([1, 1, 1280])) lv83 = R.call_tir(cls.NT_matmul, (reshape1461, model_decoder_layers_10_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1323: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv83, model_decoder_layers_10_self_attn_out_proj_bias5) add1324: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1320, add1323) layer_norm387: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1324, model_decoder_layers_10_encoder_attn_layer_norm_weight5, model_decoder_layers_10_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv84 = R.call_tir(cls.NT_matmul, (layer_norm387, model_decoder_layers_10_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1325: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv84, model_decoder_layers_10_encoder_attn_q_proj_bias5) reshape1462: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1325, R.shape([1, 1, 20, 64])) reshape1463: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1462, R.shape([1, 20, 64])) lv286 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape1463), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1464: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv286, R.shape([1, 1, 20, 64])) reshape1465: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1464, R.shape([1, 1, 1280])) lv85 = R.call_tir(cls.NT_matmul, (reshape1465, model_decoder_layers_10_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1326: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv85, model_decoder_layers_10_encoder_attn_out_proj_bias5) add1327: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1324, add1326) layer_norm388: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1327, model_decoder_layers_10_final_layer_norm_weight5, model_decoder_layers_10_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv86 = R.call_tir(cls.NT_matmul1, (layer_norm388, model_decoder_layers_10_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1328: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv86, model_decoder_layers_10_fc1_bias5) gelu140: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1328) lv87 = R.call_tir(cls.NT_matmul2, (gelu140, model_decoder_layers_10_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1329: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv87, model_decoder_layers_10_fc2_bias5) add1330: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1327, add1329) layer_norm389: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1330, model_decoder_layers_11_self_attn_layer_norm_weight5, model_decoder_layers_11_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv88 = R.call_tir(cls.NT_matmul, (layer_norm389, model_decoder_layers_11_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1331: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv88, model_decoder_layers_11_self_attn_q_proj_bias5) reshape1466: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1331, R.shape([1, 1, 20, 64])) lv89 = R.call_tir(cls.NT_matmul, (layer_norm389, model_decoder_layers_11_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1467: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv89, R.shape([1, 1, 20, 64])) lv90 = R.call_tir(cls.NT_matmul, (layer_norm389, model_decoder_layers_11_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1332: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv90, model_decoder_layers_11_self_attn_v_proj_bias5) reshape1468: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1332, R.shape([1, 1, 20, 64])) concat107: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1466, reshape1467, reshape1468), axis=2) reshape1469: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat107, R.shape([1, 60, 64])) lv287 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape1469), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1470: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv287, R.shape([1, 1, 20, 64])) reshape1471: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1470, R.shape([1, 1, 1280])) lv91 = R.call_tir(cls.NT_matmul, (reshape1471, model_decoder_layers_11_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1333: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv91, model_decoder_layers_11_self_attn_out_proj_bias5) add1334: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1330, add1333) layer_norm390: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1334, model_decoder_layers_11_encoder_attn_layer_norm_weight5, model_decoder_layers_11_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv92 = R.call_tir(cls.NT_matmul, (layer_norm390, model_decoder_layers_11_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1335: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv92, model_decoder_layers_11_encoder_attn_q_proj_bias5) reshape1472: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1335, R.shape([1, 1, 20, 64])) reshape1473: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1472, R.shape([1, 20, 64])) lv288 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape1473), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1474: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv288, R.shape([1, 1, 20, 64])) reshape1475: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1474, R.shape([1, 1, 1280])) lv93 = R.call_tir(cls.NT_matmul, (reshape1475, model_decoder_layers_11_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1336: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv93, model_decoder_layers_11_encoder_attn_out_proj_bias5) add1337: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1334, add1336) layer_norm391: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1337, model_decoder_layers_11_final_layer_norm_weight5, model_decoder_layers_11_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv94 = R.call_tir(cls.NT_matmul1, (layer_norm391, model_decoder_layers_11_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1338: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv94, model_decoder_layers_11_fc1_bias5) gelu141: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1338) lv95 = R.call_tir(cls.NT_matmul2, (gelu141, model_decoder_layers_11_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1339: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv95, model_decoder_layers_11_fc2_bias5) add1340: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1337, add1339) layer_norm392: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1340, model_decoder_layers_12_self_attn_layer_norm_weight5, model_decoder_layers_12_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv96 = R.call_tir(cls.NT_matmul, (layer_norm392, model_decoder_layers_12_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1341: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv96, model_decoder_layers_12_self_attn_q_proj_bias5) reshape1476: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1341, R.shape([1, 1, 20, 64])) lv97 = R.call_tir(cls.NT_matmul, (layer_norm392, model_decoder_layers_12_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1477: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv97, R.shape([1, 1, 20, 64])) lv98 = R.call_tir(cls.NT_matmul, (layer_norm392, model_decoder_layers_12_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1342: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv98, model_decoder_layers_12_self_attn_v_proj_bias5) reshape1478: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1342, R.shape([1, 1, 20, 64])) concat108: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1476, reshape1477, reshape1478), axis=2) reshape1479: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat108, R.shape([1, 60, 64])) lv289 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape1479), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1480: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv289, R.shape([1, 1, 20, 64])) reshape1481: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1480, R.shape([1, 1, 1280])) lv99 = R.call_tir(cls.NT_matmul, (reshape1481, model_decoder_layers_12_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1343: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv99, model_decoder_layers_12_self_attn_out_proj_bias5) add1344: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1340, add1343) layer_norm393: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1344, model_decoder_layers_12_encoder_attn_layer_norm_weight5, model_decoder_layers_12_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv100 = R.call_tir(cls.NT_matmul, (layer_norm393, model_decoder_layers_12_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1345: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv100, model_decoder_layers_12_encoder_attn_q_proj_bias5) reshape1482: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1345, R.shape([1, 1, 20, 64])) reshape1483: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1482, R.shape([1, 20, 64])) lv290 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape1483), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1484: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv290, R.shape([1, 1, 20, 64])) reshape1485: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1484, R.shape([1, 1, 1280])) lv101 = R.call_tir(cls.NT_matmul, (reshape1485, model_decoder_layers_12_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1346: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv101, model_decoder_layers_12_encoder_attn_out_proj_bias5) add1347: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1344, add1346) layer_norm394: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1347, model_decoder_layers_12_final_layer_norm_weight5, model_decoder_layers_12_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv102 = R.call_tir(cls.NT_matmul1, (layer_norm394, model_decoder_layers_12_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1348: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv102, model_decoder_layers_12_fc1_bias5) gelu142: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1348) lv103 = R.call_tir(cls.NT_matmul2, (gelu142, model_decoder_layers_12_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1349: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv103, model_decoder_layers_12_fc2_bias5) add1350: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1347, add1349) layer_norm395: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1350, model_decoder_layers_13_self_attn_layer_norm_weight5, model_decoder_layers_13_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv104 = R.call_tir(cls.NT_matmul, (layer_norm395, model_decoder_layers_13_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1351: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv104, model_decoder_layers_13_self_attn_q_proj_bias5) reshape1486: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1351, R.shape([1, 1, 20, 64])) lv105 = R.call_tir(cls.NT_matmul, (layer_norm395, model_decoder_layers_13_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1487: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv105, R.shape([1, 1, 20, 64])) lv106 = R.call_tir(cls.NT_matmul, (layer_norm395, model_decoder_layers_13_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1352: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv106, model_decoder_layers_13_self_attn_v_proj_bias5) reshape1488: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1352, R.shape([1, 1, 20, 64])) concat109: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1486, reshape1487, reshape1488), axis=2) reshape1489: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat109, R.shape([1, 60, 64])) lv291 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape1489), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1490: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv291, R.shape([1, 1, 20, 64])) reshape1491: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1490, R.shape([1, 1, 1280])) lv107 = R.call_tir(cls.NT_matmul, (reshape1491, model_decoder_layers_13_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1353: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv107, model_decoder_layers_13_self_attn_out_proj_bias5) add1354: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1350, add1353) layer_norm396: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1354, model_decoder_layers_13_encoder_attn_layer_norm_weight5, model_decoder_layers_13_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv108 = R.call_tir(cls.NT_matmul, (layer_norm396, model_decoder_layers_13_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1355: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv108, model_decoder_layers_13_encoder_attn_q_proj_bias5) reshape1492: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1355, R.shape([1, 1, 20, 64])) reshape1493: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1492, R.shape([1, 20, 64])) lv292 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape1493), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1494: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv292, R.shape([1, 1, 20, 64])) reshape1495: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1494, R.shape([1, 1, 1280])) lv109 = R.call_tir(cls.NT_matmul, (reshape1495, model_decoder_layers_13_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1356: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv109, model_decoder_layers_13_encoder_attn_out_proj_bias5) add1357: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1354, add1356) layer_norm397: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1357, model_decoder_layers_13_final_layer_norm_weight5, model_decoder_layers_13_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv110 = R.call_tir(cls.NT_matmul1, (layer_norm397, model_decoder_layers_13_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1358: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv110, model_decoder_layers_13_fc1_bias5) gelu143: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1358) lv111 = R.call_tir(cls.NT_matmul2, (gelu143, model_decoder_layers_13_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1359: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv111, model_decoder_layers_13_fc2_bias5) add1360: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1357, add1359) layer_norm398: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1360, model_decoder_layers_14_self_attn_layer_norm_weight5, model_decoder_layers_14_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv112 = R.call_tir(cls.NT_matmul, (layer_norm398, model_decoder_layers_14_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1361: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv112, model_decoder_layers_14_self_attn_q_proj_bias5) reshape1496: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1361, R.shape([1, 1, 20, 64])) lv113 = R.call_tir(cls.NT_matmul, (layer_norm398, model_decoder_layers_14_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1497: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv113, R.shape([1, 1, 20, 64])) lv114 = R.call_tir(cls.NT_matmul, (layer_norm398, model_decoder_layers_14_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1362: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv114, model_decoder_layers_14_self_attn_v_proj_bias5) reshape1498: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1362, R.shape([1, 1, 20, 64])) concat110: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1496, reshape1497, reshape1498), axis=2) reshape1499: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat110, R.shape([1, 60, 64])) lv293 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape1499), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1500: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv293, R.shape([1, 1, 20, 64])) reshape1501: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1500, R.shape([1, 1, 1280])) lv115 = R.call_tir(cls.NT_matmul, (reshape1501, model_decoder_layers_14_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1363: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv115, model_decoder_layers_14_self_attn_out_proj_bias5) add1364: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1360, add1363) layer_norm399: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1364, model_decoder_layers_14_encoder_attn_layer_norm_weight5, model_decoder_layers_14_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv116 = R.call_tir(cls.NT_matmul, (layer_norm399, model_decoder_layers_14_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1365: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv116, model_decoder_layers_14_encoder_attn_q_proj_bias5) reshape1502: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1365, R.shape([1, 1, 20, 64])) reshape1503: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1502, R.shape([1, 20, 64])) lv294 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape1503), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1504: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv294, R.shape([1, 1, 20, 64])) reshape1505: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1504, R.shape([1, 1, 1280])) lv117 = R.call_tir(cls.NT_matmul, (reshape1505, model_decoder_layers_14_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1366: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv117, model_decoder_layers_14_encoder_attn_out_proj_bias5) add1367: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1364, add1366) layer_norm400: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1367, model_decoder_layers_14_final_layer_norm_weight5, model_decoder_layers_14_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv118 = R.call_tir(cls.NT_matmul1, (layer_norm400, model_decoder_layers_14_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1368: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv118, model_decoder_layers_14_fc1_bias5) gelu144: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1368) lv119 = R.call_tir(cls.NT_matmul2, (gelu144, model_decoder_layers_14_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1369: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv119, model_decoder_layers_14_fc2_bias5) add1370: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1367, add1369) layer_norm401: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1370, model_decoder_layers_15_self_attn_layer_norm_weight5, model_decoder_layers_15_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv120 = R.call_tir(cls.NT_matmul, (layer_norm401, model_decoder_layers_15_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1371: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv120, model_decoder_layers_15_self_attn_q_proj_bias5) reshape1506: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1371, R.shape([1, 1, 20, 64])) lv121 = R.call_tir(cls.NT_matmul, (layer_norm401, model_decoder_layers_15_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1507: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv121, R.shape([1, 1, 20, 64])) lv122 = R.call_tir(cls.NT_matmul, (layer_norm401, model_decoder_layers_15_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1372: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv122, model_decoder_layers_15_self_attn_v_proj_bias5) reshape1508: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1372, R.shape([1, 1, 20, 64])) concat111: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1506, reshape1507, reshape1508), axis=2) reshape1509: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat111, R.shape([1, 60, 64])) lv295 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape1509), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1510: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv295, R.shape([1, 1, 20, 64])) reshape1511: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1510, R.shape([1, 1, 1280])) lv123 = R.call_tir(cls.NT_matmul, (reshape1511, model_decoder_layers_15_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1373: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv123, model_decoder_layers_15_self_attn_out_proj_bias5) add1374: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1370, add1373) layer_norm402: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1374, model_decoder_layers_15_encoder_attn_layer_norm_weight5, model_decoder_layers_15_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv124 = R.call_tir(cls.NT_matmul, (layer_norm402, model_decoder_layers_15_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1375: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv124, model_decoder_layers_15_encoder_attn_q_proj_bias5) reshape1512: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1375, R.shape([1, 1, 20, 64])) reshape1513: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1512, R.shape([1, 20, 64])) lv296 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape1513), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1514: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv296, R.shape([1, 1, 20, 64])) reshape1515: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1514, R.shape([1, 1, 1280])) lv125 = R.call_tir(cls.NT_matmul, (reshape1515, model_decoder_layers_15_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1376: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv125, model_decoder_layers_15_encoder_attn_out_proj_bias5) add1377: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1374, add1376) layer_norm403: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1377, model_decoder_layers_15_final_layer_norm_weight5, model_decoder_layers_15_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv126 = R.call_tir(cls.NT_matmul1, (layer_norm403, model_decoder_layers_15_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1378: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv126, model_decoder_layers_15_fc1_bias5) gelu145: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1378) lv127 = R.call_tir(cls.NT_matmul2, (gelu145, model_decoder_layers_15_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1379: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv127, model_decoder_layers_15_fc2_bias5) add1380: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1377, add1379) layer_norm404: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1380, model_decoder_layers_16_self_attn_layer_norm_weight5, model_decoder_layers_16_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv128 = R.call_tir(cls.NT_matmul, (layer_norm404, model_decoder_layers_16_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1381: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv128, model_decoder_layers_16_self_attn_q_proj_bias5) reshape1516: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1381, R.shape([1, 1, 20, 64])) lv129 = R.call_tir(cls.NT_matmul, (layer_norm404, model_decoder_layers_16_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1517: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv129, R.shape([1, 1, 20, 64])) lv130 = R.call_tir(cls.NT_matmul, (layer_norm404, model_decoder_layers_16_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1382: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv130, model_decoder_layers_16_self_attn_v_proj_bias5) reshape1518: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1382, R.shape([1, 1, 20, 64])) concat112: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1516, reshape1517, reshape1518), axis=2) reshape1519: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat112, R.shape([1, 60, 64])) lv297 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape1519), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1520: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv297, R.shape([1, 1, 20, 64])) reshape1521: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1520, R.shape([1, 1, 1280])) lv131 = R.call_tir(cls.NT_matmul, (reshape1521, model_decoder_layers_16_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1383: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv131, model_decoder_layers_16_self_attn_out_proj_bias5) add1384: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1380, add1383) layer_norm405: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1384, model_decoder_layers_16_encoder_attn_layer_norm_weight5, model_decoder_layers_16_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv132 = R.call_tir(cls.NT_matmul, (layer_norm405, model_decoder_layers_16_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1385: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv132, model_decoder_layers_16_encoder_attn_q_proj_bias5) reshape1522: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1385, R.shape([1, 1, 20, 64])) reshape1523: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1522, R.shape([1, 20, 64])) lv298 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape1523), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1524: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv298, R.shape([1, 1, 20, 64])) reshape1525: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1524, R.shape([1, 1, 1280])) lv133 = R.call_tir(cls.NT_matmul, (reshape1525, model_decoder_layers_16_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1386: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv133, model_decoder_layers_16_encoder_attn_out_proj_bias5) add1387: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1384, add1386) layer_norm406: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1387, model_decoder_layers_16_final_layer_norm_weight5, model_decoder_layers_16_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv134 = R.call_tir(cls.NT_matmul1, (layer_norm406, model_decoder_layers_16_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1388: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv134, model_decoder_layers_16_fc1_bias5) gelu146: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1388) lv135 = R.call_tir(cls.NT_matmul2, (gelu146, model_decoder_layers_16_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1389: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv135, model_decoder_layers_16_fc2_bias5) add1390: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1387, add1389) layer_norm407: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1390, model_decoder_layers_17_self_attn_layer_norm_weight5, model_decoder_layers_17_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv136 = R.call_tir(cls.NT_matmul, (layer_norm407, model_decoder_layers_17_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1391: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv136, model_decoder_layers_17_self_attn_q_proj_bias5) reshape1526: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1391, R.shape([1, 1, 20, 64])) lv137 = R.call_tir(cls.NT_matmul, (layer_norm407, model_decoder_layers_17_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1527: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv137, R.shape([1, 1, 20, 64])) lv138 = R.call_tir(cls.NT_matmul, (layer_norm407, model_decoder_layers_17_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1392: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv138, model_decoder_layers_17_self_attn_v_proj_bias5) reshape1528: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1392, R.shape([1, 1, 20, 64])) concat113: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1526, reshape1527, reshape1528), axis=2) reshape1529: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat113, R.shape([1, 60, 64])) lv299 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape1529), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1530: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv299, R.shape([1, 1, 20, 64])) reshape1531: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1530, R.shape([1, 1, 1280])) lv139 = R.call_tir(cls.NT_matmul, (reshape1531, model_decoder_layers_17_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1393: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv139, model_decoder_layers_17_self_attn_out_proj_bias5) add1394: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1390, add1393) layer_norm408: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1394, model_decoder_layers_17_encoder_attn_layer_norm_weight5, model_decoder_layers_17_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv140 = R.call_tir(cls.NT_matmul, (layer_norm408, model_decoder_layers_17_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1395: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv140, model_decoder_layers_17_encoder_attn_q_proj_bias5) reshape1532: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1395, R.shape([1, 1, 20, 64])) reshape1533: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1532, R.shape([1, 20, 64])) lv300 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape1533), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1534: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv300, R.shape([1, 1, 20, 64])) reshape1535: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1534, R.shape([1, 1, 1280])) lv141 = R.call_tir(cls.NT_matmul, (reshape1535, model_decoder_layers_17_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1396: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv141, model_decoder_layers_17_encoder_attn_out_proj_bias5) add1397: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1394, add1396) layer_norm409: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1397, model_decoder_layers_17_final_layer_norm_weight5, model_decoder_layers_17_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv142 = R.call_tir(cls.NT_matmul1, (layer_norm409, model_decoder_layers_17_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1398: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv142, model_decoder_layers_17_fc1_bias5) gelu147: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1398) lv143 = R.call_tir(cls.NT_matmul2, (gelu147, model_decoder_layers_17_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1399: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv143, model_decoder_layers_17_fc2_bias5) add1400: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1397, add1399) layer_norm410: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1400, model_decoder_layers_18_self_attn_layer_norm_weight5, model_decoder_layers_18_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv144 = R.call_tir(cls.NT_matmul, (layer_norm410, model_decoder_layers_18_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1401: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv144, model_decoder_layers_18_self_attn_q_proj_bias5) reshape1536: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1401, R.shape([1, 1, 20, 64])) lv145 = R.call_tir(cls.NT_matmul, (layer_norm410, model_decoder_layers_18_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1537: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv145, R.shape([1, 1, 20, 64])) lv146 = R.call_tir(cls.NT_matmul, (layer_norm410, model_decoder_layers_18_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1402: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv146, model_decoder_layers_18_self_attn_v_proj_bias5) reshape1538: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1402, R.shape([1, 1, 20, 64])) concat114: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1536, reshape1537, reshape1538), axis=2) reshape1539: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat114, R.shape([1, 60, 64])) lv301 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape1539), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1540: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv301, R.shape([1, 1, 20, 64])) reshape1541: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1540, R.shape([1, 1, 1280])) lv147 = R.call_tir(cls.NT_matmul, (reshape1541, model_decoder_layers_18_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1403: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv147, model_decoder_layers_18_self_attn_out_proj_bias5) add1404: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1400, add1403) layer_norm411: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1404, model_decoder_layers_18_encoder_attn_layer_norm_weight5, model_decoder_layers_18_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv148 = R.call_tir(cls.NT_matmul, (layer_norm411, model_decoder_layers_18_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1405: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv148, model_decoder_layers_18_encoder_attn_q_proj_bias5) reshape1542: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1405, R.shape([1, 1, 20, 64])) reshape1543: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1542, R.shape([1, 20, 64])) lv302 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape1543), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1544: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv302, R.shape([1, 1, 20, 64])) reshape1545: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1544, R.shape([1, 1, 1280])) lv149 = R.call_tir(cls.NT_matmul, (reshape1545, model_decoder_layers_18_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1406: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv149, model_decoder_layers_18_encoder_attn_out_proj_bias5) add1407: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1404, add1406) layer_norm412: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1407, model_decoder_layers_18_final_layer_norm_weight5, model_decoder_layers_18_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv150 = R.call_tir(cls.NT_matmul1, (layer_norm412, model_decoder_layers_18_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1408: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv150, model_decoder_layers_18_fc1_bias5) gelu148: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1408) lv151 = R.call_tir(cls.NT_matmul2, (gelu148, model_decoder_layers_18_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1409: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv151, model_decoder_layers_18_fc2_bias5) add1410: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1407, add1409) layer_norm413: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1410, model_decoder_layers_19_self_attn_layer_norm_weight5, model_decoder_layers_19_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv152 = R.call_tir(cls.NT_matmul, (layer_norm413, model_decoder_layers_19_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1411: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv152, model_decoder_layers_19_self_attn_q_proj_bias5) reshape1546: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1411, R.shape([1, 1, 20, 64])) lv153 = R.call_tir(cls.NT_matmul, (layer_norm413, model_decoder_layers_19_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1547: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv153, R.shape([1, 1, 20, 64])) lv154 = R.call_tir(cls.NT_matmul, (layer_norm413, model_decoder_layers_19_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1412: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv154, model_decoder_layers_19_self_attn_v_proj_bias5) reshape1548: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1412, R.shape([1, 1, 20, 64])) concat115: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1546, reshape1547, reshape1548), axis=2) reshape1549: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat115, R.shape([1, 60, 64])) lv303 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape1549), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1550: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv303, R.shape([1, 1, 20, 64])) reshape1551: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1550, R.shape([1, 1, 1280])) lv155 = R.call_tir(cls.NT_matmul, (reshape1551, model_decoder_layers_19_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1413: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv155, model_decoder_layers_19_self_attn_out_proj_bias5) add1414: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1410, add1413) layer_norm414: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1414, model_decoder_layers_19_encoder_attn_layer_norm_weight5, model_decoder_layers_19_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv156 = R.call_tir(cls.NT_matmul, (layer_norm414, model_decoder_layers_19_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1415: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv156, model_decoder_layers_19_encoder_attn_q_proj_bias5) reshape1552: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1415, R.shape([1, 1, 20, 64])) reshape1553: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1552, R.shape([1, 20, 64])) lv304 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape1553), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1554: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv304, R.shape([1, 1, 20, 64])) reshape1555: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1554, R.shape([1, 1, 1280])) lv157 = R.call_tir(cls.NT_matmul, (reshape1555, model_decoder_layers_19_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1416: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv157, model_decoder_layers_19_encoder_attn_out_proj_bias5) add1417: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1414, add1416) layer_norm415: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1417, model_decoder_layers_19_final_layer_norm_weight5, model_decoder_layers_19_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv158 = R.call_tir(cls.NT_matmul1, (layer_norm415, model_decoder_layers_19_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1418: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv158, model_decoder_layers_19_fc1_bias5) gelu149: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1418) lv159 = R.call_tir(cls.NT_matmul2, (gelu149, model_decoder_layers_19_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1419: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv159, model_decoder_layers_19_fc2_bias5) add1420: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1417, add1419) layer_norm416: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1420, model_decoder_layers_20_self_attn_layer_norm_weight5, model_decoder_layers_20_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv160 = R.call_tir(cls.NT_matmul, (layer_norm416, model_decoder_layers_20_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1421: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv160, model_decoder_layers_20_self_attn_q_proj_bias5) reshape1556: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1421, R.shape([1, 1, 20, 64])) lv161 = R.call_tir(cls.NT_matmul, (layer_norm416, model_decoder_layers_20_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1557: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv161, R.shape([1, 1, 20, 64])) lv162 = R.call_tir(cls.NT_matmul, (layer_norm416, model_decoder_layers_20_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1422: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv162, model_decoder_layers_20_self_attn_v_proj_bias5) reshape1558: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1422, R.shape([1, 1, 20, 64])) concat116: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1556, reshape1557, reshape1558), axis=2) reshape1559: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat116, R.shape([1, 60, 64])) lv305 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape1559), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1560: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv305, R.shape([1, 1, 20, 64])) reshape1561: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1560, R.shape([1, 1, 1280])) lv163 = R.call_tir(cls.NT_matmul, (reshape1561, model_decoder_layers_20_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1423: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv163, model_decoder_layers_20_self_attn_out_proj_bias5) add1424: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1420, add1423) layer_norm417: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1424, model_decoder_layers_20_encoder_attn_layer_norm_weight5, model_decoder_layers_20_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv164 = R.call_tir(cls.NT_matmul, (layer_norm417, model_decoder_layers_20_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1425: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv164, model_decoder_layers_20_encoder_attn_q_proj_bias5) reshape1562: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1425, R.shape([1, 1, 20, 64])) reshape1563: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1562, R.shape([1, 20, 64])) lv306 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape1563), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1564: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv306, R.shape([1, 1, 20, 64])) reshape1565: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1564, R.shape([1, 1, 1280])) lv165 = R.call_tir(cls.NT_matmul, (reshape1565, model_decoder_layers_20_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1426: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv165, model_decoder_layers_20_encoder_attn_out_proj_bias5) add1427: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1424, add1426) layer_norm418: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1427, model_decoder_layers_20_final_layer_norm_weight5, model_decoder_layers_20_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv166 = R.call_tir(cls.NT_matmul1, (layer_norm418, model_decoder_layers_20_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1428: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv166, model_decoder_layers_20_fc1_bias5) gelu150: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1428) lv167 = R.call_tir(cls.NT_matmul2, (gelu150, model_decoder_layers_20_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1429: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv167, model_decoder_layers_20_fc2_bias5) add1430: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1427, add1429) layer_norm419: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1430, model_decoder_layers_21_self_attn_layer_norm_weight5, model_decoder_layers_21_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv168 = R.call_tir(cls.NT_matmul, (layer_norm419, model_decoder_layers_21_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1431: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv168, model_decoder_layers_21_self_attn_q_proj_bias5) reshape1566: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1431, R.shape([1, 1, 20, 64])) lv169 = R.call_tir(cls.NT_matmul, (layer_norm419, model_decoder_layers_21_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1567: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv169, R.shape([1, 1, 20, 64])) lv170 = R.call_tir(cls.NT_matmul, (layer_norm419, model_decoder_layers_21_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1432: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv170, model_decoder_layers_21_self_attn_v_proj_bias5) reshape1568: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1432, R.shape([1, 1, 20, 64])) concat117: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1566, reshape1567, reshape1568), axis=2) reshape1569: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat117, R.shape([1, 60, 64])) lv307 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape1569), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1570: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv307, R.shape([1, 1, 20, 64])) reshape1571: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1570, R.shape([1, 1, 1280])) lv171 = R.call_tir(cls.NT_matmul, (reshape1571, model_decoder_layers_21_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1433: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv171, model_decoder_layers_21_self_attn_out_proj_bias5) add1434: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1430, add1433) layer_norm420: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1434, model_decoder_layers_21_encoder_attn_layer_norm_weight5, model_decoder_layers_21_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv172 = R.call_tir(cls.NT_matmul, (layer_norm420, model_decoder_layers_21_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1435: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv172, model_decoder_layers_21_encoder_attn_q_proj_bias5) reshape1572: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1435, R.shape([1, 1, 20, 64])) reshape1573: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1572, R.shape([1, 20, 64])) lv308 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape1573), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1574: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv308, R.shape([1, 1, 20, 64])) reshape1575: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1574, R.shape([1, 1, 1280])) lv173 = R.call_tir(cls.NT_matmul, (reshape1575, model_decoder_layers_21_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1436: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv173, model_decoder_layers_21_encoder_attn_out_proj_bias5) add1437: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1434, add1436) layer_norm421: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1437, model_decoder_layers_21_final_layer_norm_weight5, model_decoder_layers_21_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv174 = R.call_tir(cls.NT_matmul1, (layer_norm421, model_decoder_layers_21_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1438: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv174, model_decoder_layers_21_fc1_bias5) gelu151: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1438) lv175 = R.call_tir(cls.NT_matmul2, (gelu151, model_decoder_layers_21_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1439: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv175, model_decoder_layers_21_fc2_bias5) add1440: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1437, add1439) layer_norm422: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1440, model_decoder_layers_22_self_attn_layer_norm_weight5, model_decoder_layers_22_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv176 = R.call_tir(cls.NT_matmul, (layer_norm422, model_decoder_layers_22_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1441: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv176, model_decoder_layers_22_self_attn_q_proj_bias5) reshape1576: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1441, R.shape([1, 1, 20, 64])) lv177 = R.call_tir(cls.NT_matmul, (layer_norm422, model_decoder_layers_22_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1577: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv177, R.shape([1, 1, 20, 64])) lv178 = R.call_tir(cls.NT_matmul, (layer_norm422, model_decoder_layers_22_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1442: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv178, model_decoder_layers_22_self_attn_v_proj_bias5) reshape1578: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1442, R.shape([1, 1, 20, 64])) concat118: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1576, reshape1577, reshape1578), axis=2) reshape1579: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat118, R.shape([1, 60, 64])) lv309 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape1579), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1580: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv309, R.shape([1, 1, 20, 64])) reshape1581: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1580, R.shape([1, 1, 1280])) lv179 = R.call_tir(cls.NT_matmul, (reshape1581, model_decoder_layers_22_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1443: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv179, model_decoder_layers_22_self_attn_out_proj_bias5) add1444: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1440, add1443) layer_norm423: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1444, model_decoder_layers_22_encoder_attn_layer_norm_weight5, model_decoder_layers_22_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv180 = R.call_tir(cls.NT_matmul, (layer_norm423, model_decoder_layers_22_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1445: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv180, model_decoder_layers_22_encoder_attn_q_proj_bias5) reshape1582: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1445, R.shape([1, 1, 20, 64])) reshape1583: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1582, R.shape([1, 20, 64])) lv310 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape1583), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1584: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv310, R.shape([1, 1, 20, 64])) reshape1585: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1584, R.shape([1, 1, 1280])) lv181 = R.call_tir(cls.NT_matmul, (reshape1585, model_decoder_layers_22_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1446: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv181, model_decoder_layers_22_encoder_attn_out_proj_bias5) add1447: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1444, add1446) layer_norm424: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1447, model_decoder_layers_22_final_layer_norm_weight5, model_decoder_layers_22_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv182 = R.call_tir(cls.NT_matmul1, (layer_norm424, model_decoder_layers_22_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1448: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv182, model_decoder_layers_22_fc1_bias5) gelu152: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1448) lv183 = R.call_tir(cls.NT_matmul2, (gelu152, model_decoder_layers_22_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1449: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv183, model_decoder_layers_22_fc2_bias5) add1450: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1447, add1449) layer_norm425: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1450, model_decoder_layers_23_self_attn_layer_norm_weight5, model_decoder_layers_23_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv184 = R.call_tir(cls.NT_matmul, (layer_norm425, model_decoder_layers_23_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1451: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv184, model_decoder_layers_23_self_attn_q_proj_bias5) reshape1586: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1451, R.shape([1, 1, 20, 64])) lv185 = R.call_tir(cls.NT_matmul, (layer_norm425, model_decoder_layers_23_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1587: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv185, R.shape([1, 1, 20, 64])) lv186 = R.call_tir(cls.NT_matmul, (layer_norm425, model_decoder_layers_23_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1452: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv186, model_decoder_layers_23_self_attn_v_proj_bias5) reshape1588: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1452, R.shape([1, 1, 20, 64])) concat119: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1586, reshape1587, reshape1588), axis=2) reshape1589: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat119, R.shape([1, 60, 64])) lv311 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape1589), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1590: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv311, R.shape([1, 1, 20, 64])) reshape1591: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1590, R.shape([1, 1, 1280])) lv187 = R.call_tir(cls.NT_matmul, (reshape1591, model_decoder_layers_23_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1453: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv187, model_decoder_layers_23_self_attn_out_proj_bias5) add1454: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1450, add1453) layer_norm426: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1454, model_decoder_layers_23_encoder_attn_layer_norm_weight5, model_decoder_layers_23_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv188 = R.call_tir(cls.NT_matmul, (layer_norm426, model_decoder_layers_23_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1455: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv188, model_decoder_layers_23_encoder_attn_q_proj_bias5) reshape1592: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1455, R.shape([1, 1, 20, 64])) reshape1593: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1592, R.shape([1, 20, 64])) lv312 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape1593), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1594: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv312, R.shape([1, 1, 20, 64])) reshape1595: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1594, R.shape([1, 1, 1280])) lv189 = R.call_tir(cls.NT_matmul, (reshape1595, model_decoder_layers_23_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1456: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv189, model_decoder_layers_23_encoder_attn_out_proj_bias5) add1457: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1454, add1456) layer_norm427: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1457, model_decoder_layers_23_final_layer_norm_weight5, model_decoder_layers_23_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv190 = R.call_tir(cls.NT_matmul1, (layer_norm427, model_decoder_layers_23_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1458: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv190, model_decoder_layers_23_fc1_bias5) gelu153: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1458) lv191 = R.call_tir(cls.NT_matmul2, (gelu153, model_decoder_layers_23_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1459: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv191, model_decoder_layers_23_fc2_bias5) add1460: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1457, add1459) layer_norm428: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1460, model_decoder_layers_24_self_attn_layer_norm_weight5, model_decoder_layers_24_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv192 = R.call_tir(cls.NT_matmul, (layer_norm428, model_decoder_layers_24_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1461: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv192, model_decoder_layers_24_self_attn_q_proj_bias5) reshape1596: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1461, R.shape([1, 1, 20, 64])) lv193 = R.call_tir(cls.NT_matmul, (layer_norm428, model_decoder_layers_24_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1597: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv193, R.shape([1, 1, 20, 64])) lv194 = R.call_tir(cls.NT_matmul, (layer_norm428, model_decoder_layers_24_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1462: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv194, model_decoder_layers_24_self_attn_v_proj_bias5) reshape1598: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1462, R.shape([1, 1, 20, 64])) concat120: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1596, reshape1597, reshape1598), axis=2) reshape1599: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat120, R.shape([1, 60, 64])) lv313 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape1599), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1600: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv313, R.shape([1, 1, 20, 64])) reshape1601: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1600, R.shape([1, 1, 1280])) lv195 = R.call_tir(cls.NT_matmul, (reshape1601, model_decoder_layers_24_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1463: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv195, model_decoder_layers_24_self_attn_out_proj_bias5) add1464: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1460, add1463) layer_norm429: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1464, model_decoder_layers_24_encoder_attn_layer_norm_weight5, model_decoder_layers_24_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv196 = R.call_tir(cls.NT_matmul, (layer_norm429, model_decoder_layers_24_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1465: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv196, model_decoder_layers_24_encoder_attn_q_proj_bias5) reshape1602: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1465, R.shape([1, 1, 20, 64])) reshape1603: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1602, R.shape([1, 20, 64])) lv314 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape1603), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1604: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv314, R.shape([1, 1, 20, 64])) reshape1605: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1604, R.shape([1, 1, 1280])) lv197 = R.call_tir(cls.NT_matmul, (reshape1605, model_decoder_layers_24_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1466: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv197, model_decoder_layers_24_encoder_attn_out_proj_bias5) add1467: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1464, add1466) layer_norm430: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1467, model_decoder_layers_24_final_layer_norm_weight5, model_decoder_layers_24_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv198 = R.call_tir(cls.NT_matmul1, (layer_norm430, model_decoder_layers_24_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1468: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv198, model_decoder_layers_24_fc1_bias5) gelu154: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1468) lv199 = R.call_tir(cls.NT_matmul2, (gelu154, model_decoder_layers_24_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1469: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv199, model_decoder_layers_24_fc2_bias5) add1470: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1467, add1469) layer_norm431: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1470, model_decoder_layers_25_self_attn_layer_norm_weight5, model_decoder_layers_25_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv200 = R.call_tir(cls.NT_matmul, (layer_norm431, model_decoder_layers_25_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1471: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv200, model_decoder_layers_25_self_attn_q_proj_bias5) reshape1606: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1471, R.shape([1, 1, 20, 64])) lv201 = R.call_tir(cls.NT_matmul, (layer_norm431, model_decoder_layers_25_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1607: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv201, R.shape([1, 1, 20, 64])) lv202 = R.call_tir(cls.NT_matmul, (layer_norm431, model_decoder_layers_25_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1472: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv202, model_decoder_layers_25_self_attn_v_proj_bias5) reshape1608: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1472, R.shape([1, 1, 20, 64])) concat121: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1606, reshape1607, reshape1608), axis=2) reshape1609: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat121, R.shape([1, 60, 64])) lv315 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape1609), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1610: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv315, R.shape([1, 1, 20, 64])) reshape1611: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1610, R.shape([1, 1, 1280])) lv203 = R.call_tir(cls.NT_matmul, (reshape1611, model_decoder_layers_25_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1473: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv203, model_decoder_layers_25_self_attn_out_proj_bias5) add1474: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1470, add1473) layer_norm432: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1474, model_decoder_layers_25_encoder_attn_layer_norm_weight5, model_decoder_layers_25_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv204 = R.call_tir(cls.NT_matmul, (layer_norm432, model_decoder_layers_25_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1475: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv204, model_decoder_layers_25_encoder_attn_q_proj_bias5) reshape1612: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1475, R.shape([1, 1, 20, 64])) reshape1613: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1612, R.shape([1, 20, 64])) lv316 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape1613), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1614: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv316, R.shape([1, 1, 20, 64])) reshape1615: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1614, R.shape([1, 1, 1280])) lv205 = R.call_tir(cls.NT_matmul, (reshape1615, model_decoder_layers_25_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1476: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv205, model_decoder_layers_25_encoder_attn_out_proj_bias5) add1477: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1474, add1476) layer_norm433: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1477, model_decoder_layers_25_final_layer_norm_weight5, model_decoder_layers_25_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv206 = R.call_tir(cls.NT_matmul1, (layer_norm433, model_decoder_layers_25_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1478: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv206, model_decoder_layers_25_fc1_bias5) gelu155: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1478) lv207 = R.call_tir(cls.NT_matmul2, (gelu155, model_decoder_layers_25_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1479: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv207, model_decoder_layers_25_fc2_bias5) add1480: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1477, add1479) layer_norm434: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1480, model_decoder_layers_26_self_attn_layer_norm_weight5, model_decoder_layers_26_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv208 = R.call_tir(cls.NT_matmul, (layer_norm434, model_decoder_layers_26_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1481: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv208, model_decoder_layers_26_self_attn_q_proj_bias5) reshape1616: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1481, R.shape([1, 1, 20, 64])) lv209 = R.call_tir(cls.NT_matmul, (layer_norm434, model_decoder_layers_26_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1617: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv209, R.shape([1, 1, 20, 64])) lv210 = R.call_tir(cls.NT_matmul, (layer_norm434, model_decoder_layers_26_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1482: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv210, model_decoder_layers_26_self_attn_v_proj_bias5) reshape1618: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1482, R.shape([1, 1, 20, 64])) concat122: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1616, reshape1617, reshape1618), axis=2) reshape1619: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat122, R.shape([1, 60, 64])) lv317 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape1619), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1620: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv317, R.shape([1, 1, 20, 64])) reshape1621: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1620, R.shape([1, 1, 1280])) lv211 = R.call_tir(cls.NT_matmul, (reshape1621, model_decoder_layers_26_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1483: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv211, model_decoder_layers_26_self_attn_out_proj_bias5) add1484: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1480, add1483) layer_norm435: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1484, model_decoder_layers_26_encoder_attn_layer_norm_weight5, model_decoder_layers_26_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv212 = R.call_tir(cls.NT_matmul, (layer_norm435, model_decoder_layers_26_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1485: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv212, model_decoder_layers_26_encoder_attn_q_proj_bias5) reshape1622: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1485, R.shape([1, 1, 20, 64])) reshape1623: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1622, R.shape([1, 20, 64])) lv318 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape1623), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1624: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv318, R.shape([1, 1, 20, 64])) reshape1625: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1624, R.shape([1, 1, 1280])) lv213 = R.call_tir(cls.NT_matmul, (reshape1625, model_decoder_layers_26_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1486: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv213, model_decoder_layers_26_encoder_attn_out_proj_bias5) add1487: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1484, add1486) layer_norm436: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1487, model_decoder_layers_26_final_layer_norm_weight5, model_decoder_layers_26_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv214 = R.call_tir(cls.NT_matmul1, (layer_norm436, model_decoder_layers_26_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1488: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv214, model_decoder_layers_26_fc1_bias5) gelu156: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1488) lv215 = R.call_tir(cls.NT_matmul2, (gelu156, model_decoder_layers_26_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1489: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv215, model_decoder_layers_26_fc2_bias5) add1490: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1487, add1489) layer_norm437: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1490, model_decoder_layers_27_self_attn_layer_norm_weight5, model_decoder_layers_27_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv216 = R.call_tir(cls.NT_matmul, (layer_norm437, model_decoder_layers_27_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1491: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv216, model_decoder_layers_27_self_attn_q_proj_bias5) reshape1626: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1491, R.shape([1, 1, 20, 64])) lv217 = R.call_tir(cls.NT_matmul, (layer_norm437, model_decoder_layers_27_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1627: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv217, R.shape([1, 1, 20, 64])) lv218 = R.call_tir(cls.NT_matmul, (layer_norm437, model_decoder_layers_27_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1492: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv218, model_decoder_layers_27_self_attn_v_proj_bias5) reshape1628: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1492, R.shape([1, 1, 20, 64])) concat123: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1626, reshape1627, reshape1628), axis=2) reshape1629: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat123, R.shape([1, 60, 64])) lv319 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape1629), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1630: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv319, R.shape([1, 1, 20, 64])) reshape1631: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1630, R.shape([1, 1, 1280])) lv219 = R.call_tir(cls.NT_matmul, (reshape1631, model_decoder_layers_27_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1493: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv219, model_decoder_layers_27_self_attn_out_proj_bias5) add1494: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1490, add1493) layer_norm438: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1494, model_decoder_layers_27_encoder_attn_layer_norm_weight5, model_decoder_layers_27_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv220 = R.call_tir(cls.NT_matmul, (layer_norm438, model_decoder_layers_27_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1495: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv220, model_decoder_layers_27_encoder_attn_q_proj_bias5) reshape1632: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1495, R.shape([1, 1, 20, 64])) reshape1633: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1632, R.shape([1, 20, 64])) lv320 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape1633), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1634: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv320, R.shape([1, 1, 20, 64])) reshape1635: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1634, R.shape([1, 1, 1280])) lv221 = R.call_tir(cls.NT_matmul, (reshape1635, model_decoder_layers_27_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1496: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv221, model_decoder_layers_27_encoder_attn_out_proj_bias5) add1497: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1494, add1496) layer_norm439: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1497, model_decoder_layers_27_final_layer_norm_weight5, model_decoder_layers_27_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv222 = R.call_tir(cls.NT_matmul1, (layer_norm439, model_decoder_layers_27_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1498: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv222, model_decoder_layers_27_fc1_bias5) gelu157: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1498) lv223 = R.call_tir(cls.NT_matmul2, (gelu157, model_decoder_layers_27_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1499: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv223, model_decoder_layers_27_fc2_bias5) add1500: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1497, add1499) layer_norm440: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1500, model_decoder_layers_28_self_attn_layer_norm_weight5, model_decoder_layers_28_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv224 = R.call_tir(cls.NT_matmul, (layer_norm440, model_decoder_layers_28_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1501: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv224, model_decoder_layers_28_self_attn_q_proj_bias5) reshape1636: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1501, R.shape([1, 1, 20, 64])) lv225 = R.call_tir(cls.NT_matmul, (layer_norm440, model_decoder_layers_28_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1637: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv225, R.shape([1, 1, 20, 64])) lv226 = R.call_tir(cls.NT_matmul, (layer_norm440, model_decoder_layers_28_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1502: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv226, model_decoder_layers_28_self_attn_v_proj_bias5) reshape1638: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1502, R.shape([1, 1, 20, 64])) concat124: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1636, reshape1637, reshape1638), axis=2) reshape1639: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat124, R.shape([1, 60, 64])) lv321 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape1639), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1640: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv321, R.shape([1, 1, 20, 64])) reshape1641: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1640, R.shape([1, 1, 1280])) lv227 = R.call_tir(cls.NT_matmul, (reshape1641, model_decoder_layers_28_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1503: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv227, model_decoder_layers_28_self_attn_out_proj_bias5) add1504: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1500, add1503) layer_norm441: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1504, model_decoder_layers_28_encoder_attn_layer_norm_weight5, model_decoder_layers_28_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv228 = R.call_tir(cls.NT_matmul, (layer_norm441, model_decoder_layers_28_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1505: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv228, model_decoder_layers_28_encoder_attn_q_proj_bias5) reshape1642: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1505, R.shape([1, 1, 20, 64])) reshape1643: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1642, R.shape([1, 20, 64])) lv322 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape1643), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1644: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv322, R.shape([1, 1, 20, 64])) reshape1645: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1644, R.shape([1, 1, 1280])) lv229 = R.call_tir(cls.NT_matmul, (reshape1645, model_decoder_layers_28_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1506: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv229, model_decoder_layers_28_encoder_attn_out_proj_bias5) add1507: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1504, add1506) layer_norm442: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1507, model_decoder_layers_28_final_layer_norm_weight5, model_decoder_layers_28_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv230 = R.call_tir(cls.NT_matmul1, (layer_norm442, model_decoder_layers_28_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1508: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv230, model_decoder_layers_28_fc1_bias5) gelu158: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1508) lv231 = R.call_tir(cls.NT_matmul2, (gelu158, model_decoder_layers_28_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1509: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv231, model_decoder_layers_28_fc2_bias5) add1510: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1507, add1509) layer_norm443: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1510, model_decoder_layers_29_self_attn_layer_norm_weight5, model_decoder_layers_29_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv232 = R.call_tir(cls.NT_matmul, (layer_norm443, model_decoder_layers_29_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1511: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv232, model_decoder_layers_29_self_attn_q_proj_bias5) reshape1646: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1511, R.shape([1, 1, 20, 64])) lv233 = R.call_tir(cls.NT_matmul, (layer_norm443, model_decoder_layers_29_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1647: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv233, R.shape([1, 1, 20, 64])) lv234 = R.call_tir(cls.NT_matmul, (layer_norm443, model_decoder_layers_29_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1512: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv234, model_decoder_layers_29_self_attn_v_proj_bias5) reshape1648: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1512, R.shape([1, 1, 20, 64])) concat125: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1646, reshape1647, reshape1648), axis=2) reshape1649: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat125, R.shape([1, 60, 64])) lv323 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1649), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1650: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv323, R.shape([1, 1, 20, 64])) reshape1651: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1650, R.shape([1, 1, 1280])) lv235 = R.call_tir(cls.NT_matmul, (reshape1651, model_decoder_layers_29_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1513: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv235, model_decoder_layers_29_self_attn_out_proj_bias5) add1514: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1510, add1513) layer_norm444: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1514, model_decoder_layers_29_encoder_attn_layer_norm_weight5, model_decoder_layers_29_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv236 = R.call_tir(cls.NT_matmul, (layer_norm444, model_decoder_layers_29_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1515: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv236, model_decoder_layers_29_encoder_attn_q_proj_bias5) reshape1652: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1515, R.shape([1, 1, 20, 64])) reshape1653: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1652, R.shape([1, 20, 64])) lv324 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1653), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1654: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv324, R.shape([1, 1, 20, 64])) reshape1655: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1654, R.shape([1, 1, 1280])) lv237 = R.call_tir(cls.NT_matmul, (reshape1655, model_decoder_layers_29_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1516: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv237, model_decoder_layers_29_encoder_attn_out_proj_bias5) add1517: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1514, add1516) layer_norm445: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1517, model_decoder_layers_29_final_layer_norm_weight5, model_decoder_layers_29_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv238 = R.call_tir(cls.NT_matmul1, (layer_norm445, model_decoder_layers_29_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1518: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv238, model_decoder_layers_29_fc1_bias5) gelu159: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1518) lv239 = R.call_tir(cls.NT_matmul2, (gelu159, model_decoder_layers_29_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1519: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv239, model_decoder_layers_29_fc2_bias5) add1520: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1517, add1519) layer_norm446: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1520, model_decoder_layers_30_self_attn_layer_norm_weight5, model_decoder_layers_30_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv240 = R.call_tir(cls.NT_matmul, (layer_norm446, model_decoder_layers_30_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1521: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv240, model_decoder_layers_30_self_attn_q_proj_bias5) reshape1656: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1521, R.shape([1, 1, 20, 64])) lv241 = R.call_tir(cls.NT_matmul, (layer_norm446, model_decoder_layers_30_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1657: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv241, R.shape([1, 1, 20, 64])) lv242 = R.call_tir(cls.NT_matmul, (layer_norm446, model_decoder_layers_30_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1522: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv242, model_decoder_layers_30_self_attn_v_proj_bias5) reshape1658: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1522, R.shape([1, 1, 20, 64])) concat126: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1656, reshape1657, reshape1658), axis=2) reshape1659: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat126, R.shape([1, 60, 64])) lv325 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1659), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1660: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv325, R.shape([1, 1, 20, 64])) reshape1661: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1660, R.shape([1, 1, 1280])) lv243 = R.call_tir(cls.NT_matmul, (reshape1661, model_decoder_layers_30_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1523: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv243, model_decoder_layers_30_self_attn_out_proj_bias5) add1524: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1520, add1523) layer_norm447: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1524, model_decoder_layers_30_encoder_attn_layer_norm_weight5, model_decoder_layers_30_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv244 = R.call_tir(cls.NT_matmul, (layer_norm447, model_decoder_layers_30_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1525: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv244, model_decoder_layers_30_encoder_attn_q_proj_bias5) reshape1662: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1525, R.shape([1, 1, 20, 64])) reshape1663: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1662, R.shape([1, 20, 64])) lv326 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1663), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1664: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv326, R.shape([1, 1, 20, 64])) reshape1665: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1664, R.shape([1, 1, 1280])) lv245 = R.call_tir(cls.NT_matmul, (reshape1665, model_decoder_layers_30_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1526: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv245, model_decoder_layers_30_encoder_attn_out_proj_bias5) add1527: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1524, add1526) layer_norm448: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1527, model_decoder_layers_30_final_layer_norm_weight5, model_decoder_layers_30_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv246 = R.call_tir(cls.NT_matmul1, (layer_norm448, model_decoder_layers_30_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1528: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv246, model_decoder_layers_30_fc1_bias5) gelu160: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1528) lv247 = R.call_tir(cls.NT_matmul2, (gelu160, model_decoder_layers_30_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1529: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv247, model_decoder_layers_30_fc2_bias5) add1530: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1527, add1529) layer_norm449: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1530, model_decoder_layers_31_self_attn_layer_norm_weight5, model_decoder_layers_31_self_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv248 = R.call_tir(cls.NT_matmul, (layer_norm449, model_decoder_layers_31_self_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1531: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv248, model_decoder_layers_31_self_attn_q_proj_bias5) reshape1666: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1531, R.shape([1, 1, 20, 64])) lv249 = R.call_tir(cls.NT_matmul, (layer_norm449, model_decoder_layers_31_self_attn_k_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) reshape1667: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv249, R.shape([1, 1, 20, 64])) lv250 = R.call_tir(cls.NT_matmul, (layer_norm449, model_decoder_layers_31_self_attn_v_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1532: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv250, model_decoder_layers_31_self_attn_v_proj_bias5) reshape1668: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1532, R.shape([1, 1, 20, 64])) concat127: R.Tensor((1, 1, 60, 64), dtype="float16") = R.concat((reshape1666, reshape1667, reshape1668), axis=2) reshape1669: R.Tensor((1, 60, 64), dtype="float16") = R.reshape(concat127, R.shape([1, 60, 64])) lv327 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1669), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1670: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv327, R.shape([1, 1, 20, 64])) reshape1671: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1670, R.shape([1, 1, 1280])) lv251 = R.call_tir(cls.NT_matmul, (reshape1671, model_decoder_layers_31_self_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1533: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv251, model_decoder_layers_31_self_attn_out_proj_bias5) add1534: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1530, add1533) layer_norm450: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1534, model_decoder_layers_31_encoder_attn_layer_norm_weight5, model_decoder_layers_31_encoder_attn_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv252 = R.call_tir(cls.NT_matmul, (layer_norm450, model_decoder_layers_31_encoder_attn_q_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1535: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv252, model_decoder_layers_31_encoder_attn_q_proj_bias5) reshape1672: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(add1535, R.shape([1, 1, 20, 64])) reshape1673: R.Tensor((1, 20, 64), dtype="float16") = R.reshape(reshape1672, R.shape([1, 20, 64])) lv328 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1673), out_sinfo=R.Tensor((1, 20, 64), dtype="float16")) reshape1674: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(lv328, R.shape([1, 1, 20, 64])) reshape1675: R.Tensor((1, 1, 1280), dtype="float16") = R.reshape(reshape1674, R.shape([1, 1, 1280])) lv253 = R.call_tir(cls.NT_matmul, (reshape1675, model_decoder_layers_31_encoder_attn_out_proj_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1536: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv253, model_decoder_layers_31_encoder_attn_out_proj_bias5) add1537: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1534, add1536) layer_norm451: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1537, model_decoder_layers_31_final_layer_norm_weight5, model_decoder_layers_31_final_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv254 = R.call_tir(cls.NT_matmul1, (layer_norm451, model_decoder_layers_31_fc1_weight5), out_sinfo=R.Tensor((1, 1, 5120), dtype="float16")) add1538: R.Tensor((1, 1, 5120), dtype="float16") = R.add(lv254, model_decoder_layers_31_fc1_bias5) gelu161: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1538) lv255 = R.call_tir(cls.NT_matmul2, (gelu161, model_decoder_layers_31_fc2_weight5), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) add1539: R.Tensor((1, 1, 1280), dtype="float16") = R.add(lv255, model_decoder_layers_31_fc2_bias5) add1540: R.Tensor((1, 1, 1280), dtype="float16") = R.add(add1537, add1539) layer_norm452: R.Tensor((1, 1, 1280), dtype="float16") = R.nn.layer_norm(add1540, model_decoder_layer_norm_weight5, model_decoder_layer_norm_bias5, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv256 = R.call_tir(cls.NT_matmul3, (layer_norm452, model_decoder_embed_tokens_weight5), out_sinfo=R.Tensor((1, 1, 51866), dtype="float32")) gv5: R.Tensor((1, 1, 51866), dtype="float32") = lv256 R.output(gv5) return gv5 @R.function(private=True) def fused_relax_permute_dims_relax_matmul(model_decoder_layers_0_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16"), layer_norm356: R.Tensor((1, 1, 1280), dtype="float16")) -> R.Tensor((1, 1, 1280), dtype="float16"): R.func_attr({"Composite": "transpose_matmul_fuse", "Primitive": 1}) with R.dataflow(): permute_dims1028: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_q_proj_weight5, axes=None) gv: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm356, permute_dims1028, out_dtype="void") R.output(gv) return gv @R.function(private=True) def fused_relax_permute_dims_relax_matmul1(model_decoder_layers_0_fc1_weight5: R.Tensor((5120, 1280), dtype="float16"), layer_norm358: R.Tensor((1, 1, 1280), dtype="float16")) -> R.Tensor((1, 1, 5120), dtype="float16"): R.func_attr({"Composite": "transpose_matmul_fuse", "Primitive": 1}) with R.dataflow(): permute_dims1034: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_0_fc1_weight5, axes=None) gv: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm358, permute_dims1034, out_dtype="void") R.output(gv) return gv @R.function(private=True) def fused_relax_permute_dims_relax_matmul2(model_decoder_layers_0_fc2_weight5: R.Tensor((1280, 5120), dtype="float16"), gelu130: R.Tensor((1, 1, 5120), dtype="float16")) -> R.Tensor((1, 1, 1280), dtype="float16"): R.func_attr({"Composite": "transpose_matmul_fuse", "Primitive": 1}) with R.dataflow(): permute_dims1035: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_fc2_weight5, axes=None) gv: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu130, permute_dims1035, out_dtype="void") R.output(gv) return gv @R.function(private=True) def fused_relax_permute_dims_relax_matmul3(model_decoder_embed_tokens_weight5: R.Tensor((51866, 1280), dtype="float16"), layer_norm452: R.Tensor((1, 1, 1280), dtype="float16")) -> R.Tensor((1, 1, 51866), dtype="float32"): R.func_attr({"Composite": "transpose_matmul_fuse", "Primitive": 1}) with R.dataflow(): permute_dims1284: R.Tensor((1280, 51866), dtype="float16") = R.permute_dims(model_decoder_embed_tokens_weight5, axes=None) gv: R.Tensor((1, 1, 51866), dtype="float32") = R.matmul(layer_norm452, permute_dims1284, out_dtype="float32") R.output(gv) return gv @R.function def multinomial_from_uniform(probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32"), uniform_samples: R.Tensor(("num_samples",), dtype="float32"), sample_indices: R.Tensor(("num_samples",), dtype="int32")) -> R.Tensor(("num_samples",), dtype="int32"): num_samples = T.int64() batch_size = T.int64() vocab_size = T.int64() R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) with R.dataflow(): probs_1: R.Tensor((batch_size, vocab_size), dtype="float32") = probs uniform_samples_1: R.Tensor((num_samples, 1), dtype="float32") = R.call_pure_packed("vm.builtin.reshape", uniform_samples, R.shape([num_samples, 1]), sinfo_args=(R.Tensor((num_samples, 1), dtype="float32"),)) sample_indices_1: R.Tensor((num_samples, 1), dtype="int32") = R.call_pure_packed("vm.builtin.reshape", sample_indices, R.shape([num_samples, 1]), sinfo_args=(R.Tensor((num_samples, 1), dtype="int32"),)) nn_multinomial_from_uniform: R.Tensor((num_samples, 1), dtype="int32") = R.multinomial_from_uniform(probs_1, uniform_samples_1, sample_indices_1, dtype="int32") lv: R.Tensor((num_samples,), dtype="int32") = R.call_pure_packed("vm.builtin.reshape", nn_multinomial_from_uniform, R.shape([num_samples]), sinfo_args=(R.Tensor((num_samples,), dtype="int32"),)) gv: R.Tensor((num_samples,), dtype="int32") = lv R.output(gv) return gv @R.function def prefill(input_ids: R.Tensor((1, "seq_len"), dtype="int32"), paged_kv_cache: R.Object, packed_params: R.Tuple(R.Tensor((1280, 128, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280, 3), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1500, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((51866, 1280), dtype="float16"), R.Tensor((448, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280, 1280), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((5120, 1280), dtype="float16"), R.Tensor((5120,), dtype="float16"), R.Tensor((1280, 5120), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"), R.Tensor((1280,), dtype="float16"))) -> R.Tensor((1, 1, 51866), dtype="float32"): seq_len = T.int64() R.func_attr({"num_input": 2, "relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) cls = Module with R.dataflow(): model_decoder_embed_tokens_weight4: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] model_decoder_embed_positions_weight4: R.Tensor((448, 1280), dtype="float16") = packed_params[488] model_decoder_layers_0_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] model_decoder_layers_0_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] model_decoder_layers_0_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[491] model_decoder_layers_0_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] model_decoder_layers_0_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[493] model_decoder_layers_0_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] model_decoder_layers_0_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[495] model_decoder_layers_0_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[496] model_decoder_layers_0_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[497] model_decoder_layers_0_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] model_decoder_layers_0_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[502] model_decoder_layers_0_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] model_decoder_layers_0_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[504] model_decoder_layers_0_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[505] model_decoder_layers_0_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[506] model_decoder_layers_0_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] model_decoder_layers_0_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[508] model_decoder_layers_0_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] model_decoder_layers_0_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[510] model_decoder_layers_0_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[511] model_decoder_layers_0_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[512] model_decoder_layers_1_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] model_decoder_layers_1_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] model_decoder_layers_1_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[515] model_decoder_layers_1_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] model_decoder_layers_1_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[517] model_decoder_layers_1_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] model_decoder_layers_1_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[519] model_decoder_layers_1_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[520] model_decoder_layers_1_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[521] model_decoder_layers_1_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] model_decoder_layers_1_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[526] model_decoder_layers_1_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] model_decoder_layers_1_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[528] model_decoder_layers_1_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[529] model_decoder_layers_1_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[530] model_decoder_layers_1_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] model_decoder_layers_1_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[532] model_decoder_layers_1_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] model_decoder_layers_1_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[534] model_decoder_layers_1_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[535] model_decoder_layers_1_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[536] model_decoder_layers_2_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] model_decoder_layers_2_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] model_decoder_layers_2_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[539] model_decoder_layers_2_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] model_decoder_layers_2_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[541] model_decoder_layers_2_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] model_decoder_layers_2_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[543] model_decoder_layers_2_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[544] model_decoder_layers_2_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[545] model_decoder_layers_2_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] model_decoder_layers_2_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[550] model_decoder_layers_2_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] model_decoder_layers_2_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[552] model_decoder_layers_2_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[553] model_decoder_layers_2_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[554] model_decoder_layers_2_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] model_decoder_layers_2_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[556] model_decoder_layers_2_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] model_decoder_layers_2_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[558] model_decoder_layers_2_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[559] model_decoder_layers_2_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[560] model_decoder_layers_3_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] model_decoder_layers_3_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] model_decoder_layers_3_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[563] model_decoder_layers_3_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] model_decoder_layers_3_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[565] model_decoder_layers_3_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] model_decoder_layers_3_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[567] model_decoder_layers_3_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[568] model_decoder_layers_3_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[569] model_decoder_layers_3_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] model_decoder_layers_3_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[574] model_decoder_layers_3_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] model_decoder_layers_3_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[576] model_decoder_layers_3_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[577] model_decoder_layers_3_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[578] model_decoder_layers_3_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] model_decoder_layers_3_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[580] model_decoder_layers_3_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] model_decoder_layers_3_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[582] model_decoder_layers_3_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[583] model_decoder_layers_3_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[584] model_decoder_layers_4_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] model_decoder_layers_4_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] model_decoder_layers_4_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[587] model_decoder_layers_4_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] model_decoder_layers_4_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[589] model_decoder_layers_4_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] model_decoder_layers_4_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[591] model_decoder_layers_4_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[592] model_decoder_layers_4_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[593] model_decoder_layers_4_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] model_decoder_layers_4_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[598] model_decoder_layers_4_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] model_decoder_layers_4_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[600] model_decoder_layers_4_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[601] model_decoder_layers_4_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[602] model_decoder_layers_4_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] model_decoder_layers_4_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[604] model_decoder_layers_4_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] model_decoder_layers_4_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[606] model_decoder_layers_4_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[607] model_decoder_layers_4_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[608] model_decoder_layers_5_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] model_decoder_layers_5_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] model_decoder_layers_5_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[611] model_decoder_layers_5_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] model_decoder_layers_5_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[613] model_decoder_layers_5_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] model_decoder_layers_5_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[615] model_decoder_layers_5_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[616] model_decoder_layers_5_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[617] model_decoder_layers_5_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] model_decoder_layers_5_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[622] model_decoder_layers_5_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] model_decoder_layers_5_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[624] model_decoder_layers_5_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[625] model_decoder_layers_5_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[626] model_decoder_layers_5_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] model_decoder_layers_5_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[628] model_decoder_layers_5_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] model_decoder_layers_5_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[630] model_decoder_layers_5_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[631] model_decoder_layers_5_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[632] model_decoder_layers_6_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] model_decoder_layers_6_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] model_decoder_layers_6_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[635] model_decoder_layers_6_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] model_decoder_layers_6_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[637] model_decoder_layers_6_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] model_decoder_layers_6_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[639] model_decoder_layers_6_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[640] model_decoder_layers_6_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[641] model_decoder_layers_6_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] model_decoder_layers_6_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[646] model_decoder_layers_6_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] model_decoder_layers_6_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[648] model_decoder_layers_6_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[649] model_decoder_layers_6_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[650] model_decoder_layers_6_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] model_decoder_layers_6_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[652] model_decoder_layers_6_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] model_decoder_layers_6_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[654] model_decoder_layers_6_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[655] model_decoder_layers_6_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[656] model_decoder_layers_7_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] model_decoder_layers_7_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] model_decoder_layers_7_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[659] model_decoder_layers_7_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] model_decoder_layers_7_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[661] model_decoder_layers_7_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] model_decoder_layers_7_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[663] model_decoder_layers_7_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[664] model_decoder_layers_7_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[665] model_decoder_layers_7_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] model_decoder_layers_7_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[670] model_decoder_layers_7_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] model_decoder_layers_7_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[672] model_decoder_layers_7_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[673] model_decoder_layers_7_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[674] model_decoder_layers_7_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] model_decoder_layers_7_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[676] model_decoder_layers_7_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] model_decoder_layers_7_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[678] model_decoder_layers_7_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[679] model_decoder_layers_7_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[680] model_decoder_layers_8_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] model_decoder_layers_8_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] model_decoder_layers_8_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[683] model_decoder_layers_8_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] model_decoder_layers_8_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[685] model_decoder_layers_8_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] model_decoder_layers_8_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[687] model_decoder_layers_8_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[688] model_decoder_layers_8_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[689] model_decoder_layers_8_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] model_decoder_layers_8_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[694] model_decoder_layers_8_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] model_decoder_layers_8_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[696] model_decoder_layers_8_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[697] model_decoder_layers_8_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[698] model_decoder_layers_8_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] model_decoder_layers_8_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[700] model_decoder_layers_8_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] model_decoder_layers_8_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[702] model_decoder_layers_8_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[703] model_decoder_layers_8_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[704] model_decoder_layers_9_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] model_decoder_layers_9_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] model_decoder_layers_9_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[707] model_decoder_layers_9_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] model_decoder_layers_9_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[709] model_decoder_layers_9_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] model_decoder_layers_9_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[711] model_decoder_layers_9_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[712] model_decoder_layers_9_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[713] model_decoder_layers_9_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] model_decoder_layers_9_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[718] model_decoder_layers_9_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] model_decoder_layers_9_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[720] model_decoder_layers_9_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[721] model_decoder_layers_9_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[722] model_decoder_layers_9_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] model_decoder_layers_9_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[724] model_decoder_layers_9_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] model_decoder_layers_9_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[726] model_decoder_layers_9_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[727] model_decoder_layers_9_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[728] model_decoder_layers_10_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] model_decoder_layers_10_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] model_decoder_layers_10_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[731] model_decoder_layers_10_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] model_decoder_layers_10_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[733] model_decoder_layers_10_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] model_decoder_layers_10_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[735] model_decoder_layers_10_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[736] model_decoder_layers_10_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[737] model_decoder_layers_10_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] model_decoder_layers_10_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[742] model_decoder_layers_10_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] model_decoder_layers_10_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[744] model_decoder_layers_10_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[745] model_decoder_layers_10_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[746] model_decoder_layers_10_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] model_decoder_layers_10_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[748] model_decoder_layers_10_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] model_decoder_layers_10_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[750] model_decoder_layers_10_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[751] model_decoder_layers_10_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[752] model_decoder_layers_11_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] model_decoder_layers_11_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] model_decoder_layers_11_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[755] model_decoder_layers_11_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] model_decoder_layers_11_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[757] model_decoder_layers_11_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] model_decoder_layers_11_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[759] model_decoder_layers_11_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[760] model_decoder_layers_11_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[761] model_decoder_layers_11_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] model_decoder_layers_11_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[766] model_decoder_layers_11_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] model_decoder_layers_11_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[768] model_decoder_layers_11_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[769] model_decoder_layers_11_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[770] model_decoder_layers_11_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] model_decoder_layers_11_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[772] model_decoder_layers_11_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] model_decoder_layers_11_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[774] model_decoder_layers_11_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[775] model_decoder_layers_11_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[776] model_decoder_layers_12_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] model_decoder_layers_12_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] model_decoder_layers_12_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[779] model_decoder_layers_12_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] model_decoder_layers_12_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[781] model_decoder_layers_12_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] model_decoder_layers_12_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[783] model_decoder_layers_12_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[784] model_decoder_layers_12_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[785] model_decoder_layers_12_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] model_decoder_layers_12_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[790] model_decoder_layers_12_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] model_decoder_layers_12_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[792] model_decoder_layers_12_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[793] model_decoder_layers_12_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[794] model_decoder_layers_12_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] model_decoder_layers_12_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[796] model_decoder_layers_12_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] model_decoder_layers_12_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[798] model_decoder_layers_12_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[799] model_decoder_layers_12_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[800] model_decoder_layers_13_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] model_decoder_layers_13_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] model_decoder_layers_13_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[803] model_decoder_layers_13_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] model_decoder_layers_13_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[805] model_decoder_layers_13_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] model_decoder_layers_13_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[807] model_decoder_layers_13_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[808] model_decoder_layers_13_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[809] model_decoder_layers_13_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] model_decoder_layers_13_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[814] model_decoder_layers_13_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] model_decoder_layers_13_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[816] model_decoder_layers_13_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[817] model_decoder_layers_13_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[818] model_decoder_layers_13_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] model_decoder_layers_13_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[820] model_decoder_layers_13_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] model_decoder_layers_13_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[822] model_decoder_layers_13_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[823] model_decoder_layers_13_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[824] model_decoder_layers_14_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] model_decoder_layers_14_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] model_decoder_layers_14_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[827] model_decoder_layers_14_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] model_decoder_layers_14_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[829] model_decoder_layers_14_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] model_decoder_layers_14_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[831] model_decoder_layers_14_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[832] model_decoder_layers_14_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[833] model_decoder_layers_14_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] model_decoder_layers_14_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[838] model_decoder_layers_14_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] model_decoder_layers_14_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[840] model_decoder_layers_14_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[841] model_decoder_layers_14_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[842] model_decoder_layers_14_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] model_decoder_layers_14_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[844] model_decoder_layers_14_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] model_decoder_layers_14_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[846] model_decoder_layers_14_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[847] model_decoder_layers_14_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[848] model_decoder_layers_15_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] model_decoder_layers_15_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] model_decoder_layers_15_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[851] model_decoder_layers_15_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] model_decoder_layers_15_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[853] model_decoder_layers_15_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] model_decoder_layers_15_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[855] model_decoder_layers_15_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[856] model_decoder_layers_15_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[857] model_decoder_layers_15_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] model_decoder_layers_15_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[862] model_decoder_layers_15_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] model_decoder_layers_15_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[864] model_decoder_layers_15_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[865] model_decoder_layers_15_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[866] model_decoder_layers_15_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] model_decoder_layers_15_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[868] model_decoder_layers_15_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] model_decoder_layers_15_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[870] model_decoder_layers_15_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[871] model_decoder_layers_15_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[872] model_decoder_layers_16_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] model_decoder_layers_16_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] model_decoder_layers_16_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[875] model_decoder_layers_16_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] model_decoder_layers_16_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[877] model_decoder_layers_16_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] model_decoder_layers_16_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[879] model_decoder_layers_16_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[880] model_decoder_layers_16_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[881] model_decoder_layers_16_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] model_decoder_layers_16_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[886] model_decoder_layers_16_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] model_decoder_layers_16_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[888] model_decoder_layers_16_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[889] model_decoder_layers_16_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[890] model_decoder_layers_16_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] model_decoder_layers_16_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[892] model_decoder_layers_16_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] model_decoder_layers_16_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[894] model_decoder_layers_16_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[895] model_decoder_layers_16_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[896] model_decoder_layers_17_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] model_decoder_layers_17_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] model_decoder_layers_17_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[899] model_decoder_layers_17_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] model_decoder_layers_17_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[901] model_decoder_layers_17_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] model_decoder_layers_17_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[903] model_decoder_layers_17_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[904] model_decoder_layers_17_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[905] model_decoder_layers_17_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] model_decoder_layers_17_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[910] model_decoder_layers_17_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] model_decoder_layers_17_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[912] model_decoder_layers_17_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[913] model_decoder_layers_17_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[914] model_decoder_layers_17_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] model_decoder_layers_17_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[916] model_decoder_layers_17_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] model_decoder_layers_17_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[918] model_decoder_layers_17_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[919] model_decoder_layers_17_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[920] model_decoder_layers_18_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] model_decoder_layers_18_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] model_decoder_layers_18_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[923] model_decoder_layers_18_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] model_decoder_layers_18_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[925] model_decoder_layers_18_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] model_decoder_layers_18_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[927] model_decoder_layers_18_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[928] model_decoder_layers_18_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[929] model_decoder_layers_18_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] model_decoder_layers_18_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[934] model_decoder_layers_18_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] model_decoder_layers_18_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[936] model_decoder_layers_18_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[937] model_decoder_layers_18_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[938] model_decoder_layers_18_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] model_decoder_layers_18_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[940] model_decoder_layers_18_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] model_decoder_layers_18_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[942] model_decoder_layers_18_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[943] model_decoder_layers_18_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[944] model_decoder_layers_19_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] model_decoder_layers_19_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] model_decoder_layers_19_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[947] model_decoder_layers_19_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] model_decoder_layers_19_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[949] model_decoder_layers_19_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] model_decoder_layers_19_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[951] model_decoder_layers_19_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[952] model_decoder_layers_19_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[953] model_decoder_layers_19_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] model_decoder_layers_19_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[958] model_decoder_layers_19_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] model_decoder_layers_19_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[960] model_decoder_layers_19_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[961] model_decoder_layers_19_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[962] model_decoder_layers_19_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] model_decoder_layers_19_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[964] model_decoder_layers_19_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] model_decoder_layers_19_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[966] model_decoder_layers_19_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[967] model_decoder_layers_19_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[968] model_decoder_layers_20_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] model_decoder_layers_20_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] model_decoder_layers_20_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[971] model_decoder_layers_20_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] model_decoder_layers_20_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[973] model_decoder_layers_20_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] model_decoder_layers_20_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[975] model_decoder_layers_20_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[976] model_decoder_layers_20_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[977] model_decoder_layers_20_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] model_decoder_layers_20_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[982] model_decoder_layers_20_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] model_decoder_layers_20_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[984] model_decoder_layers_20_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[985] model_decoder_layers_20_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[986] model_decoder_layers_20_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] model_decoder_layers_20_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[988] model_decoder_layers_20_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] model_decoder_layers_20_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[990] model_decoder_layers_20_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[991] model_decoder_layers_20_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[992] model_decoder_layers_21_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] model_decoder_layers_21_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] model_decoder_layers_21_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[995] model_decoder_layers_21_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] model_decoder_layers_21_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[997] model_decoder_layers_21_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] model_decoder_layers_21_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[999] model_decoder_layers_21_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1000] model_decoder_layers_21_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1001] model_decoder_layers_21_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] model_decoder_layers_21_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1006] model_decoder_layers_21_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] model_decoder_layers_21_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1008] model_decoder_layers_21_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1009] model_decoder_layers_21_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1010] model_decoder_layers_21_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] model_decoder_layers_21_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1012] model_decoder_layers_21_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] model_decoder_layers_21_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1014] model_decoder_layers_21_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1015] model_decoder_layers_21_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1016] model_decoder_layers_22_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] model_decoder_layers_22_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] model_decoder_layers_22_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1019] model_decoder_layers_22_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] model_decoder_layers_22_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1021] model_decoder_layers_22_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] model_decoder_layers_22_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1023] model_decoder_layers_22_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1024] model_decoder_layers_22_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1025] model_decoder_layers_22_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] model_decoder_layers_22_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1030] model_decoder_layers_22_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] model_decoder_layers_22_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1032] model_decoder_layers_22_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1033] model_decoder_layers_22_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1034] model_decoder_layers_22_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] model_decoder_layers_22_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1036] model_decoder_layers_22_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] model_decoder_layers_22_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1038] model_decoder_layers_22_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1039] model_decoder_layers_22_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1040] model_decoder_layers_23_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] model_decoder_layers_23_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] model_decoder_layers_23_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1043] model_decoder_layers_23_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] model_decoder_layers_23_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1045] model_decoder_layers_23_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] model_decoder_layers_23_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1047] model_decoder_layers_23_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1048] model_decoder_layers_23_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1049] model_decoder_layers_23_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] model_decoder_layers_23_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1054] model_decoder_layers_23_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] model_decoder_layers_23_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1056] model_decoder_layers_23_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1057] model_decoder_layers_23_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1058] model_decoder_layers_23_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] model_decoder_layers_23_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1060] model_decoder_layers_23_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] model_decoder_layers_23_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1062] model_decoder_layers_23_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1063] model_decoder_layers_23_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1064] model_decoder_layers_24_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] model_decoder_layers_24_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] model_decoder_layers_24_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1067] model_decoder_layers_24_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] model_decoder_layers_24_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1069] model_decoder_layers_24_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] model_decoder_layers_24_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1071] model_decoder_layers_24_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1072] model_decoder_layers_24_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1073] model_decoder_layers_24_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] model_decoder_layers_24_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1078] model_decoder_layers_24_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] model_decoder_layers_24_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1080] model_decoder_layers_24_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1081] model_decoder_layers_24_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1082] model_decoder_layers_24_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] model_decoder_layers_24_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1084] model_decoder_layers_24_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] model_decoder_layers_24_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1086] model_decoder_layers_24_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1087] model_decoder_layers_24_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1088] model_decoder_layers_25_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] model_decoder_layers_25_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] model_decoder_layers_25_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1091] model_decoder_layers_25_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] model_decoder_layers_25_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1093] model_decoder_layers_25_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] model_decoder_layers_25_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1095] model_decoder_layers_25_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1096] model_decoder_layers_25_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1097] model_decoder_layers_25_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] model_decoder_layers_25_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1102] model_decoder_layers_25_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] model_decoder_layers_25_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1104] model_decoder_layers_25_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1105] model_decoder_layers_25_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1106] model_decoder_layers_25_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] model_decoder_layers_25_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1108] model_decoder_layers_25_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] model_decoder_layers_25_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1110] model_decoder_layers_25_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1111] model_decoder_layers_25_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1112] model_decoder_layers_26_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] model_decoder_layers_26_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] model_decoder_layers_26_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1115] model_decoder_layers_26_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] model_decoder_layers_26_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1117] model_decoder_layers_26_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] model_decoder_layers_26_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1119] model_decoder_layers_26_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1120] model_decoder_layers_26_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1121] model_decoder_layers_26_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] model_decoder_layers_26_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1126] model_decoder_layers_26_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] model_decoder_layers_26_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1128] model_decoder_layers_26_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1129] model_decoder_layers_26_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1130] model_decoder_layers_26_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] model_decoder_layers_26_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1132] model_decoder_layers_26_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] model_decoder_layers_26_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1134] model_decoder_layers_26_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1135] model_decoder_layers_26_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1136] model_decoder_layers_27_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] model_decoder_layers_27_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] model_decoder_layers_27_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1139] model_decoder_layers_27_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] model_decoder_layers_27_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1141] model_decoder_layers_27_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] model_decoder_layers_27_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1143] model_decoder_layers_27_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1144] model_decoder_layers_27_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1145] model_decoder_layers_27_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] model_decoder_layers_27_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1150] model_decoder_layers_27_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] model_decoder_layers_27_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1152] model_decoder_layers_27_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1153] model_decoder_layers_27_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1154] model_decoder_layers_27_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] model_decoder_layers_27_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1156] model_decoder_layers_27_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] model_decoder_layers_27_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1158] model_decoder_layers_27_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1159] model_decoder_layers_27_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1160] model_decoder_layers_28_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] model_decoder_layers_28_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] model_decoder_layers_28_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1163] model_decoder_layers_28_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] model_decoder_layers_28_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1165] model_decoder_layers_28_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] model_decoder_layers_28_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1167] model_decoder_layers_28_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1168] model_decoder_layers_28_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1169] model_decoder_layers_28_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] model_decoder_layers_28_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1174] model_decoder_layers_28_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] model_decoder_layers_28_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1176] model_decoder_layers_28_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1177] model_decoder_layers_28_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1178] model_decoder_layers_28_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] model_decoder_layers_28_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1180] model_decoder_layers_28_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] model_decoder_layers_28_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1182] model_decoder_layers_28_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1183] model_decoder_layers_28_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1184] model_decoder_layers_29_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] model_decoder_layers_29_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] model_decoder_layers_29_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1187] model_decoder_layers_29_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] model_decoder_layers_29_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1189] model_decoder_layers_29_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] model_decoder_layers_29_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1191] model_decoder_layers_29_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1192] model_decoder_layers_29_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1193] model_decoder_layers_29_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] model_decoder_layers_29_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1198] model_decoder_layers_29_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] model_decoder_layers_29_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1200] model_decoder_layers_29_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1201] model_decoder_layers_29_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1202] model_decoder_layers_29_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] model_decoder_layers_29_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1204] model_decoder_layers_29_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] model_decoder_layers_29_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1206] model_decoder_layers_29_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1207] model_decoder_layers_29_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1208] model_decoder_layers_30_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] model_decoder_layers_30_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] model_decoder_layers_30_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1211] model_decoder_layers_30_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] model_decoder_layers_30_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1213] model_decoder_layers_30_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] model_decoder_layers_30_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1215] model_decoder_layers_30_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1216] model_decoder_layers_30_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1217] model_decoder_layers_30_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] model_decoder_layers_30_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1222] model_decoder_layers_30_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] model_decoder_layers_30_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1224] model_decoder_layers_30_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1225] model_decoder_layers_30_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1226] model_decoder_layers_30_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] model_decoder_layers_30_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1228] model_decoder_layers_30_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] model_decoder_layers_30_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1230] model_decoder_layers_30_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1231] model_decoder_layers_30_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1232] model_decoder_layers_31_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] model_decoder_layers_31_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] model_decoder_layers_31_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1235] model_decoder_layers_31_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] model_decoder_layers_31_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1237] model_decoder_layers_31_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] model_decoder_layers_31_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1239] model_decoder_layers_31_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1240] model_decoder_layers_31_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1241] model_decoder_layers_31_encoder_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] model_decoder_layers_31_encoder_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1246] model_decoder_layers_31_encoder_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] model_decoder_layers_31_encoder_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1248] model_decoder_layers_31_encoder_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1249] model_decoder_layers_31_encoder_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1250] model_decoder_layers_31_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] model_decoder_layers_31_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[1252] model_decoder_layers_31_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] model_decoder_layers_31_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1254] model_decoder_layers_31_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1255] model_decoder_layers_31_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1256] model_decoder_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[1257] model_decoder_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1258] reshape1030: R.Tensor((seq_len,), dtype="int32") = R.reshape(input_ids, R.shape([seq_len])) take5: R.Tensor((seq_len, 1280), dtype="float16") = R.take(model_decoder_embed_tokens_weight4, reshape1030, axis=0) reshape1031: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(take5, R.shape([1, seq_len, 1280])) lv198: R.Tensor((seq_len,), dtype="int32") = R.call_pure_packed("vm.builtin.attention_kv_cache_get_query_positions", paged_kv_cache, sinfo_args=(R.Tensor((seq_len,), dtype="int32"),)) take6: R.Tensor((seq_len, 1280), dtype="float16") = R.take(model_decoder_embed_positions_weight4, lv198, axis=0) reshape1032: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(take6, R.shape([1, seq_len, 1280])) add899: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(reshape1031, reshape1032) layer_norm259: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add899, model_decoder_layers_0_self_attn_layer_norm_weight4, model_decoder_layers_0_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv32 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_self_attn_q_proj_weight4, layer_norm259, model_decoder_layers_0_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1033: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv32, R.shape([1, seq_len, 20, 64])) lv32_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_0_self_attn_k_proj_weight4, layer_norm259), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1034: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv32_1, R.shape([1, seq_len, 20, 64])) lv33 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_self_attn_v_proj_weight4, layer_norm259, model_decoder_layers_0_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1035: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv33, R.shape([1, seq_len, 20, 64])) concat64: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1033, reshape1034, reshape1035), axis=2) reshape1036: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat64, R.shape([seq_len, 60, 64])) lv199 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape1036), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1037: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv199, R.shape([1, seq_len, 20, 64])) reshape1038: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1037, R.shape([1, seq_len, 1280])) lv34 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_self_attn_out_proj_weight4, reshape1038, model_decoder_layers_0_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add903: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add899, lv34) layer_norm260: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add903, model_decoder_layers_0_encoder_attn_layer_norm_weight4, model_decoder_layers_0_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv35 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_encoder_attn_q_proj_weight4, layer_norm260, model_decoder_layers_0_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1039: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv35, R.shape([1, seq_len, 20, 64])) reshape1040: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1039, R.shape([seq_len, 20, 64])) lv200 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(0), R.prim_value(T.float32(1)), reshape1040), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1041: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv200, R.shape([1, seq_len, 20, 64])) reshape1042: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1041, R.shape([1, seq_len, 1280])) lv36 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_0_encoder_attn_out_proj_weight4, reshape1042, model_decoder_layers_0_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add906: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add903, lv36) layer_norm261: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add906, model_decoder_layers_0_final_layer_norm_weight4, model_decoder_layers_0_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_0_fc1_weight4, layer_norm261, model_decoder_layers_0_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv37 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_0_fc2_weight4, lv, model_decoder_layers_0_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add909: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add906, lv37) layer_norm262: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add909, model_decoder_layers_1_self_attn_layer_norm_weight4, model_decoder_layers_1_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv38 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_self_attn_q_proj_weight4, layer_norm262, model_decoder_layers_1_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1043: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv38, R.shape([1, seq_len, 20, 64])) lv33_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_1_self_attn_k_proj_weight4, layer_norm262), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1044: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv33_1, R.shape([1, seq_len, 20, 64])) lv39 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_self_attn_v_proj_weight4, layer_norm262, model_decoder_layers_1_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1045: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv39, R.shape([1, seq_len, 20, 64])) concat65: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1043, reshape1044, reshape1045), axis=2) reshape1046: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat65, R.shape([seq_len, 60, 64])) lv201 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape1046), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1047: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv201, R.shape([1, seq_len, 20, 64])) reshape1048: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1047, R.shape([1, seq_len, 1280])) lv40 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_self_attn_out_proj_weight4, reshape1048, model_decoder_layers_1_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add913: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add909, lv40) layer_norm263: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add913, model_decoder_layers_1_encoder_attn_layer_norm_weight4, model_decoder_layers_1_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv41 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_encoder_attn_q_proj_weight4, layer_norm263, model_decoder_layers_1_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1049: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv41, R.shape([1, seq_len, 20, 64])) reshape1050: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1049, R.shape([seq_len, 20, 64])) lv202 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(1), R.prim_value(T.float32(1)), reshape1050), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1051: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv202, R.shape([1, seq_len, 20, 64])) reshape1052: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1051, R.shape([1, seq_len, 1280])) lv42 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_1_encoder_attn_out_proj_weight4, reshape1052, model_decoder_layers_1_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add916: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add913, lv42) layer_norm264: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add916, model_decoder_layers_1_final_layer_norm_weight4, model_decoder_layers_1_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_1_fc1_weight4, layer_norm264, model_decoder_layers_1_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv43 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_1_fc2_weight4, lv1, model_decoder_layers_1_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add919: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add916, lv43) layer_norm265: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add919, model_decoder_layers_2_self_attn_layer_norm_weight4, model_decoder_layers_2_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv44 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_self_attn_q_proj_weight4, layer_norm265, model_decoder_layers_2_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1053: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv44, R.shape([1, seq_len, 20, 64])) lv34_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_2_self_attn_k_proj_weight4, layer_norm265), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1054: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv34_1, R.shape([1, seq_len, 20, 64])) lv45 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_self_attn_v_proj_weight4, layer_norm265, model_decoder_layers_2_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1055: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv45, R.shape([1, seq_len, 20, 64])) concat66: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1053, reshape1054, reshape1055), axis=2) reshape1056: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat66, R.shape([seq_len, 60, 64])) lv203 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape1056), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1057: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv203, R.shape([1, seq_len, 20, 64])) reshape1058: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1057, R.shape([1, seq_len, 1280])) lv46 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_self_attn_out_proj_weight4, reshape1058, model_decoder_layers_2_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add923: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add919, lv46) layer_norm266: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add923, model_decoder_layers_2_encoder_attn_layer_norm_weight4, model_decoder_layers_2_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv47 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_encoder_attn_q_proj_weight4, layer_norm266, model_decoder_layers_2_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1059: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv47, R.shape([1, seq_len, 20, 64])) reshape1060: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1059, R.shape([seq_len, 20, 64])) lv204 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(2), R.prim_value(T.float32(1)), reshape1060), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1061: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv204, R.shape([1, seq_len, 20, 64])) reshape1062: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1061, R.shape([1, seq_len, 1280])) lv48 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_2_encoder_attn_out_proj_weight4, reshape1062, model_decoder_layers_2_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add926: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add923, lv48) layer_norm267: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add926, model_decoder_layers_2_final_layer_norm_weight4, model_decoder_layers_2_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv2 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_2_fc1_weight4, layer_norm267, model_decoder_layers_2_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv49 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_2_fc2_weight4, lv2, model_decoder_layers_2_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add929: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add926, lv49) layer_norm268: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add929, model_decoder_layers_3_self_attn_layer_norm_weight4, model_decoder_layers_3_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv50 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_self_attn_q_proj_weight4, layer_norm268, model_decoder_layers_3_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1063: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv50, R.shape([1, seq_len, 20, 64])) lv35_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_3_self_attn_k_proj_weight4, layer_norm268), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1064: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv35_1, R.shape([1, seq_len, 20, 64])) lv51 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_self_attn_v_proj_weight4, layer_norm268, model_decoder_layers_3_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1065: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv51, R.shape([1, seq_len, 20, 64])) concat67: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1063, reshape1064, reshape1065), axis=2) reshape1066: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat67, R.shape([seq_len, 60, 64])) lv205 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape1066), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1067: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv205, R.shape([1, seq_len, 20, 64])) reshape1068: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1067, R.shape([1, seq_len, 1280])) lv52 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_self_attn_out_proj_weight4, reshape1068, model_decoder_layers_3_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add933: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add929, lv52) layer_norm269: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add933, model_decoder_layers_3_encoder_attn_layer_norm_weight4, model_decoder_layers_3_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv53 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_encoder_attn_q_proj_weight4, layer_norm269, model_decoder_layers_3_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1069: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv53, R.shape([1, seq_len, 20, 64])) reshape1070: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1069, R.shape([seq_len, 20, 64])) lv206 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(3), R.prim_value(T.float32(1)), reshape1070), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1071: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv206, R.shape([1, seq_len, 20, 64])) reshape1072: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1071, R.shape([1, seq_len, 1280])) lv54 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_3_encoder_attn_out_proj_weight4, reshape1072, model_decoder_layers_3_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add936: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add933, lv54) layer_norm270: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add936, model_decoder_layers_3_final_layer_norm_weight4, model_decoder_layers_3_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv3 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_3_fc1_weight4, layer_norm270, model_decoder_layers_3_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv55 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_3_fc2_weight4, lv3, model_decoder_layers_3_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add939: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add936, lv55) layer_norm271: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add939, model_decoder_layers_4_self_attn_layer_norm_weight4, model_decoder_layers_4_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv56 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_self_attn_q_proj_weight4, layer_norm271, model_decoder_layers_4_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1073: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv56, R.shape([1, seq_len, 20, 64])) lv36_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_4_self_attn_k_proj_weight4, layer_norm271), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1074: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv36_1, R.shape([1, seq_len, 20, 64])) lv57 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_self_attn_v_proj_weight4, layer_norm271, model_decoder_layers_4_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1075: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv57, R.shape([1, seq_len, 20, 64])) concat68: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1073, reshape1074, reshape1075), axis=2) reshape1076: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat68, R.shape([seq_len, 60, 64])) lv207 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape1076), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1077: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv207, R.shape([1, seq_len, 20, 64])) reshape1078: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1077, R.shape([1, seq_len, 1280])) lv58 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_self_attn_out_proj_weight4, reshape1078, model_decoder_layers_4_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add943: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add939, lv58) layer_norm272: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add943, model_decoder_layers_4_encoder_attn_layer_norm_weight4, model_decoder_layers_4_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv59 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_encoder_attn_q_proj_weight4, layer_norm272, model_decoder_layers_4_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1079: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv59, R.shape([1, seq_len, 20, 64])) reshape1080: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1079, R.shape([seq_len, 20, 64])) lv208 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(4), R.prim_value(T.float32(1)), reshape1080), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1081: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv208, R.shape([1, seq_len, 20, 64])) reshape1082: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1081, R.shape([1, seq_len, 1280])) lv60 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_4_encoder_attn_out_proj_weight4, reshape1082, model_decoder_layers_4_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add946: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add943, lv60) layer_norm273: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add946, model_decoder_layers_4_final_layer_norm_weight4, model_decoder_layers_4_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv4 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_4_fc1_weight4, layer_norm273, model_decoder_layers_4_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv61 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_4_fc2_weight4, lv4, model_decoder_layers_4_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add949: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add946, lv61) layer_norm274: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add949, model_decoder_layers_5_self_attn_layer_norm_weight4, model_decoder_layers_5_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv62 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_self_attn_q_proj_weight4, layer_norm274, model_decoder_layers_5_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1083: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv62, R.shape([1, seq_len, 20, 64])) lv37_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_5_self_attn_k_proj_weight4, layer_norm274), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1084: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv37_1, R.shape([1, seq_len, 20, 64])) lv63 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_self_attn_v_proj_weight4, layer_norm274, model_decoder_layers_5_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1085: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv63, R.shape([1, seq_len, 20, 64])) concat69: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1083, reshape1084, reshape1085), axis=2) reshape1086: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat69, R.shape([seq_len, 60, 64])) lv209 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape1086), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1087: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv209, R.shape([1, seq_len, 20, 64])) reshape1088: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1087, R.shape([1, seq_len, 1280])) lv64 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_self_attn_out_proj_weight4, reshape1088, model_decoder_layers_5_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add953: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add949, lv64) layer_norm275: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add953, model_decoder_layers_5_encoder_attn_layer_norm_weight4, model_decoder_layers_5_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv65 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_encoder_attn_q_proj_weight4, layer_norm275, model_decoder_layers_5_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1089: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv65, R.shape([1, seq_len, 20, 64])) reshape1090: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1089, R.shape([seq_len, 20, 64])) lv210 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(5), R.prim_value(T.float32(1)), reshape1090), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1091: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv210, R.shape([1, seq_len, 20, 64])) reshape1092: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1091, R.shape([1, seq_len, 1280])) lv66 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_5_encoder_attn_out_proj_weight4, reshape1092, model_decoder_layers_5_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add956: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add953, lv66) layer_norm276: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add956, model_decoder_layers_5_final_layer_norm_weight4, model_decoder_layers_5_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv5 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_5_fc1_weight4, layer_norm276, model_decoder_layers_5_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv67 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_5_fc2_weight4, lv5, model_decoder_layers_5_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add959: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add956, lv67) layer_norm277: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add959, model_decoder_layers_6_self_attn_layer_norm_weight4, model_decoder_layers_6_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv68 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_self_attn_q_proj_weight4, layer_norm277, model_decoder_layers_6_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1093: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv68, R.shape([1, seq_len, 20, 64])) lv38_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_6_self_attn_k_proj_weight4, layer_norm277), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1094: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv38_1, R.shape([1, seq_len, 20, 64])) lv69 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_self_attn_v_proj_weight4, layer_norm277, model_decoder_layers_6_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1095: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv69, R.shape([1, seq_len, 20, 64])) concat70: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1093, reshape1094, reshape1095), axis=2) reshape1096: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat70, R.shape([seq_len, 60, 64])) lv211 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape1096), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1097: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv211, R.shape([1, seq_len, 20, 64])) reshape1098: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1097, R.shape([1, seq_len, 1280])) lv70 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_self_attn_out_proj_weight4, reshape1098, model_decoder_layers_6_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add963: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add959, lv70) layer_norm278: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add963, model_decoder_layers_6_encoder_attn_layer_norm_weight4, model_decoder_layers_6_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv71 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_encoder_attn_q_proj_weight4, layer_norm278, model_decoder_layers_6_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1099: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv71, R.shape([1, seq_len, 20, 64])) reshape1100: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1099, R.shape([seq_len, 20, 64])) lv212 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(6), R.prim_value(T.float32(1)), reshape1100), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1101: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv212, R.shape([1, seq_len, 20, 64])) reshape1102: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1101, R.shape([1, seq_len, 1280])) lv72 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_6_encoder_attn_out_proj_weight4, reshape1102, model_decoder_layers_6_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add966: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add963, lv72) layer_norm279: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add966, model_decoder_layers_6_final_layer_norm_weight4, model_decoder_layers_6_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv6 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_6_fc1_weight4, layer_norm279, model_decoder_layers_6_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv73 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_6_fc2_weight4, lv6, model_decoder_layers_6_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add969: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add966, lv73) layer_norm280: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add969, model_decoder_layers_7_self_attn_layer_norm_weight4, model_decoder_layers_7_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv74 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_self_attn_q_proj_weight4, layer_norm280, model_decoder_layers_7_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1103: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv74, R.shape([1, seq_len, 20, 64])) lv39_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_7_self_attn_k_proj_weight4, layer_norm280), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1104: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv39_1, R.shape([1, seq_len, 20, 64])) lv75 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_self_attn_v_proj_weight4, layer_norm280, model_decoder_layers_7_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1105: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv75, R.shape([1, seq_len, 20, 64])) concat71: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1103, reshape1104, reshape1105), axis=2) reshape1106: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat71, R.shape([seq_len, 60, 64])) lv213 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape1106), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1107: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv213, R.shape([1, seq_len, 20, 64])) reshape1108: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1107, R.shape([1, seq_len, 1280])) lv76 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_self_attn_out_proj_weight4, reshape1108, model_decoder_layers_7_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add973: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add969, lv76) layer_norm281: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add973, model_decoder_layers_7_encoder_attn_layer_norm_weight4, model_decoder_layers_7_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv77 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_encoder_attn_q_proj_weight4, layer_norm281, model_decoder_layers_7_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1109: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv77, R.shape([1, seq_len, 20, 64])) reshape1110: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1109, R.shape([seq_len, 20, 64])) lv214 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(7), R.prim_value(T.float32(1)), reshape1110), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1111: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv214, R.shape([1, seq_len, 20, 64])) reshape1112: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1111, R.shape([1, seq_len, 1280])) lv78 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_7_encoder_attn_out_proj_weight4, reshape1112, model_decoder_layers_7_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add976: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add973, lv78) layer_norm282: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add976, model_decoder_layers_7_final_layer_norm_weight4, model_decoder_layers_7_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv7 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_7_fc1_weight4, layer_norm282, model_decoder_layers_7_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv79 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_7_fc2_weight4, lv7, model_decoder_layers_7_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add979: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add976, lv79) layer_norm283: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add979, model_decoder_layers_8_self_attn_layer_norm_weight4, model_decoder_layers_8_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv80 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_self_attn_q_proj_weight4, layer_norm283, model_decoder_layers_8_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1113: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv80, R.shape([1, seq_len, 20, 64])) lv40_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_8_self_attn_k_proj_weight4, layer_norm283), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1114: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv40_1, R.shape([1, seq_len, 20, 64])) lv81 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_self_attn_v_proj_weight4, layer_norm283, model_decoder_layers_8_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1115: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv81, R.shape([1, seq_len, 20, 64])) concat72: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1113, reshape1114, reshape1115), axis=2) reshape1116: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat72, R.shape([seq_len, 60, 64])) lv215 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape1116), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1117: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv215, R.shape([1, seq_len, 20, 64])) reshape1118: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1117, R.shape([1, seq_len, 1280])) lv82 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_self_attn_out_proj_weight4, reshape1118, model_decoder_layers_8_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add983: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add979, lv82) layer_norm284: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add983, model_decoder_layers_8_encoder_attn_layer_norm_weight4, model_decoder_layers_8_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv83 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_encoder_attn_q_proj_weight4, layer_norm284, model_decoder_layers_8_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1119: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv83, R.shape([1, seq_len, 20, 64])) reshape1120: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1119, R.shape([seq_len, 20, 64])) lv216 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(8), R.prim_value(T.float32(1)), reshape1120), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1121: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv216, R.shape([1, seq_len, 20, 64])) reshape1122: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1121, R.shape([1, seq_len, 1280])) lv84 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_8_encoder_attn_out_proj_weight4, reshape1122, model_decoder_layers_8_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add986: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add983, lv84) layer_norm285: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add986, model_decoder_layers_8_final_layer_norm_weight4, model_decoder_layers_8_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv8 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_8_fc1_weight4, layer_norm285, model_decoder_layers_8_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv85 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_8_fc2_weight4, lv8, model_decoder_layers_8_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add989: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add986, lv85) layer_norm286: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add989, model_decoder_layers_9_self_attn_layer_norm_weight4, model_decoder_layers_9_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv86 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_self_attn_q_proj_weight4, layer_norm286, model_decoder_layers_9_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1123: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv86, R.shape([1, seq_len, 20, 64])) lv41_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_9_self_attn_k_proj_weight4, layer_norm286), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1124: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv41_1, R.shape([1, seq_len, 20, 64])) lv87 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_self_attn_v_proj_weight4, layer_norm286, model_decoder_layers_9_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1125: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv87, R.shape([1, seq_len, 20, 64])) concat73: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1123, reshape1124, reshape1125), axis=2) reshape1126: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat73, R.shape([seq_len, 60, 64])) lv217 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape1126), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1127: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv217, R.shape([1, seq_len, 20, 64])) reshape1128: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1127, R.shape([1, seq_len, 1280])) lv88 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_self_attn_out_proj_weight4, reshape1128, model_decoder_layers_9_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add993: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add989, lv88) layer_norm287: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add993, model_decoder_layers_9_encoder_attn_layer_norm_weight4, model_decoder_layers_9_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv89 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_encoder_attn_q_proj_weight4, layer_norm287, model_decoder_layers_9_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1129: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv89, R.shape([1, seq_len, 20, 64])) reshape1130: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1129, R.shape([seq_len, 20, 64])) lv218 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(9), R.prim_value(T.float32(1)), reshape1130), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1131: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv218, R.shape([1, seq_len, 20, 64])) reshape1132: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1131, R.shape([1, seq_len, 1280])) lv90 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_9_encoder_attn_out_proj_weight4, reshape1132, model_decoder_layers_9_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add996: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add993, lv90) layer_norm288: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add996, model_decoder_layers_9_final_layer_norm_weight4, model_decoder_layers_9_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv9 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_9_fc1_weight4, layer_norm288, model_decoder_layers_9_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv91 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_9_fc2_weight4, lv9, model_decoder_layers_9_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add999: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add996, lv91) layer_norm289: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add999, model_decoder_layers_10_self_attn_layer_norm_weight4, model_decoder_layers_10_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv92 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_self_attn_q_proj_weight4, layer_norm289, model_decoder_layers_10_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1133: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv92, R.shape([1, seq_len, 20, 64])) lv42_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_10_self_attn_k_proj_weight4, layer_norm289), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1134: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv42_1, R.shape([1, seq_len, 20, 64])) lv93 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_self_attn_v_proj_weight4, layer_norm289, model_decoder_layers_10_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1135: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv93, R.shape([1, seq_len, 20, 64])) concat74: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1133, reshape1134, reshape1135), axis=2) reshape1136: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat74, R.shape([seq_len, 60, 64])) lv219 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape1136), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1137: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv219, R.shape([1, seq_len, 20, 64])) reshape1138: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1137, R.shape([1, seq_len, 1280])) lv94 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_self_attn_out_proj_weight4, reshape1138, model_decoder_layers_10_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1003: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add999, lv94) layer_norm290: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1003, model_decoder_layers_10_encoder_attn_layer_norm_weight4, model_decoder_layers_10_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv95 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_encoder_attn_q_proj_weight4, layer_norm290, model_decoder_layers_10_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1139: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv95, R.shape([1, seq_len, 20, 64])) reshape1140: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1139, R.shape([seq_len, 20, 64])) lv220 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(10), R.prim_value(T.float32(1)), reshape1140), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1141: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv220, R.shape([1, seq_len, 20, 64])) reshape1142: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1141, R.shape([1, seq_len, 1280])) lv96 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_10_encoder_attn_out_proj_weight4, reshape1142, model_decoder_layers_10_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1006: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1003, lv96) layer_norm291: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1006, model_decoder_layers_10_final_layer_norm_weight4, model_decoder_layers_10_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv10 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_10_fc1_weight4, layer_norm291, model_decoder_layers_10_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv97 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_10_fc2_weight4, lv10, model_decoder_layers_10_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1009: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1006, lv97) layer_norm292: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1009, model_decoder_layers_11_self_attn_layer_norm_weight4, model_decoder_layers_11_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv98 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_self_attn_q_proj_weight4, layer_norm292, model_decoder_layers_11_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1143: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv98, R.shape([1, seq_len, 20, 64])) lv43_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_11_self_attn_k_proj_weight4, layer_norm292), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1144: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv43_1, R.shape([1, seq_len, 20, 64])) lv99 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_self_attn_v_proj_weight4, layer_norm292, model_decoder_layers_11_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1145: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv99, R.shape([1, seq_len, 20, 64])) concat75: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1143, reshape1144, reshape1145), axis=2) reshape1146: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat75, R.shape([seq_len, 60, 64])) lv221 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape1146), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1147: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv221, R.shape([1, seq_len, 20, 64])) reshape1148: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1147, R.shape([1, seq_len, 1280])) lv100 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_self_attn_out_proj_weight4, reshape1148, model_decoder_layers_11_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1013: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1009, lv100) layer_norm293: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1013, model_decoder_layers_11_encoder_attn_layer_norm_weight4, model_decoder_layers_11_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv101 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_encoder_attn_q_proj_weight4, layer_norm293, model_decoder_layers_11_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1149: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv101, R.shape([1, seq_len, 20, 64])) reshape1150: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1149, R.shape([seq_len, 20, 64])) lv222 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(11), R.prim_value(T.float32(1)), reshape1150), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1151: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv222, R.shape([1, seq_len, 20, 64])) reshape1152: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1151, R.shape([1, seq_len, 1280])) lv102 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_11_encoder_attn_out_proj_weight4, reshape1152, model_decoder_layers_11_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1016: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1013, lv102) layer_norm294: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1016, model_decoder_layers_11_final_layer_norm_weight4, model_decoder_layers_11_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv11 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_11_fc1_weight4, layer_norm294, model_decoder_layers_11_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv103 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_11_fc2_weight4, lv11, model_decoder_layers_11_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1019: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1016, lv103) layer_norm295: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1019, model_decoder_layers_12_self_attn_layer_norm_weight4, model_decoder_layers_12_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv104 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_self_attn_q_proj_weight4, layer_norm295, model_decoder_layers_12_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1153: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv104, R.shape([1, seq_len, 20, 64])) lv44_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_12_self_attn_k_proj_weight4, layer_norm295), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1154: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv44_1, R.shape([1, seq_len, 20, 64])) lv105 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_self_attn_v_proj_weight4, layer_norm295, model_decoder_layers_12_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1155: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv105, R.shape([1, seq_len, 20, 64])) concat76: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1153, reshape1154, reshape1155), axis=2) reshape1156: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat76, R.shape([seq_len, 60, 64])) lv223 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape1156), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1157: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv223, R.shape([1, seq_len, 20, 64])) reshape1158: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1157, R.shape([1, seq_len, 1280])) lv106 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_self_attn_out_proj_weight4, reshape1158, model_decoder_layers_12_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1023: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1019, lv106) layer_norm296: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1023, model_decoder_layers_12_encoder_attn_layer_norm_weight4, model_decoder_layers_12_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv107 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_encoder_attn_q_proj_weight4, layer_norm296, model_decoder_layers_12_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1159: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv107, R.shape([1, seq_len, 20, 64])) reshape1160: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1159, R.shape([seq_len, 20, 64])) lv224 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(12), R.prim_value(T.float32(1)), reshape1160), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1161: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv224, R.shape([1, seq_len, 20, 64])) reshape1162: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1161, R.shape([1, seq_len, 1280])) lv108 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_12_encoder_attn_out_proj_weight4, reshape1162, model_decoder_layers_12_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1026: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1023, lv108) layer_norm297: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1026, model_decoder_layers_12_final_layer_norm_weight4, model_decoder_layers_12_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv12 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_12_fc1_weight4, layer_norm297, model_decoder_layers_12_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv109 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_12_fc2_weight4, lv12, model_decoder_layers_12_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1029: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1026, lv109) layer_norm298: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1029, model_decoder_layers_13_self_attn_layer_norm_weight4, model_decoder_layers_13_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv110 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_self_attn_q_proj_weight4, layer_norm298, model_decoder_layers_13_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1163: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv110, R.shape([1, seq_len, 20, 64])) lv45_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_13_self_attn_k_proj_weight4, layer_norm298), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1164: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv45_1, R.shape([1, seq_len, 20, 64])) lv111 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_self_attn_v_proj_weight4, layer_norm298, model_decoder_layers_13_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1165: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv111, R.shape([1, seq_len, 20, 64])) concat77: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1163, reshape1164, reshape1165), axis=2) reshape1166: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat77, R.shape([seq_len, 60, 64])) lv225 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape1166), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1167: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv225, R.shape([1, seq_len, 20, 64])) reshape1168: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1167, R.shape([1, seq_len, 1280])) lv112 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_self_attn_out_proj_weight4, reshape1168, model_decoder_layers_13_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1033: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1029, lv112) layer_norm299: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1033, model_decoder_layers_13_encoder_attn_layer_norm_weight4, model_decoder_layers_13_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv113 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_encoder_attn_q_proj_weight4, layer_norm299, model_decoder_layers_13_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1169: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv113, R.shape([1, seq_len, 20, 64])) reshape1170: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1169, R.shape([seq_len, 20, 64])) lv226 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(13), R.prim_value(T.float32(1)), reshape1170), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1171: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv226, R.shape([1, seq_len, 20, 64])) reshape1172: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1171, R.shape([1, seq_len, 1280])) lv114 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_13_encoder_attn_out_proj_weight4, reshape1172, model_decoder_layers_13_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1036: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1033, lv114) layer_norm300: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1036, model_decoder_layers_13_final_layer_norm_weight4, model_decoder_layers_13_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv13 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_13_fc1_weight4, layer_norm300, model_decoder_layers_13_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv115 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_13_fc2_weight4, lv13, model_decoder_layers_13_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1039: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1036, lv115) layer_norm301: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1039, model_decoder_layers_14_self_attn_layer_norm_weight4, model_decoder_layers_14_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv116 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_self_attn_q_proj_weight4, layer_norm301, model_decoder_layers_14_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1173: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv116, R.shape([1, seq_len, 20, 64])) lv46_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_14_self_attn_k_proj_weight4, layer_norm301), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1174: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv46_1, R.shape([1, seq_len, 20, 64])) lv117 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_self_attn_v_proj_weight4, layer_norm301, model_decoder_layers_14_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1175: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv117, R.shape([1, seq_len, 20, 64])) concat78: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1173, reshape1174, reshape1175), axis=2) reshape1176: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat78, R.shape([seq_len, 60, 64])) lv227 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape1176), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1177: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv227, R.shape([1, seq_len, 20, 64])) reshape1178: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1177, R.shape([1, seq_len, 1280])) lv118 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_self_attn_out_proj_weight4, reshape1178, model_decoder_layers_14_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1043: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1039, lv118) layer_norm302: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1043, model_decoder_layers_14_encoder_attn_layer_norm_weight4, model_decoder_layers_14_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv119 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_encoder_attn_q_proj_weight4, layer_norm302, model_decoder_layers_14_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1179: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv119, R.shape([1, seq_len, 20, 64])) reshape1180: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1179, R.shape([seq_len, 20, 64])) lv228 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(14), R.prim_value(T.float32(1)), reshape1180), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1181: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv228, R.shape([1, seq_len, 20, 64])) reshape1182: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1181, R.shape([1, seq_len, 1280])) lv120 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_14_encoder_attn_out_proj_weight4, reshape1182, model_decoder_layers_14_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1046: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1043, lv120) layer_norm303: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1046, model_decoder_layers_14_final_layer_norm_weight4, model_decoder_layers_14_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv14 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_14_fc1_weight4, layer_norm303, model_decoder_layers_14_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv121 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_14_fc2_weight4, lv14, model_decoder_layers_14_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1049: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1046, lv121) layer_norm304: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1049, model_decoder_layers_15_self_attn_layer_norm_weight4, model_decoder_layers_15_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv122 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_self_attn_q_proj_weight4, layer_norm304, model_decoder_layers_15_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1183: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv122, R.shape([1, seq_len, 20, 64])) lv47_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_15_self_attn_k_proj_weight4, layer_norm304), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1184: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv47_1, R.shape([1, seq_len, 20, 64])) lv123 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_self_attn_v_proj_weight4, layer_norm304, model_decoder_layers_15_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1185: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv123, R.shape([1, seq_len, 20, 64])) concat79: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1183, reshape1184, reshape1185), axis=2) reshape1186: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat79, R.shape([seq_len, 60, 64])) lv229 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape1186), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1187: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv229, R.shape([1, seq_len, 20, 64])) reshape1188: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1187, R.shape([1, seq_len, 1280])) lv124 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_self_attn_out_proj_weight4, reshape1188, model_decoder_layers_15_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1053: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1049, lv124) layer_norm305: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1053, model_decoder_layers_15_encoder_attn_layer_norm_weight4, model_decoder_layers_15_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv125 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_encoder_attn_q_proj_weight4, layer_norm305, model_decoder_layers_15_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1189: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv125, R.shape([1, seq_len, 20, 64])) reshape1190: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1189, R.shape([seq_len, 20, 64])) lv230 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(15), R.prim_value(T.float32(1)), reshape1190), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1191: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv230, R.shape([1, seq_len, 20, 64])) reshape1192: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1191, R.shape([1, seq_len, 1280])) lv126 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_15_encoder_attn_out_proj_weight4, reshape1192, model_decoder_layers_15_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1056: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1053, lv126) layer_norm306: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1056, model_decoder_layers_15_final_layer_norm_weight4, model_decoder_layers_15_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv15 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_15_fc1_weight4, layer_norm306, model_decoder_layers_15_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv127 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_15_fc2_weight4, lv15, model_decoder_layers_15_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1059: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1056, lv127) layer_norm307: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1059, model_decoder_layers_16_self_attn_layer_norm_weight4, model_decoder_layers_16_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv128 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_self_attn_q_proj_weight4, layer_norm307, model_decoder_layers_16_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1193: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv128, R.shape([1, seq_len, 20, 64])) lv48_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_16_self_attn_k_proj_weight4, layer_norm307), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1194: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv48_1, R.shape([1, seq_len, 20, 64])) lv129 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_self_attn_v_proj_weight4, layer_norm307, model_decoder_layers_16_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1195: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv129, R.shape([1, seq_len, 20, 64])) concat80: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1193, reshape1194, reshape1195), axis=2) reshape1196: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat80, R.shape([seq_len, 60, 64])) lv231 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape1196), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1197: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv231, R.shape([1, seq_len, 20, 64])) reshape1198: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1197, R.shape([1, seq_len, 1280])) lv130 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_self_attn_out_proj_weight4, reshape1198, model_decoder_layers_16_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1063: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1059, lv130) layer_norm308: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1063, model_decoder_layers_16_encoder_attn_layer_norm_weight4, model_decoder_layers_16_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv131 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_encoder_attn_q_proj_weight4, layer_norm308, model_decoder_layers_16_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1199: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv131, R.shape([1, seq_len, 20, 64])) reshape1200: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1199, R.shape([seq_len, 20, 64])) lv232 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(16), R.prim_value(T.float32(1)), reshape1200), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1201: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv232, R.shape([1, seq_len, 20, 64])) reshape1202: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1201, R.shape([1, seq_len, 1280])) lv132 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_16_encoder_attn_out_proj_weight4, reshape1202, model_decoder_layers_16_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1066: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1063, lv132) layer_norm309: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1066, model_decoder_layers_16_final_layer_norm_weight4, model_decoder_layers_16_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv16 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_16_fc1_weight4, layer_norm309, model_decoder_layers_16_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv133 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_16_fc2_weight4, lv16, model_decoder_layers_16_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1069: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1066, lv133) layer_norm310: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1069, model_decoder_layers_17_self_attn_layer_norm_weight4, model_decoder_layers_17_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv134 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_self_attn_q_proj_weight4, layer_norm310, model_decoder_layers_17_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1203: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv134, R.shape([1, seq_len, 20, 64])) lv49_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_17_self_attn_k_proj_weight4, layer_norm310), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1204: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv49_1, R.shape([1, seq_len, 20, 64])) lv135 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_self_attn_v_proj_weight4, layer_norm310, model_decoder_layers_17_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1205: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv135, R.shape([1, seq_len, 20, 64])) concat81: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1203, reshape1204, reshape1205), axis=2) reshape1206: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat81, R.shape([seq_len, 60, 64])) lv233 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape1206), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1207: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv233, R.shape([1, seq_len, 20, 64])) reshape1208: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1207, R.shape([1, seq_len, 1280])) lv136 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_self_attn_out_proj_weight4, reshape1208, model_decoder_layers_17_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1073: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1069, lv136) layer_norm311: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1073, model_decoder_layers_17_encoder_attn_layer_norm_weight4, model_decoder_layers_17_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv137 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_encoder_attn_q_proj_weight4, layer_norm311, model_decoder_layers_17_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1209: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv137, R.shape([1, seq_len, 20, 64])) reshape1210: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1209, R.shape([seq_len, 20, 64])) lv234 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(17), R.prim_value(T.float32(1)), reshape1210), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1211: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv234, R.shape([1, seq_len, 20, 64])) reshape1212: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1211, R.shape([1, seq_len, 1280])) lv138 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_17_encoder_attn_out_proj_weight4, reshape1212, model_decoder_layers_17_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1076: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1073, lv138) layer_norm312: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1076, model_decoder_layers_17_final_layer_norm_weight4, model_decoder_layers_17_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv17 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_17_fc1_weight4, layer_norm312, model_decoder_layers_17_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv139 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_17_fc2_weight4, lv17, model_decoder_layers_17_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1079: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1076, lv139) layer_norm313: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1079, model_decoder_layers_18_self_attn_layer_norm_weight4, model_decoder_layers_18_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv140 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_self_attn_q_proj_weight4, layer_norm313, model_decoder_layers_18_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1213: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv140, R.shape([1, seq_len, 20, 64])) lv50_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_18_self_attn_k_proj_weight4, layer_norm313), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1214: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv50_1, R.shape([1, seq_len, 20, 64])) lv141 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_self_attn_v_proj_weight4, layer_norm313, model_decoder_layers_18_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1215: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv141, R.shape([1, seq_len, 20, 64])) concat82: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1213, reshape1214, reshape1215), axis=2) reshape1216: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat82, R.shape([seq_len, 60, 64])) lv235 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape1216), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1217: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv235, R.shape([1, seq_len, 20, 64])) reshape1218: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1217, R.shape([1, seq_len, 1280])) lv142 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_self_attn_out_proj_weight4, reshape1218, model_decoder_layers_18_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1083: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1079, lv142) layer_norm314: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1083, model_decoder_layers_18_encoder_attn_layer_norm_weight4, model_decoder_layers_18_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv143 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_encoder_attn_q_proj_weight4, layer_norm314, model_decoder_layers_18_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1219: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv143, R.shape([1, seq_len, 20, 64])) reshape1220: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1219, R.shape([seq_len, 20, 64])) lv236 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(18), R.prim_value(T.float32(1)), reshape1220), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1221: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv236, R.shape([1, seq_len, 20, 64])) reshape1222: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1221, R.shape([1, seq_len, 1280])) lv144 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_18_encoder_attn_out_proj_weight4, reshape1222, model_decoder_layers_18_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1086: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1083, lv144) layer_norm315: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1086, model_decoder_layers_18_final_layer_norm_weight4, model_decoder_layers_18_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv18 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_18_fc1_weight4, layer_norm315, model_decoder_layers_18_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv145 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_18_fc2_weight4, lv18, model_decoder_layers_18_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1089: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1086, lv145) layer_norm316: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1089, model_decoder_layers_19_self_attn_layer_norm_weight4, model_decoder_layers_19_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv146 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_self_attn_q_proj_weight4, layer_norm316, model_decoder_layers_19_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1223: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv146, R.shape([1, seq_len, 20, 64])) lv51_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_19_self_attn_k_proj_weight4, layer_norm316), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1224: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv51_1, R.shape([1, seq_len, 20, 64])) lv147 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_self_attn_v_proj_weight4, layer_norm316, model_decoder_layers_19_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1225: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv147, R.shape([1, seq_len, 20, 64])) concat83: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1223, reshape1224, reshape1225), axis=2) reshape1226: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat83, R.shape([seq_len, 60, 64])) lv237 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape1226), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1227: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv237, R.shape([1, seq_len, 20, 64])) reshape1228: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1227, R.shape([1, seq_len, 1280])) lv148 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_self_attn_out_proj_weight4, reshape1228, model_decoder_layers_19_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1093: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1089, lv148) layer_norm317: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1093, model_decoder_layers_19_encoder_attn_layer_norm_weight4, model_decoder_layers_19_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv149 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_encoder_attn_q_proj_weight4, layer_norm317, model_decoder_layers_19_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1229: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv149, R.shape([1, seq_len, 20, 64])) reshape1230: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1229, R.shape([seq_len, 20, 64])) lv238 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(19), R.prim_value(T.float32(1)), reshape1230), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1231: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv238, R.shape([1, seq_len, 20, 64])) reshape1232: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1231, R.shape([1, seq_len, 1280])) lv150 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_19_encoder_attn_out_proj_weight4, reshape1232, model_decoder_layers_19_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1096: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1093, lv150) layer_norm318: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1096, model_decoder_layers_19_final_layer_norm_weight4, model_decoder_layers_19_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv19 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_19_fc1_weight4, layer_norm318, model_decoder_layers_19_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv151 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_19_fc2_weight4, lv19, model_decoder_layers_19_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1099: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1096, lv151) layer_norm319: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1099, model_decoder_layers_20_self_attn_layer_norm_weight4, model_decoder_layers_20_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv152 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_self_attn_q_proj_weight4, layer_norm319, model_decoder_layers_20_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1233: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv152, R.shape([1, seq_len, 20, 64])) lv52_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_20_self_attn_k_proj_weight4, layer_norm319), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1234: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv52_1, R.shape([1, seq_len, 20, 64])) lv153 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_self_attn_v_proj_weight4, layer_norm319, model_decoder_layers_20_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1235: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv153, R.shape([1, seq_len, 20, 64])) concat84: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1233, reshape1234, reshape1235), axis=2) reshape1236: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat84, R.shape([seq_len, 60, 64])) lv239 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape1236), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1237: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv239, R.shape([1, seq_len, 20, 64])) reshape1238: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1237, R.shape([1, seq_len, 1280])) lv154 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_self_attn_out_proj_weight4, reshape1238, model_decoder_layers_20_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1103: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1099, lv154) layer_norm320: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1103, model_decoder_layers_20_encoder_attn_layer_norm_weight4, model_decoder_layers_20_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv155 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_encoder_attn_q_proj_weight4, layer_norm320, model_decoder_layers_20_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1239: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv155, R.shape([1, seq_len, 20, 64])) reshape1240: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1239, R.shape([seq_len, 20, 64])) lv240 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(20), R.prim_value(T.float32(1)), reshape1240), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1241: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv240, R.shape([1, seq_len, 20, 64])) reshape1242: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1241, R.shape([1, seq_len, 1280])) lv156 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_20_encoder_attn_out_proj_weight4, reshape1242, model_decoder_layers_20_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1106: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1103, lv156) layer_norm321: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1106, model_decoder_layers_20_final_layer_norm_weight4, model_decoder_layers_20_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv20 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_20_fc1_weight4, layer_norm321, model_decoder_layers_20_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv157 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_20_fc2_weight4, lv20, model_decoder_layers_20_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1109: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1106, lv157) layer_norm322: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1109, model_decoder_layers_21_self_attn_layer_norm_weight4, model_decoder_layers_21_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv158 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_self_attn_q_proj_weight4, layer_norm322, model_decoder_layers_21_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1243: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv158, R.shape([1, seq_len, 20, 64])) lv53_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_21_self_attn_k_proj_weight4, layer_norm322), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1244: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv53_1, R.shape([1, seq_len, 20, 64])) lv159 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_self_attn_v_proj_weight4, layer_norm322, model_decoder_layers_21_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1245: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv159, R.shape([1, seq_len, 20, 64])) concat85: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1243, reshape1244, reshape1245), axis=2) reshape1246: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat85, R.shape([seq_len, 60, 64])) lv241 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape1246), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1247: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv241, R.shape([1, seq_len, 20, 64])) reshape1248: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1247, R.shape([1, seq_len, 1280])) lv160 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_self_attn_out_proj_weight4, reshape1248, model_decoder_layers_21_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1113: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1109, lv160) layer_norm323: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1113, model_decoder_layers_21_encoder_attn_layer_norm_weight4, model_decoder_layers_21_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv161 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_encoder_attn_q_proj_weight4, layer_norm323, model_decoder_layers_21_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1249: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv161, R.shape([1, seq_len, 20, 64])) reshape1250: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1249, R.shape([seq_len, 20, 64])) lv242 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(21), R.prim_value(T.float32(1)), reshape1250), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1251: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv242, R.shape([1, seq_len, 20, 64])) reshape1252: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1251, R.shape([1, seq_len, 1280])) lv162 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_21_encoder_attn_out_proj_weight4, reshape1252, model_decoder_layers_21_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1116: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1113, lv162) layer_norm324: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1116, model_decoder_layers_21_final_layer_norm_weight4, model_decoder_layers_21_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv21 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_21_fc1_weight4, layer_norm324, model_decoder_layers_21_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv163 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_21_fc2_weight4, lv21, model_decoder_layers_21_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1119: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1116, lv163) layer_norm325: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1119, model_decoder_layers_22_self_attn_layer_norm_weight4, model_decoder_layers_22_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv164 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_self_attn_q_proj_weight4, layer_norm325, model_decoder_layers_22_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1253: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv164, R.shape([1, seq_len, 20, 64])) lv54_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_22_self_attn_k_proj_weight4, layer_norm325), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1254: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv54_1, R.shape([1, seq_len, 20, 64])) lv165 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_self_attn_v_proj_weight4, layer_norm325, model_decoder_layers_22_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1255: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv165, R.shape([1, seq_len, 20, 64])) concat86: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1253, reshape1254, reshape1255), axis=2) reshape1256: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat86, R.shape([seq_len, 60, 64])) lv243 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape1256), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1257: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv243, R.shape([1, seq_len, 20, 64])) reshape1258: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1257, R.shape([1, seq_len, 1280])) lv166 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_self_attn_out_proj_weight4, reshape1258, model_decoder_layers_22_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1123: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1119, lv166) layer_norm326: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1123, model_decoder_layers_22_encoder_attn_layer_norm_weight4, model_decoder_layers_22_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv167 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_encoder_attn_q_proj_weight4, layer_norm326, model_decoder_layers_22_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1259: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv167, R.shape([1, seq_len, 20, 64])) reshape1260: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1259, R.shape([seq_len, 20, 64])) lv244 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(22), R.prim_value(T.float32(1)), reshape1260), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1261: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv244, R.shape([1, seq_len, 20, 64])) reshape1262: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1261, R.shape([1, seq_len, 1280])) lv168 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_22_encoder_attn_out_proj_weight4, reshape1262, model_decoder_layers_22_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1126: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1123, lv168) layer_norm327: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1126, model_decoder_layers_22_final_layer_norm_weight4, model_decoder_layers_22_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv22 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_22_fc1_weight4, layer_norm327, model_decoder_layers_22_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv169 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_22_fc2_weight4, lv22, model_decoder_layers_22_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1129: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1126, lv169) layer_norm328: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1129, model_decoder_layers_23_self_attn_layer_norm_weight4, model_decoder_layers_23_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv170 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_self_attn_q_proj_weight4, layer_norm328, model_decoder_layers_23_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1263: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv170, R.shape([1, seq_len, 20, 64])) lv55_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_23_self_attn_k_proj_weight4, layer_norm328), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1264: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv55_1, R.shape([1, seq_len, 20, 64])) lv171 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_self_attn_v_proj_weight4, layer_norm328, model_decoder_layers_23_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1265: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv171, R.shape([1, seq_len, 20, 64])) concat87: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1263, reshape1264, reshape1265), axis=2) reshape1266: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat87, R.shape([seq_len, 60, 64])) lv245 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape1266), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1267: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv245, R.shape([1, seq_len, 20, 64])) reshape1268: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1267, R.shape([1, seq_len, 1280])) lv172 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_self_attn_out_proj_weight4, reshape1268, model_decoder_layers_23_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1133: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1129, lv172) layer_norm329: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1133, model_decoder_layers_23_encoder_attn_layer_norm_weight4, model_decoder_layers_23_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv173 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_encoder_attn_q_proj_weight4, layer_norm329, model_decoder_layers_23_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1269: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv173, R.shape([1, seq_len, 20, 64])) reshape1270: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1269, R.shape([seq_len, 20, 64])) lv246 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(23), R.prim_value(T.float32(1)), reshape1270), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1271: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv246, R.shape([1, seq_len, 20, 64])) reshape1272: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1271, R.shape([1, seq_len, 1280])) lv174 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_23_encoder_attn_out_proj_weight4, reshape1272, model_decoder_layers_23_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1136: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1133, lv174) layer_norm330: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1136, model_decoder_layers_23_final_layer_norm_weight4, model_decoder_layers_23_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv23 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_23_fc1_weight4, layer_norm330, model_decoder_layers_23_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv175 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_23_fc2_weight4, lv23, model_decoder_layers_23_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1139: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1136, lv175) layer_norm331: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1139, model_decoder_layers_24_self_attn_layer_norm_weight4, model_decoder_layers_24_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv176 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_self_attn_q_proj_weight4, layer_norm331, model_decoder_layers_24_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1273: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv176, R.shape([1, seq_len, 20, 64])) lv56_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_24_self_attn_k_proj_weight4, layer_norm331), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1274: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv56_1, R.shape([1, seq_len, 20, 64])) lv177 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_self_attn_v_proj_weight4, layer_norm331, model_decoder_layers_24_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1275: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv177, R.shape([1, seq_len, 20, 64])) concat88: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1273, reshape1274, reshape1275), axis=2) reshape1276: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat88, R.shape([seq_len, 60, 64])) lv247 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape1276), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1277: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv247, R.shape([1, seq_len, 20, 64])) reshape1278: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1277, R.shape([1, seq_len, 1280])) lv178 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_self_attn_out_proj_weight4, reshape1278, model_decoder_layers_24_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1143: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1139, lv178) layer_norm332: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1143, model_decoder_layers_24_encoder_attn_layer_norm_weight4, model_decoder_layers_24_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv179 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_encoder_attn_q_proj_weight4, layer_norm332, model_decoder_layers_24_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1279: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv179, R.shape([1, seq_len, 20, 64])) reshape1280: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1279, R.shape([seq_len, 20, 64])) lv248 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(24), R.prim_value(T.float32(1)), reshape1280), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1281: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv248, R.shape([1, seq_len, 20, 64])) reshape1282: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1281, R.shape([1, seq_len, 1280])) lv180 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_24_encoder_attn_out_proj_weight4, reshape1282, model_decoder_layers_24_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1146: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1143, lv180) layer_norm333: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1146, model_decoder_layers_24_final_layer_norm_weight4, model_decoder_layers_24_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv24 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_24_fc1_weight4, layer_norm333, model_decoder_layers_24_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv181 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_24_fc2_weight4, lv24, model_decoder_layers_24_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1149: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1146, lv181) layer_norm334: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1149, model_decoder_layers_25_self_attn_layer_norm_weight4, model_decoder_layers_25_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv182 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_self_attn_q_proj_weight4, layer_norm334, model_decoder_layers_25_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1283: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv182, R.shape([1, seq_len, 20, 64])) lv57_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_25_self_attn_k_proj_weight4, layer_norm334), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1284: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv57_1, R.shape([1, seq_len, 20, 64])) lv183 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_self_attn_v_proj_weight4, layer_norm334, model_decoder_layers_25_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1285: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv183, R.shape([1, seq_len, 20, 64])) concat89: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1283, reshape1284, reshape1285), axis=2) reshape1286: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat89, R.shape([seq_len, 60, 64])) lv249 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape1286), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1287: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv249, R.shape([1, seq_len, 20, 64])) reshape1288: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1287, R.shape([1, seq_len, 1280])) lv184 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_self_attn_out_proj_weight4, reshape1288, model_decoder_layers_25_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1153: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1149, lv184) layer_norm335: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1153, model_decoder_layers_25_encoder_attn_layer_norm_weight4, model_decoder_layers_25_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv185 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_encoder_attn_q_proj_weight4, layer_norm335, model_decoder_layers_25_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1289: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv185, R.shape([1, seq_len, 20, 64])) reshape1290: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1289, R.shape([seq_len, 20, 64])) lv250 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(25), R.prim_value(T.float32(1)), reshape1290), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1291: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv250, R.shape([1, seq_len, 20, 64])) reshape1292: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1291, R.shape([1, seq_len, 1280])) lv186 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_25_encoder_attn_out_proj_weight4, reshape1292, model_decoder_layers_25_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1156: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1153, lv186) layer_norm336: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1156, model_decoder_layers_25_final_layer_norm_weight4, model_decoder_layers_25_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv25 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_25_fc1_weight4, layer_norm336, model_decoder_layers_25_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv187 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_25_fc2_weight4, lv25, model_decoder_layers_25_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1159: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1156, lv187) layer_norm337: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1159, model_decoder_layers_26_self_attn_layer_norm_weight4, model_decoder_layers_26_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv188 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_self_attn_q_proj_weight4, layer_norm337, model_decoder_layers_26_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1293: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv188, R.shape([1, seq_len, 20, 64])) lv58_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_26_self_attn_k_proj_weight4, layer_norm337), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1294: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv58_1, R.shape([1, seq_len, 20, 64])) lv189 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_self_attn_v_proj_weight4, layer_norm337, model_decoder_layers_26_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1295: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv189, R.shape([1, seq_len, 20, 64])) concat90: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1293, reshape1294, reshape1295), axis=2) reshape1296: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat90, R.shape([seq_len, 60, 64])) lv251 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape1296), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1297: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv251, R.shape([1, seq_len, 20, 64])) reshape1298: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1297, R.shape([1, seq_len, 1280])) lv190 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_self_attn_out_proj_weight4, reshape1298, model_decoder_layers_26_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1163: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1159, lv190) layer_norm338: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1163, model_decoder_layers_26_encoder_attn_layer_norm_weight4, model_decoder_layers_26_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv191 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_encoder_attn_q_proj_weight4, layer_norm338, model_decoder_layers_26_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1299: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv191, R.shape([1, seq_len, 20, 64])) reshape1300: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1299, R.shape([seq_len, 20, 64])) lv252 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(26), R.prim_value(T.float32(1)), reshape1300), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1301: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv252, R.shape([1, seq_len, 20, 64])) reshape1302: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1301, R.shape([1, seq_len, 1280])) lv192 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_26_encoder_attn_out_proj_weight4, reshape1302, model_decoder_layers_26_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1166: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1163, lv192) layer_norm339: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1166, model_decoder_layers_26_final_layer_norm_weight4, model_decoder_layers_26_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv26 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_26_fc1_weight4, layer_norm339, model_decoder_layers_26_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv193 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_26_fc2_weight4, lv26, model_decoder_layers_26_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1169: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1166, lv193) layer_norm340: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1169, model_decoder_layers_27_self_attn_layer_norm_weight4, model_decoder_layers_27_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv194 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_self_attn_q_proj_weight4, layer_norm340, model_decoder_layers_27_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1303: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv194, R.shape([1, seq_len, 20, 64])) lv59_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_27_self_attn_k_proj_weight4, layer_norm340), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1304: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv59_1, R.shape([1, seq_len, 20, 64])) lv195 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_self_attn_v_proj_weight4, layer_norm340, model_decoder_layers_27_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1305: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv195, R.shape([1, seq_len, 20, 64])) concat91: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1303, reshape1304, reshape1305), axis=2) reshape1306: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat91, R.shape([seq_len, 60, 64])) lv253 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape1306), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1307: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv253, R.shape([1, seq_len, 20, 64])) reshape1308: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1307, R.shape([1, seq_len, 1280])) lv196 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_self_attn_out_proj_weight4, reshape1308, model_decoder_layers_27_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1173: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1169, lv196) layer_norm341: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1173, model_decoder_layers_27_encoder_attn_layer_norm_weight4, model_decoder_layers_27_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv197 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_encoder_attn_q_proj_weight4, layer_norm341, model_decoder_layers_27_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1309: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv197, R.shape([1, seq_len, 20, 64])) reshape1310: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1309, R.shape([seq_len, 20, 64])) lv254 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(27), R.prim_value(T.float32(1)), reshape1310), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1311: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv254, R.shape([1, seq_len, 20, 64])) reshape1312: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1311, R.shape([1, seq_len, 1280])) lv198_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_27_encoder_attn_out_proj_weight4, reshape1312, model_decoder_layers_27_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1176: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1173, lv198_1) layer_norm342: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1176, model_decoder_layers_27_final_layer_norm_weight4, model_decoder_layers_27_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv27 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_27_fc1_weight4, layer_norm342, model_decoder_layers_27_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv199_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_27_fc2_weight4, lv27, model_decoder_layers_27_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1179: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1176, lv199_1) layer_norm343: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1179, model_decoder_layers_28_self_attn_layer_norm_weight4, model_decoder_layers_28_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv200_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_self_attn_q_proj_weight4, layer_norm343, model_decoder_layers_28_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1313: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv200_1, R.shape([1, seq_len, 20, 64])) lv60_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_28_self_attn_k_proj_weight4, layer_norm343), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1314: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv60_1, R.shape([1, seq_len, 20, 64])) lv201_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_self_attn_v_proj_weight4, layer_norm343, model_decoder_layers_28_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1315: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv201_1, R.shape([1, seq_len, 20, 64])) concat92: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1313, reshape1314, reshape1315), axis=2) reshape1316: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat92, R.shape([seq_len, 60, 64])) lv255 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape1316), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1317: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv255, R.shape([1, seq_len, 20, 64])) reshape1318: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1317, R.shape([1, seq_len, 1280])) lv202_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_self_attn_out_proj_weight4, reshape1318, model_decoder_layers_28_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1183: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1179, lv202_1) layer_norm344: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1183, model_decoder_layers_28_encoder_attn_layer_norm_weight4, model_decoder_layers_28_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv203_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_encoder_attn_q_proj_weight4, layer_norm344, model_decoder_layers_28_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1319: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv203_1, R.shape([1, seq_len, 20, 64])) reshape1320: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1319, R.shape([seq_len, 20, 64])) lv256 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(28), R.prim_value(T.float32(1)), reshape1320), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1321: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv256, R.shape([1, seq_len, 20, 64])) reshape1322: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1321, R.shape([1, seq_len, 1280])) lv204_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_28_encoder_attn_out_proj_weight4, reshape1322, model_decoder_layers_28_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1186: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1183, lv204_1) layer_norm345: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1186, model_decoder_layers_28_final_layer_norm_weight4, model_decoder_layers_28_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv28 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_28_fc1_weight4, layer_norm345, model_decoder_layers_28_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv205_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_28_fc2_weight4, lv28, model_decoder_layers_28_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1189: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1186, lv205_1) layer_norm346: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1189, model_decoder_layers_29_self_attn_layer_norm_weight4, model_decoder_layers_29_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv206_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_self_attn_q_proj_weight4, layer_norm346, model_decoder_layers_29_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1323: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv206_1, R.shape([1, seq_len, 20, 64])) lv61_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_29_self_attn_k_proj_weight4, layer_norm346), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1324: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv61_1, R.shape([1, seq_len, 20, 64])) lv207_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_self_attn_v_proj_weight4, layer_norm346, model_decoder_layers_29_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1325: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv207_1, R.shape([1, seq_len, 20, 64])) concat93: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1323, reshape1324, reshape1325), axis=2) reshape1326: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat93, R.shape([seq_len, 60, 64])) lv257 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1326), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1327: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv257, R.shape([1, seq_len, 20, 64])) reshape1328: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1327, R.shape([1, seq_len, 1280])) lv208_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_self_attn_out_proj_weight4, reshape1328, model_decoder_layers_29_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1193: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1189, lv208_1) layer_norm347: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1193, model_decoder_layers_29_encoder_attn_layer_norm_weight4, model_decoder_layers_29_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv209_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_encoder_attn_q_proj_weight4, layer_norm347, model_decoder_layers_29_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1329: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv209_1, R.shape([1, seq_len, 20, 64])) reshape1330: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1329, R.shape([seq_len, 20, 64])) lv258 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(29), R.prim_value(T.float32(1)), reshape1330), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1331: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv258, R.shape([1, seq_len, 20, 64])) reshape1332: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1331, R.shape([1, seq_len, 1280])) lv210_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_29_encoder_attn_out_proj_weight4, reshape1332, model_decoder_layers_29_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1196: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1193, lv210_1) layer_norm348: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1196, model_decoder_layers_29_final_layer_norm_weight4, model_decoder_layers_29_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv29 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_29_fc1_weight4, layer_norm348, model_decoder_layers_29_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv211_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_29_fc2_weight4, lv29, model_decoder_layers_29_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1199: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1196, lv211_1) layer_norm349: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1199, model_decoder_layers_30_self_attn_layer_norm_weight4, model_decoder_layers_30_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv212_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_self_attn_q_proj_weight4, layer_norm349, model_decoder_layers_30_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1333: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv212_1, R.shape([1, seq_len, 20, 64])) lv62_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_30_self_attn_k_proj_weight4, layer_norm349), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1334: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv62_1, R.shape([1, seq_len, 20, 64])) lv213_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_self_attn_v_proj_weight4, layer_norm349, model_decoder_layers_30_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1335: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv213_1, R.shape([1, seq_len, 20, 64])) concat94: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1333, reshape1334, reshape1335), axis=2) reshape1336: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat94, R.shape([seq_len, 60, 64])) lv259 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1336), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1337: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv259, R.shape([1, seq_len, 20, 64])) reshape1338: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1337, R.shape([1, seq_len, 1280])) lv214_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_self_attn_out_proj_weight4, reshape1338, model_decoder_layers_30_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1203: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1199, lv214_1) layer_norm350: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1203, model_decoder_layers_30_encoder_attn_layer_norm_weight4, model_decoder_layers_30_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv215_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_encoder_attn_q_proj_weight4, layer_norm350, model_decoder_layers_30_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1339: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv215_1, R.shape([1, seq_len, 20, 64])) reshape1340: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1339, R.shape([seq_len, 20, 64])) lv260 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(30), R.prim_value(T.float32(1)), reshape1340), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1341: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv260, R.shape([1, seq_len, 20, 64])) reshape1342: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1341, R.shape([1, seq_len, 1280])) lv216_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_30_encoder_attn_out_proj_weight4, reshape1342, model_decoder_layers_30_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1206: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1203, lv216_1) layer_norm351: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1206, model_decoder_layers_30_final_layer_norm_weight4, model_decoder_layers_30_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv30 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_30_fc1_weight4, layer_norm351, model_decoder_layers_30_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv217_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_30_fc2_weight4, lv30, model_decoder_layers_30_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1209: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1206, lv217_1) layer_norm352: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1209, model_decoder_layers_31_self_attn_layer_norm_weight4, model_decoder_layers_31_self_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv218_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_self_attn_q_proj_weight4, layer_norm352, model_decoder_layers_31_self_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1343: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv218_1, R.shape([1, seq_len, 20, 64])) lv63_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul1_cublas", (model_decoder_layers_31_self_attn_k_proj_weight4, layer_norm352), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1344: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv63_1, R.shape([1, seq_len, 20, 64])) lv219_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_self_attn_v_proj_weight4, layer_norm352, model_decoder_layers_31_self_attn_v_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1345: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv219_1, R.shape([1, seq_len, 20, 64])) concat95: R.Tensor((1, seq_len, 60, 64), dtype="float16") = R.concat((reshape1343, reshape1344, reshape1345), axis=2) reshape1346: R.Tensor((seq_len, 60, 64), dtype="float16") = R.reshape(concat95, R.shape([seq_len, 60, 64])) lv261 = R.call_dps_packed("vm.builtin.attention_kv_cache_attention_with_fused_qkv", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1346), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1347: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv261, R.shape([1, seq_len, 20, 64])) reshape1348: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1347, R.shape([1, seq_len, 1280])) lv220_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_self_attn_out_proj_weight4, reshape1348, model_decoder_layers_31_self_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1213: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1209, lv220_1) layer_norm353: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1213, model_decoder_layers_31_encoder_attn_layer_norm_weight4, model_decoder_layers_31_encoder_attn_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv221_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_encoder_attn_q_proj_weight4, layer_norm353, model_decoder_layers_31_encoder_attn_q_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) reshape1349: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv221_1, R.shape([1, seq_len, 20, 64])) reshape1350: R.Tensor((seq_len, 20, 64), dtype="float16") = R.reshape(reshape1349, R.shape([seq_len, 20, 64])) lv262 = R.call_dps_packed("vm.builtin.attention_kv_cache_cross_attention", (paged_kv_cache, R.prim_value(31), R.prim_value(T.float32(1)), reshape1350), out_sinfo=R.Tensor((seq_len, 20, 64), dtype="float16")) reshape1351: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(lv262, R.shape([1, seq_len, 20, 64])) reshape1352: R.Tensor((1, seq_len, 1280), dtype="float16") = R.reshape(reshape1351, R.shape([1, seq_len, 1280])) lv222_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add1_cublas", (model_decoder_layers_31_encoder_attn_out_proj_weight4, reshape1352, model_decoder_layers_31_encoder_attn_out_proj_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1216: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1213, lv222_1) layer_norm354: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1216, model_decoder_layers_31_final_layer_norm_weight4, model_decoder_layers_31_final_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv31 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add_relax_nn_gelu_cublas", (model_decoder_layers_31_fc1_weight4, layer_norm354, model_decoder_layers_31_fc1_bias4), out_sinfo=R.Tensor((1, seq_len, 5120), dtype="float16")) lv223_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul_relax_add2_cublas", (model_decoder_layers_31_fc2_weight4, lv31, model_decoder_layers_31_fc2_bias4), out_sinfo=R.Tensor((1, seq_len, 1280), dtype="float16")) add1219: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1216, lv223_1) layer_norm355: R.Tensor((1, seq_len, 1280), dtype="float16") = R.nn.layer_norm(add1219, model_decoder_layer_norm_weight4, model_decoder_layer_norm_bias4, axes=[-1], epsilon=1.0000000000000001e-05, center=True, scale=True) lv263 = R.call_tir(cls.index, (layer_norm355,), out_sinfo=R.Tensor((1, 1, 1280), dtype="float16")) lv64_1 = R.call_dps_packed("fused_relax_permute_dims_relax_matmul2_cublas", (model_decoder_embed_tokens_weight4, lv263), out_sinfo=R.Tensor((1, 1, 51866), dtype="float32")) gv4: R.Tensor((1, 1, 51866), dtype="float32") = lv64_1 R.output(gv4) return gv4 @R.function def renormalize_by_top_p(probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32"), top_p: R.Tensor(("batch_size",), dtype="float32"), init_pivots: R.Tensor(("batch_size", 3), dtype="float32")) -> R.Tensor(("batch_size", "vocab_size"), dtype="float32"): batch_size = T.int64() vocab_size = T.int64() R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) cls = Module with R.dataflow(): lv6 = R.call_tir(cls.top_p_pivot_cutoff, (probs, top_p, init_pivots), out_sinfo=[R.Tensor((batch_size,), dtype="float32"), R.Tensor((batch_size,), dtype="float32")]) lv7: R.Tensor((batch_size,), dtype="float32") = lv6[0] lv8: R.Tensor((batch_size,), dtype="float32") = lv6[1] gv5 = R.call_tir(cls.top_p_renorm_after_cutoff, (probs, lv7, lv8), out_sinfo=R.Tensor((batch_size, vocab_size), dtype="float32")) R.output(gv5) return gv5 @R.function def sample_with_top_p(sorted_probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32"), sorted_indices: R.Tensor(("batch_size", "vocab_size"), dtype="int32"), uniform_samples: R.Tensor(("num_samples",), dtype="float32"), sample_indices: R.Tensor(("num_samples",), dtype="int32"), top_p: R.Tensor(("batch_size",), dtype="float32")) -> R.Tensor(("num_samples",), dtype="int32"): num_samples = T.int64() batch_size = T.int64() vocab_size = T.int64() R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) cls = Module with R.dataflow(): sorted_probs_1: R.Tensor((batch_size, vocab_size), dtype="float32") = sorted_probs sorted_indices_1: R.Tensor((batch_size, vocab_size), dtype="int32") = sorted_indices uniform_samples1: R.Tensor((num_samples, 1), dtype="float32") = R.call_pure_packed("vm.builtin.reshape", uniform_samples, R.shape([num_samples, 1]), sinfo_args=(R.Tensor((num_samples, 1), dtype="float32"),)) sample_indices1: R.Tensor((num_samples, 1), dtype="int32") = R.call_pure_packed("vm.builtin.reshape", sample_indices, R.shape([num_samples, 1]), sinfo_args=(R.Tensor((num_samples, 1), dtype="int32"),)) sample_indices2: R.Tensor((batch_size, 1), dtype="float32") = R.call_pure_packed("vm.builtin.reshape", top_p, R.shape([batch_size, 1]), sinfo_args=(R.Tensor((batch_size, 1), dtype="float32"),)) lv3 = R.call_tir(cls.full, R.tuple(), out_sinfo=R.Tensor((batch_size, 1), dtype="int32"), tir_vars=R.shape([vocab_size])) cumsum: R.Tensor((batch_size, vocab_size), dtype="float32") = R.cumsum(sorted_probs_1, axis=1, dtype="void", exclusive=None) lv4 = R.call_tir(cls.get_renorm_prob, (cumsum, sample_indices2, lv3), out_sinfo=R.Tensor((batch_size, 1), dtype="float32")) lv5 = R.call_tir(cls.get_index_from_sorted, (cumsum, sorted_indices_1, lv4, uniform_samples1, sample_indices1), out_sinfo=R.Tensor((num_samples, 1), dtype="int32")) gv2: R.Tensor((num_samples,), dtype="int32") = R.call_pure_packed("vm.builtin.reshape", lv5, R.shape([num_samples]), sinfo_args=(R.Tensor((num_samples,), dtype="int32"),)) R.output(gv2) return gv2 @R.function def sampler_take_probs(unsorted_probs: R.Tensor(("batch_size", "vocab_size"), dtype="float32"), sorted_indices: R.Tensor(("batch_size", "vocab_size"), dtype="int32"), sample_indices: R.Tensor(("num_samples",), dtype="int32"), sampling_result: R.Tensor(("num_samples",), dtype="int32"), lobprob_offsets: R.Tensor(("num_positions",), dtype="int32")) -> R.Tuple(R.Tensor(("num_samples",), dtype="float32"), R.Tensor(("num_positions",), dtype="float32"), R.Tensor(("num_positions",), dtype="int32")): num_samples = T.int64() num_positions = T.int64() batch_size = T.int64() vocab_size = T.int64() R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) cls = Module with R.dataflow(): gv3 = R.call_tir(cls.sampler_take_probs_tir, (unsorted_probs, sorted_indices, sample_indices, sampling_result, lobprob_offsets), out_sinfo=[R.Tensor((num_samples,), dtype="float32"), R.Tensor((num_positions,), dtype="float32"), R.Tensor((num_positions,), dtype="int32")]) R.output(gv3) return gv3 @R.function def sampler_verify_draft_tokens(draft_probs: R.Tensor(("num_nodes", "vocab_size"), dtype="float32"), draft_tokens: R.Tensor(("num_nodes",), dtype="int32"), model_probs: R.Tensor(("num_nodes", "vocab_size"), dtype="float32"), token_tree_first_child: R.Tensor(("num_nodes",), dtype="int32"), token_tree_next_sibling: R.Tensor(("num_nodes",), dtype="int32"), uniform_samples: R.Tensor(("num_nodes",), dtype="float32"), token_tree_parent_ptr: R.Tensor(("nbatch",), dtype="int32")) -> R.Tuple(R.Tensor(("num_nodes", "vocab_size"), dtype="float32"), R.Tensor(("nbatch",), dtype="int32")): num_nodes = T.int64() vocab_size = T.int64() nbatch = T.int64() R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "num_positions": 48, "num_samples": 8}}) cls = Module with R.dataflow(): gv4: R.Tuple(R.Tensor((num_nodes, vocab_size), dtype="float32"), R.Tensor((nbatch,), dtype="int32")) = R.call_tir_inplace(cls.batch_verify_on_gpu_single_kernel, (draft_probs, draft_tokens, model_probs, token_tree_first_child, token_tree_next_sibling, uniform_samples, token_tree_parent_ptr), out_sinfo=[R.Tensor((num_nodes, vocab_size), dtype="float32"), R.Tensor((nbatch,), dtype="int32")], inplace_indices=[2, 6]) R.output(gv4) return gv4 @R.function def softmax_with_temperature(logits: R.Tensor(("batch_size", 1, "vocab_size"), dtype="float32"), temperature: R.Tensor(("batch_size",), dtype="float32")) -> R.Tensor(("batch_size", 1, "vocab_size"), dtype="float32"): batch_size = T.int64() vocab_size = T.int64() R.func_attr({"relax.memory_plan_dynamic_func_output": 1, "tir_non_negative_var": ["vocab_size"], "tir_var_upper_bound": {"batch_size": 8, "seq_len": 15000, "total_seq_len": 1500}}) cls = Module with R.dataflow(): lv: R.Tensor((batch_size, vocab_size), dtype="float32") = R.call_pure_packed("vm.builtin.reshape", logits, R.shape([batch_size, vocab_size]), sinfo_args=(R.Tensor((batch_size, vocab_size), dtype="float32"),)) lv1 = R.call_tir(cls.chunk_lse, (lv, temperature), out_sinfo=[R.Tensor((batch_size, (vocab_size + 4096 - 1) // 4096), dtype="float32"), R.Tensor((batch_size, (vocab_size + 4096 - 1) // 4096), dtype="float32")]) lv2: R.Tensor((batch_size, (vocab_size + 4096 - 1) // 4096), dtype="float32") = lv1[0] lv3: R.Tensor((batch_size, (vocab_size + 4096 - 1) // 4096), dtype="float32") = lv1[1] lv4 = R.call_tir(cls.softmax_with_chunked_sum, (lv, temperature, lv2, lv3), out_sinfo=R.Tensor((batch_size, vocab_size), dtype="float32")) gv: R.Tensor((batch_size, 1, vocab_size), dtype="float32") = R.call_pure_packed("vm.builtin.reshape", lv4, R.shape([batch_size, 1, vocab_size]), sinfo_args=(R.Tensor((batch_size, 1, vocab_size), dtype="float32"),)) R.output(gv) return gv # Metadata omitted. Use show_meta=True in script() method to show it.