diff --git "a/debug/debug-phase0.py" "b/debug/debug-phase0.py" new file mode 100644--- /dev/null +++ "b/debug/debug-phase0.py" @@ -0,0 +1,16603 @@ +# 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: + @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_encoder_conv1_weight1: R.Tensor((1280, 128, 3), dtype="float16") = packed_params[0] + model_encoder_conv1_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1] + model_encoder_conv2_weight1: R.Tensor((1280, 1280, 3), dtype="float16") = packed_params[2] + model_encoder_conv2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[3] + model_encoder_embed_positions_weight1: R.Tensor((1500, 1280), dtype="float16") = packed_params[4] + model_encoder_layers_0_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[5] + model_encoder_layers_0_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[6] + model_encoder_layers_0_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[7] + model_encoder_layers_0_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[8] + model_encoder_layers_0_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[9] + model_encoder_layers_0_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[10] + model_encoder_layers_0_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[11] + model_encoder_layers_0_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[12] + model_encoder_layers_0_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[13] + model_encoder_layers_0_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[14] + model_encoder_layers_0_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[15] + model_encoder_layers_0_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[16] + model_encoder_layers_0_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[17] + model_encoder_layers_0_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[18] + model_encoder_layers_0_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[19] + model_encoder_layers_1_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[20] + model_encoder_layers_1_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[21] + model_encoder_layers_1_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[22] + model_encoder_layers_1_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[23] + model_encoder_layers_1_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[24] + model_encoder_layers_1_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[25] + model_encoder_layers_1_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[26] + model_encoder_layers_1_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[27] + model_encoder_layers_1_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[28] + model_encoder_layers_1_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[29] + model_encoder_layers_1_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[30] + model_encoder_layers_1_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[31] + model_encoder_layers_1_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[32] + model_encoder_layers_1_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[33] + model_encoder_layers_1_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[34] + model_encoder_layers_2_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[35] + model_encoder_layers_2_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[36] + model_encoder_layers_2_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[37] + model_encoder_layers_2_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[38] + model_encoder_layers_2_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[39] + model_encoder_layers_2_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[40] + model_encoder_layers_2_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[41] + model_encoder_layers_2_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[42] + model_encoder_layers_2_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[43] + model_encoder_layers_2_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[44] + model_encoder_layers_2_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[45] + model_encoder_layers_2_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[46] + model_encoder_layers_2_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[47] + model_encoder_layers_2_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[48] + model_encoder_layers_2_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[49] + model_encoder_layers_3_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[50] + model_encoder_layers_3_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[51] + model_encoder_layers_3_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[52] + model_encoder_layers_3_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[53] + model_encoder_layers_3_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[54] + model_encoder_layers_3_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[55] + model_encoder_layers_3_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[56] + model_encoder_layers_3_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[57] + model_encoder_layers_3_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[58] + model_encoder_layers_3_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[59] + model_encoder_layers_3_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[60] + model_encoder_layers_3_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[61] + model_encoder_layers_3_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[62] + model_encoder_layers_3_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[63] + model_encoder_layers_3_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[64] + model_encoder_layers_4_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[65] + model_encoder_layers_4_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[66] + model_encoder_layers_4_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[67] + model_encoder_layers_4_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[68] + model_encoder_layers_4_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[69] + model_encoder_layers_4_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[70] + model_encoder_layers_4_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[71] + model_encoder_layers_4_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[72] + model_encoder_layers_4_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[73] + model_encoder_layers_4_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[74] + model_encoder_layers_4_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[75] + model_encoder_layers_4_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[76] + model_encoder_layers_4_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[77] + model_encoder_layers_4_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[78] + model_encoder_layers_4_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[79] + model_encoder_layers_5_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[80] + model_encoder_layers_5_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[81] + model_encoder_layers_5_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[82] + model_encoder_layers_5_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[83] + model_encoder_layers_5_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[84] + model_encoder_layers_5_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[85] + model_encoder_layers_5_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[86] + model_encoder_layers_5_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[87] + model_encoder_layers_5_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[88] + model_encoder_layers_5_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[89] + model_encoder_layers_5_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[90] + model_encoder_layers_5_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[91] + model_encoder_layers_5_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[92] + model_encoder_layers_5_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[93] + model_encoder_layers_5_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[94] + model_encoder_layers_6_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[95] + model_encoder_layers_6_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[96] + model_encoder_layers_6_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[97] + model_encoder_layers_6_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[98] + model_encoder_layers_6_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[99] + model_encoder_layers_6_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[100] + model_encoder_layers_6_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[101] + model_encoder_layers_6_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[102] + model_encoder_layers_6_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[103] + model_encoder_layers_6_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[104] + model_encoder_layers_6_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[105] + model_encoder_layers_6_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[106] + model_encoder_layers_6_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[107] + model_encoder_layers_6_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[108] + model_encoder_layers_6_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[109] + model_encoder_layers_7_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[110] + model_encoder_layers_7_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[111] + model_encoder_layers_7_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[112] + model_encoder_layers_7_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[113] + model_encoder_layers_7_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[114] + model_encoder_layers_7_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[115] + model_encoder_layers_7_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[116] + model_encoder_layers_7_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[117] + model_encoder_layers_7_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[118] + model_encoder_layers_7_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[119] + model_encoder_layers_7_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[120] + model_encoder_layers_7_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[121] + model_encoder_layers_7_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[122] + model_encoder_layers_7_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[123] + model_encoder_layers_7_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[124] + model_encoder_layers_8_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[125] + model_encoder_layers_8_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[126] + model_encoder_layers_8_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[127] + model_encoder_layers_8_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[128] + model_encoder_layers_8_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[129] + model_encoder_layers_8_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[130] + model_encoder_layers_8_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[131] + model_encoder_layers_8_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[132] + model_encoder_layers_8_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[133] + model_encoder_layers_8_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[134] + model_encoder_layers_8_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[135] + model_encoder_layers_8_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[136] + model_encoder_layers_8_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[137] + model_encoder_layers_8_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[138] + model_encoder_layers_8_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[139] + model_encoder_layers_9_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[140] + model_encoder_layers_9_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[141] + model_encoder_layers_9_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[142] + model_encoder_layers_9_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[143] + model_encoder_layers_9_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[144] + model_encoder_layers_9_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[145] + model_encoder_layers_9_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[146] + model_encoder_layers_9_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[147] + model_encoder_layers_9_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[148] + model_encoder_layers_9_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[149] + model_encoder_layers_9_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[150] + model_encoder_layers_9_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[151] + model_encoder_layers_9_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[152] + model_encoder_layers_9_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[153] + model_encoder_layers_9_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[154] + model_encoder_layers_10_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[155] + model_encoder_layers_10_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[156] + model_encoder_layers_10_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[157] + model_encoder_layers_10_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[158] + model_encoder_layers_10_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[159] + model_encoder_layers_10_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[160] + model_encoder_layers_10_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[161] + model_encoder_layers_10_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[162] + model_encoder_layers_10_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[163] + model_encoder_layers_10_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[164] + model_encoder_layers_10_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[165] + model_encoder_layers_10_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[166] + model_encoder_layers_10_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[167] + model_encoder_layers_10_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[168] + model_encoder_layers_10_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[169] + model_encoder_layers_11_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[170] + model_encoder_layers_11_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[171] + model_encoder_layers_11_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[172] + model_encoder_layers_11_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[173] + model_encoder_layers_11_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[174] + model_encoder_layers_11_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[175] + model_encoder_layers_11_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[176] + model_encoder_layers_11_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[177] + model_encoder_layers_11_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[178] + model_encoder_layers_11_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[179] + model_encoder_layers_11_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[180] + model_encoder_layers_11_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[181] + model_encoder_layers_11_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[182] + model_encoder_layers_11_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[183] + model_encoder_layers_11_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[184] + model_encoder_layers_12_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[185] + model_encoder_layers_12_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[186] + model_encoder_layers_12_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[187] + model_encoder_layers_12_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[188] + model_encoder_layers_12_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[189] + model_encoder_layers_12_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[190] + model_encoder_layers_12_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[191] + model_encoder_layers_12_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[192] + model_encoder_layers_12_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[193] + model_encoder_layers_12_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[194] + model_encoder_layers_12_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[195] + model_encoder_layers_12_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[196] + model_encoder_layers_12_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[197] + model_encoder_layers_12_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[198] + model_encoder_layers_12_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[199] + model_encoder_layers_13_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[200] + model_encoder_layers_13_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[201] + model_encoder_layers_13_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[202] + model_encoder_layers_13_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[203] + model_encoder_layers_13_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[204] + model_encoder_layers_13_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[205] + model_encoder_layers_13_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[206] + model_encoder_layers_13_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[207] + model_encoder_layers_13_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[208] + model_encoder_layers_13_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[209] + model_encoder_layers_13_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[210] + model_encoder_layers_13_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[211] + model_encoder_layers_13_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[212] + model_encoder_layers_13_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[213] + model_encoder_layers_13_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[214] + model_encoder_layers_14_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[215] + model_encoder_layers_14_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[216] + model_encoder_layers_14_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[217] + model_encoder_layers_14_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[218] + model_encoder_layers_14_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[219] + model_encoder_layers_14_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[220] + model_encoder_layers_14_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[221] + model_encoder_layers_14_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[222] + model_encoder_layers_14_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[223] + model_encoder_layers_14_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[224] + model_encoder_layers_14_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[225] + model_encoder_layers_14_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[226] + model_encoder_layers_14_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[227] + model_encoder_layers_14_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[228] + model_encoder_layers_14_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[229] + model_encoder_layers_15_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[230] + model_encoder_layers_15_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[231] + model_encoder_layers_15_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[232] + model_encoder_layers_15_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[233] + model_encoder_layers_15_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[234] + model_encoder_layers_15_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[235] + model_encoder_layers_15_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[236] + model_encoder_layers_15_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[237] + model_encoder_layers_15_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[238] + model_encoder_layers_15_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[239] + model_encoder_layers_15_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[240] + model_encoder_layers_15_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[241] + model_encoder_layers_15_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[242] + model_encoder_layers_15_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[243] + model_encoder_layers_15_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[244] + model_encoder_layers_16_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[245] + model_encoder_layers_16_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[246] + model_encoder_layers_16_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[247] + model_encoder_layers_16_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[248] + model_encoder_layers_16_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[249] + model_encoder_layers_16_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[250] + model_encoder_layers_16_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[251] + model_encoder_layers_16_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[252] + model_encoder_layers_16_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[253] + model_encoder_layers_16_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[254] + model_encoder_layers_16_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[255] + model_encoder_layers_16_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[256] + model_encoder_layers_16_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[257] + model_encoder_layers_16_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[258] + model_encoder_layers_16_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[259] + model_encoder_layers_17_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[260] + model_encoder_layers_17_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[261] + model_encoder_layers_17_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[262] + model_encoder_layers_17_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[263] + model_encoder_layers_17_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[264] + model_encoder_layers_17_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[265] + model_encoder_layers_17_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[266] + model_encoder_layers_17_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[267] + model_encoder_layers_17_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[268] + model_encoder_layers_17_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[269] + model_encoder_layers_17_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[270] + model_encoder_layers_17_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[271] + model_encoder_layers_17_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[272] + model_encoder_layers_17_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[273] + model_encoder_layers_17_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[274] + model_encoder_layers_18_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[275] + model_encoder_layers_18_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[276] + model_encoder_layers_18_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[277] + model_encoder_layers_18_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[278] + model_encoder_layers_18_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[279] + model_encoder_layers_18_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[280] + model_encoder_layers_18_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[281] + model_encoder_layers_18_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[282] + model_encoder_layers_18_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[283] + model_encoder_layers_18_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[284] + model_encoder_layers_18_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[285] + model_encoder_layers_18_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[286] + model_encoder_layers_18_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[287] + model_encoder_layers_18_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[288] + model_encoder_layers_18_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[289] + model_encoder_layers_19_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[290] + model_encoder_layers_19_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[291] + model_encoder_layers_19_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[292] + model_encoder_layers_19_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[293] + model_encoder_layers_19_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[294] + model_encoder_layers_19_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[295] + model_encoder_layers_19_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[296] + model_encoder_layers_19_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[297] + model_encoder_layers_19_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[298] + model_encoder_layers_19_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[299] + model_encoder_layers_19_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[300] + model_encoder_layers_19_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[301] + model_encoder_layers_19_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[302] + model_encoder_layers_19_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[303] + model_encoder_layers_19_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[304] + model_encoder_layers_20_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[305] + model_encoder_layers_20_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[306] + model_encoder_layers_20_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[307] + model_encoder_layers_20_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[308] + model_encoder_layers_20_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[309] + model_encoder_layers_20_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[310] + model_encoder_layers_20_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[311] + model_encoder_layers_20_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[312] + model_encoder_layers_20_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[313] + model_encoder_layers_20_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[314] + model_encoder_layers_20_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[315] + model_encoder_layers_20_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[316] + model_encoder_layers_20_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[317] + model_encoder_layers_20_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[318] + model_encoder_layers_20_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[319] + model_encoder_layers_21_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[320] + model_encoder_layers_21_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[321] + model_encoder_layers_21_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[322] + model_encoder_layers_21_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[323] + model_encoder_layers_21_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[324] + model_encoder_layers_21_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[325] + model_encoder_layers_21_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[326] + model_encoder_layers_21_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[327] + model_encoder_layers_21_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[328] + model_encoder_layers_21_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[329] + model_encoder_layers_21_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[330] + model_encoder_layers_21_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[331] + model_encoder_layers_21_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[332] + model_encoder_layers_21_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[333] + model_encoder_layers_21_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[334] + model_encoder_layers_22_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[335] + model_encoder_layers_22_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[336] + model_encoder_layers_22_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[337] + model_encoder_layers_22_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[338] + model_encoder_layers_22_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[339] + model_encoder_layers_22_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[340] + model_encoder_layers_22_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[341] + model_encoder_layers_22_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[342] + model_encoder_layers_22_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[343] + model_encoder_layers_22_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[344] + model_encoder_layers_22_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[345] + model_encoder_layers_22_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[346] + model_encoder_layers_22_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[347] + model_encoder_layers_22_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[348] + model_encoder_layers_22_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[349] + model_encoder_layers_23_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[350] + model_encoder_layers_23_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[351] + model_encoder_layers_23_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[352] + model_encoder_layers_23_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[353] + model_encoder_layers_23_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[354] + model_encoder_layers_23_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[355] + model_encoder_layers_23_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[356] + model_encoder_layers_23_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[357] + model_encoder_layers_23_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[358] + model_encoder_layers_23_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[359] + model_encoder_layers_23_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[360] + model_encoder_layers_23_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[361] + model_encoder_layers_23_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[362] + model_encoder_layers_23_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[363] + model_encoder_layers_23_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[364] + model_encoder_layers_24_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[365] + model_encoder_layers_24_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[366] + model_encoder_layers_24_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[367] + model_encoder_layers_24_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[368] + model_encoder_layers_24_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[369] + model_encoder_layers_24_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[370] + model_encoder_layers_24_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[371] + model_encoder_layers_24_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[372] + model_encoder_layers_24_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[373] + model_encoder_layers_24_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[374] + model_encoder_layers_24_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[375] + model_encoder_layers_24_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[376] + model_encoder_layers_24_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[377] + model_encoder_layers_24_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[378] + model_encoder_layers_24_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[379] + model_encoder_layers_25_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[380] + model_encoder_layers_25_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[381] + model_encoder_layers_25_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[382] + model_encoder_layers_25_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[383] + model_encoder_layers_25_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[384] + model_encoder_layers_25_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[385] + model_encoder_layers_25_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[386] + model_encoder_layers_25_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[387] + model_encoder_layers_25_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[388] + model_encoder_layers_25_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[389] + model_encoder_layers_25_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[390] + model_encoder_layers_25_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[391] + model_encoder_layers_25_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[392] + model_encoder_layers_25_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[393] + model_encoder_layers_25_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[394] + model_encoder_layers_26_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[395] + model_encoder_layers_26_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[396] + model_encoder_layers_26_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[397] + model_encoder_layers_26_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[398] + model_encoder_layers_26_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[399] + model_encoder_layers_26_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[400] + model_encoder_layers_26_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[401] + model_encoder_layers_26_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[402] + model_encoder_layers_26_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[403] + model_encoder_layers_26_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[404] + model_encoder_layers_26_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[405] + model_encoder_layers_26_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[406] + model_encoder_layers_26_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[407] + model_encoder_layers_26_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[408] + model_encoder_layers_26_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[409] + model_encoder_layers_27_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[410] + model_encoder_layers_27_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[411] + model_encoder_layers_27_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[412] + model_encoder_layers_27_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[413] + model_encoder_layers_27_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[414] + model_encoder_layers_27_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[415] + model_encoder_layers_27_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[416] + model_encoder_layers_27_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[417] + model_encoder_layers_27_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[418] + model_encoder_layers_27_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[419] + model_encoder_layers_27_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[420] + model_encoder_layers_27_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[421] + model_encoder_layers_27_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[422] + model_encoder_layers_27_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[423] + model_encoder_layers_27_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[424] + model_encoder_layers_28_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[425] + model_encoder_layers_28_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[426] + model_encoder_layers_28_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[427] + model_encoder_layers_28_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[428] + model_encoder_layers_28_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[429] + model_encoder_layers_28_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[430] + model_encoder_layers_28_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[431] + model_encoder_layers_28_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[432] + model_encoder_layers_28_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[433] + model_encoder_layers_28_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[434] + model_encoder_layers_28_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[435] + model_encoder_layers_28_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[436] + model_encoder_layers_28_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[437] + model_encoder_layers_28_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[438] + model_encoder_layers_28_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[439] + model_encoder_layers_29_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[440] + model_encoder_layers_29_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[441] + model_encoder_layers_29_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[442] + model_encoder_layers_29_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[443] + model_encoder_layers_29_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[444] + model_encoder_layers_29_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[445] + model_encoder_layers_29_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[446] + model_encoder_layers_29_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[447] + model_encoder_layers_29_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[448] + model_encoder_layers_29_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[449] + model_encoder_layers_29_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[450] + model_encoder_layers_29_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[451] + model_encoder_layers_29_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[452] + model_encoder_layers_29_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[453] + model_encoder_layers_29_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[454] + model_encoder_layers_30_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[455] + model_encoder_layers_30_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[456] + model_encoder_layers_30_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[457] + model_encoder_layers_30_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[458] + model_encoder_layers_30_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[459] + model_encoder_layers_30_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[460] + model_encoder_layers_30_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[461] + model_encoder_layers_30_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[462] + model_encoder_layers_30_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[463] + model_encoder_layers_30_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[464] + model_encoder_layers_30_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[465] + model_encoder_layers_30_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[466] + model_encoder_layers_30_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[467] + model_encoder_layers_30_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[468] + model_encoder_layers_30_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[469] + model_encoder_layers_31_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[470] + model_encoder_layers_31_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[471] + model_encoder_layers_31_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[472] + model_encoder_layers_31_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[473] + model_encoder_layers_31_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[474] + model_encoder_layers_31_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[475] + model_encoder_layers_31_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[476] + model_encoder_layers_31_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[477] + model_encoder_layers_31_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[478] + model_encoder_layers_31_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[479] + model_encoder_layers_31_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[480] + model_encoder_layers_31_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[481] + model_encoder_layers_31_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[482] + model_encoder_layers_31_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[483] + model_encoder_layers_31_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[484] + model_encoder_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[485] + model_encoder_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[486] + model_decoder_embed_tokens_weight1: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + model_decoder_embed_positions_weight1: R.Tensor((448, 1280), dtype="float16") = packed_params[488] + model_decoder_layers_0_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] + model_decoder_layers_0_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] + model_decoder_layers_0_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[491] + model_decoder_layers_0_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] + model_decoder_layers_0_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[493] + model_decoder_layers_0_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] + model_decoder_layers_0_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[495] + model_decoder_layers_0_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[496] + model_decoder_layers_0_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[497] + 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_0_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] + model_decoder_layers_0_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[502] + model_decoder_layers_0_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] + model_decoder_layers_0_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[504] + model_decoder_layers_0_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[505] + model_decoder_layers_0_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[506] + model_decoder_layers_0_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] + model_decoder_layers_0_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[508] + model_decoder_layers_0_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] + model_decoder_layers_0_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[510] + model_decoder_layers_0_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[511] + model_decoder_layers_0_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[512] + model_decoder_layers_1_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] + model_decoder_layers_1_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] + model_decoder_layers_1_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[515] + model_decoder_layers_1_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] + model_decoder_layers_1_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[517] + model_decoder_layers_1_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] + model_decoder_layers_1_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[519] + model_decoder_layers_1_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[520] + model_decoder_layers_1_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[521] + 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_1_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] + model_decoder_layers_1_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[526] + model_decoder_layers_1_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] + model_decoder_layers_1_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[528] + model_decoder_layers_1_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[529] + model_decoder_layers_1_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[530] + model_decoder_layers_1_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] + model_decoder_layers_1_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[532] + model_decoder_layers_1_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] + model_decoder_layers_1_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[534] + model_decoder_layers_1_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[535] + model_decoder_layers_1_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[536] + model_decoder_layers_2_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] + model_decoder_layers_2_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] + model_decoder_layers_2_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[539] + model_decoder_layers_2_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] + model_decoder_layers_2_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[541] + model_decoder_layers_2_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] + model_decoder_layers_2_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[543] + model_decoder_layers_2_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[544] + model_decoder_layers_2_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[545] + 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_2_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] + model_decoder_layers_2_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[550] + model_decoder_layers_2_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] + model_decoder_layers_2_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[552] + model_decoder_layers_2_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[553] + model_decoder_layers_2_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[554] + model_decoder_layers_2_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] + model_decoder_layers_2_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[556] + model_decoder_layers_2_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] + model_decoder_layers_2_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[558] + model_decoder_layers_2_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[559] + model_decoder_layers_2_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[560] + model_decoder_layers_3_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] + model_decoder_layers_3_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] + model_decoder_layers_3_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[563] + model_decoder_layers_3_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] + model_decoder_layers_3_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[565] + model_decoder_layers_3_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] + model_decoder_layers_3_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[567] + model_decoder_layers_3_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[568] + model_decoder_layers_3_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[569] + 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_3_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] + model_decoder_layers_3_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[574] + model_decoder_layers_3_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] + model_decoder_layers_3_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[576] + model_decoder_layers_3_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[577] + model_decoder_layers_3_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[578] + model_decoder_layers_3_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] + model_decoder_layers_3_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[580] + model_decoder_layers_3_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] + model_decoder_layers_3_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[582] + model_decoder_layers_3_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[583] + model_decoder_layers_3_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[584] + model_decoder_layers_4_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] + model_decoder_layers_4_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] + model_decoder_layers_4_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[587] + model_decoder_layers_4_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] + model_decoder_layers_4_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[589] + model_decoder_layers_4_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] + model_decoder_layers_4_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[591] + model_decoder_layers_4_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[592] + model_decoder_layers_4_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[593] + 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_4_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] + model_decoder_layers_4_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[598] + model_decoder_layers_4_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] + model_decoder_layers_4_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[600] + model_decoder_layers_4_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[601] + model_decoder_layers_4_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[602] + model_decoder_layers_4_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] + model_decoder_layers_4_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[604] + model_decoder_layers_4_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] + model_decoder_layers_4_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[606] + model_decoder_layers_4_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[607] + model_decoder_layers_4_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[608] + model_decoder_layers_5_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] + model_decoder_layers_5_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] + model_decoder_layers_5_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[611] + model_decoder_layers_5_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] + model_decoder_layers_5_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[613] + model_decoder_layers_5_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] + model_decoder_layers_5_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[615] + model_decoder_layers_5_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[616] + model_decoder_layers_5_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[617] + 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_5_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] + model_decoder_layers_5_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[622] + model_decoder_layers_5_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] + model_decoder_layers_5_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[624] + model_decoder_layers_5_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[625] + model_decoder_layers_5_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[626] + model_decoder_layers_5_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] + model_decoder_layers_5_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[628] + model_decoder_layers_5_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] + model_decoder_layers_5_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[630] + model_decoder_layers_5_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[631] + model_decoder_layers_5_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[632] + model_decoder_layers_6_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] + model_decoder_layers_6_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] + model_decoder_layers_6_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[635] + model_decoder_layers_6_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] + model_decoder_layers_6_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[637] + model_decoder_layers_6_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] + model_decoder_layers_6_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[639] + model_decoder_layers_6_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[640] + model_decoder_layers_6_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[641] + 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_6_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] + model_decoder_layers_6_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[646] + model_decoder_layers_6_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] + model_decoder_layers_6_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[648] + model_decoder_layers_6_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[649] + model_decoder_layers_6_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[650] + model_decoder_layers_6_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] + model_decoder_layers_6_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[652] + model_decoder_layers_6_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] + model_decoder_layers_6_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[654] + model_decoder_layers_6_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[655] + model_decoder_layers_6_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[656] + model_decoder_layers_7_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] + model_decoder_layers_7_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] + model_decoder_layers_7_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[659] + model_decoder_layers_7_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] + model_decoder_layers_7_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[661] + model_decoder_layers_7_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] + model_decoder_layers_7_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[663] + model_decoder_layers_7_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[664] + model_decoder_layers_7_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[665] + 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_7_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] + model_decoder_layers_7_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[670] + model_decoder_layers_7_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] + model_decoder_layers_7_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[672] + model_decoder_layers_7_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[673] + model_decoder_layers_7_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[674] + model_decoder_layers_7_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] + model_decoder_layers_7_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[676] + model_decoder_layers_7_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] + model_decoder_layers_7_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[678] + model_decoder_layers_7_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[679] + model_decoder_layers_7_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[680] + model_decoder_layers_8_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] + model_decoder_layers_8_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] + model_decoder_layers_8_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[683] + model_decoder_layers_8_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] + model_decoder_layers_8_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[685] + model_decoder_layers_8_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] + model_decoder_layers_8_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[687] + model_decoder_layers_8_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[688] + model_decoder_layers_8_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[689] + 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_8_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] + model_decoder_layers_8_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[694] + model_decoder_layers_8_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] + model_decoder_layers_8_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[696] + model_decoder_layers_8_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[697] + model_decoder_layers_8_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[698] + model_decoder_layers_8_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] + model_decoder_layers_8_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[700] + model_decoder_layers_8_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] + model_decoder_layers_8_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[702] + model_decoder_layers_8_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[703] + model_decoder_layers_8_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[704] + model_decoder_layers_9_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] + model_decoder_layers_9_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] + model_decoder_layers_9_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[707] + model_decoder_layers_9_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] + model_decoder_layers_9_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[709] + model_decoder_layers_9_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] + model_decoder_layers_9_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[711] + model_decoder_layers_9_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[712] + model_decoder_layers_9_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[713] + 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_9_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] + model_decoder_layers_9_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[718] + model_decoder_layers_9_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] + model_decoder_layers_9_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[720] + model_decoder_layers_9_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[721] + model_decoder_layers_9_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[722] + model_decoder_layers_9_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] + model_decoder_layers_9_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[724] + model_decoder_layers_9_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] + model_decoder_layers_9_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[726] + model_decoder_layers_9_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[727] + model_decoder_layers_9_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[728] + model_decoder_layers_10_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] + model_decoder_layers_10_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] + model_decoder_layers_10_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[731] + model_decoder_layers_10_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] + model_decoder_layers_10_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[733] + model_decoder_layers_10_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] + model_decoder_layers_10_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[735] + model_decoder_layers_10_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[736] + model_decoder_layers_10_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[737] + 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_10_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] + model_decoder_layers_10_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[742] + model_decoder_layers_10_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] + model_decoder_layers_10_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[744] + model_decoder_layers_10_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[745] + model_decoder_layers_10_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[746] + model_decoder_layers_10_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] + model_decoder_layers_10_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[748] + model_decoder_layers_10_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] + model_decoder_layers_10_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[750] + model_decoder_layers_10_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[751] + model_decoder_layers_10_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[752] + model_decoder_layers_11_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] + model_decoder_layers_11_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] + model_decoder_layers_11_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[755] + model_decoder_layers_11_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] + model_decoder_layers_11_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[757] + model_decoder_layers_11_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] + model_decoder_layers_11_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[759] + model_decoder_layers_11_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[760] + model_decoder_layers_11_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[761] + 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_11_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] + model_decoder_layers_11_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[766] + model_decoder_layers_11_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] + model_decoder_layers_11_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[768] + model_decoder_layers_11_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[769] + model_decoder_layers_11_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[770] + model_decoder_layers_11_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] + model_decoder_layers_11_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[772] + model_decoder_layers_11_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] + model_decoder_layers_11_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[774] + model_decoder_layers_11_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[775] + model_decoder_layers_11_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[776] + model_decoder_layers_12_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] + model_decoder_layers_12_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] + model_decoder_layers_12_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[779] + model_decoder_layers_12_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] + model_decoder_layers_12_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[781] + model_decoder_layers_12_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] + model_decoder_layers_12_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[783] + model_decoder_layers_12_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[784] + model_decoder_layers_12_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[785] + 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_12_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] + model_decoder_layers_12_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[790] + model_decoder_layers_12_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] + model_decoder_layers_12_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[792] + model_decoder_layers_12_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[793] + model_decoder_layers_12_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[794] + model_decoder_layers_12_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] + model_decoder_layers_12_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[796] + model_decoder_layers_12_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] + model_decoder_layers_12_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[798] + model_decoder_layers_12_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[799] + model_decoder_layers_12_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[800] + model_decoder_layers_13_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] + model_decoder_layers_13_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] + model_decoder_layers_13_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[803] + model_decoder_layers_13_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] + model_decoder_layers_13_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[805] + model_decoder_layers_13_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] + model_decoder_layers_13_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[807] + model_decoder_layers_13_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[808] + model_decoder_layers_13_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[809] + 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_13_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] + model_decoder_layers_13_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[814] + model_decoder_layers_13_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] + model_decoder_layers_13_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[816] + model_decoder_layers_13_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[817] + model_decoder_layers_13_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[818] + model_decoder_layers_13_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] + model_decoder_layers_13_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[820] + model_decoder_layers_13_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] + model_decoder_layers_13_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[822] + model_decoder_layers_13_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[823] + model_decoder_layers_13_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[824] + model_decoder_layers_14_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] + model_decoder_layers_14_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] + model_decoder_layers_14_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[827] + model_decoder_layers_14_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] + model_decoder_layers_14_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[829] + model_decoder_layers_14_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] + model_decoder_layers_14_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[831] + model_decoder_layers_14_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[832] + model_decoder_layers_14_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[833] + 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_14_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] + model_decoder_layers_14_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[838] + model_decoder_layers_14_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] + model_decoder_layers_14_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[840] + model_decoder_layers_14_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[841] + model_decoder_layers_14_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[842] + model_decoder_layers_14_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] + model_decoder_layers_14_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[844] + model_decoder_layers_14_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] + model_decoder_layers_14_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[846] + model_decoder_layers_14_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[847] + model_decoder_layers_14_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[848] + model_decoder_layers_15_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] + model_decoder_layers_15_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] + model_decoder_layers_15_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[851] + model_decoder_layers_15_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] + model_decoder_layers_15_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[853] + model_decoder_layers_15_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] + model_decoder_layers_15_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[855] + model_decoder_layers_15_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[856] + model_decoder_layers_15_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[857] + 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_15_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] + model_decoder_layers_15_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[862] + model_decoder_layers_15_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] + model_decoder_layers_15_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[864] + model_decoder_layers_15_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[865] + model_decoder_layers_15_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[866] + model_decoder_layers_15_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] + model_decoder_layers_15_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[868] + model_decoder_layers_15_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] + model_decoder_layers_15_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[870] + model_decoder_layers_15_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[871] + model_decoder_layers_15_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[872] + model_decoder_layers_16_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] + model_decoder_layers_16_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] + model_decoder_layers_16_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[875] + model_decoder_layers_16_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] + model_decoder_layers_16_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[877] + model_decoder_layers_16_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] + model_decoder_layers_16_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[879] + model_decoder_layers_16_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[880] + model_decoder_layers_16_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[881] + 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_16_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] + model_decoder_layers_16_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[886] + model_decoder_layers_16_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] + model_decoder_layers_16_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[888] + model_decoder_layers_16_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[889] + model_decoder_layers_16_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[890] + model_decoder_layers_16_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] + model_decoder_layers_16_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[892] + model_decoder_layers_16_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] + model_decoder_layers_16_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[894] + model_decoder_layers_16_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[895] + model_decoder_layers_16_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[896] + model_decoder_layers_17_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] + model_decoder_layers_17_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] + model_decoder_layers_17_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[899] + model_decoder_layers_17_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] + model_decoder_layers_17_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[901] + model_decoder_layers_17_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] + model_decoder_layers_17_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[903] + model_decoder_layers_17_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[904] + model_decoder_layers_17_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[905] + 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_17_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] + model_decoder_layers_17_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[910] + model_decoder_layers_17_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] + model_decoder_layers_17_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[912] + model_decoder_layers_17_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[913] + model_decoder_layers_17_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[914] + model_decoder_layers_17_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] + model_decoder_layers_17_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[916] + model_decoder_layers_17_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] + model_decoder_layers_17_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[918] + model_decoder_layers_17_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[919] + model_decoder_layers_17_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[920] + model_decoder_layers_18_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] + model_decoder_layers_18_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] + model_decoder_layers_18_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[923] + model_decoder_layers_18_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] + model_decoder_layers_18_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[925] + model_decoder_layers_18_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] + model_decoder_layers_18_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[927] + model_decoder_layers_18_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[928] + model_decoder_layers_18_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[929] + 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_18_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] + model_decoder_layers_18_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[934] + model_decoder_layers_18_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] + model_decoder_layers_18_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[936] + model_decoder_layers_18_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[937] + model_decoder_layers_18_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[938] + model_decoder_layers_18_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] + model_decoder_layers_18_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[940] + model_decoder_layers_18_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] + model_decoder_layers_18_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[942] + model_decoder_layers_18_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[943] + model_decoder_layers_18_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[944] + model_decoder_layers_19_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] + model_decoder_layers_19_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] + model_decoder_layers_19_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[947] + model_decoder_layers_19_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] + model_decoder_layers_19_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[949] + model_decoder_layers_19_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] + model_decoder_layers_19_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[951] + model_decoder_layers_19_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[952] + model_decoder_layers_19_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[953] + 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_19_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] + model_decoder_layers_19_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[958] + model_decoder_layers_19_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] + model_decoder_layers_19_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[960] + model_decoder_layers_19_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[961] + model_decoder_layers_19_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[962] + model_decoder_layers_19_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] + model_decoder_layers_19_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[964] + model_decoder_layers_19_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] + model_decoder_layers_19_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[966] + model_decoder_layers_19_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[967] + model_decoder_layers_19_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[968] + model_decoder_layers_20_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] + model_decoder_layers_20_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] + model_decoder_layers_20_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[971] + model_decoder_layers_20_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] + model_decoder_layers_20_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[973] + model_decoder_layers_20_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] + model_decoder_layers_20_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[975] + model_decoder_layers_20_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[976] + model_decoder_layers_20_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[977] + 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_20_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] + model_decoder_layers_20_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[982] + model_decoder_layers_20_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] + model_decoder_layers_20_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[984] + model_decoder_layers_20_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[985] + model_decoder_layers_20_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[986] + model_decoder_layers_20_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] + model_decoder_layers_20_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[988] + model_decoder_layers_20_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] + model_decoder_layers_20_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[990] + model_decoder_layers_20_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[991] + model_decoder_layers_20_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[992] + model_decoder_layers_21_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] + model_decoder_layers_21_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] + model_decoder_layers_21_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[995] + model_decoder_layers_21_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] + model_decoder_layers_21_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[997] + model_decoder_layers_21_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] + model_decoder_layers_21_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[999] + model_decoder_layers_21_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1000] + model_decoder_layers_21_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1001] + 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_21_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] + model_decoder_layers_21_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1006] + model_decoder_layers_21_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] + model_decoder_layers_21_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1008] + model_decoder_layers_21_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1009] + model_decoder_layers_21_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1010] + model_decoder_layers_21_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] + model_decoder_layers_21_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[1012] + model_decoder_layers_21_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] + model_decoder_layers_21_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1014] + model_decoder_layers_21_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1015] + model_decoder_layers_21_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1016] + model_decoder_layers_22_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] + model_decoder_layers_22_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] + model_decoder_layers_22_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1019] + model_decoder_layers_22_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] + model_decoder_layers_22_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1021] + model_decoder_layers_22_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] + model_decoder_layers_22_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1023] + model_decoder_layers_22_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1024] + model_decoder_layers_22_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1025] + 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_22_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] + model_decoder_layers_22_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1030] + model_decoder_layers_22_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] + model_decoder_layers_22_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1032] + model_decoder_layers_22_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1033] + model_decoder_layers_22_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1034] + model_decoder_layers_22_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] + model_decoder_layers_22_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[1036] + model_decoder_layers_22_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] + model_decoder_layers_22_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1038] + model_decoder_layers_22_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1039] + model_decoder_layers_22_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1040] + model_decoder_layers_23_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] + model_decoder_layers_23_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] + model_decoder_layers_23_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1043] + model_decoder_layers_23_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] + model_decoder_layers_23_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1045] + model_decoder_layers_23_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] + model_decoder_layers_23_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1047] + model_decoder_layers_23_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1048] + model_decoder_layers_23_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1049] + 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_23_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] + model_decoder_layers_23_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1054] + model_decoder_layers_23_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] + model_decoder_layers_23_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1056] + model_decoder_layers_23_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1057] + model_decoder_layers_23_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1058] + model_decoder_layers_23_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] + model_decoder_layers_23_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[1060] + model_decoder_layers_23_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] + model_decoder_layers_23_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1062] + model_decoder_layers_23_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1063] + model_decoder_layers_23_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1064] + model_decoder_layers_24_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] + model_decoder_layers_24_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] + model_decoder_layers_24_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1067] + model_decoder_layers_24_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] + model_decoder_layers_24_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1069] + model_decoder_layers_24_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] + model_decoder_layers_24_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1071] + model_decoder_layers_24_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1072] + model_decoder_layers_24_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1073] + 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_24_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] + model_decoder_layers_24_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1078] + model_decoder_layers_24_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] + model_decoder_layers_24_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1080] + model_decoder_layers_24_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1081] + model_decoder_layers_24_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1082] + model_decoder_layers_24_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] + model_decoder_layers_24_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[1084] + model_decoder_layers_24_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] + model_decoder_layers_24_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1086] + model_decoder_layers_24_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1087] + model_decoder_layers_24_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1088] + model_decoder_layers_25_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] + model_decoder_layers_25_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] + model_decoder_layers_25_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1091] + model_decoder_layers_25_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] + model_decoder_layers_25_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1093] + model_decoder_layers_25_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] + model_decoder_layers_25_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1095] + model_decoder_layers_25_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1096] + model_decoder_layers_25_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1097] + 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_25_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] + model_decoder_layers_25_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1102] + model_decoder_layers_25_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] + model_decoder_layers_25_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1104] + model_decoder_layers_25_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1105] + model_decoder_layers_25_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1106] + model_decoder_layers_25_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] + model_decoder_layers_25_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[1108] + model_decoder_layers_25_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] + model_decoder_layers_25_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1110] + model_decoder_layers_25_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1111] + model_decoder_layers_25_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1112] + model_decoder_layers_26_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] + model_decoder_layers_26_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] + model_decoder_layers_26_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1115] + model_decoder_layers_26_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] + model_decoder_layers_26_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1117] + model_decoder_layers_26_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] + model_decoder_layers_26_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1119] + model_decoder_layers_26_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1120] + model_decoder_layers_26_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1121] + 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_26_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] + model_decoder_layers_26_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1126] + model_decoder_layers_26_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] + model_decoder_layers_26_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1128] + model_decoder_layers_26_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1129] + model_decoder_layers_26_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1130] + model_decoder_layers_26_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] + model_decoder_layers_26_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[1132] + model_decoder_layers_26_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] + model_decoder_layers_26_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1134] + model_decoder_layers_26_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1135] + model_decoder_layers_26_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1136] + model_decoder_layers_27_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] + model_decoder_layers_27_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] + model_decoder_layers_27_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1139] + model_decoder_layers_27_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] + model_decoder_layers_27_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1141] + model_decoder_layers_27_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] + model_decoder_layers_27_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1143] + model_decoder_layers_27_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1144] + model_decoder_layers_27_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1145] + 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_27_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] + model_decoder_layers_27_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1150] + model_decoder_layers_27_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] + model_decoder_layers_27_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1152] + model_decoder_layers_27_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1153] + model_decoder_layers_27_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1154] + model_decoder_layers_27_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] + model_decoder_layers_27_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[1156] + model_decoder_layers_27_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] + model_decoder_layers_27_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1158] + model_decoder_layers_27_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1159] + model_decoder_layers_27_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1160] + model_decoder_layers_28_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] + model_decoder_layers_28_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] + model_decoder_layers_28_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1163] + model_decoder_layers_28_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] + model_decoder_layers_28_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1165] + model_decoder_layers_28_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] + model_decoder_layers_28_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1167] + model_decoder_layers_28_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1168] + model_decoder_layers_28_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1169] + 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_28_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] + model_decoder_layers_28_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1174] + model_decoder_layers_28_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] + model_decoder_layers_28_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1176] + model_decoder_layers_28_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1177] + model_decoder_layers_28_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1178] + model_decoder_layers_28_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] + model_decoder_layers_28_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[1180] + model_decoder_layers_28_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] + model_decoder_layers_28_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1182] + model_decoder_layers_28_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1183] + model_decoder_layers_28_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1184] + model_decoder_layers_29_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] + model_decoder_layers_29_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] + model_decoder_layers_29_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1187] + model_decoder_layers_29_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] + model_decoder_layers_29_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1189] + model_decoder_layers_29_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] + model_decoder_layers_29_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1191] + model_decoder_layers_29_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1192] + model_decoder_layers_29_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1193] + 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_29_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] + model_decoder_layers_29_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1198] + model_decoder_layers_29_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] + model_decoder_layers_29_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1200] + model_decoder_layers_29_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1201] + model_decoder_layers_29_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1202] + model_decoder_layers_29_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] + model_decoder_layers_29_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[1204] + model_decoder_layers_29_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] + model_decoder_layers_29_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1206] + model_decoder_layers_29_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1207] + model_decoder_layers_29_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1208] + model_decoder_layers_30_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] + model_decoder_layers_30_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] + model_decoder_layers_30_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1211] + model_decoder_layers_30_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] + model_decoder_layers_30_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1213] + model_decoder_layers_30_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] + model_decoder_layers_30_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1215] + model_decoder_layers_30_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1216] + model_decoder_layers_30_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1217] + 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_30_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] + model_decoder_layers_30_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1222] + model_decoder_layers_30_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] + model_decoder_layers_30_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1224] + model_decoder_layers_30_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1225] + model_decoder_layers_30_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1226] + model_decoder_layers_30_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] + model_decoder_layers_30_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[1228] + model_decoder_layers_30_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] + model_decoder_layers_30_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1230] + model_decoder_layers_30_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1231] + model_decoder_layers_30_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1232] + model_decoder_layers_31_self_attn_k_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] + model_decoder_layers_31_self_attn_v_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] + model_decoder_layers_31_self_attn_v_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1235] + model_decoder_layers_31_self_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] + model_decoder_layers_31_self_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1237] + model_decoder_layers_31_self_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] + model_decoder_layers_31_self_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1239] + model_decoder_layers_31_self_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1240] + model_decoder_layers_31_self_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1241] + 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] + model_decoder_layers_31_encoder_attn_q_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] + model_decoder_layers_31_encoder_attn_q_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1246] + model_decoder_layers_31_encoder_attn_out_proj_weight1: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] + model_decoder_layers_31_encoder_attn_out_proj_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1248] + model_decoder_layers_31_encoder_attn_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1249] + model_decoder_layers_31_encoder_attn_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1250] + model_decoder_layers_31_fc1_weight1: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] + model_decoder_layers_31_fc1_bias1: R.Tensor((5120,), dtype="float16") = packed_params[1252] + model_decoder_layers_31_fc2_weight1: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] + model_decoder_layers_31_fc2_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1254] + model_decoder_layers_31_final_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1255] + model_decoder_layers_31_final_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1256] + model_decoder_layer_norm_weight1: R.Tensor((1280,), dtype="float16") = packed_params[1257] + model_decoder_layer_norm_bias1: R.Tensor((1280,), dtype="float16") = packed_params[1258] + permute_dims193: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_encoder_attn_k_proj_weight1, axes=None) + matmul192: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims193, out_dtype="void") + reshape256: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul192, R.shape([batch_size, 1500, 20, 64])) + permute_dims194: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_encoder_attn_v_proj_weight1, axes=None) + matmul193: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims194, out_dtype="void") + add225: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul193, model_decoder_layers_0_encoder_attn_v_proj_bias1) + reshape257: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add225, 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,)) + permute_dims195: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_encoder_attn_k_proj_weight1, axes=None) + matmul194: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims195, out_dtype="void") + reshape260: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul194, R.shape([batch_size, 1500, 20, 64])) + permute_dims196: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_encoder_attn_v_proj_weight1, axes=None) + matmul195: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims196, out_dtype="void") + add226: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul195, model_decoder_layers_1_encoder_attn_v_proj_bias1) + reshape261: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add226, 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,)) + permute_dims197: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_encoder_attn_k_proj_weight1, axes=None) + matmul196: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims197, out_dtype="void") + reshape264: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul196, R.shape([batch_size, 1500, 20, 64])) + permute_dims198: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_encoder_attn_v_proj_weight1, axes=None) + matmul197: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims198, out_dtype="void") + add227: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul197, model_decoder_layers_2_encoder_attn_v_proj_bias1) + reshape265: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add227, 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,)) + permute_dims199: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_encoder_attn_k_proj_weight1, axes=None) + matmul198: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims199, out_dtype="void") + reshape268: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul198, R.shape([batch_size, 1500, 20, 64])) + permute_dims200: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_encoder_attn_v_proj_weight1, axes=None) + matmul199: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims200, out_dtype="void") + add228: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul199, model_decoder_layers_3_encoder_attn_v_proj_bias1) + reshape269: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add228, 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,)) + permute_dims201: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_encoder_attn_k_proj_weight1, axes=None) + matmul200: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims201, out_dtype="void") + reshape272: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul200, R.shape([batch_size, 1500, 20, 64])) + permute_dims202: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_encoder_attn_v_proj_weight1, axes=None) + matmul201: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims202, out_dtype="void") + add229: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul201, model_decoder_layers_4_encoder_attn_v_proj_bias1) + reshape273: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add229, 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,)) + permute_dims203: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_encoder_attn_k_proj_weight1, axes=None) + matmul202: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims203, out_dtype="void") + reshape276: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul202, R.shape([batch_size, 1500, 20, 64])) + permute_dims204: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_encoder_attn_v_proj_weight1, axes=None) + matmul203: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims204, out_dtype="void") + add230: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul203, model_decoder_layers_5_encoder_attn_v_proj_bias1) + reshape277: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add230, 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,)) + permute_dims205: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_encoder_attn_k_proj_weight1, axes=None) + matmul204: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims205, out_dtype="void") + reshape280: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul204, R.shape([batch_size, 1500, 20, 64])) + permute_dims206: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_encoder_attn_v_proj_weight1, axes=None) + matmul205: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims206, out_dtype="void") + add231: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul205, model_decoder_layers_6_encoder_attn_v_proj_bias1) + reshape281: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add231, 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,)) + permute_dims207: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_encoder_attn_k_proj_weight1, axes=None) + matmul206: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims207, out_dtype="void") + reshape284: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul206, R.shape([batch_size, 1500, 20, 64])) + permute_dims208: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_encoder_attn_v_proj_weight1, axes=None) + matmul207: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims208, out_dtype="void") + add232: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul207, model_decoder_layers_7_encoder_attn_v_proj_bias1) + reshape285: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add232, 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,)) + permute_dims209: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_encoder_attn_k_proj_weight1, axes=None) + matmul208: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims209, out_dtype="void") + reshape288: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul208, R.shape([batch_size, 1500, 20, 64])) + permute_dims210: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_encoder_attn_v_proj_weight1, axes=None) + matmul209: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims210, out_dtype="void") + add233: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul209, model_decoder_layers_8_encoder_attn_v_proj_bias1) + reshape289: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add233, 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,)) + permute_dims211: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_encoder_attn_k_proj_weight1, axes=None) + matmul210: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims211, out_dtype="void") + reshape292: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul210, R.shape([batch_size, 1500, 20, 64])) + permute_dims212: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_encoder_attn_v_proj_weight1, axes=None) + matmul211: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims212, out_dtype="void") + add234: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul211, model_decoder_layers_9_encoder_attn_v_proj_bias1) + reshape293: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add234, 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,)) + permute_dims213: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_encoder_attn_k_proj_weight1, axes=None) + matmul212: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims213, out_dtype="void") + reshape296: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul212, R.shape([batch_size, 1500, 20, 64])) + permute_dims214: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_encoder_attn_v_proj_weight1, axes=None) + matmul213: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims214, out_dtype="void") + add235: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul213, model_decoder_layers_10_encoder_attn_v_proj_bias1) + reshape297: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add235, 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,)) + permute_dims215: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_encoder_attn_k_proj_weight1, axes=None) + matmul214: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims215, out_dtype="void") + reshape300: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul214, R.shape([batch_size, 1500, 20, 64])) + permute_dims216: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_encoder_attn_v_proj_weight1, axes=None) + matmul215: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims216, out_dtype="void") + add236: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul215, model_decoder_layers_11_encoder_attn_v_proj_bias1) + reshape301: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add236, 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,)) + permute_dims217: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_encoder_attn_k_proj_weight1, axes=None) + matmul216: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims217, out_dtype="void") + reshape304: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul216, R.shape([batch_size, 1500, 20, 64])) + permute_dims218: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_encoder_attn_v_proj_weight1, axes=None) + matmul217: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims218, out_dtype="void") + add237: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul217, model_decoder_layers_12_encoder_attn_v_proj_bias1) + reshape305: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add237, 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,)) + permute_dims219: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_encoder_attn_k_proj_weight1, axes=None) + matmul218: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims219, out_dtype="void") + reshape308: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul218, R.shape([batch_size, 1500, 20, 64])) + permute_dims220: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_encoder_attn_v_proj_weight1, axes=None) + matmul219: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims220, out_dtype="void") + add238: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul219, model_decoder_layers_13_encoder_attn_v_proj_bias1) + reshape309: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add238, 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,)) + permute_dims221: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_encoder_attn_k_proj_weight1, axes=None) + matmul220: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims221, out_dtype="void") + reshape312: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul220, R.shape([batch_size, 1500, 20, 64])) + permute_dims222: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_encoder_attn_v_proj_weight1, axes=None) + matmul221: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims222, out_dtype="void") + add239: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul221, model_decoder_layers_14_encoder_attn_v_proj_bias1) + reshape313: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add239, 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,)) + permute_dims223: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_encoder_attn_k_proj_weight1, axes=None) + matmul222: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims223, out_dtype="void") + reshape316: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul222, R.shape([batch_size, 1500, 20, 64])) + permute_dims224: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_encoder_attn_v_proj_weight1, axes=None) + matmul223: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims224, out_dtype="void") + add240: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul223, model_decoder_layers_15_encoder_attn_v_proj_bias1) + reshape317: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add240, 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,)) + permute_dims225: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_encoder_attn_k_proj_weight1, axes=None) + matmul224: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims225, out_dtype="void") + reshape320: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul224, R.shape([batch_size, 1500, 20, 64])) + permute_dims226: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_encoder_attn_v_proj_weight1, axes=None) + matmul225: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims226, out_dtype="void") + add241: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul225, model_decoder_layers_16_encoder_attn_v_proj_bias1) + reshape321: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add241, 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,)) + permute_dims227: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_encoder_attn_k_proj_weight1, axes=None) + matmul226: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims227, out_dtype="void") + reshape324: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul226, R.shape([batch_size, 1500, 20, 64])) + permute_dims228: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_encoder_attn_v_proj_weight1, axes=None) + matmul227: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims228, out_dtype="void") + add242: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul227, model_decoder_layers_17_encoder_attn_v_proj_bias1) + reshape325: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add242, 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,)) + permute_dims229: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_encoder_attn_k_proj_weight1, axes=None) + matmul228: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims229, out_dtype="void") + reshape328: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul228, R.shape([batch_size, 1500, 20, 64])) + permute_dims230: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_encoder_attn_v_proj_weight1, axes=None) + matmul229: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims230, out_dtype="void") + add243: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul229, model_decoder_layers_18_encoder_attn_v_proj_bias1) + reshape329: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add243, 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,)) + permute_dims231: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_encoder_attn_k_proj_weight1, axes=None) + matmul230: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims231, out_dtype="void") + reshape332: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul230, R.shape([batch_size, 1500, 20, 64])) + permute_dims232: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_encoder_attn_v_proj_weight1, axes=None) + matmul231: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims232, out_dtype="void") + add244: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul231, model_decoder_layers_19_encoder_attn_v_proj_bias1) + reshape333: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add244, 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,)) + permute_dims233: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_encoder_attn_k_proj_weight1, axes=None) + matmul232: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims233, out_dtype="void") + reshape336: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul232, R.shape([batch_size, 1500, 20, 64])) + permute_dims234: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_encoder_attn_v_proj_weight1, axes=None) + matmul233: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims234, out_dtype="void") + add245: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul233, model_decoder_layers_20_encoder_attn_v_proj_bias1) + reshape337: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add245, 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,)) + permute_dims235: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_encoder_attn_k_proj_weight1, axes=None) + matmul234: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims235, out_dtype="void") + reshape340: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul234, R.shape([batch_size, 1500, 20, 64])) + permute_dims236: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_encoder_attn_v_proj_weight1, axes=None) + matmul235: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims236, out_dtype="void") + add246: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul235, model_decoder_layers_21_encoder_attn_v_proj_bias1) + reshape341: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add246, 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,)) + permute_dims237: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_encoder_attn_k_proj_weight1, axes=None) + matmul236: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims237, out_dtype="void") + reshape344: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul236, R.shape([batch_size, 1500, 20, 64])) + permute_dims238: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_encoder_attn_v_proj_weight1, axes=None) + matmul237: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims238, out_dtype="void") + add247: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul237, model_decoder_layers_22_encoder_attn_v_proj_bias1) + reshape345: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add247, 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,)) + permute_dims239: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_encoder_attn_k_proj_weight1, axes=None) + matmul238: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims239, out_dtype="void") + reshape348: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul238, R.shape([batch_size, 1500, 20, 64])) + permute_dims240: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_encoder_attn_v_proj_weight1, axes=None) + matmul239: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims240, out_dtype="void") + add248: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul239, model_decoder_layers_23_encoder_attn_v_proj_bias1) + reshape349: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add248, 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,)) + permute_dims241: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_encoder_attn_k_proj_weight1, axes=None) + matmul240: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims241, out_dtype="void") + reshape352: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul240, R.shape([batch_size, 1500, 20, 64])) + permute_dims242: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_encoder_attn_v_proj_weight1, axes=None) + matmul241: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims242, out_dtype="void") + add249: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul241, model_decoder_layers_24_encoder_attn_v_proj_bias1) + reshape353: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add249, 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,)) + permute_dims243: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_encoder_attn_k_proj_weight1, axes=None) + matmul242: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims243, out_dtype="void") + reshape356: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul242, R.shape([batch_size, 1500, 20, 64])) + permute_dims244: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_encoder_attn_v_proj_weight1, axes=None) + matmul243: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims244, out_dtype="void") + add250: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul243, model_decoder_layers_25_encoder_attn_v_proj_bias1) + reshape357: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add250, 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,)) + permute_dims245: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_encoder_attn_k_proj_weight1, axes=None) + matmul244: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims245, out_dtype="void") + reshape360: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul244, R.shape([batch_size, 1500, 20, 64])) + permute_dims246: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_encoder_attn_v_proj_weight1, axes=None) + matmul245: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims246, out_dtype="void") + add251: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul245, model_decoder_layers_26_encoder_attn_v_proj_bias1) + reshape361: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add251, 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,)) + permute_dims247: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_encoder_attn_k_proj_weight1, axes=None) + matmul246: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims247, out_dtype="void") + reshape364: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul246, R.shape([batch_size, 1500, 20, 64])) + permute_dims248: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_encoder_attn_v_proj_weight1, axes=None) + matmul247: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims248, out_dtype="void") + add252: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul247, model_decoder_layers_27_encoder_attn_v_proj_bias1) + reshape365: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add252, 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,)) + permute_dims249: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_encoder_attn_k_proj_weight1, axes=None) + matmul248: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims249, out_dtype="void") + reshape368: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul248, R.shape([batch_size, 1500, 20, 64])) + permute_dims250: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_encoder_attn_v_proj_weight1, axes=None) + matmul249: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims250, out_dtype="void") + add253: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul249, model_decoder_layers_28_encoder_attn_v_proj_bias1) + reshape369: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add253, 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,)) + permute_dims251: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_encoder_attn_k_proj_weight1, axes=None) + matmul250: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims251, out_dtype="void") + reshape372: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul250, R.shape([batch_size, 1500, 20, 64])) + permute_dims252: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_encoder_attn_v_proj_weight1, axes=None) + matmul251: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims252, out_dtype="void") + add254: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul251, model_decoder_layers_29_encoder_attn_v_proj_bias1) + reshape373: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add254, 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,)) + permute_dims253: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_encoder_attn_k_proj_weight1, axes=None) + matmul252: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims253, out_dtype="void") + reshape376: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul252, R.shape([batch_size, 1500, 20, 64])) + permute_dims254: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_encoder_attn_v_proj_weight1, axes=None) + matmul253: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims254, out_dtype="void") + add255: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul253, model_decoder_layers_30_encoder_attn_v_proj_bias1) + reshape377: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add255, 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,)) + permute_dims255: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_encoder_attn_k_proj_weight1, axes=None) + matmul254: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims255, out_dtype="void") + reshape380: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul254, R.shape([batch_size, 1500, 20, 64])) + permute_dims256: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_encoder_attn_v_proj_weight1, axes=None) + matmul255: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(encoder_hidden_states, permute_dims256, out_dtype="void") + add256: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul255, model_decoder_layers_31_encoder_attn_v_proj_bias1) + reshape381: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add256, 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_encoder_conv1_weight3: R.Tensor((1280, 128, 3), dtype="float16") = packed_params[0] + model_encoder_conv1_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1] + model_encoder_conv2_weight3: R.Tensor((1280, 1280, 3), dtype="float16") = packed_params[2] + model_encoder_conv2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[3] + model_encoder_embed_positions_weight3: R.Tensor((1500, 1280), dtype="float16") = packed_params[4] + model_encoder_layers_0_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[5] + model_encoder_layers_0_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[6] + model_encoder_layers_0_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[7] + model_encoder_layers_0_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[8] + model_encoder_layers_0_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[9] + model_encoder_layers_0_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[10] + model_encoder_layers_0_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[11] + model_encoder_layers_0_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[12] + model_encoder_layers_0_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[13] + model_encoder_layers_0_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[14] + model_encoder_layers_0_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[15] + model_encoder_layers_0_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[16] + model_encoder_layers_0_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[17] + model_encoder_layers_0_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[18] + model_encoder_layers_0_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[19] + model_encoder_layers_1_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[20] + model_encoder_layers_1_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[21] + model_encoder_layers_1_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[22] + model_encoder_layers_1_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[23] + model_encoder_layers_1_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[24] + model_encoder_layers_1_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[25] + model_encoder_layers_1_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[26] + model_encoder_layers_1_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[27] + model_encoder_layers_1_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[28] + model_encoder_layers_1_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[29] + model_encoder_layers_1_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[30] + model_encoder_layers_1_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[31] + model_encoder_layers_1_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[32] + model_encoder_layers_1_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[33] + model_encoder_layers_1_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[34] + model_encoder_layers_2_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[35] + model_encoder_layers_2_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[36] + model_encoder_layers_2_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[37] + model_encoder_layers_2_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[38] + model_encoder_layers_2_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[39] + model_encoder_layers_2_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[40] + model_encoder_layers_2_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[41] + model_encoder_layers_2_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[42] + model_encoder_layers_2_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[43] + model_encoder_layers_2_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[44] + model_encoder_layers_2_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[45] + model_encoder_layers_2_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[46] + model_encoder_layers_2_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[47] + model_encoder_layers_2_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[48] + model_encoder_layers_2_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[49] + model_encoder_layers_3_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[50] + model_encoder_layers_3_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[51] + model_encoder_layers_3_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[52] + model_encoder_layers_3_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[53] + model_encoder_layers_3_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[54] + model_encoder_layers_3_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[55] + model_encoder_layers_3_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[56] + model_encoder_layers_3_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[57] + model_encoder_layers_3_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[58] + model_encoder_layers_3_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[59] + model_encoder_layers_3_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[60] + model_encoder_layers_3_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[61] + model_encoder_layers_3_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[62] + model_encoder_layers_3_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[63] + model_encoder_layers_3_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[64] + model_encoder_layers_4_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[65] + model_encoder_layers_4_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[66] + model_encoder_layers_4_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[67] + model_encoder_layers_4_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[68] + model_encoder_layers_4_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[69] + model_encoder_layers_4_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[70] + model_encoder_layers_4_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[71] + model_encoder_layers_4_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[72] + model_encoder_layers_4_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[73] + model_encoder_layers_4_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[74] + model_encoder_layers_4_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[75] + model_encoder_layers_4_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[76] + model_encoder_layers_4_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[77] + model_encoder_layers_4_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[78] + model_encoder_layers_4_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[79] + model_encoder_layers_5_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[80] + model_encoder_layers_5_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[81] + model_encoder_layers_5_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[82] + model_encoder_layers_5_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[83] + model_encoder_layers_5_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[84] + model_encoder_layers_5_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[85] + model_encoder_layers_5_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[86] + model_encoder_layers_5_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[87] + model_encoder_layers_5_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[88] + model_encoder_layers_5_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[89] + model_encoder_layers_5_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[90] + model_encoder_layers_5_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[91] + model_encoder_layers_5_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[92] + model_encoder_layers_5_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[93] + model_encoder_layers_5_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[94] + model_encoder_layers_6_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[95] + model_encoder_layers_6_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[96] + model_encoder_layers_6_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[97] + model_encoder_layers_6_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[98] + model_encoder_layers_6_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[99] + model_encoder_layers_6_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[100] + model_encoder_layers_6_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[101] + model_encoder_layers_6_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[102] + model_encoder_layers_6_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[103] + model_encoder_layers_6_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[104] + model_encoder_layers_6_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[105] + model_encoder_layers_6_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[106] + model_encoder_layers_6_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[107] + model_encoder_layers_6_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[108] + model_encoder_layers_6_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[109] + model_encoder_layers_7_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[110] + model_encoder_layers_7_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[111] + model_encoder_layers_7_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[112] + model_encoder_layers_7_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[113] + model_encoder_layers_7_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[114] + model_encoder_layers_7_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[115] + model_encoder_layers_7_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[116] + model_encoder_layers_7_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[117] + model_encoder_layers_7_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[118] + model_encoder_layers_7_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[119] + model_encoder_layers_7_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[120] + model_encoder_layers_7_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[121] + model_encoder_layers_7_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[122] + model_encoder_layers_7_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[123] + model_encoder_layers_7_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[124] + model_encoder_layers_8_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[125] + model_encoder_layers_8_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[126] + model_encoder_layers_8_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[127] + model_encoder_layers_8_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[128] + model_encoder_layers_8_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[129] + model_encoder_layers_8_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[130] + model_encoder_layers_8_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[131] + model_encoder_layers_8_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[132] + model_encoder_layers_8_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[133] + model_encoder_layers_8_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[134] + model_encoder_layers_8_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[135] + model_encoder_layers_8_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[136] + model_encoder_layers_8_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[137] + model_encoder_layers_8_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[138] + model_encoder_layers_8_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[139] + model_encoder_layers_9_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[140] + model_encoder_layers_9_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[141] + model_encoder_layers_9_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[142] + model_encoder_layers_9_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[143] + model_encoder_layers_9_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[144] + model_encoder_layers_9_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[145] + model_encoder_layers_9_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[146] + model_encoder_layers_9_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[147] + model_encoder_layers_9_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[148] + model_encoder_layers_9_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[149] + model_encoder_layers_9_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[150] + model_encoder_layers_9_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[151] + model_encoder_layers_9_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[152] + model_encoder_layers_9_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[153] + model_encoder_layers_9_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[154] + model_encoder_layers_10_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[155] + model_encoder_layers_10_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[156] + model_encoder_layers_10_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[157] + model_encoder_layers_10_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[158] + model_encoder_layers_10_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[159] + model_encoder_layers_10_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[160] + model_encoder_layers_10_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[161] + model_encoder_layers_10_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[162] + model_encoder_layers_10_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[163] + model_encoder_layers_10_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[164] + model_encoder_layers_10_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[165] + model_encoder_layers_10_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[166] + model_encoder_layers_10_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[167] + model_encoder_layers_10_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[168] + model_encoder_layers_10_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[169] + model_encoder_layers_11_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[170] + model_encoder_layers_11_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[171] + model_encoder_layers_11_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[172] + model_encoder_layers_11_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[173] + model_encoder_layers_11_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[174] + model_encoder_layers_11_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[175] + model_encoder_layers_11_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[176] + model_encoder_layers_11_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[177] + model_encoder_layers_11_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[178] + model_encoder_layers_11_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[179] + model_encoder_layers_11_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[180] + model_encoder_layers_11_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[181] + model_encoder_layers_11_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[182] + model_encoder_layers_11_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[183] + model_encoder_layers_11_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[184] + model_encoder_layers_12_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[185] + model_encoder_layers_12_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[186] + model_encoder_layers_12_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[187] + model_encoder_layers_12_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[188] + model_encoder_layers_12_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[189] + model_encoder_layers_12_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[190] + model_encoder_layers_12_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[191] + model_encoder_layers_12_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[192] + model_encoder_layers_12_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[193] + model_encoder_layers_12_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[194] + model_encoder_layers_12_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[195] + model_encoder_layers_12_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[196] + model_encoder_layers_12_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[197] + model_encoder_layers_12_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[198] + model_encoder_layers_12_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[199] + model_encoder_layers_13_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[200] + model_encoder_layers_13_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[201] + model_encoder_layers_13_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[202] + model_encoder_layers_13_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[203] + model_encoder_layers_13_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[204] + model_encoder_layers_13_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[205] + model_encoder_layers_13_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[206] + model_encoder_layers_13_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[207] + model_encoder_layers_13_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[208] + model_encoder_layers_13_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[209] + model_encoder_layers_13_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[210] + model_encoder_layers_13_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[211] + model_encoder_layers_13_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[212] + model_encoder_layers_13_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[213] + model_encoder_layers_13_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[214] + model_encoder_layers_14_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[215] + model_encoder_layers_14_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[216] + model_encoder_layers_14_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[217] + model_encoder_layers_14_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[218] + model_encoder_layers_14_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[219] + model_encoder_layers_14_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[220] + model_encoder_layers_14_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[221] + model_encoder_layers_14_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[222] + model_encoder_layers_14_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[223] + model_encoder_layers_14_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[224] + model_encoder_layers_14_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[225] + model_encoder_layers_14_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[226] + model_encoder_layers_14_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[227] + model_encoder_layers_14_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[228] + model_encoder_layers_14_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[229] + model_encoder_layers_15_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[230] + model_encoder_layers_15_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[231] + model_encoder_layers_15_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[232] + model_encoder_layers_15_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[233] + model_encoder_layers_15_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[234] + model_encoder_layers_15_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[235] + model_encoder_layers_15_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[236] + model_encoder_layers_15_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[237] + model_encoder_layers_15_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[238] + model_encoder_layers_15_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[239] + model_encoder_layers_15_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[240] + model_encoder_layers_15_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[241] + model_encoder_layers_15_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[242] + model_encoder_layers_15_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[243] + model_encoder_layers_15_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[244] + model_encoder_layers_16_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[245] + model_encoder_layers_16_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[246] + model_encoder_layers_16_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[247] + model_encoder_layers_16_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[248] + model_encoder_layers_16_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[249] + model_encoder_layers_16_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[250] + model_encoder_layers_16_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[251] + model_encoder_layers_16_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[252] + model_encoder_layers_16_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[253] + model_encoder_layers_16_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[254] + model_encoder_layers_16_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[255] + model_encoder_layers_16_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[256] + model_encoder_layers_16_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[257] + model_encoder_layers_16_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[258] + model_encoder_layers_16_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[259] + model_encoder_layers_17_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[260] + model_encoder_layers_17_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[261] + model_encoder_layers_17_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[262] + model_encoder_layers_17_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[263] + model_encoder_layers_17_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[264] + model_encoder_layers_17_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[265] + model_encoder_layers_17_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[266] + model_encoder_layers_17_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[267] + model_encoder_layers_17_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[268] + model_encoder_layers_17_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[269] + model_encoder_layers_17_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[270] + model_encoder_layers_17_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[271] + model_encoder_layers_17_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[272] + model_encoder_layers_17_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[273] + model_encoder_layers_17_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[274] + model_encoder_layers_18_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[275] + model_encoder_layers_18_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[276] + model_encoder_layers_18_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[277] + model_encoder_layers_18_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[278] + model_encoder_layers_18_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[279] + model_encoder_layers_18_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[280] + model_encoder_layers_18_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[281] + model_encoder_layers_18_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[282] + model_encoder_layers_18_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[283] + model_encoder_layers_18_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[284] + model_encoder_layers_18_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[285] + model_encoder_layers_18_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[286] + model_encoder_layers_18_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[287] + model_encoder_layers_18_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[288] + model_encoder_layers_18_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[289] + model_encoder_layers_19_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[290] + model_encoder_layers_19_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[291] + model_encoder_layers_19_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[292] + model_encoder_layers_19_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[293] + model_encoder_layers_19_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[294] + model_encoder_layers_19_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[295] + model_encoder_layers_19_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[296] + model_encoder_layers_19_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[297] + model_encoder_layers_19_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[298] + model_encoder_layers_19_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[299] + model_encoder_layers_19_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[300] + model_encoder_layers_19_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[301] + model_encoder_layers_19_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[302] + model_encoder_layers_19_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[303] + model_encoder_layers_19_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[304] + model_encoder_layers_20_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[305] + model_encoder_layers_20_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[306] + model_encoder_layers_20_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[307] + model_encoder_layers_20_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[308] + model_encoder_layers_20_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[309] + model_encoder_layers_20_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[310] + model_encoder_layers_20_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[311] + model_encoder_layers_20_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[312] + model_encoder_layers_20_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[313] + model_encoder_layers_20_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[314] + model_encoder_layers_20_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[315] + model_encoder_layers_20_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[316] + model_encoder_layers_20_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[317] + model_encoder_layers_20_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[318] + model_encoder_layers_20_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[319] + model_encoder_layers_21_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[320] + model_encoder_layers_21_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[321] + model_encoder_layers_21_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[322] + model_encoder_layers_21_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[323] + model_encoder_layers_21_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[324] + model_encoder_layers_21_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[325] + model_encoder_layers_21_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[326] + model_encoder_layers_21_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[327] + model_encoder_layers_21_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[328] + model_encoder_layers_21_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[329] + model_encoder_layers_21_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[330] + model_encoder_layers_21_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[331] + model_encoder_layers_21_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[332] + model_encoder_layers_21_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[333] + model_encoder_layers_21_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[334] + model_encoder_layers_22_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[335] + model_encoder_layers_22_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[336] + model_encoder_layers_22_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[337] + model_encoder_layers_22_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[338] + model_encoder_layers_22_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[339] + model_encoder_layers_22_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[340] + model_encoder_layers_22_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[341] + model_encoder_layers_22_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[342] + model_encoder_layers_22_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[343] + model_encoder_layers_22_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[344] + model_encoder_layers_22_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[345] + model_encoder_layers_22_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[346] + model_encoder_layers_22_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[347] + model_encoder_layers_22_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[348] + model_encoder_layers_22_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[349] + model_encoder_layers_23_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[350] + model_encoder_layers_23_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[351] + model_encoder_layers_23_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[352] + model_encoder_layers_23_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[353] + model_encoder_layers_23_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[354] + model_encoder_layers_23_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[355] + model_encoder_layers_23_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[356] + model_encoder_layers_23_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[357] + model_encoder_layers_23_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[358] + model_encoder_layers_23_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[359] + model_encoder_layers_23_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[360] + model_encoder_layers_23_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[361] + model_encoder_layers_23_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[362] + model_encoder_layers_23_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[363] + model_encoder_layers_23_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[364] + model_encoder_layers_24_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[365] + model_encoder_layers_24_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[366] + model_encoder_layers_24_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[367] + model_encoder_layers_24_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[368] + model_encoder_layers_24_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[369] + model_encoder_layers_24_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[370] + model_encoder_layers_24_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[371] + model_encoder_layers_24_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[372] + model_encoder_layers_24_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[373] + model_encoder_layers_24_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[374] + model_encoder_layers_24_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[375] + model_encoder_layers_24_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[376] + model_encoder_layers_24_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[377] + model_encoder_layers_24_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[378] + model_encoder_layers_24_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[379] + model_encoder_layers_25_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[380] + model_encoder_layers_25_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[381] + model_encoder_layers_25_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[382] + model_encoder_layers_25_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[383] + model_encoder_layers_25_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[384] + model_encoder_layers_25_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[385] + model_encoder_layers_25_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[386] + model_encoder_layers_25_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[387] + model_encoder_layers_25_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[388] + model_encoder_layers_25_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[389] + model_encoder_layers_25_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[390] + model_encoder_layers_25_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[391] + model_encoder_layers_25_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[392] + model_encoder_layers_25_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[393] + model_encoder_layers_25_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[394] + model_encoder_layers_26_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[395] + model_encoder_layers_26_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[396] + model_encoder_layers_26_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[397] + model_encoder_layers_26_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[398] + model_encoder_layers_26_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[399] + model_encoder_layers_26_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[400] + model_encoder_layers_26_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[401] + model_encoder_layers_26_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[402] + model_encoder_layers_26_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[403] + model_encoder_layers_26_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[404] + model_encoder_layers_26_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[405] + model_encoder_layers_26_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[406] + model_encoder_layers_26_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[407] + model_encoder_layers_26_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[408] + model_encoder_layers_26_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[409] + model_encoder_layers_27_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[410] + model_encoder_layers_27_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[411] + model_encoder_layers_27_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[412] + model_encoder_layers_27_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[413] + model_encoder_layers_27_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[414] + model_encoder_layers_27_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[415] + model_encoder_layers_27_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[416] + model_encoder_layers_27_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[417] + model_encoder_layers_27_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[418] + model_encoder_layers_27_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[419] + model_encoder_layers_27_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[420] + model_encoder_layers_27_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[421] + model_encoder_layers_27_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[422] + model_encoder_layers_27_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[423] + model_encoder_layers_27_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[424] + model_encoder_layers_28_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[425] + model_encoder_layers_28_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[426] + model_encoder_layers_28_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[427] + model_encoder_layers_28_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[428] + model_encoder_layers_28_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[429] + model_encoder_layers_28_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[430] + model_encoder_layers_28_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[431] + model_encoder_layers_28_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[432] + model_encoder_layers_28_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[433] + model_encoder_layers_28_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[434] + model_encoder_layers_28_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[435] + model_encoder_layers_28_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[436] + model_encoder_layers_28_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[437] + model_encoder_layers_28_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[438] + model_encoder_layers_28_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[439] + model_encoder_layers_29_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[440] + model_encoder_layers_29_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[441] + model_encoder_layers_29_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[442] + model_encoder_layers_29_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[443] + model_encoder_layers_29_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[444] + model_encoder_layers_29_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[445] + model_encoder_layers_29_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[446] + model_encoder_layers_29_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[447] + model_encoder_layers_29_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[448] + model_encoder_layers_29_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[449] + model_encoder_layers_29_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[450] + model_encoder_layers_29_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[451] + model_encoder_layers_29_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[452] + model_encoder_layers_29_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[453] + model_encoder_layers_29_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[454] + model_encoder_layers_30_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[455] + model_encoder_layers_30_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[456] + model_encoder_layers_30_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[457] + model_encoder_layers_30_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[458] + model_encoder_layers_30_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[459] + model_encoder_layers_30_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[460] + model_encoder_layers_30_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[461] + model_encoder_layers_30_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[462] + model_encoder_layers_30_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[463] + model_encoder_layers_30_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[464] + model_encoder_layers_30_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[465] + model_encoder_layers_30_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[466] + model_encoder_layers_30_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[467] + model_encoder_layers_30_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[468] + model_encoder_layers_30_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[469] + model_encoder_layers_31_self_attn_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[470] + model_encoder_layers_31_self_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[471] + model_encoder_layers_31_self_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[472] + model_encoder_layers_31_self_attn_q_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[473] + model_encoder_layers_31_self_attn_q_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[474] + model_encoder_layers_31_self_attn_out_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[475] + model_encoder_layers_31_self_attn_out_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[476] + model_encoder_layers_31_self_attn_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[477] + model_encoder_layers_31_self_attn_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[478] + model_encoder_layers_31_fc1_weight3: R.Tensor((5120, 1280), dtype="float16") = packed_params[479] + model_encoder_layers_31_fc1_bias3: R.Tensor((5120,), dtype="float16") = packed_params[480] + model_encoder_layers_31_fc2_weight3: R.Tensor((1280, 5120), dtype="float16") = packed_params[481] + model_encoder_layers_31_fc2_bias3: R.Tensor((1280,), dtype="float16") = packed_params[482] + model_encoder_layers_31_final_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[483] + model_encoder_layers_31_final_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[484] + model_encoder_layer_norm_weight3: R.Tensor((1280,), dtype="float16") = packed_params[485] + model_encoder_layer_norm_bias3: R.Tensor((1280,), dtype="float16") = packed_params[486] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[498] + model_decoder_layers_0_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[499] + model_decoder_layers_0_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[500] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[522] + model_decoder_layers_1_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[523] + model_decoder_layers_1_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[524] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[546] + model_decoder_layers_2_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[547] + model_decoder_layers_2_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[548] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[570] + model_decoder_layers_3_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[571] + model_decoder_layers_3_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[572] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[594] + model_decoder_layers_4_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[595] + model_decoder_layers_4_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[596] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[618] + model_decoder_layers_5_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[619] + model_decoder_layers_5_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[620] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[642] + model_decoder_layers_6_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[643] + model_decoder_layers_6_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[644] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[666] + model_decoder_layers_7_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[667] + model_decoder_layers_7_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[668] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[690] + model_decoder_layers_8_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[691] + model_decoder_layers_8_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[692] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[714] + model_decoder_layers_9_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[715] + model_decoder_layers_9_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[716] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[738] + model_decoder_layers_10_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[739] + model_decoder_layers_10_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[740] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[762] + model_decoder_layers_11_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[763] + model_decoder_layers_11_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[764] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[786] + model_decoder_layers_12_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[787] + model_decoder_layers_12_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[788] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[810] + model_decoder_layers_13_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[811] + model_decoder_layers_13_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[812] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[834] + model_decoder_layers_14_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[835] + model_decoder_layers_14_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[836] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[858] + model_decoder_layers_15_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[859] + model_decoder_layers_15_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[860] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[882] + model_decoder_layers_16_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[883] + model_decoder_layers_16_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[884] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[906] + model_decoder_layers_17_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[907] + model_decoder_layers_17_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[908] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[930] + model_decoder_layers_18_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[931] + model_decoder_layers_18_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[932] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[954] + model_decoder_layers_19_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[955] + model_decoder_layers_19_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[956] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[978] + model_decoder_layers_20_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[979] + model_decoder_layers_20_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[980] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1002] + model_decoder_layers_21_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1003] + model_decoder_layers_21_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1004] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1026] + model_decoder_layers_22_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1027] + model_decoder_layers_22_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1028] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1050] + model_decoder_layers_23_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1051] + model_decoder_layers_23_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1052] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1074] + model_decoder_layers_24_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1075] + model_decoder_layers_24_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1076] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1098] + model_decoder_layers_25_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1099] + model_decoder_layers_25_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1100] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1122] + model_decoder_layers_26_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1123] + model_decoder_layers_26_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1124] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1146] + model_decoder_layers_27_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1147] + model_decoder_layers_27_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1148] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1170] + model_decoder_layers_28_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1171] + model_decoder_layers_28_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1172] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1194] + model_decoder_layers_29_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1195] + model_decoder_layers_29_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1196] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1218] + model_decoder_layers_30_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1219] + model_decoder_layers_30_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1220] + 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_k_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1242] + model_decoder_layers_31_encoder_attn_v_proj_weight3: R.Tensor((1280, 1280), dtype="float16") = packed_params[1243] + model_decoder_layers_31_encoder_attn_v_proj_bias3: R.Tensor((1280,), dtype="float16") = packed_params[1244] + 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) + permute_dims514: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_q_proj_weight3, axes=None) + matmul513: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm162, permute_dims514, out_dtype="void") + add579: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul513, model_decoder_layers_0_self_attn_q_proj_bias3) + reshape710: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add579, R.shape([batch_size, 1, 20, 64])) + permute_dims515: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_k_proj_weight3, axes=None) + matmul514: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm162, permute_dims515, out_dtype="void") + reshape711: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul514, R.shape([batch_size, 1, 20, 64])) + permute_dims516: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_v_proj_weight3, axes=None) + matmul515: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm162, permute_dims516, out_dtype="void") + add580: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul515, model_decoder_layers_0_self_attn_v_proj_bias3) + reshape712: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add580, 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])) + permute_dims517: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_out_proj_weight3, axes=None) + matmul516: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape715, permute_dims517, out_dtype="void") + add581: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul516, model_decoder_layers_0_self_attn_out_proj_bias3) + add582: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add578, add581) + 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) + permute_dims518: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_encoder_attn_q_proj_weight3, axes=None) + matmul517: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm163, permute_dims518, out_dtype="void") + add583: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul517, model_decoder_layers_0_encoder_attn_q_proj_bias3) + reshape716: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add583, 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])) + permute_dims519: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_encoder_attn_out_proj_weight3, axes=None) + matmul518: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape719, permute_dims519, out_dtype="void") + add584: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul518, model_decoder_layers_0_encoder_attn_out_proj_bias3) + add585: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add582, add584) + 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) + permute_dims520: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_0_fc1_weight3, axes=None) + matmul519: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm164, permute_dims520, out_dtype="void") + add586: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul519, model_decoder_layers_0_fc1_bias3) + gelu66: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add586) + permute_dims521: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_fc2_weight3, axes=None) + matmul520: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu66, permute_dims521, out_dtype="void") + add587: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul520, model_decoder_layers_0_fc2_bias3) + add588: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add585, add587) + 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) + permute_dims522: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_q_proj_weight3, axes=None) + matmul521: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm165, permute_dims522, out_dtype="void") + add589: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul521, model_decoder_layers_1_self_attn_q_proj_bias3) + reshape720: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add589, R.shape([batch_size, 1, 20, 64])) + permute_dims523: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_k_proj_weight3, axes=None) + matmul522: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm165, permute_dims523, out_dtype="void") + reshape721: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul522, R.shape([batch_size, 1, 20, 64])) + permute_dims524: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_v_proj_weight3, axes=None) + matmul523: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm165, permute_dims524, out_dtype="void") + add590: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul523, model_decoder_layers_1_self_attn_v_proj_bias3) + reshape722: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add590, 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])) + permute_dims525: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_out_proj_weight3, axes=None) + matmul524: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape725, permute_dims525, out_dtype="void") + add591: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul524, model_decoder_layers_1_self_attn_out_proj_bias3) + add592: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add588, add591) + 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) + permute_dims526: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_encoder_attn_q_proj_weight3, axes=None) + matmul525: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm166, permute_dims526, out_dtype="void") + add593: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul525, model_decoder_layers_1_encoder_attn_q_proj_bias3) + reshape726: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add593, 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])) + permute_dims527: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_encoder_attn_out_proj_weight3, axes=None) + matmul526: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape729, permute_dims527, out_dtype="void") + add594: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul526, model_decoder_layers_1_encoder_attn_out_proj_bias3) + add595: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add592, add594) + 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) + permute_dims528: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_1_fc1_weight3, axes=None) + matmul527: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm167, permute_dims528, out_dtype="void") + add596: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul527, model_decoder_layers_1_fc1_bias3) + gelu67: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add596) + permute_dims529: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_fc2_weight3, axes=None) + matmul528: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu67, permute_dims529, out_dtype="void") + add597: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul528, model_decoder_layers_1_fc2_bias3) + add598: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add595, add597) + 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) + permute_dims530: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_q_proj_weight3, axes=None) + matmul529: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm168, permute_dims530, out_dtype="void") + add599: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul529, model_decoder_layers_2_self_attn_q_proj_bias3) + reshape730: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add599, R.shape([batch_size, 1, 20, 64])) + permute_dims531: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_k_proj_weight3, axes=None) + matmul530: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm168, permute_dims531, out_dtype="void") + reshape731: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul530, R.shape([batch_size, 1, 20, 64])) + permute_dims532: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_v_proj_weight3, axes=None) + matmul531: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm168, permute_dims532, out_dtype="void") + add600: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul531, model_decoder_layers_2_self_attn_v_proj_bias3) + reshape732: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add600, 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])) + permute_dims533: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_out_proj_weight3, axes=None) + matmul532: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape735, permute_dims533, out_dtype="void") + add601: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul532, model_decoder_layers_2_self_attn_out_proj_bias3) + add602: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add598, add601) + 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) + permute_dims534: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_encoder_attn_q_proj_weight3, axes=None) + matmul533: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm169, permute_dims534, out_dtype="void") + add603: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul533, model_decoder_layers_2_encoder_attn_q_proj_bias3) + reshape736: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add603, 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])) + permute_dims535: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_encoder_attn_out_proj_weight3, axes=None) + matmul534: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape739, permute_dims535, out_dtype="void") + add604: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul534, model_decoder_layers_2_encoder_attn_out_proj_bias3) + add605: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add602, add604) + 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) + permute_dims536: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_2_fc1_weight3, axes=None) + matmul535: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm170, permute_dims536, out_dtype="void") + add606: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul535, model_decoder_layers_2_fc1_bias3) + gelu68: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add606) + permute_dims537: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_fc2_weight3, axes=None) + matmul536: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu68, permute_dims537, out_dtype="void") + add607: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul536, model_decoder_layers_2_fc2_bias3) + add608: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add605, add607) + 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) + permute_dims538: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_q_proj_weight3, axes=None) + matmul537: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm171, permute_dims538, out_dtype="void") + add609: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul537, model_decoder_layers_3_self_attn_q_proj_bias3) + reshape740: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add609, R.shape([batch_size, 1, 20, 64])) + permute_dims539: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_k_proj_weight3, axes=None) + matmul538: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm171, permute_dims539, out_dtype="void") + reshape741: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul538, R.shape([batch_size, 1, 20, 64])) + permute_dims540: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_v_proj_weight3, axes=None) + matmul539: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm171, permute_dims540, out_dtype="void") + add610: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul539, model_decoder_layers_3_self_attn_v_proj_bias3) + reshape742: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add610, 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])) + permute_dims541: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_out_proj_weight3, axes=None) + matmul540: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape745, permute_dims541, out_dtype="void") + add611: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul540, model_decoder_layers_3_self_attn_out_proj_bias3) + add612: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add608, add611) + 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) + permute_dims542: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_encoder_attn_q_proj_weight3, axes=None) + matmul541: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm172, permute_dims542, out_dtype="void") + add613: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul541, model_decoder_layers_3_encoder_attn_q_proj_bias3) + reshape746: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add613, 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])) + permute_dims543: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_encoder_attn_out_proj_weight3, axes=None) + matmul542: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape749, permute_dims543, out_dtype="void") + add614: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul542, model_decoder_layers_3_encoder_attn_out_proj_bias3) + add615: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add612, add614) + 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) + permute_dims544: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_3_fc1_weight3, axes=None) + matmul543: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm173, permute_dims544, out_dtype="void") + add616: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul543, model_decoder_layers_3_fc1_bias3) + gelu69: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add616) + permute_dims545: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_fc2_weight3, axes=None) + matmul544: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu69, permute_dims545, out_dtype="void") + add617: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul544, model_decoder_layers_3_fc2_bias3) + add618: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add615, add617) + 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) + permute_dims546: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_q_proj_weight3, axes=None) + matmul545: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm174, permute_dims546, out_dtype="void") + add619: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul545, model_decoder_layers_4_self_attn_q_proj_bias3) + reshape750: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add619, R.shape([batch_size, 1, 20, 64])) + permute_dims547: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_k_proj_weight3, axes=None) + matmul546: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm174, permute_dims547, out_dtype="void") + reshape751: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul546, R.shape([batch_size, 1, 20, 64])) + permute_dims548: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_v_proj_weight3, axes=None) + matmul547: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm174, permute_dims548, out_dtype="void") + add620: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul547, model_decoder_layers_4_self_attn_v_proj_bias3) + reshape752: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add620, 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])) + permute_dims549: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_out_proj_weight3, axes=None) + matmul548: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape755, permute_dims549, out_dtype="void") + add621: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul548, model_decoder_layers_4_self_attn_out_proj_bias3) + add622: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add618, add621) + 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) + permute_dims550: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_encoder_attn_q_proj_weight3, axes=None) + matmul549: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm175, permute_dims550, out_dtype="void") + add623: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul549, model_decoder_layers_4_encoder_attn_q_proj_bias3) + reshape756: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add623, 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])) + permute_dims551: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_encoder_attn_out_proj_weight3, axes=None) + matmul550: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape759, permute_dims551, out_dtype="void") + add624: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul550, model_decoder_layers_4_encoder_attn_out_proj_bias3) + add625: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add622, add624) + 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) + permute_dims552: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_4_fc1_weight3, axes=None) + matmul551: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm176, permute_dims552, out_dtype="void") + add626: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul551, model_decoder_layers_4_fc1_bias3) + gelu70: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add626) + permute_dims553: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_fc2_weight3, axes=None) + matmul552: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu70, permute_dims553, out_dtype="void") + add627: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul552, model_decoder_layers_4_fc2_bias3) + add628: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add625, add627) + 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) + permute_dims554: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_q_proj_weight3, axes=None) + matmul553: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm177, permute_dims554, out_dtype="void") + add629: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul553, model_decoder_layers_5_self_attn_q_proj_bias3) + reshape760: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add629, R.shape([batch_size, 1, 20, 64])) + permute_dims555: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_k_proj_weight3, axes=None) + matmul554: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm177, permute_dims555, out_dtype="void") + reshape761: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul554, R.shape([batch_size, 1, 20, 64])) + permute_dims556: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_v_proj_weight3, axes=None) + matmul555: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm177, permute_dims556, out_dtype="void") + add630: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul555, model_decoder_layers_5_self_attn_v_proj_bias3) + reshape762: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add630, 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])) + permute_dims557: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_out_proj_weight3, axes=None) + matmul556: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape765, permute_dims557, out_dtype="void") + add631: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul556, model_decoder_layers_5_self_attn_out_proj_bias3) + add632: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add628, add631) + 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) + permute_dims558: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_encoder_attn_q_proj_weight3, axes=None) + matmul557: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm178, permute_dims558, out_dtype="void") + add633: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul557, model_decoder_layers_5_encoder_attn_q_proj_bias3) + reshape766: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add633, 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])) + permute_dims559: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_encoder_attn_out_proj_weight3, axes=None) + matmul558: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape769, permute_dims559, out_dtype="void") + add634: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul558, model_decoder_layers_5_encoder_attn_out_proj_bias3) + add635: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add632, add634) + 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) + permute_dims560: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_5_fc1_weight3, axes=None) + matmul559: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm179, permute_dims560, out_dtype="void") + add636: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul559, model_decoder_layers_5_fc1_bias3) + gelu71: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add636) + permute_dims561: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_fc2_weight3, axes=None) + matmul560: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu71, permute_dims561, out_dtype="void") + add637: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul560, model_decoder_layers_5_fc2_bias3) + add638: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add635, add637) + 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) + permute_dims562: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_q_proj_weight3, axes=None) + matmul561: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm180, permute_dims562, out_dtype="void") + add639: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul561, model_decoder_layers_6_self_attn_q_proj_bias3) + reshape770: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add639, R.shape([batch_size, 1, 20, 64])) + permute_dims563: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_k_proj_weight3, axes=None) + matmul562: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm180, permute_dims563, out_dtype="void") + reshape771: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul562, R.shape([batch_size, 1, 20, 64])) + permute_dims564: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_v_proj_weight3, axes=None) + matmul563: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm180, permute_dims564, out_dtype="void") + add640: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul563, model_decoder_layers_6_self_attn_v_proj_bias3) + reshape772: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add640, 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])) + permute_dims565: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_out_proj_weight3, axes=None) + matmul564: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape775, permute_dims565, out_dtype="void") + add641: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul564, model_decoder_layers_6_self_attn_out_proj_bias3) + add642: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add638, add641) + 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) + permute_dims566: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_encoder_attn_q_proj_weight3, axes=None) + matmul565: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm181, permute_dims566, out_dtype="void") + add643: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul565, model_decoder_layers_6_encoder_attn_q_proj_bias3) + reshape776: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add643, 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])) + permute_dims567: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_encoder_attn_out_proj_weight3, axes=None) + matmul566: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape779, permute_dims567, out_dtype="void") + add644: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul566, model_decoder_layers_6_encoder_attn_out_proj_bias3) + add645: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add642, add644) + 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) + permute_dims568: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_6_fc1_weight3, axes=None) + matmul567: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm182, permute_dims568, out_dtype="void") + add646: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul567, model_decoder_layers_6_fc1_bias3) + gelu72: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add646) + permute_dims569: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_fc2_weight3, axes=None) + matmul568: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu72, permute_dims569, out_dtype="void") + add647: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul568, model_decoder_layers_6_fc2_bias3) + add648: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add645, add647) + 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) + permute_dims570: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_q_proj_weight3, axes=None) + matmul569: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm183, permute_dims570, out_dtype="void") + add649: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul569, model_decoder_layers_7_self_attn_q_proj_bias3) + reshape780: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add649, R.shape([batch_size, 1, 20, 64])) + permute_dims571: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_k_proj_weight3, axes=None) + matmul570: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm183, permute_dims571, out_dtype="void") + reshape781: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul570, R.shape([batch_size, 1, 20, 64])) + permute_dims572: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_v_proj_weight3, axes=None) + matmul571: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm183, permute_dims572, out_dtype="void") + add650: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul571, model_decoder_layers_7_self_attn_v_proj_bias3) + reshape782: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add650, 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])) + permute_dims573: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_out_proj_weight3, axes=None) + matmul572: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape785, permute_dims573, out_dtype="void") + add651: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul572, model_decoder_layers_7_self_attn_out_proj_bias3) + add652: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add648, add651) + 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) + permute_dims574: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_encoder_attn_q_proj_weight3, axes=None) + matmul573: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm184, permute_dims574, out_dtype="void") + add653: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul573, model_decoder_layers_7_encoder_attn_q_proj_bias3) + reshape786: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add653, 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])) + permute_dims575: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_encoder_attn_out_proj_weight3, axes=None) + matmul574: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape789, permute_dims575, out_dtype="void") + add654: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul574, model_decoder_layers_7_encoder_attn_out_proj_bias3) + add655: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add652, add654) + 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) + permute_dims576: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_7_fc1_weight3, axes=None) + matmul575: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm185, permute_dims576, out_dtype="void") + add656: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul575, model_decoder_layers_7_fc1_bias3) + gelu73: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add656) + permute_dims577: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_fc2_weight3, axes=None) + matmul576: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu73, permute_dims577, out_dtype="void") + add657: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul576, model_decoder_layers_7_fc2_bias3) + add658: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add655, add657) + 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) + permute_dims578: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_q_proj_weight3, axes=None) + matmul577: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm186, permute_dims578, out_dtype="void") + add659: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul577, model_decoder_layers_8_self_attn_q_proj_bias3) + reshape790: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add659, R.shape([batch_size, 1, 20, 64])) + permute_dims579: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_k_proj_weight3, axes=None) + matmul578: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm186, permute_dims579, out_dtype="void") + reshape791: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul578, R.shape([batch_size, 1, 20, 64])) + permute_dims580: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_v_proj_weight3, axes=None) + matmul579: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm186, permute_dims580, out_dtype="void") + add660: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul579, model_decoder_layers_8_self_attn_v_proj_bias3) + reshape792: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add660, 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])) + permute_dims581: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_out_proj_weight3, axes=None) + matmul580: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape795, permute_dims581, out_dtype="void") + add661: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul580, model_decoder_layers_8_self_attn_out_proj_bias3) + add662: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add658, add661) + 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) + permute_dims582: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_encoder_attn_q_proj_weight3, axes=None) + matmul581: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm187, permute_dims582, out_dtype="void") + add663: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul581, model_decoder_layers_8_encoder_attn_q_proj_bias3) + reshape796: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add663, 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])) + permute_dims583: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_encoder_attn_out_proj_weight3, axes=None) + matmul582: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape799, permute_dims583, out_dtype="void") + add664: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul582, model_decoder_layers_8_encoder_attn_out_proj_bias3) + add665: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add662, add664) + 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) + permute_dims584: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_8_fc1_weight3, axes=None) + matmul583: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm188, permute_dims584, out_dtype="void") + add666: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul583, model_decoder_layers_8_fc1_bias3) + gelu74: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add666) + permute_dims585: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_fc2_weight3, axes=None) + matmul584: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu74, permute_dims585, out_dtype="void") + add667: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul584, model_decoder_layers_8_fc2_bias3) + add668: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add665, add667) + 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) + permute_dims586: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_q_proj_weight3, axes=None) + matmul585: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm189, permute_dims586, out_dtype="void") + add669: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul585, model_decoder_layers_9_self_attn_q_proj_bias3) + reshape800: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add669, R.shape([batch_size, 1, 20, 64])) + permute_dims587: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_k_proj_weight3, axes=None) + matmul586: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm189, permute_dims587, out_dtype="void") + reshape801: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul586, R.shape([batch_size, 1, 20, 64])) + permute_dims588: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_v_proj_weight3, axes=None) + matmul587: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm189, permute_dims588, out_dtype="void") + add670: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul587, model_decoder_layers_9_self_attn_v_proj_bias3) + reshape802: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add670, 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])) + permute_dims589: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_out_proj_weight3, axes=None) + matmul588: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape805, permute_dims589, out_dtype="void") + add671: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul588, model_decoder_layers_9_self_attn_out_proj_bias3) + add672: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add668, add671) + 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) + permute_dims590: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_encoder_attn_q_proj_weight3, axes=None) + matmul589: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm190, permute_dims590, out_dtype="void") + add673: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul589, model_decoder_layers_9_encoder_attn_q_proj_bias3) + reshape806: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add673, 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])) + permute_dims591: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_encoder_attn_out_proj_weight3, axes=None) + matmul590: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape809, permute_dims591, out_dtype="void") + add674: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul590, model_decoder_layers_9_encoder_attn_out_proj_bias3) + add675: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add672, add674) + 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) + permute_dims592: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_9_fc1_weight3, axes=None) + matmul591: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm191, permute_dims592, out_dtype="void") + add676: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul591, model_decoder_layers_9_fc1_bias3) + gelu75: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add676) + permute_dims593: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_fc2_weight3, axes=None) + matmul592: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu75, permute_dims593, out_dtype="void") + add677: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul592, model_decoder_layers_9_fc2_bias3) + add678: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add675, add677) + 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) + permute_dims594: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_q_proj_weight3, axes=None) + matmul593: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm192, permute_dims594, out_dtype="void") + add679: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul593, model_decoder_layers_10_self_attn_q_proj_bias3) + reshape810: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add679, R.shape([batch_size, 1, 20, 64])) + permute_dims595: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_k_proj_weight3, axes=None) + matmul594: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm192, permute_dims595, out_dtype="void") + reshape811: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul594, R.shape([batch_size, 1, 20, 64])) + permute_dims596: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_v_proj_weight3, axes=None) + matmul595: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm192, permute_dims596, out_dtype="void") + add680: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul595, model_decoder_layers_10_self_attn_v_proj_bias3) + reshape812: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add680, 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])) + permute_dims597: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_out_proj_weight3, axes=None) + matmul596: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape815, permute_dims597, out_dtype="void") + add681: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul596, model_decoder_layers_10_self_attn_out_proj_bias3) + add682: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add678, add681) + 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) + permute_dims598: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_encoder_attn_q_proj_weight3, axes=None) + matmul597: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm193, permute_dims598, out_dtype="void") + add683: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul597, model_decoder_layers_10_encoder_attn_q_proj_bias3) + reshape816: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add683, 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])) + permute_dims599: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_encoder_attn_out_proj_weight3, axes=None) + matmul598: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape819, permute_dims599, out_dtype="void") + add684: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul598, model_decoder_layers_10_encoder_attn_out_proj_bias3) + add685: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add682, add684) + 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) + permute_dims600: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_10_fc1_weight3, axes=None) + matmul599: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm194, permute_dims600, out_dtype="void") + add686: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul599, model_decoder_layers_10_fc1_bias3) + gelu76: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add686) + permute_dims601: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_fc2_weight3, axes=None) + matmul600: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu76, permute_dims601, out_dtype="void") + add687: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul600, model_decoder_layers_10_fc2_bias3) + add688: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add685, add687) + 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) + permute_dims602: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_q_proj_weight3, axes=None) + matmul601: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm195, permute_dims602, out_dtype="void") + add689: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul601, model_decoder_layers_11_self_attn_q_proj_bias3) + reshape820: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add689, R.shape([batch_size, 1, 20, 64])) + permute_dims603: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_k_proj_weight3, axes=None) + matmul602: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm195, permute_dims603, out_dtype="void") + reshape821: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul602, R.shape([batch_size, 1, 20, 64])) + permute_dims604: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_v_proj_weight3, axes=None) + matmul603: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm195, permute_dims604, out_dtype="void") + add690: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul603, model_decoder_layers_11_self_attn_v_proj_bias3) + reshape822: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add690, 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])) + permute_dims605: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_out_proj_weight3, axes=None) + matmul604: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape825, permute_dims605, out_dtype="void") + add691: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul604, model_decoder_layers_11_self_attn_out_proj_bias3) + add692: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add688, add691) + 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) + permute_dims606: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_encoder_attn_q_proj_weight3, axes=None) + matmul605: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm196, permute_dims606, out_dtype="void") + add693: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul605, model_decoder_layers_11_encoder_attn_q_proj_bias3) + reshape826: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add693, 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])) + permute_dims607: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_encoder_attn_out_proj_weight3, axes=None) + matmul606: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape829, permute_dims607, out_dtype="void") + add694: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul606, model_decoder_layers_11_encoder_attn_out_proj_bias3) + add695: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add692, add694) + 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) + permute_dims608: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_11_fc1_weight3, axes=None) + matmul607: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm197, permute_dims608, out_dtype="void") + add696: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul607, model_decoder_layers_11_fc1_bias3) + gelu77: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add696) + permute_dims609: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_fc2_weight3, axes=None) + matmul608: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu77, permute_dims609, out_dtype="void") + add697: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul608, model_decoder_layers_11_fc2_bias3) + add698: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add695, add697) + 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) + permute_dims610: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_q_proj_weight3, axes=None) + matmul609: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm198, permute_dims610, out_dtype="void") + add699: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul609, model_decoder_layers_12_self_attn_q_proj_bias3) + reshape830: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add699, R.shape([batch_size, 1, 20, 64])) + permute_dims611: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_k_proj_weight3, axes=None) + matmul610: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm198, permute_dims611, out_dtype="void") + reshape831: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul610, R.shape([batch_size, 1, 20, 64])) + permute_dims612: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_v_proj_weight3, axes=None) + matmul611: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm198, permute_dims612, out_dtype="void") + add700: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul611, model_decoder_layers_12_self_attn_v_proj_bias3) + reshape832: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add700, 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])) + permute_dims613: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_out_proj_weight3, axes=None) + matmul612: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape835, permute_dims613, out_dtype="void") + add701: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul612, model_decoder_layers_12_self_attn_out_proj_bias3) + add702: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add698, add701) + 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) + permute_dims614: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_encoder_attn_q_proj_weight3, axes=None) + matmul613: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm199, permute_dims614, out_dtype="void") + add703: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul613, model_decoder_layers_12_encoder_attn_q_proj_bias3) + reshape836: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add703, 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])) + permute_dims615: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_encoder_attn_out_proj_weight3, axes=None) + matmul614: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape839, permute_dims615, out_dtype="void") + add704: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul614, model_decoder_layers_12_encoder_attn_out_proj_bias3) + add705: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add702, add704) + 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) + permute_dims616: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_12_fc1_weight3, axes=None) + matmul615: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm200, permute_dims616, out_dtype="void") + add706: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul615, model_decoder_layers_12_fc1_bias3) + gelu78: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add706) + permute_dims617: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_fc2_weight3, axes=None) + matmul616: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu78, permute_dims617, out_dtype="void") + add707: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul616, model_decoder_layers_12_fc2_bias3) + add708: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add705, add707) + 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) + permute_dims618: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_q_proj_weight3, axes=None) + matmul617: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm201, permute_dims618, out_dtype="void") + add709: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul617, model_decoder_layers_13_self_attn_q_proj_bias3) + reshape840: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add709, R.shape([batch_size, 1, 20, 64])) + permute_dims619: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_k_proj_weight3, axes=None) + matmul618: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm201, permute_dims619, out_dtype="void") + reshape841: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul618, R.shape([batch_size, 1, 20, 64])) + permute_dims620: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_v_proj_weight3, axes=None) + matmul619: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm201, permute_dims620, out_dtype="void") + add710: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul619, model_decoder_layers_13_self_attn_v_proj_bias3) + reshape842: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add710, 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])) + permute_dims621: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_out_proj_weight3, axes=None) + matmul620: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape845, permute_dims621, out_dtype="void") + add711: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul620, model_decoder_layers_13_self_attn_out_proj_bias3) + add712: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add708, add711) + 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) + permute_dims622: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_encoder_attn_q_proj_weight3, axes=None) + matmul621: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm202, permute_dims622, out_dtype="void") + add713: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul621, model_decoder_layers_13_encoder_attn_q_proj_bias3) + reshape846: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add713, 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])) + permute_dims623: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_encoder_attn_out_proj_weight3, axes=None) + matmul622: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape849, permute_dims623, out_dtype="void") + add714: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul622, model_decoder_layers_13_encoder_attn_out_proj_bias3) + add715: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add712, add714) + 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) + permute_dims624: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_13_fc1_weight3, axes=None) + matmul623: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm203, permute_dims624, out_dtype="void") + add716: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul623, model_decoder_layers_13_fc1_bias3) + gelu79: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add716) + permute_dims625: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_fc2_weight3, axes=None) + matmul624: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu79, permute_dims625, out_dtype="void") + add717: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul624, model_decoder_layers_13_fc2_bias3) + add718: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add715, add717) + 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) + permute_dims626: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_q_proj_weight3, axes=None) + matmul625: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm204, permute_dims626, out_dtype="void") + add719: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul625, model_decoder_layers_14_self_attn_q_proj_bias3) + reshape850: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add719, R.shape([batch_size, 1, 20, 64])) + permute_dims627: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_k_proj_weight3, axes=None) + matmul626: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm204, permute_dims627, out_dtype="void") + reshape851: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul626, R.shape([batch_size, 1, 20, 64])) + permute_dims628: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_v_proj_weight3, axes=None) + matmul627: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm204, permute_dims628, out_dtype="void") + add720: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul627, model_decoder_layers_14_self_attn_v_proj_bias3) + reshape852: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add720, 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])) + permute_dims629: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_out_proj_weight3, axes=None) + matmul628: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape855, permute_dims629, out_dtype="void") + add721: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul628, model_decoder_layers_14_self_attn_out_proj_bias3) + add722: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add718, add721) + 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) + permute_dims630: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_encoder_attn_q_proj_weight3, axes=None) + matmul629: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm205, permute_dims630, out_dtype="void") + add723: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul629, model_decoder_layers_14_encoder_attn_q_proj_bias3) + reshape856: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add723, 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])) + permute_dims631: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_encoder_attn_out_proj_weight3, axes=None) + matmul630: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape859, permute_dims631, out_dtype="void") + add724: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul630, model_decoder_layers_14_encoder_attn_out_proj_bias3) + add725: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add722, add724) + 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) + permute_dims632: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_14_fc1_weight3, axes=None) + matmul631: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm206, permute_dims632, out_dtype="void") + add726: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul631, model_decoder_layers_14_fc1_bias3) + gelu80: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add726) + permute_dims633: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_fc2_weight3, axes=None) + matmul632: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu80, permute_dims633, out_dtype="void") + add727: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul632, model_decoder_layers_14_fc2_bias3) + add728: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add725, add727) + 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) + permute_dims634: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_q_proj_weight3, axes=None) + matmul633: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm207, permute_dims634, out_dtype="void") + add729: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul633, model_decoder_layers_15_self_attn_q_proj_bias3) + reshape860: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add729, R.shape([batch_size, 1, 20, 64])) + permute_dims635: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_k_proj_weight3, axes=None) + matmul634: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm207, permute_dims635, out_dtype="void") + reshape861: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul634, R.shape([batch_size, 1, 20, 64])) + permute_dims636: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_v_proj_weight3, axes=None) + matmul635: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm207, permute_dims636, out_dtype="void") + add730: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul635, model_decoder_layers_15_self_attn_v_proj_bias3) + reshape862: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add730, 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])) + permute_dims637: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_out_proj_weight3, axes=None) + matmul636: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape865, permute_dims637, out_dtype="void") + add731: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul636, model_decoder_layers_15_self_attn_out_proj_bias3) + add732: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add728, add731) + 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) + permute_dims638: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_encoder_attn_q_proj_weight3, axes=None) + matmul637: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm208, permute_dims638, out_dtype="void") + add733: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul637, model_decoder_layers_15_encoder_attn_q_proj_bias3) + reshape866: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add733, 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])) + permute_dims639: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_encoder_attn_out_proj_weight3, axes=None) + matmul638: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape869, permute_dims639, out_dtype="void") + add734: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul638, model_decoder_layers_15_encoder_attn_out_proj_bias3) + add735: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add732, add734) + 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) + permute_dims640: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_15_fc1_weight3, axes=None) + matmul639: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm209, permute_dims640, out_dtype="void") + add736: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul639, model_decoder_layers_15_fc1_bias3) + gelu81: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add736) + permute_dims641: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_fc2_weight3, axes=None) + matmul640: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu81, permute_dims641, out_dtype="void") + add737: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul640, model_decoder_layers_15_fc2_bias3) + add738: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add735, add737) + 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) + permute_dims642: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_q_proj_weight3, axes=None) + matmul641: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm210, permute_dims642, out_dtype="void") + add739: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul641, model_decoder_layers_16_self_attn_q_proj_bias3) + reshape870: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add739, R.shape([batch_size, 1, 20, 64])) + permute_dims643: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_k_proj_weight3, axes=None) + matmul642: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm210, permute_dims643, out_dtype="void") + reshape871: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul642, R.shape([batch_size, 1, 20, 64])) + permute_dims644: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_v_proj_weight3, axes=None) + matmul643: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm210, permute_dims644, out_dtype="void") + add740: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul643, model_decoder_layers_16_self_attn_v_proj_bias3) + reshape872: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add740, 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])) + permute_dims645: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_out_proj_weight3, axes=None) + matmul644: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape875, permute_dims645, out_dtype="void") + add741: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul644, model_decoder_layers_16_self_attn_out_proj_bias3) + add742: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add738, add741) + 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) + permute_dims646: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_encoder_attn_q_proj_weight3, axes=None) + matmul645: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm211, permute_dims646, out_dtype="void") + add743: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul645, model_decoder_layers_16_encoder_attn_q_proj_bias3) + reshape876: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add743, 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])) + permute_dims647: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_encoder_attn_out_proj_weight3, axes=None) + matmul646: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape879, permute_dims647, out_dtype="void") + add744: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul646, model_decoder_layers_16_encoder_attn_out_proj_bias3) + add745: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add742, add744) + 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) + permute_dims648: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_16_fc1_weight3, axes=None) + matmul647: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm212, permute_dims648, out_dtype="void") + add746: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul647, model_decoder_layers_16_fc1_bias3) + gelu82: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add746) + permute_dims649: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_fc2_weight3, axes=None) + matmul648: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu82, permute_dims649, out_dtype="void") + add747: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul648, model_decoder_layers_16_fc2_bias3) + add748: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add745, add747) + 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) + permute_dims650: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_q_proj_weight3, axes=None) + matmul649: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm213, permute_dims650, out_dtype="void") + add749: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul649, model_decoder_layers_17_self_attn_q_proj_bias3) + reshape880: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add749, R.shape([batch_size, 1, 20, 64])) + permute_dims651: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_k_proj_weight3, axes=None) + matmul650: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm213, permute_dims651, out_dtype="void") + reshape881: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul650, R.shape([batch_size, 1, 20, 64])) + permute_dims652: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_v_proj_weight3, axes=None) + matmul651: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm213, permute_dims652, out_dtype="void") + add750: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul651, model_decoder_layers_17_self_attn_v_proj_bias3) + reshape882: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add750, 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])) + permute_dims653: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_out_proj_weight3, axes=None) + matmul652: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape885, permute_dims653, out_dtype="void") + add751: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul652, model_decoder_layers_17_self_attn_out_proj_bias3) + add752: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add748, add751) + 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) + permute_dims654: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_encoder_attn_q_proj_weight3, axes=None) + matmul653: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm214, permute_dims654, out_dtype="void") + add753: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul653, model_decoder_layers_17_encoder_attn_q_proj_bias3) + reshape886: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add753, 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])) + permute_dims655: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_encoder_attn_out_proj_weight3, axes=None) + matmul654: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape889, permute_dims655, out_dtype="void") + add754: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul654, model_decoder_layers_17_encoder_attn_out_proj_bias3) + add755: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add752, add754) + 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) + permute_dims656: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_17_fc1_weight3, axes=None) + matmul655: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm215, permute_dims656, out_dtype="void") + add756: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul655, model_decoder_layers_17_fc1_bias3) + gelu83: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add756) + permute_dims657: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_fc2_weight3, axes=None) + matmul656: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu83, permute_dims657, out_dtype="void") + add757: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul656, model_decoder_layers_17_fc2_bias3) + add758: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add755, add757) + 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) + permute_dims658: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_q_proj_weight3, axes=None) + matmul657: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm216, permute_dims658, out_dtype="void") + add759: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul657, model_decoder_layers_18_self_attn_q_proj_bias3) + reshape890: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add759, R.shape([batch_size, 1, 20, 64])) + permute_dims659: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_k_proj_weight3, axes=None) + matmul658: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm216, permute_dims659, out_dtype="void") + reshape891: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul658, R.shape([batch_size, 1, 20, 64])) + permute_dims660: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_v_proj_weight3, axes=None) + matmul659: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm216, permute_dims660, out_dtype="void") + add760: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul659, model_decoder_layers_18_self_attn_v_proj_bias3) + reshape892: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add760, 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])) + permute_dims661: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_out_proj_weight3, axes=None) + matmul660: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape895, permute_dims661, out_dtype="void") + add761: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul660, model_decoder_layers_18_self_attn_out_proj_bias3) + add762: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add758, add761) + 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) + permute_dims662: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_encoder_attn_q_proj_weight3, axes=None) + matmul661: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm217, permute_dims662, out_dtype="void") + add763: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul661, model_decoder_layers_18_encoder_attn_q_proj_bias3) + reshape896: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add763, 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])) + permute_dims663: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_encoder_attn_out_proj_weight3, axes=None) + matmul662: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape899, permute_dims663, out_dtype="void") + add764: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul662, model_decoder_layers_18_encoder_attn_out_proj_bias3) + add765: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add762, add764) + 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) + permute_dims664: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_18_fc1_weight3, axes=None) + matmul663: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm218, permute_dims664, out_dtype="void") + add766: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul663, model_decoder_layers_18_fc1_bias3) + gelu84: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add766) + permute_dims665: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_fc2_weight3, axes=None) + matmul664: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu84, permute_dims665, out_dtype="void") + add767: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul664, model_decoder_layers_18_fc2_bias3) + add768: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add765, add767) + 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) + permute_dims666: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_q_proj_weight3, axes=None) + matmul665: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm219, permute_dims666, out_dtype="void") + add769: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul665, model_decoder_layers_19_self_attn_q_proj_bias3) + reshape900: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add769, R.shape([batch_size, 1, 20, 64])) + permute_dims667: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_k_proj_weight3, axes=None) + matmul666: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm219, permute_dims667, out_dtype="void") + reshape901: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul666, R.shape([batch_size, 1, 20, 64])) + permute_dims668: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_v_proj_weight3, axes=None) + matmul667: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm219, permute_dims668, out_dtype="void") + add770: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul667, model_decoder_layers_19_self_attn_v_proj_bias3) + reshape902: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add770, 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])) + permute_dims669: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_out_proj_weight3, axes=None) + matmul668: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape905, permute_dims669, out_dtype="void") + add771: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul668, model_decoder_layers_19_self_attn_out_proj_bias3) + add772: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add768, add771) + 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) + permute_dims670: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_encoder_attn_q_proj_weight3, axes=None) + matmul669: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm220, permute_dims670, out_dtype="void") + add773: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul669, model_decoder_layers_19_encoder_attn_q_proj_bias3) + reshape906: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add773, 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])) + permute_dims671: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_encoder_attn_out_proj_weight3, axes=None) + matmul670: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape909, permute_dims671, out_dtype="void") + add774: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul670, model_decoder_layers_19_encoder_attn_out_proj_bias3) + add775: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add772, add774) + 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) + permute_dims672: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_19_fc1_weight3, axes=None) + matmul671: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm221, permute_dims672, out_dtype="void") + add776: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul671, model_decoder_layers_19_fc1_bias3) + gelu85: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add776) + permute_dims673: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_fc2_weight3, axes=None) + matmul672: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu85, permute_dims673, out_dtype="void") + add777: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul672, model_decoder_layers_19_fc2_bias3) + add778: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add775, add777) + 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) + permute_dims674: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_q_proj_weight3, axes=None) + matmul673: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm222, permute_dims674, out_dtype="void") + add779: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul673, model_decoder_layers_20_self_attn_q_proj_bias3) + reshape910: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add779, R.shape([batch_size, 1, 20, 64])) + permute_dims675: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_k_proj_weight3, axes=None) + matmul674: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm222, permute_dims675, out_dtype="void") + reshape911: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul674, R.shape([batch_size, 1, 20, 64])) + permute_dims676: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_v_proj_weight3, axes=None) + matmul675: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm222, permute_dims676, out_dtype="void") + add780: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul675, model_decoder_layers_20_self_attn_v_proj_bias3) + reshape912: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add780, 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])) + permute_dims677: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_out_proj_weight3, axes=None) + matmul676: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape915, permute_dims677, out_dtype="void") + add781: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul676, model_decoder_layers_20_self_attn_out_proj_bias3) + add782: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add778, add781) + 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) + permute_dims678: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_encoder_attn_q_proj_weight3, axes=None) + matmul677: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm223, permute_dims678, out_dtype="void") + add783: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul677, model_decoder_layers_20_encoder_attn_q_proj_bias3) + reshape916: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add783, 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])) + permute_dims679: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_encoder_attn_out_proj_weight3, axes=None) + matmul678: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape919, permute_dims679, out_dtype="void") + add784: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul678, model_decoder_layers_20_encoder_attn_out_proj_bias3) + add785: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add782, add784) + 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) + permute_dims680: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_20_fc1_weight3, axes=None) + matmul679: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm224, permute_dims680, out_dtype="void") + add786: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul679, model_decoder_layers_20_fc1_bias3) + gelu86: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add786) + permute_dims681: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_fc2_weight3, axes=None) + matmul680: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu86, permute_dims681, out_dtype="void") + add787: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul680, model_decoder_layers_20_fc2_bias3) + add788: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add785, add787) + 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) + permute_dims682: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_q_proj_weight3, axes=None) + matmul681: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm225, permute_dims682, out_dtype="void") + add789: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul681, model_decoder_layers_21_self_attn_q_proj_bias3) + reshape920: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add789, R.shape([batch_size, 1, 20, 64])) + permute_dims683: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_k_proj_weight3, axes=None) + matmul682: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm225, permute_dims683, out_dtype="void") + reshape921: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul682, R.shape([batch_size, 1, 20, 64])) + permute_dims684: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_v_proj_weight3, axes=None) + matmul683: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm225, permute_dims684, out_dtype="void") + add790: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul683, model_decoder_layers_21_self_attn_v_proj_bias3) + reshape922: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add790, 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])) + permute_dims685: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_out_proj_weight3, axes=None) + matmul684: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape925, permute_dims685, out_dtype="void") + add791: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul684, model_decoder_layers_21_self_attn_out_proj_bias3) + add792: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add788, add791) + 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) + permute_dims686: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_encoder_attn_q_proj_weight3, axes=None) + matmul685: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm226, permute_dims686, out_dtype="void") + add793: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul685, model_decoder_layers_21_encoder_attn_q_proj_bias3) + reshape926: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add793, 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])) + permute_dims687: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_encoder_attn_out_proj_weight3, axes=None) + matmul686: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape929, permute_dims687, out_dtype="void") + add794: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul686, model_decoder_layers_21_encoder_attn_out_proj_bias3) + add795: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add792, add794) + 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) + permute_dims688: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_21_fc1_weight3, axes=None) + matmul687: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm227, permute_dims688, out_dtype="void") + add796: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul687, model_decoder_layers_21_fc1_bias3) + gelu87: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add796) + permute_dims689: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_fc2_weight3, axes=None) + matmul688: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu87, permute_dims689, out_dtype="void") + add797: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul688, model_decoder_layers_21_fc2_bias3) + add798: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add795, add797) + 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) + permute_dims690: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_q_proj_weight3, axes=None) + matmul689: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm228, permute_dims690, out_dtype="void") + add799: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul689, model_decoder_layers_22_self_attn_q_proj_bias3) + reshape930: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add799, R.shape([batch_size, 1, 20, 64])) + permute_dims691: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_k_proj_weight3, axes=None) + matmul690: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm228, permute_dims691, out_dtype="void") + reshape931: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul690, R.shape([batch_size, 1, 20, 64])) + permute_dims692: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_v_proj_weight3, axes=None) + matmul691: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm228, permute_dims692, out_dtype="void") + add800: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul691, model_decoder_layers_22_self_attn_v_proj_bias3) + reshape932: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add800, 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])) + permute_dims693: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_out_proj_weight3, axes=None) + matmul692: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape935, permute_dims693, out_dtype="void") + add801: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul692, model_decoder_layers_22_self_attn_out_proj_bias3) + add802: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add798, add801) + 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) + permute_dims694: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_encoder_attn_q_proj_weight3, axes=None) + matmul693: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm229, permute_dims694, out_dtype="void") + add803: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul693, model_decoder_layers_22_encoder_attn_q_proj_bias3) + reshape936: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add803, 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])) + permute_dims695: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_encoder_attn_out_proj_weight3, axes=None) + matmul694: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape939, permute_dims695, out_dtype="void") + add804: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul694, model_decoder_layers_22_encoder_attn_out_proj_bias3) + add805: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add802, add804) + 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) + permute_dims696: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_22_fc1_weight3, axes=None) + matmul695: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm230, permute_dims696, out_dtype="void") + add806: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul695, model_decoder_layers_22_fc1_bias3) + gelu88: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add806) + permute_dims697: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_fc2_weight3, axes=None) + matmul696: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu88, permute_dims697, out_dtype="void") + add807: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul696, model_decoder_layers_22_fc2_bias3) + add808: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add805, add807) + 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) + permute_dims698: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_q_proj_weight3, axes=None) + matmul697: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm231, permute_dims698, out_dtype="void") + add809: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul697, model_decoder_layers_23_self_attn_q_proj_bias3) + reshape940: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add809, R.shape([batch_size, 1, 20, 64])) + permute_dims699: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_k_proj_weight3, axes=None) + matmul698: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm231, permute_dims699, out_dtype="void") + reshape941: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul698, R.shape([batch_size, 1, 20, 64])) + permute_dims700: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_v_proj_weight3, axes=None) + matmul699: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm231, permute_dims700, out_dtype="void") + add810: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul699, model_decoder_layers_23_self_attn_v_proj_bias3) + reshape942: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add810, 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])) + permute_dims701: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_out_proj_weight3, axes=None) + matmul700: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape945, permute_dims701, out_dtype="void") + add811: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul700, model_decoder_layers_23_self_attn_out_proj_bias3) + add812: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add808, add811) + 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) + permute_dims702: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_encoder_attn_q_proj_weight3, axes=None) + matmul701: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm232, permute_dims702, out_dtype="void") + add813: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul701, model_decoder_layers_23_encoder_attn_q_proj_bias3) + reshape946: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add813, 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])) + permute_dims703: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_encoder_attn_out_proj_weight3, axes=None) + matmul702: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape949, permute_dims703, out_dtype="void") + add814: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul702, model_decoder_layers_23_encoder_attn_out_proj_bias3) + add815: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add812, add814) + 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) + permute_dims704: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_23_fc1_weight3, axes=None) + matmul703: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm233, permute_dims704, out_dtype="void") + add816: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul703, model_decoder_layers_23_fc1_bias3) + gelu89: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add816) + permute_dims705: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_fc2_weight3, axes=None) + matmul704: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu89, permute_dims705, out_dtype="void") + add817: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul704, model_decoder_layers_23_fc2_bias3) + add818: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add815, add817) + 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) + permute_dims706: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_q_proj_weight3, axes=None) + matmul705: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm234, permute_dims706, out_dtype="void") + add819: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul705, model_decoder_layers_24_self_attn_q_proj_bias3) + reshape950: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add819, R.shape([batch_size, 1, 20, 64])) + permute_dims707: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_k_proj_weight3, axes=None) + matmul706: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm234, permute_dims707, out_dtype="void") + reshape951: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul706, R.shape([batch_size, 1, 20, 64])) + permute_dims708: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_v_proj_weight3, axes=None) + matmul707: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm234, permute_dims708, out_dtype="void") + add820: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul707, model_decoder_layers_24_self_attn_v_proj_bias3) + reshape952: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add820, 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])) + permute_dims709: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_out_proj_weight3, axes=None) + matmul708: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape955, permute_dims709, out_dtype="void") + add821: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul708, model_decoder_layers_24_self_attn_out_proj_bias3) + add822: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add818, add821) + 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) + permute_dims710: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_encoder_attn_q_proj_weight3, axes=None) + matmul709: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm235, permute_dims710, out_dtype="void") + add823: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul709, model_decoder_layers_24_encoder_attn_q_proj_bias3) + reshape956: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add823, 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])) + permute_dims711: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_encoder_attn_out_proj_weight3, axes=None) + matmul710: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape959, permute_dims711, out_dtype="void") + add824: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul710, model_decoder_layers_24_encoder_attn_out_proj_bias3) + add825: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add822, add824) + 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) + permute_dims712: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_24_fc1_weight3, axes=None) + matmul711: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm236, permute_dims712, out_dtype="void") + add826: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul711, model_decoder_layers_24_fc1_bias3) + gelu90: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add826) + permute_dims713: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_fc2_weight3, axes=None) + matmul712: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu90, permute_dims713, out_dtype="void") + add827: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul712, model_decoder_layers_24_fc2_bias3) + add828: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add825, add827) + 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) + permute_dims714: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_q_proj_weight3, axes=None) + matmul713: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm237, permute_dims714, out_dtype="void") + add829: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul713, model_decoder_layers_25_self_attn_q_proj_bias3) + reshape960: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add829, R.shape([batch_size, 1, 20, 64])) + permute_dims715: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_k_proj_weight3, axes=None) + matmul714: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm237, permute_dims715, out_dtype="void") + reshape961: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul714, R.shape([batch_size, 1, 20, 64])) + permute_dims716: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_v_proj_weight3, axes=None) + matmul715: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm237, permute_dims716, out_dtype="void") + add830: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul715, model_decoder_layers_25_self_attn_v_proj_bias3) + reshape962: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add830, 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])) + permute_dims717: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_out_proj_weight3, axes=None) + matmul716: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape965, permute_dims717, out_dtype="void") + add831: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul716, model_decoder_layers_25_self_attn_out_proj_bias3) + add832: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add828, add831) + 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) + permute_dims718: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_encoder_attn_q_proj_weight3, axes=None) + matmul717: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm238, permute_dims718, out_dtype="void") + add833: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul717, model_decoder_layers_25_encoder_attn_q_proj_bias3) + reshape966: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add833, 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])) + permute_dims719: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_encoder_attn_out_proj_weight3, axes=None) + matmul718: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape969, permute_dims719, out_dtype="void") + add834: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul718, model_decoder_layers_25_encoder_attn_out_proj_bias3) + add835: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add832, add834) + 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) + permute_dims720: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_25_fc1_weight3, axes=None) + matmul719: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm239, permute_dims720, out_dtype="void") + add836: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul719, model_decoder_layers_25_fc1_bias3) + gelu91: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add836) + permute_dims721: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_fc2_weight3, axes=None) + matmul720: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu91, permute_dims721, out_dtype="void") + add837: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul720, model_decoder_layers_25_fc2_bias3) + add838: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add835, add837) + 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) + permute_dims722: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_q_proj_weight3, axes=None) + matmul721: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm240, permute_dims722, out_dtype="void") + add839: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul721, model_decoder_layers_26_self_attn_q_proj_bias3) + reshape970: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add839, R.shape([batch_size, 1, 20, 64])) + permute_dims723: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_k_proj_weight3, axes=None) + matmul722: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm240, permute_dims723, out_dtype="void") + reshape971: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul722, R.shape([batch_size, 1, 20, 64])) + permute_dims724: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_v_proj_weight3, axes=None) + matmul723: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm240, permute_dims724, out_dtype="void") + add840: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul723, model_decoder_layers_26_self_attn_v_proj_bias3) + reshape972: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add840, 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])) + permute_dims725: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_out_proj_weight3, axes=None) + matmul724: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape975, permute_dims725, out_dtype="void") + add841: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul724, model_decoder_layers_26_self_attn_out_proj_bias3) + add842: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add838, add841) + 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) + permute_dims726: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_encoder_attn_q_proj_weight3, axes=None) + matmul725: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm241, permute_dims726, out_dtype="void") + add843: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul725, model_decoder_layers_26_encoder_attn_q_proj_bias3) + reshape976: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add843, 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])) + permute_dims727: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_encoder_attn_out_proj_weight3, axes=None) + matmul726: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape979, permute_dims727, out_dtype="void") + add844: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul726, model_decoder_layers_26_encoder_attn_out_proj_bias3) + add845: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add842, add844) + 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) + permute_dims728: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_26_fc1_weight3, axes=None) + matmul727: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm242, permute_dims728, out_dtype="void") + add846: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul727, model_decoder_layers_26_fc1_bias3) + gelu92: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add846) + permute_dims729: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_fc2_weight3, axes=None) + matmul728: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu92, permute_dims729, out_dtype="void") + add847: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul728, model_decoder_layers_26_fc2_bias3) + add848: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add845, add847) + 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) + permute_dims730: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_q_proj_weight3, axes=None) + matmul729: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm243, permute_dims730, out_dtype="void") + add849: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul729, model_decoder_layers_27_self_attn_q_proj_bias3) + reshape980: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add849, R.shape([batch_size, 1, 20, 64])) + permute_dims731: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_k_proj_weight3, axes=None) + matmul730: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm243, permute_dims731, out_dtype="void") + reshape981: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul730, R.shape([batch_size, 1, 20, 64])) + permute_dims732: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_v_proj_weight3, axes=None) + matmul731: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm243, permute_dims732, out_dtype="void") + add850: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul731, model_decoder_layers_27_self_attn_v_proj_bias3) + reshape982: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add850, 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])) + permute_dims733: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_out_proj_weight3, axes=None) + matmul732: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape985, permute_dims733, out_dtype="void") + add851: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul732, model_decoder_layers_27_self_attn_out_proj_bias3) + add852: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add848, add851) + 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) + permute_dims734: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_encoder_attn_q_proj_weight3, axes=None) + matmul733: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm244, permute_dims734, out_dtype="void") + add853: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul733, model_decoder_layers_27_encoder_attn_q_proj_bias3) + reshape986: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add853, 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])) + permute_dims735: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_encoder_attn_out_proj_weight3, axes=None) + matmul734: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape989, permute_dims735, out_dtype="void") + add854: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul734, model_decoder_layers_27_encoder_attn_out_proj_bias3) + add855: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add852, add854) + 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) + permute_dims736: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_27_fc1_weight3, axes=None) + matmul735: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm245, permute_dims736, out_dtype="void") + add856: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul735, model_decoder_layers_27_fc1_bias3) + gelu93: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add856) + permute_dims737: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_fc2_weight3, axes=None) + matmul736: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu93, permute_dims737, out_dtype="void") + add857: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul736, model_decoder_layers_27_fc2_bias3) + add858: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add855, add857) + 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) + permute_dims738: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_q_proj_weight3, axes=None) + matmul737: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm246, permute_dims738, out_dtype="void") + add859: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul737, model_decoder_layers_28_self_attn_q_proj_bias3) + reshape990: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add859, R.shape([batch_size, 1, 20, 64])) + permute_dims739: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_k_proj_weight3, axes=None) + matmul738: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm246, permute_dims739, out_dtype="void") + reshape991: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul738, R.shape([batch_size, 1, 20, 64])) + permute_dims740: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_v_proj_weight3, axes=None) + matmul739: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm246, permute_dims740, out_dtype="void") + add860: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul739, model_decoder_layers_28_self_attn_v_proj_bias3) + reshape992: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add860, 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])) + permute_dims741: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_out_proj_weight3, axes=None) + matmul740: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape995, permute_dims741, out_dtype="void") + add861: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul740, model_decoder_layers_28_self_attn_out_proj_bias3) + add862: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add858, add861) + 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) + permute_dims742: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_encoder_attn_q_proj_weight3, axes=None) + matmul741: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm247, permute_dims742, out_dtype="void") + add863: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul741, model_decoder_layers_28_encoder_attn_q_proj_bias3) + reshape996: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add863, 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])) + permute_dims743: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_encoder_attn_out_proj_weight3, axes=None) + matmul742: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape999, permute_dims743, out_dtype="void") + add864: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul742, model_decoder_layers_28_encoder_attn_out_proj_bias3) + add865: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add862, add864) + 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) + permute_dims744: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_28_fc1_weight3, axes=None) + matmul743: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm248, permute_dims744, out_dtype="void") + add866: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul743, model_decoder_layers_28_fc1_bias3) + gelu94: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add866) + permute_dims745: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_fc2_weight3, axes=None) + matmul744: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu94, permute_dims745, out_dtype="void") + add867: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul744, model_decoder_layers_28_fc2_bias3) + add868: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add865, add867) + 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) + permute_dims746: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_q_proj_weight3, axes=None) + matmul745: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm249, permute_dims746, out_dtype="void") + add869: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul745, model_decoder_layers_29_self_attn_q_proj_bias3) + reshape1000: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add869, R.shape([batch_size, 1, 20, 64])) + permute_dims747: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_k_proj_weight3, axes=None) + matmul746: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm249, permute_dims747, out_dtype="void") + reshape1001: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul746, R.shape([batch_size, 1, 20, 64])) + permute_dims748: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_v_proj_weight3, axes=None) + matmul747: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm249, permute_dims748, out_dtype="void") + add870: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul747, model_decoder_layers_29_self_attn_v_proj_bias3) + reshape1002: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add870, 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])) + permute_dims749: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_out_proj_weight3, axes=None) + matmul748: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape1005, permute_dims749, out_dtype="void") + add871: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul748, model_decoder_layers_29_self_attn_out_proj_bias3) + add872: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add868, add871) + 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) + permute_dims750: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_encoder_attn_q_proj_weight3, axes=None) + matmul749: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm250, permute_dims750, out_dtype="void") + add873: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul749, model_decoder_layers_29_encoder_attn_q_proj_bias3) + reshape1006: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add873, 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])) + permute_dims751: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_encoder_attn_out_proj_weight3, axes=None) + matmul750: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape1009, permute_dims751, out_dtype="void") + add874: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul750, model_decoder_layers_29_encoder_attn_out_proj_bias3) + add875: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add872, add874) + 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) + permute_dims752: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_29_fc1_weight3, axes=None) + matmul751: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm251, permute_dims752, out_dtype="void") + add876: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul751, model_decoder_layers_29_fc1_bias3) + gelu95: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add876) + permute_dims753: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_fc2_weight3, axes=None) + matmul752: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu95, permute_dims753, out_dtype="void") + add877: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul752, model_decoder_layers_29_fc2_bias3) + add878: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add875, add877) + 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) + permute_dims754: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_q_proj_weight3, axes=None) + matmul753: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm252, permute_dims754, out_dtype="void") + add879: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul753, model_decoder_layers_30_self_attn_q_proj_bias3) + reshape1010: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add879, R.shape([batch_size, 1, 20, 64])) + permute_dims755: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_k_proj_weight3, axes=None) + matmul754: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm252, permute_dims755, out_dtype="void") + reshape1011: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul754, R.shape([batch_size, 1, 20, 64])) + permute_dims756: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_v_proj_weight3, axes=None) + matmul755: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm252, permute_dims756, out_dtype="void") + add880: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul755, model_decoder_layers_30_self_attn_v_proj_bias3) + reshape1012: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add880, 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])) + permute_dims757: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_out_proj_weight3, axes=None) + matmul756: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape1015, permute_dims757, out_dtype="void") + add881: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul756, model_decoder_layers_30_self_attn_out_proj_bias3) + add882: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add878, add881) + 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) + permute_dims758: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_encoder_attn_q_proj_weight3, axes=None) + matmul757: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm253, permute_dims758, out_dtype="void") + add883: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul757, model_decoder_layers_30_encoder_attn_q_proj_bias3) + reshape1016: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add883, 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])) + permute_dims759: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_encoder_attn_out_proj_weight3, axes=None) + matmul758: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape1019, permute_dims759, out_dtype="void") + add884: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul758, model_decoder_layers_30_encoder_attn_out_proj_bias3) + add885: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add882, add884) + 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) + permute_dims760: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_30_fc1_weight3, axes=None) + matmul759: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm254, permute_dims760, out_dtype="void") + add886: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul759, model_decoder_layers_30_fc1_bias3) + gelu96: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add886) + permute_dims761: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_fc2_weight3, axes=None) + matmul760: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu96, permute_dims761, out_dtype="void") + add887: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul760, model_decoder_layers_30_fc2_bias3) + add888: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add885, add887) + 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) + permute_dims762: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_q_proj_weight3, axes=None) + matmul761: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm255, permute_dims762, out_dtype="void") + add889: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul761, model_decoder_layers_31_self_attn_q_proj_bias3) + reshape1020: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add889, R.shape([batch_size, 1, 20, 64])) + permute_dims763: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_k_proj_weight3, axes=None) + matmul762: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm255, permute_dims763, out_dtype="void") + reshape1021: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(matmul762, R.shape([batch_size, 1, 20, 64])) + permute_dims764: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_v_proj_weight3, axes=None) + matmul763: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm255, permute_dims764, out_dtype="void") + add890: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul763, model_decoder_layers_31_self_attn_v_proj_bias3) + reshape1022: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add890, 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])) + permute_dims765: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_out_proj_weight3, axes=None) + matmul764: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape1025, permute_dims765, out_dtype="void") + add891: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul764, model_decoder_layers_31_self_attn_out_proj_bias3) + add892: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add888, add891) + 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) + permute_dims766: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_encoder_attn_q_proj_weight3, axes=None) + matmul765: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(layer_norm256, permute_dims766, out_dtype="void") + add893: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul765, model_decoder_layers_31_encoder_attn_q_proj_bias3) + reshape1026: R.Tensor((batch_size, 1, 20, 64), dtype="float16") = R.reshape(add893, 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])) + permute_dims767: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_encoder_attn_out_proj_weight3, axes=None) + matmul766: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(reshape1029, permute_dims767, out_dtype="void") + add894: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul766, model_decoder_layers_31_encoder_attn_out_proj_bias3) + add895: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add892, add894) + 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) + permute_dims768: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_31_fc1_weight3, axes=None) + matmul767: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.matmul(layer_norm257, permute_dims768, out_dtype="void") + add896: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.add(matmul767, model_decoder_layers_31_fc1_bias3) + gelu97: R.Tensor((batch_size, 1, 5120), dtype="float16") = R.nn.gelu(add896) + permute_dims769: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_fc2_weight3, axes=None) + matmul768: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.matmul(gelu97, permute_dims769, out_dtype="void") + add897: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(matmul768, model_decoder_layers_31_fc2_bias3) + add898: R.Tensor((batch_size, 1, 1280), dtype="float16") = R.add(add895, add897) + 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) + permute_dims770: R.Tensor((1280, 51866), dtype="float16") = R.permute_dims(model_decoder_embed_tokens_weight3, axes=None) + matmul769: R.Tensor((batch_size, 1, 51866), dtype="float32") = R.matmul(layer_norm258, permute_dims770, out_dtype="float32") + gv3: R.Tensor((batch_size, 1, 51866), dtype="float32") = matmul769 + 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] + model_decoder_embed_tokens_weight: R.Tensor((51866, 1280), dtype="float16") = packed_params[487] + model_decoder_embed_positions_weight: R.Tensor((448, 1280), dtype="float16") = packed_params[488] + model_decoder_layers_0_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[489] + model_decoder_layers_0_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[490] + model_decoder_layers_0_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[491] + model_decoder_layers_0_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[492] + model_decoder_layers_0_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[493] + model_decoder_layers_0_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[494] + model_decoder_layers_0_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[495] + model_decoder_layers_0_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[496] + model_decoder_layers_0_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[497] + model_decoder_layers_0_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[498] + model_decoder_layers_0_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[499] + model_decoder_layers_0_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[500] + model_decoder_layers_0_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[501] + model_decoder_layers_0_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[502] + model_decoder_layers_0_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[503] + model_decoder_layers_0_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[504] + model_decoder_layers_0_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[505] + model_decoder_layers_0_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[506] + model_decoder_layers_0_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[507] + model_decoder_layers_0_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[508] + model_decoder_layers_0_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[509] + model_decoder_layers_0_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[510] + model_decoder_layers_0_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[511] + model_decoder_layers_0_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[512] + model_decoder_layers_1_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[513] + model_decoder_layers_1_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[514] + model_decoder_layers_1_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[515] + model_decoder_layers_1_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[516] + model_decoder_layers_1_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[517] + model_decoder_layers_1_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[518] + model_decoder_layers_1_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[519] + model_decoder_layers_1_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[520] + model_decoder_layers_1_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[521] + model_decoder_layers_1_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[522] + model_decoder_layers_1_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[523] + model_decoder_layers_1_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[524] + model_decoder_layers_1_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[525] + model_decoder_layers_1_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[526] + model_decoder_layers_1_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[527] + model_decoder_layers_1_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[528] + model_decoder_layers_1_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[529] + model_decoder_layers_1_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[530] + model_decoder_layers_1_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[531] + model_decoder_layers_1_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[532] + model_decoder_layers_1_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[533] + model_decoder_layers_1_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[534] + model_decoder_layers_1_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[535] + model_decoder_layers_1_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[536] + model_decoder_layers_2_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[537] + model_decoder_layers_2_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[538] + model_decoder_layers_2_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[539] + model_decoder_layers_2_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[540] + model_decoder_layers_2_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[541] + model_decoder_layers_2_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[542] + model_decoder_layers_2_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[543] + model_decoder_layers_2_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[544] + model_decoder_layers_2_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[545] + model_decoder_layers_2_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[546] + model_decoder_layers_2_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[547] + model_decoder_layers_2_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[548] + model_decoder_layers_2_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[549] + model_decoder_layers_2_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[550] + model_decoder_layers_2_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[551] + model_decoder_layers_2_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[552] + model_decoder_layers_2_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[553] + model_decoder_layers_2_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[554] + model_decoder_layers_2_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[555] + model_decoder_layers_2_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[556] + model_decoder_layers_2_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[557] + model_decoder_layers_2_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[558] + model_decoder_layers_2_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[559] + model_decoder_layers_2_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[560] + model_decoder_layers_3_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[561] + model_decoder_layers_3_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[562] + model_decoder_layers_3_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[563] + model_decoder_layers_3_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[564] + model_decoder_layers_3_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[565] + model_decoder_layers_3_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[566] + model_decoder_layers_3_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[567] + model_decoder_layers_3_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[568] + model_decoder_layers_3_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[569] + model_decoder_layers_3_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[570] + model_decoder_layers_3_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[571] + model_decoder_layers_3_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[572] + model_decoder_layers_3_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[573] + model_decoder_layers_3_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[574] + model_decoder_layers_3_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[575] + model_decoder_layers_3_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[576] + model_decoder_layers_3_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[577] + model_decoder_layers_3_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[578] + model_decoder_layers_3_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[579] + model_decoder_layers_3_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[580] + model_decoder_layers_3_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[581] + model_decoder_layers_3_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[582] + model_decoder_layers_3_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[583] + model_decoder_layers_3_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[584] + model_decoder_layers_4_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[585] + model_decoder_layers_4_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[586] + model_decoder_layers_4_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[587] + model_decoder_layers_4_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[588] + model_decoder_layers_4_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[589] + model_decoder_layers_4_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[590] + model_decoder_layers_4_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[591] + model_decoder_layers_4_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[592] + model_decoder_layers_4_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[593] + model_decoder_layers_4_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[594] + model_decoder_layers_4_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[595] + model_decoder_layers_4_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[596] + model_decoder_layers_4_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[597] + model_decoder_layers_4_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[598] + model_decoder_layers_4_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[599] + model_decoder_layers_4_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[600] + model_decoder_layers_4_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[601] + model_decoder_layers_4_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[602] + model_decoder_layers_4_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[603] + model_decoder_layers_4_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[604] + model_decoder_layers_4_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[605] + model_decoder_layers_4_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[606] + model_decoder_layers_4_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[607] + model_decoder_layers_4_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[608] + model_decoder_layers_5_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[609] + model_decoder_layers_5_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[610] + model_decoder_layers_5_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[611] + model_decoder_layers_5_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[612] + model_decoder_layers_5_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[613] + model_decoder_layers_5_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[614] + model_decoder_layers_5_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[615] + model_decoder_layers_5_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[616] + model_decoder_layers_5_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[617] + model_decoder_layers_5_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[618] + model_decoder_layers_5_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[619] + model_decoder_layers_5_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[620] + model_decoder_layers_5_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[621] + model_decoder_layers_5_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[622] + model_decoder_layers_5_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[623] + model_decoder_layers_5_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[624] + model_decoder_layers_5_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[625] + model_decoder_layers_5_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[626] + model_decoder_layers_5_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[627] + model_decoder_layers_5_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[628] + model_decoder_layers_5_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[629] + model_decoder_layers_5_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[630] + model_decoder_layers_5_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[631] + model_decoder_layers_5_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[632] + model_decoder_layers_6_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[633] + model_decoder_layers_6_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[634] + model_decoder_layers_6_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[635] + model_decoder_layers_6_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[636] + model_decoder_layers_6_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[637] + model_decoder_layers_6_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[638] + model_decoder_layers_6_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[639] + model_decoder_layers_6_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[640] + model_decoder_layers_6_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[641] + model_decoder_layers_6_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[642] + model_decoder_layers_6_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[643] + model_decoder_layers_6_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[644] + model_decoder_layers_6_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[645] + model_decoder_layers_6_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[646] + model_decoder_layers_6_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[647] + model_decoder_layers_6_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[648] + model_decoder_layers_6_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[649] + model_decoder_layers_6_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[650] + model_decoder_layers_6_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[651] + model_decoder_layers_6_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[652] + model_decoder_layers_6_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[653] + model_decoder_layers_6_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[654] + model_decoder_layers_6_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[655] + model_decoder_layers_6_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[656] + model_decoder_layers_7_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[657] + model_decoder_layers_7_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[658] + model_decoder_layers_7_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[659] + model_decoder_layers_7_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[660] + model_decoder_layers_7_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[661] + model_decoder_layers_7_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[662] + model_decoder_layers_7_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[663] + model_decoder_layers_7_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[664] + model_decoder_layers_7_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[665] + model_decoder_layers_7_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[666] + model_decoder_layers_7_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[667] + model_decoder_layers_7_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[668] + model_decoder_layers_7_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[669] + model_decoder_layers_7_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[670] + model_decoder_layers_7_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[671] + model_decoder_layers_7_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[672] + model_decoder_layers_7_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[673] + model_decoder_layers_7_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[674] + model_decoder_layers_7_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[675] + model_decoder_layers_7_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[676] + model_decoder_layers_7_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[677] + model_decoder_layers_7_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[678] + model_decoder_layers_7_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[679] + model_decoder_layers_7_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[680] + model_decoder_layers_8_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[681] + model_decoder_layers_8_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[682] + model_decoder_layers_8_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[683] + model_decoder_layers_8_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[684] + model_decoder_layers_8_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[685] + model_decoder_layers_8_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[686] + model_decoder_layers_8_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[687] + model_decoder_layers_8_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[688] + model_decoder_layers_8_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[689] + model_decoder_layers_8_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[690] + model_decoder_layers_8_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[691] + model_decoder_layers_8_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[692] + model_decoder_layers_8_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[693] + model_decoder_layers_8_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[694] + model_decoder_layers_8_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[695] + model_decoder_layers_8_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[696] + model_decoder_layers_8_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[697] + model_decoder_layers_8_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[698] + model_decoder_layers_8_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[699] + model_decoder_layers_8_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[700] + model_decoder_layers_8_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[701] + model_decoder_layers_8_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[702] + model_decoder_layers_8_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[703] + model_decoder_layers_8_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[704] + model_decoder_layers_9_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[705] + model_decoder_layers_9_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[706] + model_decoder_layers_9_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[707] + model_decoder_layers_9_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[708] + model_decoder_layers_9_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[709] + model_decoder_layers_9_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[710] + model_decoder_layers_9_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[711] + model_decoder_layers_9_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[712] + model_decoder_layers_9_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[713] + model_decoder_layers_9_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[714] + model_decoder_layers_9_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[715] + model_decoder_layers_9_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[716] + model_decoder_layers_9_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[717] + model_decoder_layers_9_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[718] + model_decoder_layers_9_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[719] + model_decoder_layers_9_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[720] + model_decoder_layers_9_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[721] + model_decoder_layers_9_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[722] + model_decoder_layers_9_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[723] + model_decoder_layers_9_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[724] + model_decoder_layers_9_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[725] + model_decoder_layers_9_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[726] + model_decoder_layers_9_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[727] + model_decoder_layers_9_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[728] + model_decoder_layers_10_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[729] + model_decoder_layers_10_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[730] + model_decoder_layers_10_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[731] + model_decoder_layers_10_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[732] + model_decoder_layers_10_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[733] + model_decoder_layers_10_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[734] + model_decoder_layers_10_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[735] + model_decoder_layers_10_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[736] + model_decoder_layers_10_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[737] + model_decoder_layers_10_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[738] + model_decoder_layers_10_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[739] + model_decoder_layers_10_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[740] + model_decoder_layers_10_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[741] + model_decoder_layers_10_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[742] + model_decoder_layers_10_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[743] + model_decoder_layers_10_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[744] + model_decoder_layers_10_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[745] + model_decoder_layers_10_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[746] + model_decoder_layers_10_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[747] + model_decoder_layers_10_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[748] + model_decoder_layers_10_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[749] + model_decoder_layers_10_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[750] + model_decoder_layers_10_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[751] + model_decoder_layers_10_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[752] + model_decoder_layers_11_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[753] + model_decoder_layers_11_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[754] + model_decoder_layers_11_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[755] + model_decoder_layers_11_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[756] + model_decoder_layers_11_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[757] + model_decoder_layers_11_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[758] + model_decoder_layers_11_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[759] + model_decoder_layers_11_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[760] + model_decoder_layers_11_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[761] + model_decoder_layers_11_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[762] + model_decoder_layers_11_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[763] + model_decoder_layers_11_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[764] + model_decoder_layers_11_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[765] + model_decoder_layers_11_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[766] + model_decoder_layers_11_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[767] + model_decoder_layers_11_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[768] + model_decoder_layers_11_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[769] + model_decoder_layers_11_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[770] + model_decoder_layers_11_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[771] + model_decoder_layers_11_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[772] + model_decoder_layers_11_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[773] + model_decoder_layers_11_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[774] + model_decoder_layers_11_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[775] + model_decoder_layers_11_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[776] + model_decoder_layers_12_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[777] + model_decoder_layers_12_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[778] + model_decoder_layers_12_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[779] + model_decoder_layers_12_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[780] + model_decoder_layers_12_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[781] + model_decoder_layers_12_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[782] + model_decoder_layers_12_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[783] + model_decoder_layers_12_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[784] + model_decoder_layers_12_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[785] + model_decoder_layers_12_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[786] + model_decoder_layers_12_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[787] + model_decoder_layers_12_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[788] + model_decoder_layers_12_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[789] + model_decoder_layers_12_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[790] + model_decoder_layers_12_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[791] + model_decoder_layers_12_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[792] + model_decoder_layers_12_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[793] + model_decoder_layers_12_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[794] + model_decoder_layers_12_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[795] + model_decoder_layers_12_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[796] + model_decoder_layers_12_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[797] + model_decoder_layers_12_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[798] + model_decoder_layers_12_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[799] + model_decoder_layers_12_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[800] + model_decoder_layers_13_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[801] + model_decoder_layers_13_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[802] + model_decoder_layers_13_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[803] + model_decoder_layers_13_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[804] + model_decoder_layers_13_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[805] + model_decoder_layers_13_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[806] + model_decoder_layers_13_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[807] + model_decoder_layers_13_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[808] + model_decoder_layers_13_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[809] + model_decoder_layers_13_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[810] + model_decoder_layers_13_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[811] + model_decoder_layers_13_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[812] + model_decoder_layers_13_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[813] + model_decoder_layers_13_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[814] + model_decoder_layers_13_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[815] + model_decoder_layers_13_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[816] + model_decoder_layers_13_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[817] + model_decoder_layers_13_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[818] + model_decoder_layers_13_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[819] + model_decoder_layers_13_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[820] + model_decoder_layers_13_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[821] + model_decoder_layers_13_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[822] + model_decoder_layers_13_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[823] + model_decoder_layers_13_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[824] + model_decoder_layers_14_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[825] + model_decoder_layers_14_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[826] + model_decoder_layers_14_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[827] + model_decoder_layers_14_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[828] + model_decoder_layers_14_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[829] + model_decoder_layers_14_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[830] + model_decoder_layers_14_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[831] + model_decoder_layers_14_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[832] + model_decoder_layers_14_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[833] + model_decoder_layers_14_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[834] + model_decoder_layers_14_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[835] + model_decoder_layers_14_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[836] + model_decoder_layers_14_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[837] + model_decoder_layers_14_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[838] + model_decoder_layers_14_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[839] + model_decoder_layers_14_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[840] + model_decoder_layers_14_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[841] + model_decoder_layers_14_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[842] + model_decoder_layers_14_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[843] + model_decoder_layers_14_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[844] + model_decoder_layers_14_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[845] + model_decoder_layers_14_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[846] + model_decoder_layers_14_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[847] + model_decoder_layers_14_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[848] + model_decoder_layers_15_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[849] + model_decoder_layers_15_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[850] + model_decoder_layers_15_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[851] + model_decoder_layers_15_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[852] + model_decoder_layers_15_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[853] + model_decoder_layers_15_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[854] + model_decoder_layers_15_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[855] + model_decoder_layers_15_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[856] + model_decoder_layers_15_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[857] + model_decoder_layers_15_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[858] + model_decoder_layers_15_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[859] + model_decoder_layers_15_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[860] + model_decoder_layers_15_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[861] + model_decoder_layers_15_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[862] + model_decoder_layers_15_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[863] + model_decoder_layers_15_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[864] + model_decoder_layers_15_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[865] + model_decoder_layers_15_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[866] + model_decoder_layers_15_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[867] + model_decoder_layers_15_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[868] + model_decoder_layers_15_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[869] + model_decoder_layers_15_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[870] + model_decoder_layers_15_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[871] + model_decoder_layers_15_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[872] + model_decoder_layers_16_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[873] + model_decoder_layers_16_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[874] + model_decoder_layers_16_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[875] + model_decoder_layers_16_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[876] + model_decoder_layers_16_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[877] + model_decoder_layers_16_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[878] + model_decoder_layers_16_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[879] + model_decoder_layers_16_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[880] + model_decoder_layers_16_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[881] + model_decoder_layers_16_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[882] + model_decoder_layers_16_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[883] + model_decoder_layers_16_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[884] + model_decoder_layers_16_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[885] + model_decoder_layers_16_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[886] + model_decoder_layers_16_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[887] + model_decoder_layers_16_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[888] + model_decoder_layers_16_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[889] + model_decoder_layers_16_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[890] + model_decoder_layers_16_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[891] + model_decoder_layers_16_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[892] + model_decoder_layers_16_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[893] + model_decoder_layers_16_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[894] + model_decoder_layers_16_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[895] + model_decoder_layers_16_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[896] + model_decoder_layers_17_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[897] + model_decoder_layers_17_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[898] + model_decoder_layers_17_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[899] + model_decoder_layers_17_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[900] + model_decoder_layers_17_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[901] + model_decoder_layers_17_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[902] + model_decoder_layers_17_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[903] + model_decoder_layers_17_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[904] + model_decoder_layers_17_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[905] + model_decoder_layers_17_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[906] + model_decoder_layers_17_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[907] + model_decoder_layers_17_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[908] + model_decoder_layers_17_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[909] + model_decoder_layers_17_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[910] + model_decoder_layers_17_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[911] + model_decoder_layers_17_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[912] + model_decoder_layers_17_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[913] + model_decoder_layers_17_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[914] + model_decoder_layers_17_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[915] + model_decoder_layers_17_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[916] + model_decoder_layers_17_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[917] + model_decoder_layers_17_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[918] + model_decoder_layers_17_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[919] + model_decoder_layers_17_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[920] + model_decoder_layers_18_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[921] + model_decoder_layers_18_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[922] + model_decoder_layers_18_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[923] + model_decoder_layers_18_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[924] + model_decoder_layers_18_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[925] + model_decoder_layers_18_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[926] + model_decoder_layers_18_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[927] + model_decoder_layers_18_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[928] + model_decoder_layers_18_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[929] + model_decoder_layers_18_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[930] + model_decoder_layers_18_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[931] + model_decoder_layers_18_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[932] + model_decoder_layers_18_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[933] + model_decoder_layers_18_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[934] + model_decoder_layers_18_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[935] + model_decoder_layers_18_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[936] + model_decoder_layers_18_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[937] + model_decoder_layers_18_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[938] + model_decoder_layers_18_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[939] + model_decoder_layers_18_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[940] + model_decoder_layers_18_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[941] + model_decoder_layers_18_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[942] + model_decoder_layers_18_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[943] + model_decoder_layers_18_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[944] + model_decoder_layers_19_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[945] + model_decoder_layers_19_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[946] + model_decoder_layers_19_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[947] + model_decoder_layers_19_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[948] + model_decoder_layers_19_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[949] + model_decoder_layers_19_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[950] + model_decoder_layers_19_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[951] + model_decoder_layers_19_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[952] + model_decoder_layers_19_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[953] + model_decoder_layers_19_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[954] + model_decoder_layers_19_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[955] + model_decoder_layers_19_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[956] + model_decoder_layers_19_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[957] + model_decoder_layers_19_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[958] + model_decoder_layers_19_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[959] + model_decoder_layers_19_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[960] + model_decoder_layers_19_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[961] + model_decoder_layers_19_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[962] + model_decoder_layers_19_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[963] + model_decoder_layers_19_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[964] + model_decoder_layers_19_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[965] + model_decoder_layers_19_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[966] + model_decoder_layers_19_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[967] + model_decoder_layers_19_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[968] + model_decoder_layers_20_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[969] + model_decoder_layers_20_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[970] + model_decoder_layers_20_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[971] + model_decoder_layers_20_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[972] + model_decoder_layers_20_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[973] + model_decoder_layers_20_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[974] + model_decoder_layers_20_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[975] + model_decoder_layers_20_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[976] + model_decoder_layers_20_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[977] + model_decoder_layers_20_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[978] + model_decoder_layers_20_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[979] + model_decoder_layers_20_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[980] + model_decoder_layers_20_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[981] + model_decoder_layers_20_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[982] + model_decoder_layers_20_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[983] + model_decoder_layers_20_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[984] + model_decoder_layers_20_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[985] + model_decoder_layers_20_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[986] + model_decoder_layers_20_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[987] + model_decoder_layers_20_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[988] + model_decoder_layers_20_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[989] + model_decoder_layers_20_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[990] + model_decoder_layers_20_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[991] + model_decoder_layers_20_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[992] + model_decoder_layers_21_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[993] + model_decoder_layers_21_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[994] + model_decoder_layers_21_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[995] + model_decoder_layers_21_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[996] + model_decoder_layers_21_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[997] + model_decoder_layers_21_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[998] + model_decoder_layers_21_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[999] + model_decoder_layers_21_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1000] + model_decoder_layers_21_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1001] + model_decoder_layers_21_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1002] + model_decoder_layers_21_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1003] + model_decoder_layers_21_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1004] + model_decoder_layers_21_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1005] + model_decoder_layers_21_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1006] + model_decoder_layers_21_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1007] + model_decoder_layers_21_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1008] + model_decoder_layers_21_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1009] + model_decoder_layers_21_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1010] + model_decoder_layers_21_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[1011] + model_decoder_layers_21_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[1012] + model_decoder_layers_21_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[1013] + model_decoder_layers_21_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[1014] + model_decoder_layers_21_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1015] + model_decoder_layers_21_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1016] + model_decoder_layers_22_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1017] + model_decoder_layers_22_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1018] + model_decoder_layers_22_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1019] + model_decoder_layers_22_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1020] + model_decoder_layers_22_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1021] + model_decoder_layers_22_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1022] + model_decoder_layers_22_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1023] + model_decoder_layers_22_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1024] + model_decoder_layers_22_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1025] + model_decoder_layers_22_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1026] + model_decoder_layers_22_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1027] + model_decoder_layers_22_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1028] + model_decoder_layers_22_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1029] + model_decoder_layers_22_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1030] + model_decoder_layers_22_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1031] + model_decoder_layers_22_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1032] + model_decoder_layers_22_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1033] + model_decoder_layers_22_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1034] + model_decoder_layers_22_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[1035] + model_decoder_layers_22_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[1036] + model_decoder_layers_22_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[1037] + model_decoder_layers_22_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[1038] + model_decoder_layers_22_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1039] + model_decoder_layers_22_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1040] + model_decoder_layers_23_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1041] + model_decoder_layers_23_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1042] + model_decoder_layers_23_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1043] + model_decoder_layers_23_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1044] + model_decoder_layers_23_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1045] + model_decoder_layers_23_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1046] + model_decoder_layers_23_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1047] + model_decoder_layers_23_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1048] + model_decoder_layers_23_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1049] + model_decoder_layers_23_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1050] + model_decoder_layers_23_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1051] + model_decoder_layers_23_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1052] + model_decoder_layers_23_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1053] + model_decoder_layers_23_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1054] + model_decoder_layers_23_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1055] + model_decoder_layers_23_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1056] + model_decoder_layers_23_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1057] + model_decoder_layers_23_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1058] + model_decoder_layers_23_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[1059] + model_decoder_layers_23_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[1060] + model_decoder_layers_23_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[1061] + model_decoder_layers_23_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[1062] + model_decoder_layers_23_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1063] + model_decoder_layers_23_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1064] + model_decoder_layers_24_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1065] + model_decoder_layers_24_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1066] + model_decoder_layers_24_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1067] + model_decoder_layers_24_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1068] + model_decoder_layers_24_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1069] + model_decoder_layers_24_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1070] + model_decoder_layers_24_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1071] + model_decoder_layers_24_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1072] + model_decoder_layers_24_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1073] + model_decoder_layers_24_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1074] + model_decoder_layers_24_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1075] + model_decoder_layers_24_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1076] + model_decoder_layers_24_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1077] + model_decoder_layers_24_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1078] + model_decoder_layers_24_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1079] + model_decoder_layers_24_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1080] + model_decoder_layers_24_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1081] + model_decoder_layers_24_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1082] + model_decoder_layers_24_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[1083] + model_decoder_layers_24_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[1084] + model_decoder_layers_24_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[1085] + model_decoder_layers_24_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[1086] + model_decoder_layers_24_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1087] + model_decoder_layers_24_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1088] + model_decoder_layers_25_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1089] + model_decoder_layers_25_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1090] + model_decoder_layers_25_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1091] + model_decoder_layers_25_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1092] + model_decoder_layers_25_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1093] + model_decoder_layers_25_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1094] + model_decoder_layers_25_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1095] + model_decoder_layers_25_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1096] + model_decoder_layers_25_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1097] + model_decoder_layers_25_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1098] + model_decoder_layers_25_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1099] + model_decoder_layers_25_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1100] + model_decoder_layers_25_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1101] + model_decoder_layers_25_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1102] + model_decoder_layers_25_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1103] + model_decoder_layers_25_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1104] + model_decoder_layers_25_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1105] + model_decoder_layers_25_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1106] + model_decoder_layers_25_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[1107] + model_decoder_layers_25_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[1108] + model_decoder_layers_25_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[1109] + model_decoder_layers_25_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[1110] + model_decoder_layers_25_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1111] + model_decoder_layers_25_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1112] + model_decoder_layers_26_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1113] + model_decoder_layers_26_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1114] + model_decoder_layers_26_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1115] + model_decoder_layers_26_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1116] + model_decoder_layers_26_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1117] + model_decoder_layers_26_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1118] + model_decoder_layers_26_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1119] + model_decoder_layers_26_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1120] + model_decoder_layers_26_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1121] + model_decoder_layers_26_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1122] + model_decoder_layers_26_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1123] + model_decoder_layers_26_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1124] + model_decoder_layers_26_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1125] + model_decoder_layers_26_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1126] + model_decoder_layers_26_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1127] + model_decoder_layers_26_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1128] + model_decoder_layers_26_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1129] + model_decoder_layers_26_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1130] + model_decoder_layers_26_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[1131] + model_decoder_layers_26_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[1132] + model_decoder_layers_26_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[1133] + model_decoder_layers_26_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[1134] + model_decoder_layers_26_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1135] + model_decoder_layers_26_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1136] + model_decoder_layers_27_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1137] + model_decoder_layers_27_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1138] + model_decoder_layers_27_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1139] + model_decoder_layers_27_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1140] + model_decoder_layers_27_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1141] + model_decoder_layers_27_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1142] + model_decoder_layers_27_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1143] + model_decoder_layers_27_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1144] + model_decoder_layers_27_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1145] + model_decoder_layers_27_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1146] + model_decoder_layers_27_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1147] + model_decoder_layers_27_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1148] + model_decoder_layers_27_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1149] + model_decoder_layers_27_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1150] + model_decoder_layers_27_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1151] + model_decoder_layers_27_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1152] + model_decoder_layers_27_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1153] + model_decoder_layers_27_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1154] + model_decoder_layers_27_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[1155] + model_decoder_layers_27_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[1156] + model_decoder_layers_27_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[1157] + model_decoder_layers_27_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[1158] + model_decoder_layers_27_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1159] + model_decoder_layers_27_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1160] + model_decoder_layers_28_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1161] + model_decoder_layers_28_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1162] + model_decoder_layers_28_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1163] + model_decoder_layers_28_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1164] + model_decoder_layers_28_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1165] + model_decoder_layers_28_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1166] + model_decoder_layers_28_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1167] + model_decoder_layers_28_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1168] + model_decoder_layers_28_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1169] + model_decoder_layers_28_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1170] + model_decoder_layers_28_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1171] + model_decoder_layers_28_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1172] + model_decoder_layers_28_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1173] + model_decoder_layers_28_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1174] + model_decoder_layers_28_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1175] + model_decoder_layers_28_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1176] + model_decoder_layers_28_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1177] + model_decoder_layers_28_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1178] + model_decoder_layers_28_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[1179] + model_decoder_layers_28_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[1180] + model_decoder_layers_28_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[1181] + model_decoder_layers_28_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[1182] + model_decoder_layers_28_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1183] + model_decoder_layers_28_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1184] + model_decoder_layers_29_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1185] + model_decoder_layers_29_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1186] + model_decoder_layers_29_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1187] + model_decoder_layers_29_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1188] + model_decoder_layers_29_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1189] + model_decoder_layers_29_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1190] + model_decoder_layers_29_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1191] + model_decoder_layers_29_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1192] + model_decoder_layers_29_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1193] + model_decoder_layers_29_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1194] + model_decoder_layers_29_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1195] + model_decoder_layers_29_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1196] + model_decoder_layers_29_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1197] + model_decoder_layers_29_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1198] + model_decoder_layers_29_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1199] + model_decoder_layers_29_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1200] + model_decoder_layers_29_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1201] + model_decoder_layers_29_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1202] + model_decoder_layers_29_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[1203] + model_decoder_layers_29_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[1204] + model_decoder_layers_29_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[1205] + model_decoder_layers_29_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[1206] + model_decoder_layers_29_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1207] + model_decoder_layers_29_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1208] + model_decoder_layers_30_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1209] + model_decoder_layers_30_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1210] + model_decoder_layers_30_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1211] + model_decoder_layers_30_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1212] + model_decoder_layers_30_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1213] + model_decoder_layers_30_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1214] + model_decoder_layers_30_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1215] + model_decoder_layers_30_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1216] + model_decoder_layers_30_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1217] + model_decoder_layers_30_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1218] + model_decoder_layers_30_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1219] + model_decoder_layers_30_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1220] + model_decoder_layers_30_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1221] + model_decoder_layers_30_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1222] + model_decoder_layers_30_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1223] + model_decoder_layers_30_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1224] + model_decoder_layers_30_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1225] + model_decoder_layers_30_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1226] + model_decoder_layers_30_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[1227] + model_decoder_layers_30_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[1228] + model_decoder_layers_30_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[1229] + model_decoder_layers_30_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[1230] + model_decoder_layers_30_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1231] + model_decoder_layers_30_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1232] + model_decoder_layers_31_self_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1233] + model_decoder_layers_31_self_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1234] + model_decoder_layers_31_self_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1235] + model_decoder_layers_31_self_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1236] + model_decoder_layers_31_self_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1237] + model_decoder_layers_31_self_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1238] + model_decoder_layers_31_self_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1239] + model_decoder_layers_31_self_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1240] + model_decoder_layers_31_self_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1241] + model_decoder_layers_31_encoder_attn_k_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1242] + model_decoder_layers_31_encoder_attn_v_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1243] + model_decoder_layers_31_encoder_attn_v_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1244] + model_decoder_layers_31_encoder_attn_q_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1245] + model_decoder_layers_31_encoder_attn_q_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1246] + model_decoder_layers_31_encoder_attn_out_proj_weight: R.Tensor((1280, 1280), dtype="float16") = packed_params[1247] + model_decoder_layers_31_encoder_attn_out_proj_bias: R.Tensor((1280,), dtype="float16") = packed_params[1248] + model_decoder_layers_31_encoder_attn_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1249] + model_decoder_layers_31_encoder_attn_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1250] + model_decoder_layers_31_fc1_weight: R.Tensor((5120, 1280), dtype="float16") = packed_params[1251] + model_decoder_layers_31_fc1_bias: R.Tensor((5120,), dtype="float16") = packed_params[1252] + model_decoder_layers_31_fc2_weight: R.Tensor((1280, 5120), dtype="float16") = packed_params[1253] + model_decoder_layers_31_fc2_bias: R.Tensor((1280,), dtype="float16") = packed_params[1254] + model_decoder_layers_31_final_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1255] + model_decoder_layers_31_final_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1256] + model_decoder_layer_norm_weight: R.Tensor((1280,), dtype="float16") = packed_params[1257] + model_decoder_layer_norm_bias: R.Tensor((1280,), dtype="float16") = packed_params[1258] + 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) + permute_dims1: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_0_self_attn_q_proj_weight, axes=None) + matmul: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm, permute_dims1, out_dtype="void") + add1: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul, model_encoder_layers_0_self_attn_q_proj_bias) + reshape: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add1, R.shape([batch_size, 1500, 20, 64])) + permute_dims2: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_0_self_attn_k_proj_weight, axes=None) + matmul1: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm, permute_dims2, out_dtype="void") + reshape1: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul1, R.shape([batch_size, 1500, 20, 64])) + permute_dims3: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_0_self_attn_v_proj_weight, axes=None) + matmul2: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm, permute_dims3, out_dtype="void") + add2: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul2, model_encoder_layers_0_self_attn_v_proj_bias) + reshape2: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add2, 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])) + permute_dims4: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_0_self_attn_out_proj_weight, axes=None) + matmul3: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape7, permute_dims4, out_dtype="void") + add3: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul3, model_encoder_layers_0_self_attn_out_proj_bias) + add4: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add, add3) + 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) + permute_dims5: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_0_fc1_weight, axes=None) + matmul4: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm1, permute_dims5, out_dtype="void") + add5: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul4, model_encoder_layers_0_fc1_bias) + gelu2: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add5) + permute_dims6: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_0_fc2_weight, axes=None) + matmul5: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu2, permute_dims6, out_dtype="void") + add6: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul5, model_encoder_layers_0_fc2_bias) + add7: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add4, add6) + 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) + permute_dims7: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_1_self_attn_q_proj_weight, axes=None) + matmul6: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm2, permute_dims7, out_dtype="void") + add8: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul6, model_encoder_layers_1_self_attn_q_proj_bias) + reshape8: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add8, R.shape([batch_size, 1500, 20, 64])) + permute_dims8: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_1_self_attn_k_proj_weight, axes=None) + matmul7: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm2, permute_dims8, out_dtype="void") + reshape9: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul7, R.shape([batch_size, 1500, 20, 64])) + permute_dims9: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_1_self_attn_v_proj_weight, axes=None) + matmul8: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm2, permute_dims9, out_dtype="void") + add9: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul8, model_encoder_layers_1_self_attn_v_proj_bias) + reshape10: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add9, 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])) + permute_dims10: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_1_self_attn_out_proj_weight, axes=None) + matmul9: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape15, permute_dims10, out_dtype="void") + add10: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul9, model_encoder_layers_1_self_attn_out_proj_bias) + add11: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum, add10) + 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) + permute_dims11: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_1_fc1_weight, axes=None) + matmul10: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm3, permute_dims11, out_dtype="void") + add12: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul10, model_encoder_layers_1_fc1_bias) + gelu3: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add12) + permute_dims12: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_1_fc2_weight, axes=None) + matmul11: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu3, permute_dims12, out_dtype="void") + add13: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul11, model_encoder_layers_1_fc2_bias) + add14: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add11, add13) + 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) + permute_dims13: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_2_self_attn_q_proj_weight, axes=None) + matmul12: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm4, permute_dims13, out_dtype="void") + add15: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul12, model_encoder_layers_2_self_attn_q_proj_bias) + reshape16: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add15, R.shape([batch_size, 1500, 20, 64])) + permute_dims14: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_2_self_attn_k_proj_weight, axes=None) + matmul13: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm4, permute_dims14, out_dtype="void") + reshape17: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul13, R.shape([batch_size, 1500, 20, 64])) + permute_dims15: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_2_self_attn_v_proj_weight, axes=None) + matmul14: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm4, permute_dims15, out_dtype="void") + add16: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul14, model_encoder_layers_2_self_attn_v_proj_bias) + reshape18: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add16, 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])) + permute_dims16: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_2_self_attn_out_proj_weight, axes=None) + matmul15: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape23, permute_dims16, out_dtype="void") + add17: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul15, model_encoder_layers_2_self_attn_out_proj_bias) + add18: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum1, add17) + 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) + permute_dims17: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_2_fc1_weight, axes=None) + matmul16: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm5, permute_dims17, out_dtype="void") + add19: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul16, model_encoder_layers_2_fc1_bias) + gelu4: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add19) + permute_dims18: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_2_fc2_weight, axes=None) + matmul17: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu4, permute_dims18, out_dtype="void") + add20: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul17, model_encoder_layers_2_fc2_bias) + add21: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add18, add20) + 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) + permute_dims19: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_3_self_attn_q_proj_weight, axes=None) + matmul18: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm6, permute_dims19, out_dtype="void") + add22: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul18, model_encoder_layers_3_self_attn_q_proj_bias) + reshape24: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add22, R.shape([batch_size, 1500, 20, 64])) + permute_dims20: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_3_self_attn_k_proj_weight, axes=None) + matmul19: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm6, permute_dims20, out_dtype="void") + reshape25: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul19, R.shape([batch_size, 1500, 20, 64])) + permute_dims21: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_3_self_attn_v_proj_weight, axes=None) + matmul20: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm6, permute_dims21, out_dtype="void") + add23: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul20, model_encoder_layers_3_self_attn_v_proj_bias) + reshape26: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add23, 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])) + permute_dims22: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_3_self_attn_out_proj_weight, axes=None) + matmul21: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape31, permute_dims22, out_dtype="void") + add24: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul21, model_encoder_layers_3_self_attn_out_proj_bias) + add25: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum2, add24) + 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) + permute_dims23: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_3_fc1_weight, axes=None) + matmul22: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm7, permute_dims23, out_dtype="void") + add26: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul22, model_encoder_layers_3_fc1_bias) + gelu5: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add26) + permute_dims24: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_3_fc2_weight, axes=None) + matmul23: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu5, permute_dims24, out_dtype="void") + add27: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul23, model_encoder_layers_3_fc2_bias) + add28: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add25, add27) + 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) + permute_dims25: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_4_self_attn_q_proj_weight, axes=None) + matmul24: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm8, permute_dims25, out_dtype="void") + add29: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul24, model_encoder_layers_4_self_attn_q_proj_bias) + reshape32: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add29, R.shape([batch_size, 1500, 20, 64])) + permute_dims26: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_4_self_attn_k_proj_weight, axes=None) + matmul25: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm8, permute_dims26, out_dtype="void") + reshape33: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul25, R.shape([batch_size, 1500, 20, 64])) + permute_dims27: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_4_self_attn_v_proj_weight, axes=None) + matmul26: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm8, permute_dims27, out_dtype="void") + add30: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul26, model_encoder_layers_4_self_attn_v_proj_bias) + reshape34: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add30, 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])) + permute_dims28: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_4_self_attn_out_proj_weight, axes=None) + matmul27: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape39, permute_dims28, out_dtype="void") + add31: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul27, model_encoder_layers_4_self_attn_out_proj_bias) + add32: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum3, add31) + 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) + permute_dims29: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_4_fc1_weight, axes=None) + matmul28: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm9, permute_dims29, out_dtype="void") + add33: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul28, model_encoder_layers_4_fc1_bias) + gelu6: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add33) + permute_dims30: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_4_fc2_weight, axes=None) + matmul29: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu6, permute_dims30, out_dtype="void") + add34: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul29, model_encoder_layers_4_fc2_bias) + add35: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add32, add34) + 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) + permute_dims31: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_5_self_attn_q_proj_weight, axes=None) + matmul30: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm10, permute_dims31, out_dtype="void") + add36: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul30, model_encoder_layers_5_self_attn_q_proj_bias) + reshape40: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add36, R.shape([batch_size, 1500, 20, 64])) + permute_dims32: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_5_self_attn_k_proj_weight, axes=None) + matmul31: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm10, permute_dims32, out_dtype="void") + reshape41: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul31, R.shape([batch_size, 1500, 20, 64])) + permute_dims33: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_5_self_attn_v_proj_weight, axes=None) + matmul32: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm10, permute_dims33, out_dtype="void") + add37: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul32, model_encoder_layers_5_self_attn_v_proj_bias) + reshape42: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add37, 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])) + permute_dims34: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_5_self_attn_out_proj_weight, axes=None) + matmul33: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape47, permute_dims34, out_dtype="void") + add38: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul33, model_encoder_layers_5_self_attn_out_proj_bias) + add39: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum4, add38) + 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) + permute_dims35: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_5_fc1_weight, axes=None) + matmul34: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm11, permute_dims35, out_dtype="void") + add40: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul34, model_encoder_layers_5_fc1_bias) + gelu7: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add40) + permute_dims36: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_5_fc2_weight, axes=None) + matmul35: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu7, permute_dims36, out_dtype="void") + add41: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul35, model_encoder_layers_5_fc2_bias) + add42: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add39, add41) + 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) + permute_dims37: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_6_self_attn_q_proj_weight, axes=None) + matmul36: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm12, permute_dims37, out_dtype="void") + add43: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul36, model_encoder_layers_6_self_attn_q_proj_bias) + reshape48: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add43, R.shape([batch_size, 1500, 20, 64])) + permute_dims38: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_6_self_attn_k_proj_weight, axes=None) + matmul37: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm12, permute_dims38, out_dtype="void") + reshape49: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul37, R.shape([batch_size, 1500, 20, 64])) + permute_dims39: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_6_self_attn_v_proj_weight, axes=None) + matmul38: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm12, permute_dims39, out_dtype="void") + add44: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul38, model_encoder_layers_6_self_attn_v_proj_bias) + reshape50: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add44, 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])) + permute_dims40: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_6_self_attn_out_proj_weight, axes=None) + matmul39: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape55, permute_dims40, out_dtype="void") + add45: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul39, model_encoder_layers_6_self_attn_out_proj_bias) + add46: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum5, add45) + 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) + permute_dims41: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_6_fc1_weight, axes=None) + matmul40: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm13, permute_dims41, out_dtype="void") + add47: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul40, model_encoder_layers_6_fc1_bias) + gelu8: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add47) + permute_dims42: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_6_fc2_weight, axes=None) + matmul41: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu8, permute_dims42, out_dtype="void") + add48: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul41, model_encoder_layers_6_fc2_bias) + add49: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add46, add48) + 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) + permute_dims43: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_7_self_attn_q_proj_weight, axes=None) + matmul42: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm14, permute_dims43, out_dtype="void") + add50: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul42, model_encoder_layers_7_self_attn_q_proj_bias) + reshape56: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add50, R.shape([batch_size, 1500, 20, 64])) + permute_dims44: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_7_self_attn_k_proj_weight, axes=None) + matmul43: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm14, permute_dims44, out_dtype="void") + reshape57: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul43, R.shape([batch_size, 1500, 20, 64])) + permute_dims45: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_7_self_attn_v_proj_weight, axes=None) + matmul44: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm14, permute_dims45, out_dtype="void") + add51: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul44, model_encoder_layers_7_self_attn_v_proj_bias) + reshape58: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add51, 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])) + permute_dims46: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_7_self_attn_out_proj_weight, axes=None) + matmul45: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape63, permute_dims46, out_dtype="void") + add52: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul45, model_encoder_layers_7_self_attn_out_proj_bias) + add53: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum6, add52) + 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) + permute_dims47: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_7_fc1_weight, axes=None) + matmul46: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm15, permute_dims47, out_dtype="void") + add54: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul46, model_encoder_layers_7_fc1_bias) + gelu9: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add54) + permute_dims48: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_7_fc2_weight, axes=None) + matmul47: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu9, permute_dims48, out_dtype="void") + add55: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul47, model_encoder_layers_7_fc2_bias) + add56: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add53, add55) + 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) + permute_dims49: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_8_self_attn_q_proj_weight, axes=None) + matmul48: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm16, permute_dims49, out_dtype="void") + add57: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul48, model_encoder_layers_8_self_attn_q_proj_bias) + reshape64: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add57, R.shape([batch_size, 1500, 20, 64])) + permute_dims50: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_8_self_attn_k_proj_weight, axes=None) + matmul49: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm16, permute_dims50, out_dtype="void") + reshape65: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul49, R.shape([batch_size, 1500, 20, 64])) + permute_dims51: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_8_self_attn_v_proj_weight, axes=None) + matmul50: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm16, permute_dims51, out_dtype="void") + add58: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul50, model_encoder_layers_8_self_attn_v_proj_bias) + reshape66: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add58, 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])) + permute_dims52: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_8_self_attn_out_proj_weight, axes=None) + matmul51: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape71, permute_dims52, out_dtype="void") + add59: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul51, model_encoder_layers_8_self_attn_out_proj_bias) + add60: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum7, add59) + 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) + permute_dims53: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_8_fc1_weight, axes=None) + matmul52: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm17, permute_dims53, out_dtype="void") + add61: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul52, model_encoder_layers_8_fc1_bias) + gelu10: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add61) + permute_dims54: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_8_fc2_weight, axes=None) + matmul53: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu10, permute_dims54, out_dtype="void") + add62: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul53, model_encoder_layers_8_fc2_bias) + add63: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add60, add62) + 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) + permute_dims55: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_9_self_attn_q_proj_weight, axes=None) + matmul54: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm18, permute_dims55, out_dtype="void") + add64: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul54, model_encoder_layers_9_self_attn_q_proj_bias) + reshape72: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add64, R.shape([batch_size, 1500, 20, 64])) + permute_dims56: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_9_self_attn_k_proj_weight, axes=None) + matmul55: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm18, permute_dims56, out_dtype="void") + reshape73: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul55, R.shape([batch_size, 1500, 20, 64])) + permute_dims57: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_9_self_attn_v_proj_weight, axes=None) + matmul56: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm18, permute_dims57, out_dtype="void") + add65: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul56, model_encoder_layers_9_self_attn_v_proj_bias) + reshape74: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add65, 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])) + permute_dims58: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_9_self_attn_out_proj_weight, axes=None) + matmul57: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape79, permute_dims58, out_dtype="void") + add66: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul57, model_encoder_layers_9_self_attn_out_proj_bias) + add67: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum8, add66) + 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) + permute_dims59: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_9_fc1_weight, axes=None) + matmul58: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm19, permute_dims59, out_dtype="void") + add68: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul58, model_encoder_layers_9_fc1_bias) + gelu11: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add68) + permute_dims60: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_9_fc2_weight, axes=None) + matmul59: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu11, permute_dims60, out_dtype="void") + add69: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul59, model_encoder_layers_9_fc2_bias) + add70: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add67, add69) + 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) + permute_dims61: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_10_self_attn_q_proj_weight, axes=None) + matmul60: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm20, permute_dims61, out_dtype="void") + add71: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul60, model_encoder_layers_10_self_attn_q_proj_bias) + reshape80: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add71, R.shape([batch_size, 1500, 20, 64])) + permute_dims62: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_10_self_attn_k_proj_weight, axes=None) + matmul61: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm20, permute_dims62, out_dtype="void") + reshape81: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul61, R.shape([batch_size, 1500, 20, 64])) + permute_dims63: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_10_self_attn_v_proj_weight, axes=None) + matmul62: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm20, permute_dims63, out_dtype="void") + add72: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul62, model_encoder_layers_10_self_attn_v_proj_bias) + reshape82: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add72, 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])) + permute_dims64: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_10_self_attn_out_proj_weight, axes=None) + matmul63: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape87, permute_dims64, out_dtype="void") + add73: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul63, model_encoder_layers_10_self_attn_out_proj_bias) + add74: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum9, add73) + 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) + permute_dims65: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_10_fc1_weight, axes=None) + matmul64: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm21, permute_dims65, out_dtype="void") + add75: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul64, model_encoder_layers_10_fc1_bias) + gelu12: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add75) + permute_dims66: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_10_fc2_weight, axes=None) + matmul65: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu12, permute_dims66, out_dtype="void") + add76: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul65, model_encoder_layers_10_fc2_bias) + add77: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add74, add76) + 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) + permute_dims67: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_11_self_attn_q_proj_weight, axes=None) + matmul66: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm22, permute_dims67, out_dtype="void") + add78: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul66, model_encoder_layers_11_self_attn_q_proj_bias) + reshape88: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add78, R.shape([batch_size, 1500, 20, 64])) + permute_dims68: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_11_self_attn_k_proj_weight, axes=None) + matmul67: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm22, permute_dims68, out_dtype="void") + reshape89: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul67, R.shape([batch_size, 1500, 20, 64])) + permute_dims69: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_11_self_attn_v_proj_weight, axes=None) + matmul68: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm22, permute_dims69, out_dtype="void") + add79: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul68, model_encoder_layers_11_self_attn_v_proj_bias) + reshape90: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add79, 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])) + permute_dims70: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_11_self_attn_out_proj_weight, axes=None) + matmul69: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape95, permute_dims70, out_dtype="void") + add80: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul69, model_encoder_layers_11_self_attn_out_proj_bias) + add81: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum10, add80) + 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) + permute_dims71: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_11_fc1_weight, axes=None) + matmul70: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm23, permute_dims71, out_dtype="void") + add82: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul70, model_encoder_layers_11_fc1_bias) + gelu13: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add82) + permute_dims72: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_11_fc2_weight, axes=None) + matmul71: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu13, permute_dims72, out_dtype="void") + add83: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul71, model_encoder_layers_11_fc2_bias) + add84: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add81, add83) + 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) + permute_dims73: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_12_self_attn_q_proj_weight, axes=None) + matmul72: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm24, permute_dims73, out_dtype="void") + add85: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul72, model_encoder_layers_12_self_attn_q_proj_bias) + reshape96: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add85, R.shape([batch_size, 1500, 20, 64])) + permute_dims74: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_12_self_attn_k_proj_weight, axes=None) + matmul73: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm24, permute_dims74, out_dtype="void") + reshape97: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul73, R.shape([batch_size, 1500, 20, 64])) + permute_dims75: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_12_self_attn_v_proj_weight, axes=None) + matmul74: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm24, permute_dims75, out_dtype="void") + add86: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul74, model_encoder_layers_12_self_attn_v_proj_bias) + reshape98: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add86, 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])) + permute_dims76: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_12_self_attn_out_proj_weight, axes=None) + matmul75: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape103, permute_dims76, out_dtype="void") + add87: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul75, model_encoder_layers_12_self_attn_out_proj_bias) + add88: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum11, add87) + 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) + permute_dims77: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_12_fc1_weight, axes=None) + matmul76: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm25, permute_dims77, out_dtype="void") + add89: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul76, model_encoder_layers_12_fc1_bias) + gelu14: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add89) + permute_dims78: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_12_fc2_weight, axes=None) + matmul77: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu14, permute_dims78, out_dtype="void") + add90: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul77, model_encoder_layers_12_fc2_bias) + add91: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add88, add90) + 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) + permute_dims79: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_13_self_attn_q_proj_weight, axes=None) + matmul78: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm26, permute_dims79, out_dtype="void") + add92: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul78, model_encoder_layers_13_self_attn_q_proj_bias) + reshape104: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add92, R.shape([batch_size, 1500, 20, 64])) + permute_dims80: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_13_self_attn_k_proj_weight, axes=None) + matmul79: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm26, permute_dims80, out_dtype="void") + reshape105: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul79, R.shape([batch_size, 1500, 20, 64])) + permute_dims81: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_13_self_attn_v_proj_weight, axes=None) + matmul80: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm26, permute_dims81, out_dtype="void") + add93: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul80, model_encoder_layers_13_self_attn_v_proj_bias) + reshape106: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add93, 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])) + permute_dims82: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_13_self_attn_out_proj_weight, axes=None) + matmul81: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape111, permute_dims82, out_dtype="void") + add94: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul81, model_encoder_layers_13_self_attn_out_proj_bias) + add95: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum12, add94) + 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) + permute_dims83: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_13_fc1_weight, axes=None) + matmul82: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm27, permute_dims83, out_dtype="void") + add96: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul82, model_encoder_layers_13_fc1_bias) + gelu15: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add96) + permute_dims84: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_13_fc2_weight, axes=None) + matmul83: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu15, permute_dims84, out_dtype="void") + add97: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul83, model_encoder_layers_13_fc2_bias) + add98: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add95, add97) + 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) + permute_dims85: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_14_self_attn_q_proj_weight, axes=None) + matmul84: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm28, permute_dims85, out_dtype="void") + add99: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul84, model_encoder_layers_14_self_attn_q_proj_bias) + reshape112: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add99, R.shape([batch_size, 1500, 20, 64])) + permute_dims86: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_14_self_attn_k_proj_weight, axes=None) + matmul85: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm28, permute_dims86, out_dtype="void") + reshape113: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul85, R.shape([batch_size, 1500, 20, 64])) + permute_dims87: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_14_self_attn_v_proj_weight, axes=None) + matmul86: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm28, permute_dims87, out_dtype="void") + add100: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul86, model_encoder_layers_14_self_attn_v_proj_bias) + reshape114: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add100, 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])) + permute_dims88: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_14_self_attn_out_proj_weight, axes=None) + matmul87: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape119, permute_dims88, out_dtype="void") + add101: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul87, model_encoder_layers_14_self_attn_out_proj_bias) + add102: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum13, add101) + 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) + permute_dims89: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_14_fc1_weight, axes=None) + matmul88: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm29, permute_dims89, out_dtype="void") + add103: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul88, model_encoder_layers_14_fc1_bias) + gelu16: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add103) + permute_dims90: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_14_fc2_weight, axes=None) + matmul89: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu16, permute_dims90, out_dtype="void") + add104: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul89, model_encoder_layers_14_fc2_bias) + add105: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add102, add104) + 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) + permute_dims91: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_15_self_attn_q_proj_weight, axes=None) + matmul90: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm30, permute_dims91, out_dtype="void") + add106: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul90, model_encoder_layers_15_self_attn_q_proj_bias) + reshape120: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add106, R.shape([batch_size, 1500, 20, 64])) + permute_dims92: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_15_self_attn_k_proj_weight, axes=None) + matmul91: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm30, permute_dims92, out_dtype="void") + reshape121: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul91, R.shape([batch_size, 1500, 20, 64])) + permute_dims93: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_15_self_attn_v_proj_weight, axes=None) + matmul92: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm30, permute_dims93, out_dtype="void") + add107: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul92, model_encoder_layers_15_self_attn_v_proj_bias) + reshape122: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add107, 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])) + permute_dims94: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_15_self_attn_out_proj_weight, axes=None) + matmul93: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape127, permute_dims94, out_dtype="void") + add108: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul93, model_encoder_layers_15_self_attn_out_proj_bias) + add109: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum14, add108) + 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) + permute_dims95: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_15_fc1_weight, axes=None) + matmul94: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm31, permute_dims95, out_dtype="void") + add110: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul94, model_encoder_layers_15_fc1_bias) + gelu17: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add110) + permute_dims96: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_15_fc2_weight, axes=None) + matmul95: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu17, permute_dims96, out_dtype="void") + add111: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul95, model_encoder_layers_15_fc2_bias) + add112: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add109, add111) + 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) + permute_dims97: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_16_self_attn_q_proj_weight, axes=None) + matmul96: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm32, permute_dims97, out_dtype="void") + add113: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul96, model_encoder_layers_16_self_attn_q_proj_bias) + reshape128: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add113, R.shape([batch_size, 1500, 20, 64])) + permute_dims98: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_16_self_attn_k_proj_weight, axes=None) + matmul97: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm32, permute_dims98, out_dtype="void") + reshape129: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul97, R.shape([batch_size, 1500, 20, 64])) + permute_dims99: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_16_self_attn_v_proj_weight, axes=None) + matmul98: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm32, permute_dims99, out_dtype="void") + add114: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul98, model_encoder_layers_16_self_attn_v_proj_bias) + reshape130: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add114, 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])) + permute_dims100: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_16_self_attn_out_proj_weight, axes=None) + matmul99: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape135, permute_dims100, out_dtype="void") + add115: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul99, model_encoder_layers_16_self_attn_out_proj_bias) + add116: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum15, add115) + 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) + permute_dims101: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_16_fc1_weight, axes=None) + matmul100: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm33, permute_dims101, out_dtype="void") + add117: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul100, model_encoder_layers_16_fc1_bias) + gelu18: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add117) + permute_dims102: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_16_fc2_weight, axes=None) + matmul101: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu18, permute_dims102, out_dtype="void") + add118: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul101, model_encoder_layers_16_fc2_bias) + add119: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add116, add118) + 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) + permute_dims103: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_17_self_attn_q_proj_weight, axes=None) + matmul102: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm34, permute_dims103, out_dtype="void") + add120: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul102, model_encoder_layers_17_self_attn_q_proj_bias) + reshape136: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add120, R.shape([batch_size, 1500, 20, 64])) + permute_dims104: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_17_self_attn_k_proj_weight, axes=None) + matmul103: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm34, permute_dims104, out_dtype="void") + reshape137: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul103, R.shape([batch_size, 1500, 20, 64])) + permute_dims105: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_17_self_attn_v_proj_weight, axes=None) + matmul104: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm34, permute_dims105, out_dtype="void") + add121: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul104, model_encoder_layers_17_self_attn_v_proj_bias) + reshape138: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add121, 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])) + permute_dims106: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_17_self_attn_out_proj_weight, axes=None) + matmul105: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape143, permute_dims106, out_dtype="void") + add122: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul105, model_encoder_layers_17_self_attn_out_proj_bias) + add123: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum16, add122) + 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) + permute_dims107: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_17_fc1_weight, axes=None) + matmul106: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm35, permute_dims107, out_dtype="void") + add124: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul106, model_encoder_layers_17_fc1_bias) + gelu19: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add124) + permute_dims108: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_17_fc2_weight, axes=None) + matmul107: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu19, permute_dims108, out_dtype="void") + add125: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul107, model_encoder_layers_17_fc2_bias) + add126: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add123, add125) + 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) + permute_dims109: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_18_self_attn_q_proj_weight, axes=None) + matmul108: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm36, permute_dims109, out_dtype="void") + add127: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul108, model_encoder_layers_18_self_attn_q_proj_bias) + reshape144: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add127, R.shape([batch_size, 1500, 20, 64])) + permute_dims110: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_18_self_attn_k_proj_weight, axes=None) + matmul109: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm36, permute_dims110, out_dtype="void") + reshape145: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul109, R.shape([batch_size, 1500, 20, 64])) + permute_dims111: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_18_self_attn_v_proj_weight, axes=None) + matmul110: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm36, permute_dims111, out_dtype="void") + add128: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul110, model_encoder_layers_18_self_attn_v_proj_bias) + reshape146: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add128, 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])) + permute_dims112: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_18_self_attn_out_proj_weight, axes=None) + matmul111: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape151, permute_dims112, out_dtype="void") + add129: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul111, model_encoder_layers_18_self_attn_out_proj_bias) + add130: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum17, add129) + 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) + permute_dims113: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_18_fc1_weight, axes=None) + matmul112: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm37, permute_dims113, out_dtype="void") + add131: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul112, model_encoder_layers_18_fc1_bias) + gelu20: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add131) + permute_dims114: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_18_fc2_weight, axes=None) + matmul113: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu20, permute_dims114, out_dtype="void") + add132: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul113, model_encoder_layers_18_fc2_bias) + add133: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add130, add132) + 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) + permute_dims115: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_19_self_attn_q_proj_weight, axes=None) + matmul114: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm38, permute_dims115, out_dtype="void") + add134: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul114, model_encoder_layers_19_self_attn_q_proj_bias) + reshape152: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add134, R.shape([batch_size, 1500, 20, 64])) + permute_dims116: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_19_self_attn_k_proj_weight, axes=None) + matmul115: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm38, permute_dims116, out_dtype="void") + reshape153: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul115, R.shape([batch_size, 1500, 20, 64])) + permute_dims117: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_19_self_attn_v_proj_weight, axes=None) + matmul116: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm38, permute_dims117, out_dtype="void") + add135: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul116, model_encoder_layers_19_self_attn_v_proj_bias) + reshape154: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add135, 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])) + permute_dims118: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_19_self_attn_out_proj_weight, axes=None) + matmul117: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape159, permute_dims118, out_dtype="void") + add136: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul117, model_encoder_layers_19_self_attn_out_proj_bias) + add137: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum18, add136) + 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) + permute_dims119: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_19_fc1_weight, axes=None) + matmul118: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm39, permute_dims119, out_dtype="void") + add138: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul118, model_encoder_layers_19_fc1_bias) + gelu21: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add138) + permute_dims120: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_19_fc2_weight, axes=None) + matmul119: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu21, permute_dims120, out_dtype="void") + add139: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul119, model_encoder_layers_19_fc2_bias) + add140: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add137, add139) + 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) + permute_dims121: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_20_self_attn_q_proj_weight, axes=None) + matmul120: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm40, permute_dims121, out_dtype="void") + add141: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul120, model_encoder_layers_20_self_attn_q_proj_bias) + reshape160: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add141, R.shape([batch_size, 1500, 20, 64])) + permute_dims122: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_20_self_attn_k_proj_weight, axes=None) + matmul121: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm40, permute_dims122, out_dtype="void") + reshape161: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul121, R.shape([batch_size, 1500, 20, 64])) + permute_dims123: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_20_self_attn_v_proj_weight, axes=None) + matmul122: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm40, permute_dims123, out_dtype="void") + add142: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul122, model_encoder_layers_20_self_attn_v_proj_bias) + reshape162: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add142, 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])) + permute_dims124: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_20_self_attn_out_proj_weight, axes=None) + matmul123: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape167, permute_dims124, out_dtype="void") + add143: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul123, model_encoder_layers_20_self_attn_out_proj_bias) + add144: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum19, add143) + 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) + permute_dims125: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_20_fc1_weight, axes=None) + matmul124: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm41, permute_dims125, out_dtype="void") + add145: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul124, model_encoder_layers_20_fc1_bias) + gelu22: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add145) + permute_dims126: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_20_fc2_weight, axes=None) + matmul125: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu22, permute_dims126, out_dtype="void") + add146: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul125, model_encoder_layers_20_fc2_bias) + add147: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add144, add146) + 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) + permute_dims127: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_21_self_attn_q_proj_weight, axes=None) + matmul126: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm42, permute_dims127, out_dtype="void") + add148: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul126, model_encoder_layers_21_self_attn_q_proj_bias) + reshape168: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add148, R.shape([batch_size, 1500, 20, 64])) + permute_dims128: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_21_self_attn_k_proj_weight, axes=None) + matmul127: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm42, permute_dims128, out_dtype="void") + reshape169: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul127, R.shape([batch_size, 1500, 20, 64])) + permute_dims129: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_21_self_attn_v_proj_weight, axes=None) + matmul128: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm42, permute_dims129, out_dtype="void") + add149: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul128, model_encoder_layers_21_self_attn_v_proj_bias) + reshape170: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add149, 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])) + permute_dims130: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_21_self_attn_out_proj_weight, axes=None) + matmul129: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape175, permute_dims130, out_dtype="void") + add150: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul129, model_encoder_layers_21_self_attn_out_proj_bias) + add151: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum20, add150) + 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) + permute_dims131: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_21_fc1_weight, axes=None) + matmul130: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm43, permute_dims131, out_dtype="void") + add152: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul130, model_encoder_layers_21_fc1_bias) + gelu23: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add152) + permute_dims132: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_21_fc2_weight, axes=None) + matmul131: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu23, permute_dims132, out_dtype="void") + add153: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul131, model_encoder_layers_21_fc2_bias) + add154: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add151, add153) + 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) + permute_dims133: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_22_self_attn_q_proj_weight, axes=None) + matmul132: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm44, permute_dims133, out_dtype="void") + add155: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul132, model_encoder_layers_22_self_attn_q_proj_bias) + reshape176: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add155, R.shape([batch_size, 1500, 20, 64])) + permute_dims134: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_22_self_attn_k_proj_weight, axes=None) + matmul133: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm44, permute_dims134, out_dtype="void") + reshape177: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul133, R.shape([batch_size, 1500, 20, 64])) + permute_dims135: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_22_self_attn_v_proj_weight, axes=None) + matmul134: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm44, permute_dims135, out_dtype="void") + add156: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul134, model_encoder_layers_22_self_attn_v_proj_bias) + reshape178: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add156, 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])) + permute_dims136: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_22_self_attn_out_proj_weight, axes=None) + matmul135: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape183, permute_dims136, out_dtype="void") + add157: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul135, model_encoder_layers_22_self_attn_out_proj_bias) + add158: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum21, add157) + 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) + permute_dims137: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_22_fc1_weight, axes=None) + matmul136: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm45, permute_dims137, out_dtype="void") + add159: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul136, model_encoder_layers_22_fc1_bias) + gelu24: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add159) + permute_dims138: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_22_fc2_weight, axes=None) + matmul137: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu24, permute_dims138, out_dtype="void") + add160: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul137, model_encoder_layers_22_fc2_bias) + add161: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add158, add160) + 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) + permute_dims139: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_23_self_attn_q_proj_weight, axes=None) + matmul138: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm46, permute_dims139, out_dtype="void") + add162: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul138, model_encoder_layers_23_self_attn_q_proj_bias) + reshape184: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add162, R.shape([batch_size, 1500, 20, 64])) + permute_dims140: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_23_self_attn_k_proj_weight, axes=None) + matmul139: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm46, permute_dims140, out_dtype="void") + reshape185: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul139, R.shape([batch_size, 1500, 20, 64])) + permute_dims141: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_23_self_attn_v_proj_weight, axes=None) + matmul140: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm46, permute_dims141, out_dtype="void") + add163: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul140, model_encoder_layers_23_self_attn_v_proj_bias) + reshape186: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add163, 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])) + permute_dims142: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_23_self_attn_out_proj_weight, axes=None) + matmul141: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape191, permute_dims142, out_dtype="void") + add164: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul141, model_encoder_layers_23_self_attn_out_proj_bias) + add165: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum22, add164) + 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) + permute_dims143: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_23_fc1_weight, axes=None) + matmul142: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm47, permute_dims143, out_dtype="void") + add166: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul142, model_encoder_layers_23_fc1_bias) + gelu25: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add166) + permute_dims144: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_23_fc2_weight, axes=None) + matmul143: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu25, permute_dims144, out_dtype="void") + add167: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul143, model_encoder_layers_23_fc2_bias) + add168: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add165, add167) + 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) + permute_dims145: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_24_self_attn_q_proj_weight, axes=None) + matmul144: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm48, permute_dims145, out_dtype="void") + add169: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul144, model_encoder_layers_24_self_attn_q_proj_bias) + reshape192: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add169, R.shape([batch_size, 1500, 20, 64])) + permute_dims146: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_24_self_attn_k_proj_weight, axes=None) + matmul145: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm48, permute_dims146, out_dtype="void") + reshape193: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul145, R.shape([batch_size, 1500, 20, 64])) + permute_dims147: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_24_self_attn_v_proj_weight, axes=None) + matmul146: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm48, permute_dims147, out_dtype="void") + add170: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul146, model_encoder_layers_24_self_attn_v_proj_bias) + reshape194: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add170, 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])) + permute_dims148: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_24_self_attn_out_proj_weight, axes=None) + matmul147: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape199, permute_dims148, out_dtype="void") + add171: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul147, model_encoder_layers_24_self_attn_out_proj_bias) + add172: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum23, add171) + 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) + permute_dims149: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_24_fc1_weight, axes=None) + matmul148: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm49, permute_dims149, out_dtype="void") + add173: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul148, model_encoder_layers_24_fc1_bias) + gelu26: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add173) + permute_dims150: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_24_fc2_weight, axes=None) + matmul149: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu26, permute_dims150, out_dtype="void") + add174: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul149, model_encoder_layers_24_fc2_bias) + add175: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add172, add174) + 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) + permute_dims151: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_25_self_attn_q_proj_weight, axes=None) + matmul150: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm50, permute_dims151, out_dtype="void") + add176: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul150, model_encoder_layers_25_self_attn_q_proj_bias) + reshape200: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add176, R.shape([batch_size, 1500, 20, 64])) + permute_dims152: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_25_self_attn_k_proj_weight, axes=None) + matmul151: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm50, permute_dims152, out_dtype="void") + reshape201: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul151, R.shape([batch_size, 1500, 20, 64])) + permute_dims153: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_25_self_attn_v_proj_weight, axes=None) + matmul152: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm50, permute_dims153, out_dtype="void") + add177: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul152, model_encoder_layers_25_self_attn_v_proj_bias) + reshape202: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add177, 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])) + permute_dims154: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_25_self_attn_out_proj_weight, axes=None) + matmul153: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape207, permute_dims154, out_dtype="void") + add178: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul153, model_encoder_layers_25_self_attn_out_proj_bias) + add179: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum24, add178) + 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) + permute_dims155: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_25_fc1_weight, axes=None) + matmul154: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm51, permute_dims155, out_dtype="void") + add180: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul154, model_encoder_layers_25_fc1_bias) + gelu27: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add180) + permute_dims156: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_25_fc2_weight, axes=None) + matmul155: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu27, permute_dims156, out_dtype="void") + add181: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul155, model_encoder_layers_25_fc2_bias) + add182: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add179, add181) + 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) + permute_dims157: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_26_self_attn_q_proj_weight, axes=None) + matmul156: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm52, permute_dims157, out_dtype="void") + add183: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul156, model_encoder_layers_26_self_attn_q_proj_bias) + reshape208: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add183, R.shape([batch_size, 1500, 20, 64])) + permute_dims158: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_26_self_attn_k_proj_weight, axes=None) + matmul157: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm52, permute_dims158, out_dtype="void") + reshape209: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul157, R.shape([batch_size, 1500, 20, 64])) + permute_dims159: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_26_self_attn_v_proj_weight, axes=None) + matmul158: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm52, permute_dims159, out_dtype="void") + add184: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul158, model_encoder_layers_26_self_attn_v_proj_bias) + reshape210: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add184, 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])) + permute_dims160: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_26_self_attn_out_proj_weight, axes=None) + matmul159: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape215, permute_dims160, out_dtype="void") + add185: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul159, model_encoder_layers_26_self_attn_out_proj_bias) + add186: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum25, add185) + 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) + permute_dims161: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_26_fc1_weight, axes=None) + matmul160: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm53, permute_dims161, out_dtype="void") + add187: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul160, model_encoder_layers_26_fc1_bias) + gelu28: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add187) + permute_dims162: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_26_fc2_weight, axes=None) + matmul161: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu28, permute_dims162, out_dtype="void") + add188: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul161, model_encoder_layers_26_fc2_bias) + add189: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add186, add188) + 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) + permute_dims163: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_27_self_attn_q_proj_weight, axes=None) + matmul162: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm54, permute_dims163, out_dtype="void") + add190: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul162, model_encoder_layers_27_self_attn_q_proj_bias) + reshape216: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add190, R.shape([batch_size, 1500, 20, 64])) + permute_dims164: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_27_self_attn_k_proj_weight, axes=None) + matmul163: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm54, permute_dims164, out_dtype="void") + reshape217: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul163, R.shape([batch_size, 1500, 20, 64])) + permute_dims165: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_27_self_attn_v_proj_weight, axes=None) + matmul164: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm54, permute_dims165, out_dtype="void") + add191: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul164, model_encoder_layers_27_self_attn_v_proj_bias) + reshape218: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add191, 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])) + permute_dims166: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_27_self_attn_out_proj_weight, axes=None) + matmul165: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape223, permute_dims166, out_dtype="void") + add192: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul165, model_encoder_layers_27_self_attn_out_proj_bias) + add193: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum26, add192) + 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) + permute_dims167: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_27_fc1_weight, axes=None) + matmul166: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm55, permute_dims167, out_dtype="void") + add194: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul166, model_encoder_layers_27_fc1_bias) + gelu29: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add194) + permute_dims168: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_27_fc2_weight, axes=None) + matmul167: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu29, permute_dims168, out_dtype="void") + add195: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul167, model_encoder_layers_27_fc2_bias) + add196: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add193, add195) + 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) + permute_dims169: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_28_self_attn_q_proj_weight, axes=None) + matmul168: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm56, permute_dims169, out_dtype="void") + add197: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul168, model_encoder_layers_28_self_attn_q_proj_bias) + reshape224: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add197, R.shape([batch_size, 1500, 20, 64])) + permute_dims170: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_28_self_attn_k_proj_weight, axes=None) + matmul169: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm56, permute_dims170, out_dtype="void") + reshape225: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul169, R.shape([batch_size, 1500, 20, 64])) + permute_dims171: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_28_self_attn_v_proj_weight, axes=None) + matmul170: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm56, permute_dims171, out_dtype="void") + add198: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul170, model_encoder_layers_28_self_attn_v_proj_bias) + reshape226: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add198, 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])) + permute_dims172: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_28_self_attn_out_proj_weight, axes=None) + matmul171: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape231, permute_dims172, out_dtype="void") + add199: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul171, model_encoder_layers_28_self_attn_out_proj_bias) + add200: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum27, add199) + 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) + permute_dims173: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_28_fc1_weight, axes=None) + matmul172: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm57, permute_dims173, out_dtype="void") + add201: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul172, model_encoder_layers_28_fc1_bias) + gelu30: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add201) + permute_dims174: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_28_fc2_weight, axes=None) + matmul173: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu30, permute_dims174, out_dtype="void") + add202: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul173, model_encoder_layers_28_fc2_bias) + add203: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add200, add202) + 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) + permute_dims175: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_29_self_attn_q_proj_weight, axes=None) + matmul174: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm58, permute_dims175, out_dtype="void") + add204: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul174, model_encoder_layers_29_self_attn_q_proj_bias) + reshape232: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add204, R.shape([batch_size, 1500, 20, 64])) + permute_dims176: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_29_self_attn_k_proj_weight, axes=None) + matmul175: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm58, permute_dims176, out_dtype="void") + reshape233: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul175, R.shape([batch_size, 1500, 20, 64])) + permute_dims177: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_29_self_attn_v_proj_weight, axes=None) + matmul176: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm58, permute_dims177, out_dtype="void") + add205: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul176, model_encoder_layers_29_self_attn_v_proj_bias) + reshape234: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add205, 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])) + permute_dims178: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_29_self_attn_out_proj_weight, axes=None) + matmul177: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape239, permute_dims178, out_dtype="void") + add206: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul177, model_encoder_layers_29_self_attn_out_proj_bias) + add207: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum28, add206) + 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) + permute_dims179: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_29_fc1_weight, axes=None) + matmul178: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm59, permute_dims179, out_dtype="void") + add208: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul178, model_encoder_layers_29_fc1_bias) + gelu31: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add208) + permute_dims180: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_29_fc2_weight, axes=None) + matmul179: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu31, permute_dims180, out_dtype="void") + add209: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul179, model_encoder_layers_29_fc2_bias) + add210: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add207, add209) + 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) + permute_dims181: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_30_self_attn_q_proj_weight, axes=None) + matmul180: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm60, permute_dims181, out_dtype="void") + add211: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul180, model_encoder_layers_30_self_attn_q_proj_bias) + reshape240: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add211, R.shape([batch_size, 1500, 20, 64])) + permute_dims182: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_30_self_attn_k_proj_weight, axes=None) + matmul181: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm60, permute_dims182, out_dtype="void") + reshape241: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul181, R.shape([batch_size, 1500, 20, 64])) + permute_dims183: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_30_self_attn_v_proj_weight, axes=None) + matmul182: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm60, permute_dims183, out_dtype="void") + add212: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul182, model_encoder_layers_30_self_attn_v_proj_bias) + reshape242: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add212, 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])) + permute_dims184: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_30_self_attn_out_proj_weight, axes=None) + matmul183: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape247, permute_dims184, out_dtype="void") + add213: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul183, model_encoder_layers_30_self_attn_out_proj_bias) + add214: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum29, add213) + 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) + permute_dims185: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_30_fc1_weight, axes=None) + matmul184: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm61, permute_dims185, out_dtype="void") + add215: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul184, model_encoder_layers_30_fc1_bias) + gelu32: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add215) + permute_dims186: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_30_fc2_weight, axes=None) + matmul185: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu32, permute_dims186, out_dtype="void") + add216: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul185, model_encoder_layers_30_fc2_bias) + add217: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add214, add216) + 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) + permute_dims187: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_31_self_attn_q_proj_weight, axes=None) + matmul186: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm62, permute_dims187, out_dtype="void") + add218: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul186, model_encoder_layers_31_self_attn_q_proj_bias) + reshape248: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add218, R.shape([batch_size, 1500, 20, 64])) + permute_dims188: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_31_self_attn_k_proj_weight, axes=None) + matmul187: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm62, permute_dims188, out_dtype="void") + reshape249: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(matmul187, R.shape([batch_size, 1500, 20, 64])) + permute_dims189: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_31_self_attn_v_proj_weight, axes=None) + matmul188: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(layer_norm62, permute_dims189, out_dtype="void") + add219: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul188, model_encoder_layers_31_self_attn_v_proj_bias) + reshape250: R.Tensor((batch_size, 1500, 20, 64), dtype="float16") = R.reshape(add219, 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])) + permute_dims190: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_31_self_attn_out_proj_weight, axes=None) + matmul189: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(reshape255, permute_dims190, out_dtype="void") + add220: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul189, model_encoder_layers_31_self_attn_out_proj_bias) + add221: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(minimum30, add220) + 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) + permute_dims191: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_encoder_layers_31_fc1_weight, axes=None) + matmul190: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.matmul(layer_norm63, permute_dims191, out_dtype="void") + add222: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.add(matmul190, model_encoder_layers_31_fc1_bias) + gelu33: R.Tensor((batch_size, 1500, 5120), dtype="float16") = R.nn.gelu(add222) + permute_dims192: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_encoder_layers_31_fc2_weight, axes=None) + matmul191: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.matmul(gelu33, permute_dims192, out_dtype="void") + add223: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(matmul191, model_encoder_layers_31_fc2_bias) + add224: R.Tensor((batch_size, 1500, 1280), dtype="float16") = R.add(add221, add223) + 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_encoder_conv1_weight2: R.Tensor((1280, 128, 3), dtype="float16") = packed_params[0] + model_encoder_conv1_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1] + model_encoder_conv2_weight2: R.Tensor((1280, 1280, 3), dtype="float16") = packed_params[2] + model_encoder_conv2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[3] + model_encoder_embed_positions_weight2: R.Tensor((1500, 1280), dtype="float16") = packed_params[4] + model_encoder_layers_0_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[5] + model_encoder_layers_0_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[6] + model_encoder_layers_0_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[7] + model_encoder_layers_0_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[8] + model_encoder_layers_0_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[9] + model_encoder_layers_0_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[10] + model_encoder_layers_0_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[11] + model_encoder_layers_0_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[12] + model_encoder_layers_0_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[13] + model_encoder_layers_0_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[14] + model_encoder_layers_0_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[15] + model_encoder_layers_0_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[16] + model_encoder_layers_0_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[17] + model_encoder_layers_0_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[18] + model_encoder_layers_0_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[19] + model_encoder_layers_1_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[20] + model_encoder_layers_1_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[21] + model_encoder_layers_1_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[22] + model_encoder_layers_1_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[23] + model_encoder_layers_1_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[24] + model_encoder_layers_1_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[25] + model_encoder_layers_1_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[26] + model_encoder_layers_1_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[27] + model_encoder_layers_1_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[28] + model_encoder_layers_1_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[29] + model_encoder_layers_1_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[30] + model_encoder_layers_1_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[31] + model_encoder_layers_1_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[32] + model_encoder_layers_1_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[33] + model_encoder_layers_1_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[34] + model_encoder_layers_2_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[35] + model_encoder_layers_2_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[36] + model_encoder_layers_2_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[37] + model_encoder_layers_2_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[38] + model_encoder_layers_2_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[39] + model_encoder_layers_2_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[40] + model_encoder_layers_2_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[41] + model_encoder_layers_2_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[42] + model_encoder_layers_2_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[43] + model_encoder_layers_2_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[44] + model_encoder_layers_2_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[45] + model_encoder_layers_2_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[46] + model_encoder_layers_2_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[47] + model_encoder_layers_2_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[48] + model_encoder_layers_2_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[49] + model_encoder_layers_3_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[50] + model_encoder_layers_3_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[51] + model_encoder_layers_3_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[52] + model_encoder_layers_3_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[53] + model_encoder_layers_3_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[54] + model_encoder_layers_3_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[55] + model_encoder_layers_3_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[56] + model_encoder_layers_3_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[57] + model_encoder_layers_3_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[58] + model_encoder_layers_3_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[59] + model_encoder_layers_3_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[60] + model_encoder_layers_3_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[61] + model_encoder_layers_3_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[62] + model_encoder_layers_3_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[63] + model_encoder_layers_3_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[64] + model_encoder_layers_4_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[65] + model_encoder_layers_4_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[66] + model_encoder_layers_4_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[67] + model_encoder_layers_4_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[68] + model_encoder_layers_4_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[69] + model_encoder_layers_4_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[70] + model_encoder_layers_4_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[71] + model_encoder_layers_4_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[72] + model_encoder_layers_4_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[73] + model_encoder_layers_4_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[74] + model_encoder_layers_4_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[75] + model_encoder_layers_4_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[76] + model_encoder_layers_4_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[77] + model_encoder_layers_4_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[78] + model_encoder_layers_4_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[79] + model_encoder_layers_5_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[80] + model_encoder_layers_5_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[81] + model_encoder_layers_5_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[82] + model_encoder_layers_5_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[83] + model_encoder_layers_5_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[84] + model_encoder_layers_5_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[85] + model_encoder_layers_5_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[86] + model_encoder_layers_5_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[87] + model_encoder_layers_5_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[88] + model_encoder_layers_5_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[89] + model_encoder_layers_5_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[90] + model_encoder_layers_5_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[91] + model_encoder_layers_5_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[92] + model_encoder_layers_5_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[93] + model_encoder_layers_5_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[94] + model_encoder_layers_6_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[95] + model_encoder_layers_6_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[96] + model_encoder_layers_6_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[97] + model_encoder_layers_6_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[98] + model_encoder_layers_6_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[99] + model_encoder_layers_6_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[100] + model_encoder_layers_6_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[101] + model_encoder_layers_6_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[102] + model_encoder_layers_6_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[103] + model_encoder_layers_6_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[104] + model_encoder_layers_6_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[105] + model_encoder_layers_6_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[106] + model_encoder_layers_6_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[107] + model_encoder_layers_6_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[108] + model_encoder_layers_6_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[109] + model_encoder_layers_7_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[110] + model_encoder_layers_7_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[111] + model_encoder_layers_7_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[112] + model_encoder_layers_7_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[113] + model_encoder_layers_7_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[114] + model_encoder_layers_7_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[115] + model_encoder_layers_7_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[116] + model_encoder_layers_7_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[117] + model_encoder_layers_7_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[118] + model_encoder_layers_7_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[119] + model_encoder_layers_7_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[120] + model_encoder_layers_7_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[121] + model_encoder_layers_7_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[122] + model_encoder_layers_7_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[123] + model_encoder_layers_7_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[124] + model_encoder_layers_8_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[125] + model_encoder_layers_8_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[126] + model_encoder_layers_8_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[127] + model_encoder_layers_8_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[128] + model_encoder_layers_8_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[129] + model_encoder_layers_8_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[130] + model_encoder_layers_8_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[131] + model_encoder_layers_8_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[132] + model_encoder_layers_8_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[133] + model_encoder_layers_8_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[134] + model_encoder_layers_8_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[135] + model_encoder_layers_8_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[136] + model_encoder_layers_8_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[137] + model_encoder_layers_8_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[138] + model_encoder_layers_8_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[139] + model_encoder_layers_9_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[140] + model_encoder_layers_9_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[141] + model_encoder_layers_9_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[142] + model_encoder_layers_9_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[143] + model_encoder_layers_9_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[144] + model_encoder_layers_9_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[145] + model_encoder_layers_9_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[146] + model_encoder_layers_9_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[147] + model_encoder_layers_9_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[148] + model_encoder_layers_9_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[149] + model_encoder_layers_9_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[150] + model_encoder_layers_9_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[151] + model_encoder_layers_9_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[152] + model_encoder_layers_9_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[153] + model_encoder_layers_9_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[154] + model_encoder_layers_10_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[155] + model_encoder_layers_10_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[156] + model_encoder_layers_10_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[157] + model_encoder_layers_10_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[158] + model_encoder_layers_10_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[159] + model_encoder_layers_10_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[160] + model_encoder_layers_10_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[161] + model_encoder_layers_10_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[162] + model_encoder_layers_10_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[163] + model_encoder_layers_10_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[164] + model_encoder_layers_10_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[165] + model_encoder_layers_10_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[166] + model_encoder_layers_10_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[167] + model_encoder_layers_10_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[168] + model_encoder_layers_10_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[169] + model_encoder_layers_11_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[170] + model_encoder_layers_11_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[171] + model_encoder_layers_11_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[172] + model_encoder_layers_11_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[173] + model_encoder_layers_11_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[174] + model_encoder_layers_11_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[175] + model_encoder_layers_11_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[176] + model_encoder_layers_11_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[177] + model_encoder_layers_11_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[178] + model_encoder_layers_11_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[179] + model_encoder_layers_11_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[180] + model_encoder_layers_11_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[181] + model_encoder_layers_11_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[182] + model_encoder_layers_11_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[183] + model_encoder_layers_11_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[184] + model_encoder_layers_12_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[185] + model_encoder_layers_12_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[186] + model_encoder_layers_12_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[187] + model_encoder_layers_12_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[188] + model_encoder_layers_12_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[189] + model_encoder_layers_12_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[190] + model_encoder_layers_12_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[191] + model_encoder_layers_12_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[192] + model_encoder_layers_12_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[193] + model_encoder_layers_12_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[194] + model_encoder_layers_12_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[195] + model_encoder_layers_12_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[196] + model_encoder_layers_12_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[197] + model_encoder_layers_12_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[198] + model_encoder_layers_12_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[199] + model_encoder_layers_13_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[200] + model_encoder_layers_13_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[201] + model_encoder_layers_13_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[202] + model_encoder_layers_13_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[203] + model_encoder_layers_13_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[204] + model_encoder_layers_13_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[205] + model_encoder_layers_13_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[206] + model_encoder_layers_13_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[207] + model_encoder_layers_13_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[208] + model_encoder_layers_13_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[209] + model_encoder_layers_13_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[210] + model_encoder_layers_13_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[211] + model_encoder_layers_13_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[212] + model_encoder_layers_13_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[213] + model_encoder_layers_13_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[214] + model_encoder_layers_14_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[215] + model_encoder_layers_14_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[216] + model_encoder_layers_14_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[217] + model_encoder_layers_14_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[218] + model_encoder_layers_14_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[219] + model_encoder_layers_14_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[220] + model_encoder_layers_14_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[221] + model_encoder_layers_14_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[222] + model_encoder_layers_14_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[223] + model_encoder_layers_14_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[224] + model_encoder_layers_14_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[225] + model_encoder_layers_14_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[226] + model_encoder_layers_14_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[227] + model_encoder_layers_14_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[228] + model_encoder_layers_14_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[229] + model_encoder_layers_15_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[230] + model_encoder_layers_15_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[231] + model_encoder_layers_15_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[232] + model_encoder_layers_15_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[233] + model_encoder_layers_15_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[234] + model_encoder_layers_15_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[235] + model_encoder_layers_15_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[236] + model_encoder_layers_15_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[237] + model_encoder_layers_15_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[238] + model_encoder_layers_15_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[239] + model_encoder_layers_15_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[240] + model_encoder_layers_15_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[241] + model_encoder_layers_15_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[242] + model_encoder_layers_15_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[243] + model_encoder_layers_15_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[244] + model_encoder_layers_16_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[245] + model_encoder_layers_16_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[246] + model_encoder_layers_16_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[247] + model_encoder_layers_16_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[248] + model_encoder_layers_16_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[249] + model_encoder_layers_16_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[250] + model_encoder_layers_16_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[251] + model_encoder_layers_16_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[252] + model_encoder_layers_16_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[253] + model_encoder_layers_16_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[254] + model_encoder_layers_16_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[255] + model_encoder_layers_16_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[256] + model_encoder_layers_16_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[257] + model_encoder_layers_16_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[258] + model_encoder_layers_16_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[259] + model_encoder_layers_17_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[260] + model_encoder_layers_17_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[261] + model_encoder_layers_17_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[262] + model_encoder_layers_17_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[263] + model_encoder_layers_17_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[264] + model_encoder_layers_17_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[265] + model_encoder_layers_17_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[266] + model_encoder_layers_17_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[267] + model_encoder_layers_17_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[268] + model_encoder_layers_17_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[269] + model_encoder_layers_17_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[270] + model_encoder_layers_17_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[271] + model_encoder_layers_17_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[272] + model_encoder_layers_17_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[273] + model_encoder_layers_17_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[274] + model_encoder_layers_18_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[275] + model_encoder_layers_18_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[276] + model_encoder_layers_18_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[277] + model_encoder_layers_18_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[278] + model_encoder_layers_18_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[279] + model_encoder_layers_18_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[280] + model_encoder_layers_18_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[281] + model_encoder_layers_18_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[282] + model_encoder_layers_18_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[283] + model_encoder_layers_18_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[284] + model_encoder_layers_18_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[285] + model_encoder_layers_18_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[286] + model_encoder_layers_18_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[287] + model_encoder_layers_18_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[288] + model_encoder_layers_18_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[289] + model_encoder_layers_19_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[290] + model_encoder_layers_19_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[291] + model_encoder_layers_19_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[292] + model_encoder_layers_19_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[293] + model_encoder_layers_19_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[294] + model_encoder_layers_19_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[295] + model_encoder_layers_19_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[296] + model_encoder_layers_19_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[297] + model_encoder_layers_19_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[298] + model_encoder_layers_19_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[299] + model_encoder_layers_19_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[300] + model_encoder_layers_19_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[301] + model_encoder_layers_19_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[302] + model_encoder_layers_19_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[303] + model_encoder_layers_19_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[304] + model_encoder_layers_20_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[305] + model_encoder_layers_20_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[306] + model_encoder_layers_20_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[307] + model_encoder_layers_20_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[308] + model_encoder_layers_20_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[309] + model_encoder_layers_20_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[310] + model_encoder_layers_20_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[311] + model_encoder_layers_20_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[312] + model_encoder_layers_20_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[313] + model_encoder_layers_20_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[314] + model_encoder_layers_20_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[315] + model_encoder_layers_20_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[316] + model_encoder_layers_20_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[317] + model_encoder_layers_20_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[318] + model_encoder_layers_20_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[319] + model_encoder_layers_21_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[320] + model_encoder_layers_21_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[321] + model_encoder_layers_21_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[322] + model_encoder_layers_21_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[323] + model_encoder_layers_21_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[324] + model_encoder_layers_21_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[325] + model_encoder_layers_21_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[326] + model_encoder_layers_21_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[327] + model_encoder_layers_21_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[328] + model_encoder_layers_21_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[329] + model_encoder_layers_21_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[330] + model_encoder_layers_21_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[331] + model_encoder_layers_21_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[332] + model_encoder_layers_21_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[333] + model_encoder_layers_21_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[334] + model_encoder_layers_22_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[335] + model_encoder_layers_22_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[336] + model_encoder_layers_22_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[337] + model_encoder_layers_22_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[338] + model_encoder_layers_22_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[339] + model_encoder_layers_22_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[340] + model_encoder_layers_22_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[341] + model_encoder_layers_22_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[342] + model_encoder_layers_22_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[343] + model_encoder_layers_22_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[344] + model_encoder_layers_22_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[345] + model_encoder_layers_22_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[346] + model_encoder_layers_22_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[347] + model_encoder_layers_22_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[348] + model_encoder_layers_22_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[349] + model_encoder_layers_23_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[350] + model_encoder_layers_23_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[351] + model_encoder_layers_23_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[352] + model_encoder_layers_23_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[353] + model_encoder_layers_23_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[354] + model_encoder_layers_23_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[355] + model_encoder_layers_23_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[356] + model_encoder_layers_23_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[357] + model_encoder_layers_23_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[358] + model_encoder_layers_23_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[359] + model_encoder_layers_23_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[360] + model_encoder_layers_23_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[361] + model_encoder_layers_23_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[362] + model_encoder_layers_23_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[363] + model_encoder_layers_23_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[364] + model_encoder_layers_24_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[365] + model_encoder_layers_24_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[366] + model_encoder_layers_24_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[367] + model_encoder_layers_24_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[368] + model_encoder_layers_24_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[369] + model_encoder_layers_24_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[370] + model_encoder_layers_24_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[371] + model_encoder_layers_24_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[372] + model_encoder_layers_24_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[373] + model_encoder_layers_24_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[374] + model_encoder_layers_24_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[375] + model_encoder_layers_24_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[376] + model_encoder_layers_24_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[377] + model_encoder_layers_24_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[378] + model_encoder_layers_24_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[379] + model_encoder_layers_25_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[380] + model_encoder_layers_25_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[381] + model_encoder_layers_25_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[382] + model_encoder_layers_25_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[383] + model_encoder_layers_25_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[384] + model_encoder_layers_25_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[385] + model_encoder_layers_25_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[386] + model_encoder_layers_25_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[387] + model_encoder_layers_25_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[388] + model_encoder_layers_25_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[389] + model_encoder_layers_25_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[390] + model_encoder_layers_25_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[391] + model_encoder_layers_25_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[392] + model_encoder_layers_25_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[393] + model_encoder_layers_25_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[394] + model_encoder_layers_26_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[395] + model_encoder_layers_26_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[396] + model_encoder_layers_26_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[397] + model_encoder_layers_26_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[398] + model_encoder_layers_26_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[399] + model_encoder_layers_26_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[400] + model_encoder_layers_26_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[401] + model_encoder_layers_26_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[402] + model_encoder_layers_26_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[403] + model_encoder_layers_26_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[404] + model_encoder_layers_26_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[405] + model_encoder_layers_26_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[406] + model_encoder_layers_26_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[407] + model_encoder_layers_26_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[408] + model_encoder_layers_26_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[409] + model_encoder_layers_27_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[410] + model_encoder_layers_27_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[411] + model_encoder_layers_27_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[412] + model_encoder_layers_27_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[413] + model_encoder_layers_27_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[414] + model_encoder_layers_27_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[415] + model_encoder_layers_27_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[416] + model_encoder_layers_27_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[417] + model_encoder_layers_27_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[418] + model_encoder_layers_27_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[419] + model_encoder_layers_27_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[420] + model_encoder_layers_27_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[421] + model_encoder_layers_27_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[422] + model_encoder_layers_27_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[423] + model_encoder_layers_27_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[424] + model_encoder_layers_28_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[425] + model_encoder_layers_28_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[426] + model_encoder_layers_28_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[427] + model_encoder_layers_28_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[428] + model_encoder_layers_28_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[429] + model_encoder_layers_28_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[430] + model_encoder_layers_28_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[431] + model_encoder_layers_28_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[432] + model_encoder_layers_28_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[433] + model_encoder_layers_28_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[434] + model_encoder_layers_28_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[435] + model_encoder_layers_28_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[436] + model_encoder_layers_28_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[437] + model_encoder_layers_28_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[438] + model_encoder_layers_28_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[439] + model_encoder_layers_29_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[440] + model_encoder_layers_29_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[441] + model_encoder_layers_29_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[442] + model_encoder_layers_29_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[443] + model_encoder_layers_29_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[444] + model_encoder_layers_29_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[445] + model_encoder_layers_29_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[446] + model_encoder_layers_29_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[447] + model_encoder_layers_29_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[448] + model_encoder_layers_29_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[449] + model_encoder_layers_29_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[450] + model_encoder_layers_29_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[451] + model_encoder_layers_29_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[452] + model_encoder_layers_29_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[453] + model_encoder_layers_29_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[454] + model_encoder_layers_30_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[455] + model_encoder_layers_30_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[456] + model_encoder_layers_30_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[457] + model_encoder_layers_30_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[458] + model_encoder_layers_30_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[459] + model_encoder_layers_30_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[460] + model_encoder_layers_30_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[461] + model_encoder_layers_30_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[462] + model_encoder_layers_30_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[463] + model_encoder_layers_30_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[464] + model_encoder_layers_30_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[465] + model_encoder_layers_30_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[466] + model_encoder_layers_30_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[467] + model_encoder_layers_30_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[468] + model_encoder_layers_30_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[469] + model_encoder_layers_31_self_attn_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[470] + model_encoder_layers_31_self_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[471] + model_encoder_layers_31_self_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[472] + model_encoder_layers_31_self_attn_q_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[473] + model_encoder_layers_31_self_attn_q_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[474] + model_encoder_layers_31_self_attn_out_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[475] + model_encoder_layers_31_self_attn_out_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[476] + model_encoder_layers_31_self_attn_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[477] + model_encoder_layers_31_self_attn_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[478] + model_encoder_layers_31_fc1_weight2: R.Tensor((5120, 1280), dtype="float16") = packed_params[479] + model_encoder_layers_31_fc1_bias2: R.Tensor((5120,), dtype="float16") = packed_params[480] + model_encoder_layers_31_fc2_weight2: R.Tensor((1280, 5120), dtype="float16") = packed_params[481] + model_encoder_layers_31_fc2_bias2: R.Tensor((1280,), dtype="float16") = packed_params[482] + model_encoder_layers_31_final_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[483] + model_encoder_layers_31_final_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[484] + model_encoder_layer_norm_weight2: R.Tensor((1280,), dtype="float16") = packed_params[485] + model_encoder_layer_norm_bias2: R.Tensor((1280,), dtype="float16") = packed_params[486] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[498] + model_decoder_layers_0_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[499] + model_decoder_layers_0_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[500] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[522] + model_decoder_layers_1_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[523] + model_decoder_layers_1_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[524] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[546] + model_decoder_layers_2_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[547] + model_decoder_layers_2_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[548] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[570] + model_decoder_layers_3_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[571] + model_decoder_layers_3_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[572] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[594] + model_decoder_layers_4_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[595] + model_decoder_layers_4_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[596] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[618] + model_decoder_layers_5_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[619] + model_decoder_layers_5_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[620] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[642] + model_decoder_layers_6_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[643] + model_decoder_layers_6_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[644] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[666] + model_decoder_layers_7_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[667] + model_decoder_layers_7_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[668] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[690] + model_decoder_layers_8_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[691] + model_decoder_layers_8_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[692] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[714] + model_decoder_layers_9_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[715] + model_decoder_layers_9_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[716] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[738] + model_decoder_layers_10_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[739] + model_decoder_layers_10_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[740] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[762] + model_decoder_layers_11_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[763] + model_decoder_layers_11_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[764] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[786] + model_decoder_layers_12_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[787] + model_decoder_layers_12_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[788] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[810] + model_decoder_layers_13_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[811] + model_decoder_layers_13_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[812] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[834] + model_decoder_layers_14_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[835] + model_decoder_layers_14_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[836] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[858] + model_decoder_layers_15_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[859] + model_decoder_layers_15_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[860] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[882] + model_decoder_layers_16_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[883] + model_decoder_layers_16_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[884] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[906] + model_decoder_layers_17_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[907] + model_decoder_layers_17_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[908] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[930] + model_decoder_layers_18_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[931] + model_decoder_layers_18_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[932] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[954] + model_decoder_layers_19_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[955] + model_decoder_layers_19_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[956] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[978] + model_decoder_layers_20_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[979] + model_decoder_layers_20_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[980] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1002] + model_decoder_layers_21_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1003] + model_decoder_layers_21_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1004] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1026] + model_decoder_layers_22_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1027] + model_decoder_layers_22_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1028] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1050] + model_decoder_layers_23_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1051] + model_decoder_layers_23_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1052] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1074] + model_decoder_layers_24_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1075] + model_decoder_layers_24_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1076] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1098] + model_decoder_layers_25_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1099] + model_decoder_layers_25_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1100] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1122] + model_decoder_layers_26_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1123] + model_decoder_layers_26_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1124] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1146] + model_decoder_layers_27_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1147] + model_decoder_layers_27_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1148] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1170] + model_decoder_layers_28_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1171] + model_decoder_layers_28_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1172] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1194] + model_decoder_layers_29_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1195] + model_decoder_layers_29_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1196] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1218] + model_decoder_layers_30_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1219] + model_decoder_layers_30_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1220] + 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_k_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1242] + model_decoder_layers_31_encoder_attn_v_proj_weight2: R.Tensor((1280, 1280), dtype="float16") = packed_params[1243] + model_decoder_layers_31_encoder_attn_v_proj_bias2: R.Tensor((1280,), dtype="float16") = packed_params[1244] + 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) + permute_dims257: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_q_proj_weight2, axes=None) + matmul256: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm65, permute_dims257, out_dtype="void") + add258: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul256, model_decoder_layers_0_self_attn_q_proj_bias2) + reshape387: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add258, R.shape([1, seq_len, 20, 64])) + permute_dims258: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_k_proj_weight2, axes=None) + matmul257: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm65, permute_dims258, out_dtype="void") + reshape388: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul257, R.shape([1, seq_len, 20, 64])) + permute_dims259: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_v_proj_weight2, axes=None) + matmul258: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm65, permute_dims259, out_dtype="void") + add259: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul258, model_decoder_layers_0_self_attn_v_proj_bias2) + reshape389: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add259, 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])) + permute_dims260: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_out_proj_weight2, axes=None) + matmul259: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape392, permute_dims260, out_dtype="void") + add260: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul259, model_decoder_layers_0_self_attn_out_proj_bias2) + add261: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add257, add260) + 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) + permute_dims261: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_encoder_attn_q_proj_weight2, axes=None) + matmul260: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm66, permute_dims261, out_dtype="void") + add262: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul260, model_decoder_layers_0_encoder_attn_q_proj_bias2) + reshape393: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add262, 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])) + permute_dims262: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_encoder_attn_out_proj_weight2, axes=None) + matmul261: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape396, permute_dims262, out_dtype="void") + add263: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul261, model_decoder_layers_0_encoder_attn_out_proj_bias2) + add264: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add261, add263) + 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) + permute_dims263: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_0_fc1_weight2, axes=None) + matmul262: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm67, permute_dims263, out_dtype="void") + add265: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul262, model_decoder_layers_0_fc1_bias2) + gelu34: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add265) + permute_dims264: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_fc2_weight2, axes=None) + matmul263: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu34, permute_dims264, out_dtype="void") + add266: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul263, model_decoder_layers_0_fc2_bias2) + add267: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add264, add266) + 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) + permute_dims265: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_q_proj_weight2, axes=None) + matmul264: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm68, permute_dims265, out_dtype="void") + add268: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul264, model_decoder_layers_1_self_attn_q_proj_bias2) + reshape397: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add268, R.shape([1, seq_len, 20, 64])) + permute_dims266: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_k_proj_weight2, axes=None) + matmul265: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm68, permute_dims266, out_dtype="void") + reshape398: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul265, R.shape([1, seq_len, 20, 64])) + permute_dims267: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_v_proj_weight2, axes=None) + matmul266: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm68, permute_dims267, out_dtype="void") + add269: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul266, model_decoder_layers_1_self_attn_v_proj_bias2) + reshape399: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add269, 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])) + permute_dims268: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_out_proj_weight2, axes=None) + matmul267: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape402, permute_dims268, out_dtype="void") + add270: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul267, model_decoder_layers_1_self_attn_out_proj_bias2) + add271: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add267, add270) + 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) + permute_dims269: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_encoder_attn_q_proj_weight2, axes=None) + matmul268: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm69, permute_dims269, out_dtype="void") + add272: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul268, model_decoder_layers_1_encoder_attn_q_proj_bias2) + reshape403: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add272, 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])) + permute_dims270: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_encoder_attn_out_proj_weight2, axes=None) + matmul269: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape406, permute_dims270, out_dtype="void") + add273: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul269, model_decoder_layers_1_encoder_attn_out_proj_bias2) + add274: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add271, add273) + 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) + permute_dims271: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_1_fc1_weight2, axes=None) + matmul270: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm70, permute_dims271, out_dtype="void") + add275: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul270, model_decoder_layers_1_fc1_bias2) + gelu35: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add275) + permute_dims272: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_fc2_weight2, axes=None) + matmul271: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu35, permute_dims272, out_dtype="void") + add276: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul271, model_decoder_layers_1_fc2_bias2) + add277: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add274, add276) + 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) + permute_dims273: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_q_proj_weight2, axes=None) + matmul272: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm71, permute_dims273, out_dtype="void") + add278: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul272, model_decoder_layers_2_self_attn_q_proj_bias2) + reshape407: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add278, R.shape([1, seq_len, 20, 64])) + permute_dims274: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_k_proj_weight2, axes=None) + matmul273: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm71, permute_dims274, out_dtype="void") + reshape408: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul273, R.shape([1, seq_len, 20, 64])) + permute_dims275: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_v_proj_weight2, axes=None) + matmul274: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm71, permute_dims275, out_dtype="void") + add279: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul274, model_decoder_layers_2_self_attn_v_proj_bias2) + reshape409: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add279, 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])) + permute_dims276: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_out_proj_weight2, axes=None) + matmul275: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape412, permute_dims276, out_dtype="void") + add280: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul275, model_decoder_layers_2_self_attn_out_proj_bias2) + add281: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add277, add280) + 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) + permute_dims277: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_encoder_attn_q_proj_weight2, axes=None) + matmul276: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm72, permute_dims277, out_dtype="void") + add282: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul276, model_decoder_layers_2_encoder_attn_q_proj_bias2) + reshape413: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add282, 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])) + permute_dims278: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_encoder_attn_out_proj_weight2, axes=None) + matmul277: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape416, permute_dims278, out_dtype="void") + add283: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul277, model_decoder_layers_2_encoder_attn_out_proj_bias2) + add284: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add281, add283) + 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) + permute_dims279: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_2_fc1_weight2, axes=None) + matmul278: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm73, permute_dims279, out_dtype="void") + add285: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul278, model_decoder_layers_2_fc1_bias2) + gelu36: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add285) + permute_dims280: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_fc2_weight2, axes=None) + matmul279: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu36, permute_dims280, out_dtype="void") + add286: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul279, model_decoder_layers_2_fc2_bias2) + add287: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add284, add286) + 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) + permute_dims281: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_q_proj_weight2, axes=None) + matmul280: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm74, permute_dims281, out_dtype="void") + add288: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul280, model_decoder_layers_3_self_attn_q_proj_bias2) + reshape417: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add288, R.shape([1, seq_len, 20, 64])) + permute_dims282: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_k_proj_weight2, axes=None) + matmul281: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm74, permute_dims282, out_dtype="void") + reshape418: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul281, R.shape([1, seq_len, 20, 64])) + permute_dims283: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_v_proj_weight2, axes=None) + matmul282: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm74, permute_dims283, out_dtype="void") + add289: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul282, model_decoder_layers_3_self_attn_v_proj_bias2) + reshape419: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add289, 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])) + permute_dims284: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_out_proj_weight2, axes=None) + matmul283: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape422, permute_dims284, out_dtype="void") + add290: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul283, model_decoder_layers_3_self_attn_out_proj_bias2) + add291: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add287, add290) + 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) + permute_dims285: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_encoder_attn_q_proj_weight2, axes=None) + matmul284: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm75, permute_dims285, out_dtype="void") + add292: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul284, model_decoder_layers_3_encoder_attn_q_proj_bias2) + reshape423: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add292, 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])) + permute_dims286: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_encoder_attn_out_proj_weight2, axes=None) + matmul285: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape426, permute_dims286, out_dtype="void") + add293: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul285, model_decoder_layers_3_encoder_attn_out_proj_bias2) + add294: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add291, add293) + 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) + permute_dims287: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_3_fc1_weight2, axes=None) + matmul286: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm76, permute_dims287, out_dtype="void") + add295: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul286, model_decoder_layers_3_fc1_bias2) + gelu37: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add295) + permute_dims288: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_fc2_weight2, axes=None) + matmul287: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu37, permute_dims288, out_dtype="void") + add296: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul287, model_decoder_layers_3_fc2_bias2) + add297: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add294, add296) + 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) + permute_dims289: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_q_proj_weight2, axes=None) + matmul288: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm77, permute_dims289, out_dtype="void") + add298: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul288, model_decoder_layers_4_self_attn_q_proj_bias2) + reshape427: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add298, R.shape([1, seq_len, 20, 64])) + permute_dims290: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_k_proj_weight2, axes=None) + matmul289: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm77, permute_dims290, out_dtype="void") + reshape428: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul289, R.shape([1, seq_len, 20, 64])) + permute_dims291: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_v_proj_weight2, axes=None) + matmul290: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm77, permute_dims291, out_dtype="void") + add299: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul290, model_decoder_layers_4_self_attn_v_proj_bias2) + reshape429: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add299, 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])) + permute_dims292: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_out_proj_weight2, axes=None) + matmul291: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape432, permute_dims292, out_dtype="void") + add300: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul291, model_decoder_layers_4_self_attn_out_proj_bias2) + add301: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add297, add300) + 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) + permute_dims293: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_encoder_attn_q_proj_weight2, axes=None) + matmul292: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm78, permute_dims293, out_dtype="void") + add302: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul292, model_decoder_layers_4_encoder_attn_q_proj_bias2) + reshape433: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add302, 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])) + permute_dims294: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_encoder_attn_out_proj_weight2, axes=None) + matmul293: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape436, permute_dims294, out_dtype="void") + add303: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul293, model_decoder_layers_4_encoder_attn_out_proj_bias2) + add304: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add301, add303) + 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) + permute_dims295: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_4_fc1_weight2, axes=None) + matmul294: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm79, permute_dims295, out_dtype="void") + add305: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul294, model_decoder_layers_4_fc1_bias2) + gelu38: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add305) + permute_dims296: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_fc2_weight2, axes=None) + matmul295: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu38, permute_dims296, out_dtype="void") + add306: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul295, model_decoder_layers_4_fc2_bias2) + add307: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add304, add306) + 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) + permute_dims297: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_q_proj_weight2, axes=None) + matmul296: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm80, permute_dims297, out_dtype="void") + add308: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul296, model_decoder_layers_5_self_attn_q_proj_bias2) + reshape437: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add308, R.shape([1, seq_len, 20, 64])) + permute_dims298: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_k_proj_weight2, axes=None) + matmul297: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm80, permute_dims298, out_dtype="void") + reshape438: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul297, R.shape([1, seq_len, 20, 64])) + permute_dims299: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_v_proj_weight2, axes=None) + matmul298: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm80, permute_dims299, out_dtype="void") + add309: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul298, model_decoder_layers_5_self_attn_v_proj_bias2) + reshape439: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add309, 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])) + permute_dims300: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_out_proj_weight2, axes=None) + matmul299: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape442, permute_dims300, out_dtype="void") + add310: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul299, model_decoder_layers_5_self_attn_out_proj_bias2) + add311: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add307, add310) + 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) + permute_dims301: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_encoder_attn_q_proj_weight2, axes=None) + matmul300: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm81, permute_dims301, out_dtype="void") + add312: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul300, model_decoder_layers_5_encoder_attn_q_proj_bias2) + reshape443: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add312, 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])) + permute_dims302: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_encoder_attn_out_proj_weight2, axes=None) + matmul301: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape446, permute_dims302, out_dtype="void") + add313: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul301, model_decoder_layers_5_encoder_attn_out_proj_bias2) + add314: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add311, add313) + 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) + permute_dims303: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_5_fc1_weight2, axes=None) + matmul302: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm82, permute_dims303, out_dtype="void") + add315: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul302, model_decoder_layers_5_fc1_bias2) + gelu39: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add315) + permute_dims304: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_fc2_weight2, axes=None) + matmul303: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu39, permute_dims304, out_dtype="void") + add316: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul303, model_decoder_layers_5_fc2_bias2) + add317: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add314, add316) + 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) + permute_dims305: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_q_proj_weight2, axes=None) + matmul304: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm83, permute_dims305, out_dtype="void") + add318: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul304, model_decoder_layers_6_self_attn_q_proj_bias2) + reshape447: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add318, R.shape([1, seq_len, 20, 64])) + permute_dims306: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_k_proj_weight2, axes=None) + matmul305: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm83, permute_dims306, out_dtype="void") + reshape448: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul305, R.shape([1, seq_len, 20, 64])) + permute_dims307: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_v_proj_weight2, axes=None) + matmul306: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm83, permute_dims307, out_dtype="void") + add319: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul306, model_decoder_layers_6_self_attn_v_proj_bias2) + reshape449: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add319, 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])) + permute_dims308: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_out_proj_weight2, axes=None) + matmul307: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape452, permute_dims308, out_dtype="void") + add320: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul307, model_decoder_layers_6_self_attn_out_proj_bias2) + add321: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add317, add320) + 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) + permute_dims309: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_encoder_attn_q_proj_weight2, axes=None) + matmul308: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm84, permute_dims309, out_dtype="void") + add322: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul308, model_decoder_layers_6_encoder_attn_q_proj_bias2) + reshape453: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add322, 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])) + permute_dims310: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_encoder_attn_out_proj_weight2, axes=None) + matmul309: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape456, permute_dims310, out_dtype="void") + add323: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul309, model_decoder_layers_6_encoder_attn_out_proj_bias2) + add324: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add321, add323) + 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) + permute_dims311: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_6_fc1_weight2, axes=None) + matmul310: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm85, permute_dims311, out_dtype="void") + add325: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul310, model_decoder_layers_6_fc1_bias2) + gelu40: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add325) + permute_dims312: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_fc2_weight2, axes=None) + matmul311: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu40, permute_dims312, out_dtype="void") + add326: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul311, model_decoder_layers_6_fc2_bias2) + add327: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add324, add326) + 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) + permute_dims313: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_q_proj_weight2, axes=None) + matmul312: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm86, permute_dims313, out_dtype="void") + add328: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul312, model_decoder_layers_7_self_attn_q_proj_bias2) + reshape457: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add328, R.shape([1, seq_len, 20, 64])) + permute_dims314: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_k_proj_weight2, axes=None) + matmul313: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm86, permute_dims314, out_dtype="void") + reshape458: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul313, R.shape([1, seq_len, 20, 64])) + permute_dims315: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_v_proj_weight2, axes=None) + matmul314: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm86, permute_dims315, out_dtype="void") + add329: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul314, model_decoder_layers_7_self_attn_v_proj_bias2) + reshape459: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add329, 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])) + permute_dims316: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_out_proj_weight2, axes=None) + matmul315: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape462, permute_dims316, out_dtype="void") + add330: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul315, model_decoder_layers_7_self_attn_out_proj_bias2) + add331: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add327, add330) + 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) + permute_dims317: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_encoder_attn_q_proj_weight2, axes=None) + matmul316: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm87, permute_dims317, out_dtype="void") + add332: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul316, model_decoder_layers_7_encoder_attn_q_proj_bias2) + reshape463: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add332, 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])) + permute_dims318: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_encoder_attn_out_proj_weight2, axes=None) + matmul317: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape466, permute_dims318, out_dtype="void") + add333: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul317, model_decoder_layers_7_encoder_attn_out_proj_bias2) + add334: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add331, add333) + 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) + permute_dims319: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_7_fc1_weight2, axes=None) + matmul318: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm88, permute_dims319, out_dtype="void") + add335: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul318, model_decoder_layers_7_fc1_bias2) + gelu41: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add335) + permute_dims320: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_fc2_weight2, axes=None) + matmul319: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu41, permute_dims320, out_dtype="void") + add336: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul319, model_decoder_layers_7_fc2_bias2) + add337: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add334, add336) + 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) + permute_dims321: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_q_proj_weight2, axes=None) + matmul320: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm89, permute_dims321, out_dtype="void") + add338: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul320, model_decoder_layers_8_self_attn_q_proj_bias2) + reshape467: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add338, R.shape([1, seq_len, 20, 64])) + permute_dims322: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_k_proj_weight2, axes=None) + matmul321: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm89, permute_dims322, out_dtype="void") + reshape468: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul321, R.shape([1, seq_len, 20, 64])) + permute_dims323: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_v_proj_weight2, axes=None) + matmul322: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm89, permute_dims323, out_dtype="void") + add339: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul322, model_decoder_layers_8_self_attn_v_proj_bias2) + reshape469: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add339, 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])) + permute_dims324: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_out_proj_weight2, axes=None) + matmul323: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape472, permute_dims324, out_dtype="void") + add340: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul323, model_decoder_layers_8_self_attn_out_proj_bias2) + add341: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add337, add340) + 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) + permute_dims325: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_encoder_attn_q_proj_weight2, axes=None) + matmul324: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm90, permute_dims325, out_dtype="void") + add342: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul324, model_decoder_layers_8_encoder_attn_q_proj_bias2) + reshape473: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add342, 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])) + permute_dims326: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_encoder_attn_out_proj_weight2, axes=None) + matmul325: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape476, permute_dims326, out_dtype="void") + add343: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul325, model_decoder_layers_8_encoder_attn_out_proj_bias2) + add344: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add341, add343) + 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) + permute_dims327: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_8_fc1_weight2, axes=None) + matmul326: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm91, permute_dims327, out_dtype="void") + add345: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul326, model_decoder_layers_8_fc1_bias2) + gelu42: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add345) + permute_dims328: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_fc2_weight2, axes=None) + matmul327: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu42, permute_dims328, out_dtype="void") + add346: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul327, model_decoder_layers_8_fc2_bias2) + add347: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add344, add346) + 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) + permute_dims329: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_q_proj_weight2, axes=None) + matmul328: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm92, permute_dims329, out_dtype="void") + add348: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul328, model_decoder_layers_9_self_attn_q_proj_bias2) + reshape477: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add348, R.shape([1, seq_len, 20, 64])) + permute_dims330: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_k_proj_weight2, axes=None) + matmul329: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm92, permute_dims330, out_dtype="void") + reshape478: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul329, R.shape([1, seq_len, 20, 64])) + permute_dims331: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_v_proj_weight2, axes=None) + matmul330: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm92, permute_dims331, out_dtype="void") + add349: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul330, model_decoder_layers_9_self_attn_v_proj_bias2) + reshape479: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add349, 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])) + permute_dims332: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_out_proj_weight2, axes=None) + matmul331: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape482, permute_dims332, out_dtype="void") + add350: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul331, model_decoder_layers_9_self_attn_out_proj_bias2) + add351: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add347, add350) + 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) + permute_dims333: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_encoder_attn_q_proj_weight2, axes=None) + matmul332: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm93, permute_dims333, out_dtype="void") + add352: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul332, model_decoder_layers_9_encoder_attn_q_proj_bias2) + reshape483: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add352, 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])) + permute_dims334: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_encoder_attn_out_proj_weight2, axes=None) + matmul333: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape486, permute_dims334, out_dtype="void") + add353: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul333, model_decoder_layers_9_encoder_attn_out_proj_bias2) + add354: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add351, add353) + 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) + permute_dims335: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_9_fc1_weight2, axes=None) + matmul334: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm94, permute_dims335, out_dtype="void") + add355: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul334, model_decoder_layers_9_fc1_bias2) + gelu43: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add355) + permute_dims336: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_fc2_weight2, axes=None) + matmul335: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu43, permute_dims336, out_dtype="void") + add356: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul335, model_decoder_layers_9_fc2_bias2) + add357: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add354, add356) + 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) + permute_dims337: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_q_proj_weight2, axes=None) + matmul336: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm95, permute_dims337, out_dtype="void") + add358: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul336, model_decoder_layers_10_self_attn_q_proj_bias2) + reshape487: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add358, R.shape([1, seq_len, 20, 64])) + permute_dims338: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_k_proj_weight2, axes=None) + matmul337: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm95, permute_dims338, out_dtype="void") + reshape488: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul337, R.shape([1, seq_len, 20, 64])) + permute_dims339: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_v_proj_weight2, axes=None) + matmul338: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm95, permute_dims339, out_dtype="void") + add359: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul338, model_decoder_layers_10_self_attn_v_proj_bias2) + reshape489: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add359, 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])) + permute_dims340: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_out_proj_weight2, axes=None) + matmul339: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape492, permute_dims340, out_dtype="void") + add360: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul339, model_decoder_layers_10_self_attn_out_proj_bias2) + add361: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add357, add360) + 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) + permute_dims341: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_encoder_attn_q_proj_weight2, axes=None) + matmul340: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm96, permute_dims341, out_dtype="void") + add362: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul340, model_decoder_layers_10_encoder_attn_q_proj_bias2) + reshape493: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add362, 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])) + permute_dims342: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_encoder_attn_out_proj_weight2, axes=None) + matmul341: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape496, permute_dims342, out_dtype="void") + add363: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul341, model_decoder_layers_10_encoder_attn_out_proj_bias2) + add364: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add361, add363) + 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) + permute_dims343: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_10_fc1_weight2, axes=None) + matmul342: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm97, permute_dims343, out_dtype="void") + add365: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul342, model_decoder_layers_10_fc1_bias2) + gelu44: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add365) + permute_dims344: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_fc2_weight2, axes=None) + matmul343: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu44, permute_dims344, out_dtype="void") + add366: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul343, model_decoder_layers_10_fc2_bias2) + add367: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add364, add366) + 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) + permute_dims345: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_q_proj_weight2, axes=None) + matmul344: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm98, permute_dims345, out_dtype="void") + add368: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul344, model_decoder_layers_11_self_attn_q_proj_bias2) + reshape497: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add368, R.shape([1, seq_len, 20, 64])) + permute_dims346: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_k_proj_weight2, axes=None) + matmul345: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm98, permute_dims346, out_dtype="void") + reshape498: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul345, R.shape([1, seq_len, 20, 64])) + permute_dims347: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_v_proj_weight2, axes=None) + matmul346: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm98, permute_dims347, out_dtype="void") + add369: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul346, model_decoder_layers_11_self_attn_v_proj_bias2) + reshape499: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add369, 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])) + permute_dims348: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_out_proj_weight2, axes=None) + matmul347: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape502, permute_dims348, out_dtype="void") + add370: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul347, model_decoder_layers_11_self_attn_out_proj_bias2) + add371: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add367, add370) + 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) + permute_dims349: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_encoder_attn_q_proj_weight2, axes=None) + matmul348: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm99, permute_dims349, out_dtype="void") + add372: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul348, model_decoder_layers_11_encoder_attn_q_proj_bias2) + reshape503: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add372, 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])) + permute_dims350: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_encoder_attn_out_proj_weight2, axes=None) + matmul349: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape506, permute_dims350, out_dtype="void") + add373: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul349, model_decoder_layers_11_encoder_attn_out_proj_bias2) + add374: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add371, add373) + 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) + permute_dims351: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_11_fc1_weight2, axes=None) + matmul350: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm100, permute_dims351, out_dtype="void") + add375: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul350, model_decoder_layers_11_fc1_bias2) + gelu45: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add375) + permute_dims352: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_fc2_weight2, axes=None) + matmul351: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu45, permute_dims352, out_dtype="void") + add376: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul351, model_decoder_layers_11_fc2_bias2) + add377: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add374, add376) + 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) + permute_dims353: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_q_proj_weight2, axes=None) + matmul352: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm101, permute_dims353, out_dtype="void") + add378: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul352, model_decoder_layers_12_self_attn_q_proj_bias2) + reshape507: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add378, R.shape([1, seq_len, 20, 64])) + permute_dims354: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_k_proj_weight2, axes=None) + matmul353: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm101, permute_dims354, out_dtype="void") + reshape508: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul353, R.shape([1, seq_len, 20, 64])) + permute_dims355: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_v_proj_weight2, axes=None) + matmul354: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm101, permute_dims355, out_dtype="void") + add379: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul354, model_decoder_layers_12_self_attn_v_proj_bias2) + reshape509: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add379, 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])) + permute_dims356: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_out_proj_weight2, axes=None) + matmul355: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape512, permute_dims356, out_dtype="void") + add380: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul355, model_decoder_layers_12_self_attn_out_proj_bias2) + add381: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add377, add380) + 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) + permute_dims357: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_encoder_attn_q_proj_weight2, axes=None) + matmul356: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm102, permute_dims357, out_dtype="void") + add382: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul356, model_decoder_layers_12_encoder_attn_q_proj_bias2) + reshape513: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add382, 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])) + permute_dims358: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_encoder_attn_out_proj_weight2, axes=None) + matmul357: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape516, permute_dims358, out_dtype="void") + add383: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul357, model_decoder_layers_12_encoder_attn_out_proj_bias2) + add384: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add381, add383) + 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) + permute_dims359: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_12_fc1_weight2, axes=None) + matmul358: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm103, permute_dims359, out_dtype="void") + add385: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul358, model_decoder_layers_12_fc1_bias2) + gelu46: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add385) + permute_dims360: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_fc2_weight2, axes=None) + matmul359: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu46, permute_dims360, out_dtype="void") + add386: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul359, model_decoder_layers_12_fc2_bias2) + add387: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add384, add386) + 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) + permute_dims361: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_q_proj_weight2, axes=None) + matmul360: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm104, permute_dims361, out_dtype="void") + add388: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul360, model_decoder_layers_13_self_attn_q_proj_bias2) + reshape517: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add388, R.shape([1, seq_len, 20, 64])) + permute_dims362: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_k_proj_weight2, axes=None) + matmul361: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm104, permute_dims362, out_dtype="void") + reshape518: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul361, R.shape([1, seq_len, 20, 64])) + permute_dims363: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_v_proj_weight2, axes=None) + matmul362: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm104, permute_dims363, out_dtype="void") + add389: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul362, model_decoder_layers_13_self_attn_v_proj_bias2) + reshape519: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add389, 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])) + permute_dims364: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_out_proj_weight2, axes=None) + matmul363: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape522, permute_dims364, out_dtype="void") + add390: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul363, model_decoder_layers_13_self_attn_out_proj_bias2) + add391: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add387, add390) + 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) + permute_dims365: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_encoder_attn_q_proj_weight2, axes=None) + matmul364: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm105, permute_dims365, out_dtype="void") + add392: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul364, model_decoder_layers_13_encoder_attn_q_proj_bias2) + reshape523: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add392, 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])) + permute_dims366: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_encoder_attn_out_proj_weight2, axes=None) + matmul365: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape526, permute_dims366, out_dtype="void") + add393: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul365, model_decoder_layers_13_encoder_attn_out_proj_bias2) + add394: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add391, add393) + 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) + permute_dims367: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_13_fc1_weight2, axes=None) + matmul366: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm106, permute_dims367, out_dtype="void") + add395: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul366, model_decoder_layers_13_fc1_bias2) + gelu47: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add395) + permute_dims368: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_fc2_weight2, axes=None) + matmul367: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu47, permute_dims368, out_dtype="void") + add396: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul367, model_decoder_layers_13_fc2_bias2) + add397: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add394, add396) + 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) + permute_dims369: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_q_proj_weight2, axes=None) + matmul368: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm107, permute_dims369, out_dtype="void") + add398: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul368, model_decoder_layers_14_self_attn_q_proj_bias2) + reshape527: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add398, R.shape([1, seq_len, 20, 64])) + permute_dims370: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_k_proj_weight2, axes=None) + matmul369: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm107, permute_dims370, out_dtype="void") + reshape528: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul369, R.shape([1, seq_len, 20, 64])) + permute_dims371: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_v_proj_weight2, axes=None) + matmul370: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm107, permute_dims371, out_dtype="void") + add399: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul370, model_decoder_layers_14_self_attn_v_proj_bias2) + reshape529: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add399, 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])) + permute_dims372: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_out_proj_weight2, axes=None) + matmul371: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape532, permute_dims372, out_dtype="void") + add400: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul371, model_decoder_layers_14_self_attn_out_proj_bias2) + add401: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add397, add400) + 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) + permute_dims373: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_encoder_attn_q_proj_weight2, axes=None) + matmul372: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm108, permute_dims373, out_dtype="void") + add402: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul372, model_decoder_layers_14_encoder_attn_q_proj_bias2) + reshape533: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add402, 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 = 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, 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])) + permute_dims374: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_encoder_attn_out_proj_weight2, axes=None) + matmul373: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape536, permute_dims374, out_dtype="void") + add403: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul373, model_decoder_layers_14_encoder_attn_out_proj_bias2) + add404: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add401, add403) + 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) + permute_dims375: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_14_fc1_weight2, axes=None) + matmul374: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm109, permute_dims375, out_dtype="void") + add405: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul374, model_decoder_layers_14_fc1_bias2) + gelu48: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add405) + permute_dims376: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_fc2_weight2, axes=None) + matmul375: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu48, permute_dims376, out_dtype="void") + add406: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul375, model_decoder_layers_14_fc2_bias2) + add407: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add404, add406) + 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) + permute_dims377: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_q_proj_weight2, axes=None) + matmul376: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm110, permute_dims377, out_dtype="void") + add408: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul376, model_decoder_layers_15_self_attn_q_proj_bias2) + reshape537: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add408, R.shape([1, seq_len, 20, 64])) + permute_dims378: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_k_proj_weight2, axes=None) + matmul377: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm110, permute_dims378, out_dtype="void") + reshape538: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul377, R.shape([1, seq_len, 20, 64])) + permute_dims379: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_v_proj_weight2, axes=None) + matmul378: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm110, permute_dims379, out_dtype="void") + add409: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul378, model_decoder_layers_15_self_attn_v_proj_bias2) + reshape539: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add409, 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 = 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, 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])) + permute_dims380: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_out_proj_weight2, axes=None) + matmul379: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape542, permute_dims380, out_dtype="void") + add410: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul379, model_decoder_layers_15_self_attn_out_proj_bias2) + add411: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add407, add410) + 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) + permute_dims381: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_encoder_attn_q_proj_weight2, axes=None) + matmul380: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm111, permute_dims381, out_dtype="void") + add412: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul380, model_decoder_layers_15_encoder_attn_q_proj_bias2) + reshape543: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add412, 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 = 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, 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])) + permute_dims382: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_encoder_attn_out_proj_weight2, axes=None) + matmul381: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape546, permute_dims382, out_dtype="void") + add413: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul381, model_decoder_layers_15_encoder_attn_out_proj_bias2) + add414: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add411, add413) + 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) + permute_dims383: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_15_fc1_weight2, axes=None) + matmul382: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm112, permute_dims383, out_dtype="void") + add415: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul382, model_decoder_layers_15_fc1_bias2) + gelu49: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add415) + permute_dims384: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_fc2_weight2, axes=None) + matmul383: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu49, permute_dims384, out_dtype="void") + add416: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul383, model_decoder_layers_15_fc2_bias2) + add417: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add414, add416) + 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) + permute_dims385: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_q_proj_weight2, axes=None) + matmul384: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm113, permute_dims385, out_dtype="void") + add418: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul384, model_decoder_layers_16_self_attn_q_proj_bias2) + reshape547: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add418, R.shape([1, seq_len, 20, 64])) + permute_dims386: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_k_proj_weight2, axes=None) + matmul385: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm113, permute_dims386, out_dtype="void") + reshape548: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul385, R.shape([1, seq_len, 20, 64])) + permute_dims387: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_v_proj_weight2, axes=None) + matmul386: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm113, permute_dims387, out_dtype="void") + add419: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul386, model_decoder_layers_16_self_attn_v_proj_bias2) + reshape549: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add419, 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 = 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, 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])) + permute_dims388: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_out_proj_weight2, axes=None) + matmul387: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape552, permute_dims388, out_dtype="void") + add420: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul387, model_decoder_layers_16_self_attn_out_proj_bias2) + add421: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add417, add420) + 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) + permute_dims389: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_encoder_attn_q_proj_weight2, axes=None) + matmul388: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm114, permute_dims389, out_dtype="void") + add422: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul388, model_decoder_layers_16_encoder_attn_q_proj_bias2) + reshape553: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add422, 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 = 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, 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])) + permute_dims390: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_encoder_attn_out_proj_weight2, axes=None) + matmul389: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape556, permute_dims390, out_dtype="void") + add423: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul389, model_decoder_layers_16_encoder_attn_out_proj_bias2) + add424: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add421, add423) + 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) + permute_dims391: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_16_fc1_weight2, axes=None) + matmul390: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm115, permute_dims391, out_dtype="void") + add425: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul390, model_decoder_layers_16_fc1_bias2) + gelu50: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add425) + permute_dims392: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_fc2_weight2, axes=None) + matmul391: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu50, permute_dims392, out_dtype="void") + add426: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul391, model_decoder_layers_16_fc2_bias2) + add427: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add424, add426) + 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) + permute_dims393: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_q_proj_weight2, axes=None) + matmul392: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm116, permute_dims393, out_dtype="void") + add428: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul392, model_decoder_layers_17_self_attn_q_proj_bias2) + reshape557: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add428, R.shape([1, seq_len, 20, 64])) + permute_dims394: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_k_proj_weight2, axes=None) + matmul393: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm116, permute_dims394, out_dtype="void") + reshape558: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul393, R.shape([1, seq_len, 20, 64])) + permute_dims395: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_v_proj_weight2, axes=None) + matmul394: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm116, permute_dims395, out_dtype="void") + add429: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul394, model_decoder_layers_17_self_attn_v_proj_bias2) + reshape559: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add429, 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 = 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, 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])) + permute_dims396: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_out_proj_weight2, axes=None) + matmul395: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape562, permute_dims396, out_dtype="void") + add430: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul395, model_decoder_layers_17_self_attn_out_proj_bias2) + add431: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add427, add430) + 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) + permute_dims397: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_encoder_attn_q_proj_weight2, axes=None) + matmul396: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm117, permute_dims397, out_dtype="void") + add432: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul396, model_decoder_layers_17_encoder_attn_q_proj_bias2) + reshape563: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add432, 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 = 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, 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])) + permute_dims398: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_encoder_attn_out_proj_weight2, axes=None) + matmul397: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape566, permute_dims398, out_dtype="void") + add433: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul397, model_decoder_layers_17_encoder_attn_out_proj_bias2) + add434: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add431, add433) + 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) + permute_dims399: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_17_fc1_weight2, axes=None) + matmul398: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm118, permute_dims399, out_dtype="void") + add435: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul398, model_decoder_layers_17_fc1_bias2) + gelu51: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add435) + permute_dims400: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_fc2_weight2, axes=None) + matmul399: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu51, permute_dims400, out_dtype="void") + add436: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul399, model_decoder_layers_17_fc2_bias2) + add437: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add434, add436) + 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) + permute_dims401: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_q_proj_weight2, axes=None) + matmul400: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm119, permute_dims401, out_dtype="void") + add438: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul400, model_decoder_layers_18_self_attn_q_proj_bias2) + reshape567: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add438, R.shape([1, seq_len, 20, 64])) + permute_dims402: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_k_proj_weight2, axes=None) + matmul401: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm119, permute_dims402, out_dtype="void") + reshape568: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul401, R.shape([1, seq_len, 20, 64])) + permute_dims403: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_v_proj_weight2, axes=None) + matmul402: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm119, permute_dims403, out_dtype="void") + add439: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul402, model_decoder_layers_18_self_attn_v_proj_bias2) + reshape569: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add439, 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 = 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, 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])) + permute_dims404: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_out_proj_weight2, axes=None) + matmul403: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape572, permute_dims404, out_dtype="void") + add440: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul403, model_decoder_layers_18_self_attn_out_proj_bias2) + add441: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add437, add440) + 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) + permute_dims405: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_encoder_attn_q_proj_weight2, axes=None) + matmul404: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm120, permute_dims405, out_dtype="void") + add442: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul404, model_decoder_layers_18_encoder_attn_q_proj_bias2) + reshape573: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add442, 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 = 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, 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])) + permute_dims406: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_encoder_attn_out_proj_weight2, axes=None) + matmul405: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape576, permute_dims406, out_dtype="void") + add443: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul405, model_decoder_layers_18_encoder_attn_out_proj_bias2) + add444: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add441, add443) + 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) + permute_dims407: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_18_fc1_weight2, axes=None) + matmul406: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm121, permute_dims407, out_dtype="void") + add445: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul406, model_decoder_layers_18_fc1_bias2) + gelu52: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add445) + permute_dims408: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_fc2_weight2, axes=None) + matmul407: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu52, permute_dims408, out_dtype="void") + add446: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul407, model_decoder_layers_18_fc2_bias2) + add447: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add444, add446) + 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) + permute_dims409: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_q_proj_weight2, axes=None) + matmul408: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm122, permute_dims409, out_dtype="void") + add448: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul408, model_decoder_layers_19_self_attn_q_proj_bias2) + reshape577: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add448, R.shape([1, seq_len, 20, 64])) + permute_dims410: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_k_proj_weight2, axes=None) + matmul409: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm122, permute_dims410, out_dtype="void") + reshape578: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul409, R.shape([1, seq_len, 20, 64])) + permute_dims411: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_v_proj_weight2, axes=None) + matmul410: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm122, permute_dims411, out_dtype="void") + add449: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul410, model_decoder_layers_19_self_attn_v_proj_bias2) + reshape579: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add449, 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 = 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, 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])) + permute_dims412: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_out_proj_weight2, axes=None) + matmul411: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape582, permute_dims412, out_dtype="void") + add450: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul411, model_decoder_layers_19_self_attn_out_proj_bias2) + add451: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add447, add450) + 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) + permute_dims413: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_encoder_attn_q_proj_weight2, axes=None) + matmul412: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm123, permute_dims413, out_dtype="void") + add452: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul412, model_decoder_layers_19_encoder_attn_q_proj_bias2) + reshape583: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add452, 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 = 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, 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])) + permute_dims414: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_encoder_attn_out_proj_weight2, axes=None) + matmul413: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape586, permute_dims414, out_dtype="void") + add453: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul413, model_decoder_layers_19_encoder_attn_out_proj_bias2) + add454: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add451, add453) + 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) + permute_dims415: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_19_fc1_weight2, axes=None) + matmul414: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm124, permute_dims415, out_dtype="void") + add455: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul414, model_decoder_layers_19_fc1_bias2) + gelu53: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add455) + permute_dims416: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_fc2_weight2, axes=None) + matmul415: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu53, permute_dims416, out_dtype="void") + add456: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul415, model_decoder_layers_19_fc2_bias2) + add457: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add454, add456) + 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) + permute_dims417: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_q_proj_weight2, axes=None) + matmul416: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm125, permute_dims417, out_dtype="void") + add458: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul416, model_decoder_layers_20_self_attn_q_proj_bias2) + reshape587: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add458, R.shape([1, seq_len, 20, 64])) + permute_dims418: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_k_proj_weight2, axes=None) + matmul417: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm125, permute_dims418, out_dtype="void") + reshape588: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul417, R.shape([1, seq_len, 20, 64])) + permute_dims419: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_v_proj_weight2, axes=None) + matmul418: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm125, permute_dims419, out_dtype="void") + add459: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul418, model_decoder_layers_20_self_attn_v_proj_bias2) + reshape589: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add459, 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 = 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, 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])) + permute_dims420: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_out_proj_weight2, axes=None) + matmul419: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape592, permute_dims420, out_dtype="void") + add460: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul419, model_decoder_layers_20_self_attn_out_proj_bias2) + add461: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add457, add460) + 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) + permute_dims421: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_encoder_attn_q_proj_weight2, axes=None) + matmul420: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm126, permute_dims421, out_dtype="void") + add462: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul420, model_decoder_layers_20_encoder_attn_q_proj_bias2) + reshape593: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add462, 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 = 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, 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])) + permute_dims422: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_encoder_attn_out_proj_weight2, axes=None) + matmul421: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape596, permute_dims422, out_dtype="void") + add463: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul421, model_decoder_layers_20_encoder_attn_out_proj_bias2) + add464: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add461, add463) + 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) + permute_dims423: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_20_fc1_weight2, axes=None) + matmul422: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm127, permute_dims423, out_dtype="void") + add465: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul422, model_decoder_layers_20_fc1_bias2) + gelu54: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add465) + permute_dims424: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_fc2_weight2, axes=None) + matmul423: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu54, permute_dims424, out_dtype="void") + add466: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul423, model_decoder_layers_20_fc2_bias2) + add467: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add464, add466) + 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) + permute_dims425: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_q_proj_weight2, axes=None) + matmul424: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm128, permute_dims425, out_dtype="void") + add468: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul424, model_decoder_layers_21_self_attn_q_proj_bias2) + reshape597: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add468, R.shape([1, seq_len, 20, 64])) + permute_dims426: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_k_proj_weight2, axes=None) + matmul425: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm128, permute_dims426, out_dtype="void") + reshape598: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul425, R.shape([1, seq_len, 20, 64])) + permute_dims427: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_v_proj_weight2, axes=None) + matmul426: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm128, permute_dims427, out_dtype="void") + add469: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul426, model_decoder_layers_21_self_attn_v_proj_bias2) + reshape599: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add469, 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 = 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, 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])) + permute_dims428: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_out_proj_weight2, axes=None) + matmul427: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape602, permute_dims428, out_dtype="void") + add470: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul427, model_decoder_layers_21_self_attn_out_proj_bias2) + add471: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add467, add470) + 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) + permute_dims429: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_encoder_attn_q_proj_weight2, axes=None) + matmul428: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm129, permute_dims429, out_dtype="void") + add472: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul428, model_decoder_layers_21_encoder_attn_q_proj_bias2) + reshape603: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add472, 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 = 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, 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])) + permute_dims430: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_encoder_attn_out_proj_weight2, axes=None) + matmul429: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape606, permute_dims430, out_dtype="void") + add473: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul429, model_decoder_layers_21_encoder_attn_out_proj_bias2) + add474: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add471, add473) + 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) + permute_dims431: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_21_fc1_weight2, axes=None) + matmul430: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm130, permute_dims431, out_dtype="void") + add475: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul430, model_decoder_layers_21_fc1_bias2) + gelu55: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add475) + permute_dims432: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_fc2_weight2, axes=None) + matmul431: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu55, permute_dims432, out_dtype="void") + add476: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul431, model_decoder_layers_21_fc2_bias2) + add477: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add474, add476) + 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) + permute_dims433: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_q_proj_weight2, axes=None) + matmul432: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm131, permute_dims433, out_dtype="void") + add478: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul432, model_decoder_layers_22_self_attn_q_proj_bias2) + reshape607: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add478, R.shape([1, seq_len, 20, 64])) + permute_dims434: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_k_proj_weight2, axes=None) + matmul433: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm131, permute_dims434, out_dtype="void") + reshape608: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul433, R.shape([1, seq_len, 20, 64])) + permute_dims435: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_v_proj_weight2, axes=None) + matmul434: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm131, permute_dims435, out_dtype="void") + add479: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul434, model_decoder_layers_22_self_attn_v_proj_bias2) + reshape609: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add479, 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 = 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, 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])) + permute_dims436: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_out_proj_weight2, axes=None) + matmul435: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape612, permute_dims436, out_dtype="void") + add480: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul435, model_decoder_layers_22_self_attn_out_proj_bias2) + add481: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add477, add480) + 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) + permute_dims437: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_encoder_attn_q_proj_weight2, axes=None) + matmul436: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm132, permute_dims437, out_dtype="void") + add482: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul436, model_decoder_layers_22_encoder_attn_q_proj_bias2) + reshape613: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add482, 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 = 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, 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])) + permute_dims438: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_encoder_attn_out_proj_weight2, axes=None) + matmul437: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape616, permute_dims438, out_dtype="void") + add483: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul437, model_decoder_layers_22_encoder_attn_out_proj_bias2) + add484: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add481, add483) + 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) + permute_dims439: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_22_fc1_weight2, axes=None) + matmul438: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm133, permute_dims439, out_dtype="void") + add485: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul438, model_decoder_layers_22_fc1_bias2) + gelu56: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add485) + permute_dims440: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_fc2_weight2, axes=None) + matmul439: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu56, permute_dims440, out_dtype="void") + add486: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul439, model_decoder_layers_22_fc2_bias2) + add487: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add484, add486) + 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) + permute_dims441: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_q_proj_weight2, axes=None) + matmul440: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm134, permute_dims441, out_dtype="void") + add488: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul440, model_decoder_layers_23_self_attn_q_proj_bias2) + reshape617: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add488, R.shape([1, seq_len, 20, 64])) + permute_dims442: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_k_proj_weight2, axes=None) + matmul441: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm134, permute_dims442, out_dtype="void") + reshape618: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul441, R.shape([1, seq_len, 20, 64])) + permute_dims443: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_v_proj_weight2, axes=None) + matmul442: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm134, permute_dims443, out_dtype="void") + add489: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul442, model_decoder_layers_23_self_attn_v_proj_bias2) + reshape619: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add489, 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 = 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, 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])) + permute_dims444: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_out_proj_weight2, axes=None) + matmul443: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape622, permute_dims444, out_dtype="void") + add490: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul443, model_decoder_layers_23_self_attn_out_proj_bias2) + add491: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add487, add490) + 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) + permute_dims445: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_encoder_attn_q_proj_weight2, axes=None) + matmul444: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm135, permute_dims445, out_dtype="void") + add492: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul444, model_decoder_layers_23_encoder_attn_q_proj_bias2) + reshape623: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add492, 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 = 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, 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])) + permute_dims446: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_encoder_attn_out_proj_weight2, axes=None) + matmul445: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape626, permute_dims446, out_dtype="void") + add493: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul445, model_decoder_layers_23_encoder_attn_out_proj_bias2) + add494: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add491, add493) + 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) + permute_dims447: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_23_fc1_weight2, axes=None) + matmul446: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm136, permute_dims447, out_dtype="void") + add495: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul446, model_decoder_layers_23_fc1_bias2) + gelu57: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add495) + permute_dims448: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_fc2_weight2, axes=None) + matmul447: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu57, permute_dims448, out_dtype="void") + add496: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul447, model_decoder_layers_23_fc2_bias2) + add497: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add494, add496) + 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) + permute_dims449: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_q_proj_weight2, axes=None) + matmul448: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm137, permute_dims449, out_dtype="void") + add498: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul448, model_decoder_layers_24_self_attn_q_proj_bias2) + reshape627: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add498, R.shape([1, seq_len, 20, 64])) + permute_dims450: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_k_proj_weight2, axes=None) + matmul449: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm137, permute_dims450, out_dtype="void") + reshape628: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul449, R.shape([1, seq_len, 20, 64])) + permute_dims451: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_v_proj_weight2, axes=None) + matmul450: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm137, permute_dims451, out_dtype="void") + add499: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul450, model_decoder_layers_24_self_attn_v_proj_bias2) + reshape629: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add499, 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 = 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, 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])) + permute_dims452: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_out_proj_weight2, axes=None) + matmul451: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape632, permute_dims452, out_dtype="void") + add500: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul451, model_decoder_layers_24_self_attn_out_proj_bias2) + add501: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add497, add500) + 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) + permute_dims453: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_encoder_attn_q_proj_weight2, axes=None) + matmul452: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm138, permute_dims453, out_dtype="void") + add502: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul452, model_decoder_layers_24_encoder_attn_q_proj_bias2) + reshape633: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add502, 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 = 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, 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])) + permute_dims454: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_encoder_attn_out_proj_weight2, axes=None) + matmul453: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape636, permute_dims454, out_dtype="void") + add503: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul453, model_decoder_layers_24_encoder_attn_out_proj_bias2) + add504: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add501, add503) + 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) + permute_dims455: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_24_fc1_weight2, axes=None) + matmul454: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm139, permute_dims455, out_dtype="void") + add505: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul454, model_decoder_layers_24_fc1_bias2) + gelu58: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add505) + permute_dims456: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_fc2_weight2, axes=None) + matmul455: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu58, permute_dims456, out_dtype="void") + add506: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul455, model_decoder_layers_24_fc2_bias2) + add507: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add504, add506) + 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) + permute_dims457: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_q_proj_weight2, axes=None) + matmul456: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm140, permute_dims457, out_dtype="void") + add508: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul456, model_decoder_layers_25_self_attn_q_proj_bias2) + reshape637: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add508, R.shape([1, seq_len, 20, 64])) + permute_dims458: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_k_proj_weight2, axes=None) + matmul457: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm140, permute_dims458, out_dtype="void") + reshape638: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul457, R.shape([1, seq_len, 20, 64])) + permute_dims459: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_v_proj_weight2, axes=None) + matmul458: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm140, permute_dims459, out_dtype="void") + add509: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul458, model_decoder_layers_25_self_attn_v_proj_bias2) + reshape639: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add509, 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 = 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, 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])) + permute_dims460: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_out_proj_weight2, axes=None) + matmul459: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape642, permute_dims460, out_dtype="void") + add510: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul459, model_decoder_layers_25_self_attn_out_proj_bias2) + add511: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add507, add510) + 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) + permute_dims461: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_encoder_attn_q_proj_weight2, axes=None) + matmul460: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm141, permute_dims461, out_dtype="void") + add512: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul460, model_decoder_layers_25_encoder_attn_q_proj_bias2) + reshape643: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add512, 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 = 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, 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])) + permute_dims462: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_encoder_attn_out_proj_weight2, axes=None) + matmul461: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape646, permute_dims462, out_dtype="void") + add513: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul461, model_decoder_layers_25_encoder_attn_out_proj_bias2) + add514: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add511, add513) + 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) + permute_dims463: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_25_fc1_weight2, axes=None) + matmul462: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm142, permute_dims463, out_dtype="void") + add515: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul462, model_decoder_layers_25_fc1_bias2) + gelu59: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add515) + permute_dims464: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_fc2_weight2, axes=None) + matmul463: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu59, permute_dims464, out_dtype="void") + add516: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul463, model_decoder_layers_25_fc2_bias2) + add517: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add514, add516) + 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) + permute_dims465: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_q_proj_weight2, axes=None) + matmul464: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm143, permute_dims465, out_dtype="void") + add518: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul464, model_decoder_layers_26_self_attn_q_proj_bias2) + reshape647: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add518, R.shape([1, seq_len, 20, 64])) + permute_dims466: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_k_proj_weight2, axes=None) + matmul465: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm143, permute_dims466, out_dtype="void") + reshape648: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul465, R.shape([1, seq_len, 20, 64])) + permute_dims467: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_v_proj_weight2, axes=None) + matmul466: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm143, permute_dims467, out_dtype="void") + add519: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul466, model_decoder_layers_26_self_attn_v_proj_bias2) + reshape649: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add519, 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 = 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, 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])) + permute_dims468: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_out_proj_weight2, axes=None) + matmul467: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape652, permute_dims468, out_dtype="void") + add520: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul467, model_decoder_layers_26_self_attn_out_proj_bias2) + add521: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add517, add520) + 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) + permute_dims469: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_encoder_attn_q_proj_weight2, axes=None) + matmul468: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm144, permute_dims469, out_dtype="void") + add522: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul468, model_decoder_layers_26_encoder_attn_q_proj_bias2) + reshape653: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add522, 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 = 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, 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])) + permute_dims470: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_encoder_attn_out_proj_weight2, axes=None) + matmul469: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape656, permute_dims470, out_dtype="void") + add523: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul469, model_decoder_layers_26_encoder_attn_out_proj_bias2) + add524: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add521, add523) + 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) + permute_dims471: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_26_fc1_weight2, axes=None) + matmul470: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm145, permute_dims471, out_dtype="void") + add525: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul470, model_decoder_layers_26_fc1_bias2) + gelu60: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add525) + permute_dims472: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_fc2_weight2, axes=None) + matmul471: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu60, permute_dims472, out_dtype="void") + add526: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul471, model_decoder_layers_26_fc2_bias2) + add527: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add524, add526) + 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) + permute_dims473: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_q_proj_weight2, axes=None) + matmul472: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm146, permute_dims473, out_dtype="void") + add528: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul472, model_decoder_layers_27_self_attn_q_proj_bias2) + reshape657: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add528, R.shape([1, seq_len, 20, 64])) + permute_dims474: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_k_proj_weight2, axes=None) + matmul473: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm146, permute_dims474, out_dtype="void") + reshape658: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul473, R.shape([1, seq_len, 20, 64])) + permute_dims475: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_v_proj_weight2, axes=None) + matmul474: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm146, permute_dims475, out_dtype="void") + add529: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul474, model_decoder_layers_27_self_attn_v_proj_bias2) + reshape659: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add529, 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 = 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, 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])) + permute_dims476: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_out_proj_weight2, axes=None) + matmul475: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape662, permute_dims476, out_dtype="void") + add530: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul475, model_decoder_layers_27_self_attn_out_proj_bias2) + add531: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add527, add530) + 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) + permute_dims477: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_encoder_attn_q_proj_weight2, axes=None) + matmul476: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm147, permute_dims477, out_dtype="void") + add532: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul476, model_decoder_layers_27_encoder_attn_q_proj_bias2) + reshape663: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add532, 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 = 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, 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])) + permute_dims478: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_encoder_attn_out_proj_weight2, axes=None) + matmul477: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape666, permute_dims478, out_dtype="void") + add533: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul477, model_decoder_layers_27_encoder_attn_out_proj_bias2) + add534: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add531, add533) + 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) + permute_dims479: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_27_fc1_weight2, axes=None) + matmul478: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm148, permute_dims479, out_dtype="void") + add535: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul478, model_decoder_layers_27_fc1_bias2) + gelu61: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add535) + permute_dims480: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_fc2_weight2, axes=None) + matmul479: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu61, permute_dims480, out_dtype="void") + add536: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul479, model_decoder_layers_27_fc2_bias2) + add537: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add534, add536) + 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) + permute_dims481: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_q_proj_weight2, axes=None) + matmul480: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm149, permute_dims481, out_dtype="void") + add538: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul480, model_decoder_layers_28_self_attn_q_proj_bias2) + reshape667: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add538, R.shape([1, seq_len, 20, 64])) + permute_dims482: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_k_proj_weight2, axes=None) + matmul481: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm149, permute_dims482, out_dtype="void") + reshape668: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul481, R.shape([1, seq_len, 20, 64])) + permute_dims483: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_v_proj_weight2, axes=None) + matmul482: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm149, permute_dims483, out_dtype="void") + add539: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul482, model_decoder_layers_28_self_attn_v_proj_bias2) + reshape669: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add539, 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 = 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, 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])) + permute_dims484: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_out_proj_weight2, axes=None) + matmul483: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape672, permute_dims484, out_dtype="void") + add540: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul483, model_decoder_layers_28_self_attn_out_proj_bias2) + add541: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add537, add540) + 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) + permute_dims485: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_encoder_attn_q_proj_weight2, axes=None) + matmul484: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm150, permute_dims485, out_dtype="void") + add542: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul484, model_decoder_layers_28_encoder_attn_q_proj_bias2) + reshape673: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add542, 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 = 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, 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])) + permute_dims486: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_encoder_attn_out_proj_weight2, axes=None) + matmul485: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape676, permute_dims486, out_dtype="void") + add543: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul485, model_decoder_layers_28_encoder_attn_out_proj_bias2) + add544: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add541, add543) + 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) + permute_dims487: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_28_fc1_weight2, axes=None) + matmul486: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm151, permute_dims487, out_dtype="void") + add545: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul486, model_decoder_layers_28_fc1_bias2) + gelu62: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add545) + permute_dims488: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_fc2_weight2, axes=None) + matmul487: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu62, permute_dims488, out_dtype="void") + add546: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul487, model_decoder_layers_28_fc2_bias2) + add547: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add544, add546) + 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) + permute_dims489: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_q_proj_weight2, axes=None) + matmul488: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm152, permute_dims489, out_dtype="void") + add548: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul488, model_decoder_layers_29_self_attn_q_proj_bias2) + reshape677: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add548, R.shape([1, seq_len, 20, 64])) + permute_dims490: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_k_proj_weight2, axes=None) + matmul489: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm152, permute_dims490, out_dtype="void") + reshape678: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul489, R.shape([1, seq_len, 20, 64])) + permute_dims491: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_v_proj_weight2, axes=None) + matmul490: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm152, permute_dims491, out_dtype="void") + add549: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul490, model_decoder_layers_29_self_attn_v_proj_bias2) + reshape679: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add549, 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 = 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, 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])) + permute_dims492: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_out_proj_weight2, axes=None) + matmul491: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape682, permute_dims492, out_dtype="void") + add550: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul491, model_decoder_layers_29_self_attn_out_proj_bias2) + add551: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add547, add550) + 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) + permute_dims493: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_encoder_attn_q_proj_weight2, axes=None) + matmul492: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm153, permute_dims493, out_dtype="void") + add552: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul492, model_decoder_layers_29_encoder_attn_q_proj_bias2) + reshape683: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add552, 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])) + permute_dims494: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_encoder_attn_out_proj_weight2, axes=None) + matmul493: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape686, permute_dims494, out_dtype="void") + add553: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul493, model_decoder_layers_29_encoder_attn_out_proj_bias2) + add554: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add551, add553) + 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) + permute_dims495: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_29_fc1_weight2, axes=None) + matmul494: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm154, permute_dims495, out_dtype="void") + add555: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul494, model_decoder_layers_29_fc1_bias2) + gelu63: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add555) + permute_dims496: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_fc2_weight2, axes=None) + matmul495: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu63, permute_dims496, out_dtype="void") + add556: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul495, model_decoder_layers_29_fc2_bias2) + add557: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add554, add556) + 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) + permute_dims497: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_q_proj_weight2, axes=None) + matmul496: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm155, permute_dims497, out_dtype="void") + add558: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul496, model_decoder_layers_30_self_attn_q_proj_bias2) + reshape687: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add558, R.shape([1, seq_len, 20, 64])) + permute_dims498: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_k_proj_weight2, axes=None) + matmul497: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm155, permute_dims498, out_dtype="void") + reshape688: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul497, R.shape([1, seq_len, 20, 64])) + permute_dims499: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_v_proj_weight2, axes=None) + matmul498: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm155, permute_dims499, out_dtype="void") + add559: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul498, model_decoder_layers_30_self_attn_v_proj_bias2) + reshape689: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add559, 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])) + permute_dims500: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_out_proj_weight2, axes=None) + matmul499: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape692, permute_dims500, out_dtype="void") + add560: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul499, model_decoder_layers_30_self_attn_out_proj_bias2) + add561: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add557, add560) + 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) + permute_dims501: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_encoder_attn_q_proj_weight2, axes=None) + matmul500: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm156, permute_dims501, out_dtype="void") + add562: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul500, model_decoder_layers_30_encoder_attn_q_proj_bias2) + reshape693: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add562, 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])) + permute_dims502: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_encoder_attn_out_proj_weight2, axes=None) + matmul501: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape696, permute_dims502, out_dtype="void") + add563: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul501, model_decoder_layers_30_encoder_attn_out_proj_bias2) + add564: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add561, add563) + 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) + permute_dims503: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_30_fc1_weight2, axes=None) + matmul502: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm157, permute_dims503, out_dtype="void") + add565: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul502, model_decoder_layers_30_fc1_bias2) + gelu64: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add565) + permute_dims504: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_fc2_weight2, axes=None) + matmul503: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu64, permute_dims504, out_dtype="void") + add566: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul503, model_decoder_layers_30_fc2_bias2) + add567: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add564, add566) + 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) + permute_dims505: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_q_proj_weight2, axes=None) + matmul504: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm158, permute_dims505, out_dtype="void") + add568: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul504, model_decoder_layers_31_self_attn_q_proj_bias2) + reshape697: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add568, R.shape([1, seq_len, 20, 64])) + permute_dims506: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_k_proj_weight2, axes=None) + matmul505: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm158, permute_dims506, out_dtype="void") + reshape698: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul505, R.shape([1, seq_len, 20, 64])) + permute_dims507: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_v_proj_weight2, axes=None) + matmul506: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm158, permute_dims507, out_dtype="void") + add569: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul506, model_decoder_layers_31_self_attn_v_proj_bias2) + reshape699: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add569, 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])) + permute_dims508: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_out_proj_weight2, axes=None) + matmul507: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape702, permute_dims508, out_dtype="void") + add570: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul507, model_decoder_layers_31_self_attn_out_proj_bias2) + add571: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add567, add570) + 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) + permute_dims509: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_encoder_attn_q_proj_weight2, axes=None) + matmul508: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm159, permute_dims509, out_dtype="void") + add572: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul508, model_decoder_layers_31_encoder_attn_q_proj_bias2) + reshape703: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add572, 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])) + permute_dims510: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_encoder_attn_out_proj_weight2, axes=None) + matmul509: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape706, permute_dims510, out_dtype="void") + add573: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul509, model_decoder_layers_31_encoder_attn_out_proj_bias2) + add574: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add571, add573) + 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) + permute_dims511: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_31_fc1_weight2, axes=None) + matmul510: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm160, permute_dims511, out_dtype="void") + add575: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul510, model_decoder_layers_31_fc1_bias2) + gelu65: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add575) + permute_dims512: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_fc2_weight2, axes=None) + matmul511: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu65, permute_dims512, out_dtype="void") + add576: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul511, model_decoder_layers_31_fc2_bias2) + add577: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add574, add576) + 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) + permute_dims513: R.Tensor((1280, 51866), dtype="float16") = R.permute_dims(model_decoder_embed_tokens_weight2, axes=None) + matmul512: R.Tensor((1, batch_size, 51866), dtype="float32") = R.matmul(take2, permute_dims513, out_dtype="float32") + gv2: R.Tensor((1, batch_size, 51866), dtype="float32") = matmul512 + 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}}) + with R.dataflow(): + model_encoder_conv1_weight5: R.Tensor((1280, 128, 3), dtype="float16") = packed_params[0] + model_encoder_conv1_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1] + model_encoder_conv2_weight5: R.Tensor((1280, 1280, 3), dtype="float16") = packed_params[2] + model_encoder_conv2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[3] + model_encoder_embed_positions_weight5: R.Tensor((1500, 1280), dtype="float16") = packed_params[4] + model_encoder_layers_0_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[5] + model_encoder_layers_0_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[6] + model_encoder_layers_0_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[7] + model_encoder_layers_0_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[8] + model_encoder_layers_0_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[9] + model_encoder_layers_0_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[10] + model_encoder_layers_0_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[11] + model_encoder_layers_0_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[12] + model_encoder_layers_0_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[13] + model_encoder_layers_0_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[14] + model_encoder_layers_0_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[15] + model_encoder_layers_0_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[16] + model_encoder_layers_0_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[17] + model_encoder_layers_0_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[18] + model_encoder_layers_0_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[19] + model_encoder_layers_1_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[20] + model_encoder_layers_1_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[21] + model_encoder_layers_1_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[22] + model_encoder_layers_1_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[23] + model_encoder_layers_1_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[24] + model_encoder_layers_1_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[25] + model_encoder_layers_1_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[26] + model_encoder_layers_1_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[27] + model_encoder_layers_1_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[28] + model_encoder_layers_1_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[29] + model_encoder_layers_1_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[30] + model_encoder_layers_1_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[31] + model_encoder_layers_1_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[32] + model_encoder_layers_1_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[33] + model_encoder_layers_1_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[34] + model_encoder_layers_2_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[35] + model_encoder_layers_2_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[36] + model_encoder_layers_2_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[37] + model_encoder_layers_2_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[38] + model_encoder_layers_2_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[39] + model_encoder_layers_2_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[40] + model_encoder_layers_2_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[41] + model_encoder_layers_2_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[42] + model_encoder_layers_2_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[43] + model_encoder_layers_2_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[44] + model_encoder_layers_2_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[45] + model_encoder_layers_2_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[46] + model_encoder_layers_2_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[47] + model_encoder_layers_2_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[48] + model_encoder_layers_2_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[49] + model_encoder_layers_3_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[50] + model_encoder_layers_3_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[51] + model_encoder_layers_3_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[52] + model_encoder_layers_3_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[53] + model_encoder_layers_3_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[54] + model_encoder_layers_3_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[55] + model_encoder_layers_3_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[56] + model_encoder_layers_3_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[57] + model_encoder_layers_3_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[58] + model_encoder_layers_3_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[59] + model_encoder_layers_3_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[60] + model_encoder_layers_3_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[61] + model_encoder_layers_3_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[62] + model_encoder_layers_3_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[63] + model_encoder_layers_3_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[64] + model_encoder_layers_4_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[65] + model_encoder_layers_4_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[66] + model_encoder_layers_4_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[67] + model_encoder_layers_4_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[68] + model_encoder_layers_4_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[69] + model_encoder_layers_4_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[70] + model_encoder_layers_4_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[71] + model_encoder_layers_4_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[72] + model_encoder_layers_4_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[73] + model_encoder_layers_4_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[74] + model_encoder_layers_4_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[75] + model_encoder_layers_4_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[76] + model_encoder_layers_4_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[77] + model_encoder_layers_4_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[78] + model_encoder_layers_4_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[79] + model_encoder_layers_5_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[80] + model_encoder_layers_5_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[81] + model_encoder_layers_5_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[82] + model_encoder_layers_5_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[83] + model_encoder_layers_5_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[84] + model_encoder_layers_5_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[85] + model_encoder_layers_5_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[86] + model_encoder_layers_5_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[87] + model_encoder_layers_5_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[88] + model_encoder_layers_5_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[89] + model_encoder_layers_5_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[90] + model_encoder_layers_5_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[91] + model_encoder_layers_5_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[92] + model_encoder_layers_5_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[93] + model_encoder_layers_5_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[94] + model_encoder_layers_6_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[95] + model_encoder_layers_6_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[96] + model_encoder_layers_6_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[97] + model_encoder_layers_6_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[98] + model_encoder_layers_6_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[99] + model_encoder_layers_6_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[100] + model_encoder_layers_6_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[101] + model_encoder_layers_6_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[102] + model_encoder_layers_6_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[103] + model_encoder_layers_6_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[104] + model_encoder_layers_6_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[105] + model_encoder_layers_6_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[106] + model_encoder_layers_6_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[107] + model_encoder_layers_6_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[108] + model_encoder_layers_6_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[109] + model_encoder_layers_7_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[110] + model_encoder_layers_7_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[111] + model_encoder_layers_7_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[112] + model_encoder_layers_7_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[113] + model_encoder_layers_7_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[114] + model_encoder_layers_7_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[115] + model_encoder_layers_7_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[116] + model_encoder_layers_7_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[117] + model_encoder_layers_7_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[118] + model_encoder_layers_7_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[119] + model_encoder_layers_7_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[120] + model_encoder_layers_7_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[121] + model_encoder_layers_7_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[122] + model_encoder_layers_7_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[123] + model_encoder_layers_7_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[124] + model_encoder_layers_8_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[125] + model_encoder_layers_8_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[126] + model_encoder_layers_8_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[127] + model_encoder_layers_8_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[128] + model_encoder_layers_8_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[129] + model_encoder_layers_8_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[130] + model_encoder_layers_8_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[131] + model_encoder_layers_8_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[132] + model_encoder_layers_8_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[133] + model_encoder_layers_8_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[134] + model_encoder_layers_8_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[135] + model_encoder_layers_8_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[136] + model_encoder_layers_8_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[137] + model_encoder_layers_8_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[138] + model_encoder_layers_8_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[139] + model_encoder_layers_9_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[140] + model_encoder_layers_9_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[141] + model_encoder_layers_9_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[142] + model_encoder_layers_9_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[143] + model_encoder_layers_9_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[144] + model_encoder_layers_9_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[145] + model_encoder_layers_9_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[146] + model_encoder_layers_9_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[147] + model_encoder_layers_9_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[148] + model_encoder_layers_9_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[149] + model_encoder_layers_9_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[150] + model_encoder_layers_9_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[151] + model_encoder_layers_9_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[152] + model_encoder_layers_9_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[153] + model_encoder_layers_9_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[154] + model_encoder_layers_10_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[155] + model_encoder_layers_10_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[156] + model_encoder_layers_10_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[157] + model_encoder_layers_10_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[158] + model_encoder_layers_10_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[159] + model_encoder_layers_10_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[160] + model_encoder_layers_10_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[161] + model_encoder_layers_10_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[162] + model_encoder_layers_10_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[163] + model_encoder_layers_10_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[164] + model_encoder_layers_10_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[165] + model_encoder_layers_10_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[166] + model_encoder_layers_10_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[167] + model_encoder_layers_10_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[168] + model_encoder_layers_10_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[169] + model_encoder_layers_11_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[170] + model_encoder_layers_11_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[171] + model_encoder_layers_11_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[172] + model_encoder_layers_11_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[173] + model_encoder_layers_11_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[174] + model_encoder_layers_11_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[175] + model_encoder_layers_11_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[176] + model_encoder_layers_11_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[177] + model_encoder_layers_11_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[178] + model_encoder_layers_11_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[179] + model_encoder_layers_11_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[180] + model_encoder_layers_11_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[181] + model_encoder_layers_11_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[182] + model_encoder_layers_11_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[183] + model_encoder_layers_11_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[184] + model_encoder_layers_12_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[185] + model_encoder_layers_12_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[186] + model_encoder_layers_12_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[187] + model_encoder_layers_12_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[188] + model_encoder_layers_12_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[189] + model_encoder_layers_12_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[190] + model_encoder_layers_12_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[191] + model_encoder_layers_12_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[192] + model_encoder_layers_12_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[193] + model_encoder_layers_12_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[194] + model_encoder_layers_12_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[195] + model_encoder_layers_12_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[196] + model_encoder_layers_12_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[197] + model_encoder_layers_12_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[198] + model_encoder_layers_12_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[199] + model_encoder_layers_13_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[200] + model_encoder_layers_13_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[201] + model_encoder_layers_13_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[202] + model_encoder_layers_13_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[203] + model_encoder_layers_13_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[204] + model_encoder_layers_13_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[205] + model_encoder_layers_13_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[206] + model_encoder_layers_13_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[207] + model_encoder_layers_13_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[208] + model_encoder_layers_13_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[209] + model_encoder_layers_13_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[210] + model_encoder_layers_13_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[211] + model_encoder_layers_13_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[212] + model_encoder_layers_13_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[213] + model_encoder_layers_13_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[214] + model_encoder_layers_14_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[215] + model_encoder_layers_14_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[216] + model_encoder_layers_14_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[217] + model_encoder_layers_14_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[218] + model_encoder_layers_14_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[219] + model_encoder_layers_14_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[220] + model_encoder_layers_14_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[221] + model_encoder_layers_14_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[222] + model_encoder_layers_14_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[223] + model_encoder_layers_14_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[224] + model_encoder_layers_14_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[225] + model_encoder_layers_14_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[226] + model_encoder_layers_14_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[227] + model_encoder_layers_14_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[228] + model_encoder_layers_14_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[229] + model_encoder_layers_15_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[230] + model_encoder_layers_15_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[231] + model_encoder_layers_15_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[232] + model_encoder_layers_15_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[233] + model_encoder_layers_15_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[234] + model_encoder_layers_15_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[235] + model_encoder_layers_15_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[236] + model_encoder_layers_15_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[237] + model_encoder_layers_15_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[238] + model_encoder_layers_15_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[239] + model_encoder_layers_15_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[240] + model_encoder_layers_15_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[241] + model_encoder_layers_15_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[242] + model_encoder_layers_15_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[243] + model_encoder_layers_15_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[244] + model_encoder_layers_16_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[245] + model_encoder_layers_16_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[246] + model_encoder_layers_16_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[247] + model_encoder_layers_16_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[248] + model_encoder_layers_16_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[249] + model_encoder_layers_16_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[250] + model_encoder_layers_16_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[251] + model_encoder_layers_16_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[252] + model_encoder_layers_16_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[253] + model_encoder_layers_16_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[254] + model_encoder_layers_16_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[255] + model_encoder_layers_16_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[256] + model_encoder_layers_16_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[257] + model_encoder_layers_16_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[258] + model_encoder_layers_16_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[259] + model_encoder_layers_17_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[260] + model_encoder_layers_17_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[261] + model_encoder_layers_17_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[262] + model_encoder_layers_17_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[263] + model_encoder_layers_17_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[264] + model_encoder_layers_17_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[265] + model_encoder_layers_17_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[266] + model_encoder_layers_17_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[267] + model_encoder_layers_17_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[268] + model_encoder_layers_17_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[269] + model_encoder_layers_17_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[270] + model_encoder_layers_17_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[271] + model_encoder_layers_17_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[272] + model_encoder_layers_17_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[273] + model_encoder_layers_17_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[274] + model_encoder_layers_18_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[275] + model_encoder_layers_18_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[276] + model_encoder_layers_18_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[277] + model_encoder_layers_18_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[278] + model_encoder_layers_18_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[279] + model_encoder_layers_18_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[280] + model_encoder_layers_18_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[281] + model_encoder_layers_18_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[282] + model_encoder_layers_18_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[283] + model_encoder_layers_18_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[284] + model_encoder_layers_18_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[285] + model_encoder_layers_18_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[286] + model_encoder_layers_18_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[287] + model_encoder_layers_18_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[288] + model_encoder_layers_18_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[289] + model_encoder_layers_19_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[290] + model_encoder_layers_19_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[291] + model_encoder_layers_19_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[292] + model_encoder_layers_19_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[293] + model_encoder_layers_19_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[294] + model_encoder_layers_19_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[295] + model_encoder_layers_19_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[296] + model_encoder_layers_19_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[297] + model_encoder_layers_19_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[298] + model_encoder_layers_19_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[299] + model_encoder_layers_19_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[300] + model_encoder_layers_19_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[301] + model_encoder_layers_19_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[302] + model_encoder_layers_19_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[303] + model_encoder_layers_19_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[304] + model_encoder_layers_20_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[305] + model_encoder_layers_20_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[306] + model_encoder_layers_20_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[307] + model_encoder_layers_20_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[308] + model_encoder_layers_20_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[309] + model_encoder_layers_20_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[310] + model_encoder_layers_20_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[311] + model_encoder_layers_20_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[312] + model_encoder_layers_20_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[313] + model_encoder_layers_20_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[314] + model_encoder_layers_20_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[315] + model_encoder_layers_20_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[316] + model_encoder_layers_20_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[317] + model_encoder_layers_20_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[318] + model_encoder_layers_20_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[319] + model_encoder_layers_21_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[320] + model_encoder_layers_21_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[321] + model_encoder_layers_21_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[322] + model_encoder_layers_21_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[323] + model_encoder_layers_21_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[324] + model_encoder_layers_21_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[325] + model_encoder_layers_21_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[326] + model_encoder_layers_21_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[327] + model_encoder_layers_21_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[328] + model_encoder_layers_21_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[329] + model_encoder_layers_21_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[330] + model_encoder_layers_21_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[331] + model_encoder_layers_21_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[332] + model_encoder_layers_21_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[333] + model_encoder_layers_21_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[334] + model_encoder_layers_22_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[335] + model_encoder_layers_22_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[336] + model_encoder_layers_22_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[337] + model_encoder_layers_22_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[338] + model_encoder_layers_22_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[339] + model_encoder_layers_22_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[340] + model_encoder_layers_22_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[341] + model_encoder_layers_22_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[342] + model_encoder_layers_22_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[343] + model_encoder_layers_22_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[344] + model_encoder_layers_22_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[345] + model_encoder_layers_22_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[346] + model_encoder_layers_22_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[347] + model_encoder_layers_22_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[348] + model_encoder_layers_22_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[349] + model_encoder_layers_23_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[350] + model_encoder_layers_23_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[351] + model_encoder_layers_23_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[352] + model_encoder_layers_23_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[353] + model_encoder_layers_23_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[354] + model_encoder_layers_23_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[355] + model_encoder_layers_23_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[356] + model_encoder_layers_23_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[357] + model_encoder_layers_23_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[358] + model_encoder_layers_23_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[359] + model_encoder_layers_23_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[360] + model_encoder_layers_23_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[361] + model_encoder_layers_23_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[362] + model_encoder_layers_23_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[363] + model_encoder_layers_23_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[364] + model_encoder_layers_24_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[365] + model_encoder_layers_24_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[366] + model_encoder_layers_24_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[367] + model_encoder_layers_24_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[368] + model_encoder_layers_24_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[369] + model_encoder_layers_24_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[370] + model_encoder_layers_24_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[371] + model_encoder_layers_24_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[372] + model_encoder_layers_24_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[373] + model_encoder_layers_24_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[374] + model_encoder_layers_24_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[375] + model_encoder_layers_24_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[376] + model_encoder_layers_24_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[377] + model_encoder_layers_24_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[378] + model_encoder_layers_24_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[379] + model_encoder_layers_25_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[380] + model_encoder_layers_25_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[381] + model_encoder_layers_25_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[382] + model_encoder_layers_25_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[383] + model_encoder_layers_25_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[384] + model_encoder_layers_25_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[385] + model_encoder_layers_25_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[386] + model_encoder_layers_25_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[387] + model_encoder_layers_25_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[388] + model_encoder_layers_25_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[389] + model_encoder_layers_25_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[390] + model_encoder_layers_25_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[391] + model_encoder_layers_25_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[392] + model_encoder_layers_25_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[393] + model_encoder_layers_25_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[394] + model_encoder_layers_26_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[395] + model_encoder_layers_26_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[396] + model_encoder_layers_26_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[397] + model_encoder_layers_26_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[398] + model_encoder_layers_26_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[399] + model_encoder_layers_26_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[400] + model_encoder_layers_26_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[401] + model_encoder_layers_26_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[402] + model_encoder_layers_26_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[403] + model_encoder_layers_26_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[404] + model_encoder_layers_26_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[405] + model_encoder_layers_26_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[406] + model_encoder_layers_26_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[407] + model_encoder_layers_26_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[408] + model_encoder_layers_26_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[409] + model_encoder_layers_27_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[410] + model_encoder_layers_27_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[411] + model_encoder_layers_27_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[412] + model_encoder_layers_27_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[413] + model_encoder_layers_27_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[414] + model_encoder_layers_27_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[415] + model_encoder_layers_27_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[416] + model_encoder_layers_27_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[417] + model_encoder_layers_27_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[418] + model_encoder_layers_27_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[419] + model_encoder_layers_27_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[420] + model_encoder_layers_27_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[421] + model_encoder_layers_27_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[422] + model_encoder_layers_27_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[423] + model_encoder_layers_27_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[424] + model_encoder_layers_28_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[425] + model_encoder_layers_28_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[426] + model_encoder_layers_28_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[427] + model_encoder_layers_28_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[428] + model_encoder_layers_28_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[429] + model_encoder_layers_28_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[430] + model_encoder_layers_28_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[431] + model_encoder_layers_28_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[432] + model_encoder_layers_28_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[433] + model_encoder_layers_28_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[434] + model_encoder_layers_28_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[435] + model_encoder_layers_28_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[436] + model_encoder_layers_28_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[437] + model_encoder_layers_28_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[438] + model_encoder_layers_28_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[439] + model_encoder_layers_29_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[440] + model_encoder_layers_29_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[441] + model_encoder_layers_29_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[442] + model_encoder_layers_29_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[443] + model_encoder_layers_29_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[444] + model_encoder_layers_29_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[445] + model_encoder_layers_29_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[446] + model_encoder_layers_29_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[447] + model_encoder_layers_29_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[448] + model_encoder_layers_29_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[449] + model_encoder_layers_29_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[450] + model_encoder_layers_29_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[451] + model_encoder_layers_29_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[452] + model_encoder_layers_29_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[453] + model_encoder_layers_29_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[454] + model_encoder_layers_30_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[455] + model_encoder_layers_30_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[456] + model_encoder_layers_30_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[457] + model_encoder_layers_30_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[458] + model_encoder_layers_30_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[459] + model_encoder_layers_30_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[460] + model_encoder_layers_30_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[461] + model_encoder_layers_30_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[462] + model_encoder_layers_30_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[463] + model_encoder_layers_30_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[464] + model_encoder_layers_30_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[465] + model_encoder_layers_30_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[466] + model_encoder_layers_30_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[467] + model_encoder_layers_30_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[468] + model_encoder_layers_30_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[469] + model_encoder_layers_31_self_attn_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[470] + model_encoder_layers_31_self_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[471] + model_encoder_layers_31_self_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[472] + model_encoder_layers_31_self_attn_q_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[473] + model_encoder_layers_31_self_attn_q_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[474] + model_encoder_layers_31_self_attn_out_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[475] + model_encoder_layers_31_self_attn_out_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[476] + model_encoder_layers_31_self_attn_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[477] + model_encoder_layers_31_self_attn_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[478] + model_encoder_layers_31_fc1_weight5: R.Tensor((5120, 1280), dtype="float16") = packed_params[479] + model_encoder_layers_31_fc1_bias5: R.Tensor((5120,), dtype="float16") = packed_params[480] + model_encoder_layers_31_fc2_weight5: R.Tensor((1280, 5120), dtype="float16") = packed_params[481] + model_encoder_layers_31_fc2_bias5: R.Tensor((1280,), dtype="float16") = packed_params[482] + model_encoder_layers_31_final_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[483] + model_encoder_layers_31_final_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[484] + model_encoder_layer_norm_weight5: R.Tensor((1280,), dtype="float16") = packed_params[485] + model_encoder_layer_norm_bias5: R.Tensor((1280,), dtype="float16") = packed_params[486] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[498] + model_decoder_layers_0_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[499] + model_decoder_layers_0_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[500] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[522] + model_decoder_layers_1_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[523] + model_decoder_layers_1_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[524] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[546] + model_decoder_layers_2_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[547] + model_decoder_layers_2_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[548] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[570] + model_decoder_layers_3_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[571] + model_decoder_layers_3_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[572] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[594] + model_decoder_layers_4_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[595] + model_decoder_layers_4_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[596] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[618] + model_decoder_layers_5_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[619] + model_decoder_layers_5_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[620] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[642] + model_decoder_layers_6_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[643] + model_decoder_layers_6_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[644] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[666] + model_decoder_layers_7_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[667] + model_decoder_layers_7_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[668] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[690] + model_decoder_layers_8_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[691] + model_decoder_layers_8_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[692] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[714] + model_decoder_layers_9_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[715] + model_decoder_layers_9_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[716] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[738] + model_decoder_layers_10_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[739] + model_decoder_layers_10_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[740] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[762] + model_decoder_layers_11_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[763] + model_decoder_layers_11_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[764] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[786] + model_decoder_layers_12_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[787] + model_decoder_layers_12_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[788] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[810] + model_decoder_layers_13_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[811] + model_decoder_layers_13_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[812] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[834] + model_decoder_layers_14_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[835] + model_decoder_layers_14_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[836] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[858] + model_decoder_layers_15_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[859] + model_decoder_layers_15_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[860] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[882] + model_decoder_layers_16_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[883] + model_decoder_layers_16_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[884] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[906] + model_decoder_layers_17_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[907] + model_decoder_layers_17_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[908] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[930] + model_decoder_layers_18_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[931] + model_decoder_layers_18_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[932] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[954] + model_decoder_layers_19_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[955] + model_decoder_layers_19_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[956] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[978] + model_decoder_layers_20_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[979] + model_decoder_layers_20_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[980] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1002] + model_decoder_layers_21_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1003] + model_decoder_layers_21_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1004] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1026] + model_decoder_layers_22_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1027] + model_decoder_layers_22_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1028] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1050] + model_decoder_layers_23_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1051] + model_decoder_layers_23_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1052] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1074] + model_decoder_layers_24_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1075] + model_decoder_layers_24_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1076] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1098] + model_decoder_layers_25_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1099] + model_decoder_layers_25_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1100] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1122] + model_decoder_layers_26_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1123] + model_decoder_layers_26_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1124] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1146] + model_decoder_layers_27_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1147] + model_decoder_layers_27_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1148] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1170] + model_decoder_layers_28_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1171] + model_decoder_layers_28_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1172] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1194] + model_decoder_layers_29_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1195] + model_decoder_layers_29_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1196] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1218] + model_decoder_layers_30_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1219] + model_decoder_layers_30_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1220] + 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_k_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1242] + model_decoder_layers_31_encoder_attn_v_proj_weight5: R.Tensor((1280, 1280), dtype="float16") = packed_params[1243] + model_decoder_layers_31_encoder_attn_v_proj_bias5: R.Tensor((1280,), dtype="float16") = packed_params[1244] + 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) + permute_dims1028: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_q_proj_weight5, axes=None) + matmul1027: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm356, permute_dims1028, out_dtype="void") + add1221: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1027, 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])) + permute_dims1029: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_k_proj_weight5, axes=None) + matmul1028: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm356, permute_dims1029, out_dtype="void") + reshape1357: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1028, R.shape([1, 1, 20, 64])) + permute_dims1030: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_v_proj_weight5, axes=None) + matmul1029: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm356, permute_dims1030, out_dtype="void") + add1222: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1029, 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])) + permute_dims1031: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_out_proj_weight5, axes=None) + matmul1030: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1361, permute_dims1031, out_dtype="void") + add1223: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1030, 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) + permute_dims1032: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_encoder_attn_q_proj_weight5, axes=None) + matmul1031: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm357, permute_dims1032, out_dtype="void") + add1225: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1031, 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])) + permute_dims1033: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_encoder_attn_out_proj_weight5, axes=None) + matmul1032: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1365, permute_dims1033, out_dtype="void") + add1226: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1032, 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) + permute_dims1034: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_0_fc1_weight5, axes=None) + matmul1033: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm358, permute_dims1034, out_dtype="void") + add1228: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1033, model_decoder_layers_0_fc1_bias5) + gelu130: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1228) + permute_dims1035: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_fc2_weight5, axes=None) + matmul1034: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu130, permute_dims1035, out_dtype="void") + add1229: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1034, 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) + permute_dims1036: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_q_proj_weight5, axes=None) + matmul1035: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm359, permute_dims1036, out_dtype="void") + add1231: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1035, 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])) + permute_dims1037: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_k_proj_weight5, axes=None) + matmul1036: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm359, permute_dims1037, out_dtype="void") + reshape1367: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1036, R.shape([1, 1, 20, 64])) + permute_dims1038: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_v_proj_weight5, axes=None) + matmul1037: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm359, permute_dims1038, out_dtype="void") + add1232: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1037, 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])) + permute_dims1039: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_out_proj_weight5, axes=None) + matmul1038: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1371, permute_dims1039, out_dtype="void") + add1233: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1038, 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) + permute_dims1040: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_encoder_attn_q_proj_weight5, axes=None) + matmul1039: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm360, permute_dims1040, out_dtype="void") + add1235: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1039, 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])) + permute_dims1041: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_encoder_attn_out_proj_weight5, axes=None) + matmul1040: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1375, permute_dims1041, out_dtype="void") + add1236: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1040, 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) + permute_dims1042: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_1_fc1_weight5, axes=None) + matmul1041: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm361, permute_dims1042, out_dtype="void") + add1238: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1041, model_decoder_layers_1_fc1_bias5) + gelu131: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1238) + permute_dims1043: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_fc2_weight5, axes=None) + matmul1042: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu131, permute_dims1043, out_dtype="void") + add1239: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1042, 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) + permute_dims1044: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_q_proj_weight5, axes=None) + matmul1043: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm362, permute_dims1044, out_dtype="void") + add1241: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1043, 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])) + permute_dims1045: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_k_proj_weight5, axes=None) + matmul1044: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm362, permute_dims1045, out_dtype="void") + reshape1377: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1044, R.shape([1, 1, 20, 64])) + permute_dims1046: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_v_proj_weight5, axes=None) + matmul1045: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm362, permute_dims1046, out_dtype="void") + add1242: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1045, 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])) + permute_dims1047: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_out_proj_weight5, axes=None) + matmul1046: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1381, permute_dims1047, out_dtype="void") + add1243: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1046, 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) + permute_dims1048: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_encoder_attn_q_proj_weight5, axes=None) + matmul1047: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm363, permute_dims1048, out_dtype="void") + add1245: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1047, 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])) + permute_dims1049: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_encoder_attn_out_proj_weight5, axes=None) + matmul1048: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1385, permute_dims1049, out_dtype="void") + add1246: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1048, 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) + permute_dims1050: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_2_fc1_weight5, axes=None) + matmul1049: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm364, permute_dims1050, out_dtype="void") + add1248: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1049, model_decoder_layers_2_fc1_bias5) + gelu132: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1248) + permute_dims1051: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_fc2_weight5, axes=None) + matmul1050: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu132, permute_dims1051, out_dtype="void") + add1249: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1050, 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) + permute_dims1052: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_q_proj_weight5, axes=None) + matmul1051: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm365, permute_dims1052, out_dtype="void") + add1251: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1051, 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])) + permute_dims1053: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_k_proj_weight5, axes=None) + matmul1052: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm365, permute_dims1053, out_dtype="void") + reshape1387: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1052, R.shape([1, 1, 20, 64])) + permute_dims1054: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_v_proj_weight5, axes=None) + matmul1053: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm365, permute_dims1054, out_dtype="void") + add1252: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1053, 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])) + permute_dims1055: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_out_proj_weight5, axes=None) + matmul1054: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1391, permute_dims1055, out_dtype="void") + add1253: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1054, 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) + permute_dims1056: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_encoder_attn_q_proj_weight5, axes=None) + matmul1055: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm366, permute_dims1056, out_dtype="void") + add1255: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1055, 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])) + permute_dims1057: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_encoder_attn_out_proj_weight5, axes=None) + matmul1056: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1395, permute_dims1057, out_dtype="void") + add1256: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1056, 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) + permute_dims1058: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_3_fc1_weight5, axes=None) + matmul1057: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm367, permute_dims1058, out_dtype="void") + add1258: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1057, model_decoder_layers_3_fc1_bias5) + gelu133: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1258) + permute_dims1059: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_fc2_weight5, axes=None) + matmul1058: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu133, permute_dims1059, out_dtype="void") + add1259: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1058, 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) + permute_dims1060: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_q_proj_weight5, axes=None) + matmul1059: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm368, permute_dims1060, out_dtype="void") + add1261: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1059, 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])) + permute_dims1061: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_k_proj_weight5, axes=None) + matmul1060: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm368, permute_dims1061, out_dtype="void") + reshape1397: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1060, R.shape([1, 1, 20, 64])) + permute_dims1062: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_v_proj_weight5, axes=None) + matmul1061: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm368, permute_dims1062, out_dtype="void") + add1262: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1061, 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])) + permute_dims1063: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_out_proj_weight5, axes=None) + matmul1062: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1401, permute_dims1063, out_dtype="void") + add1263: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1062, 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) + permute_dims1064: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_encoder_attn_q_proj_weight5, axes=None) + matmul1063: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm369, permute_dims1064, out_dtype="void") + add1265: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1063, 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])) + permute_dims1065: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_encoder_attn_out_proj_weight5, axes=None) + matmul1064: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1405, permute_dims1065, out_dtype="void") + add1266: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1064, 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) + permute_dims1066: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_4_fc1_weight5, axes=None) + matmul1065: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm370, permute_dims1066, out_dtype="void") + add1268: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1065, model_decoder_layers_4_fc1_bias5) + gelu134: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1268) + permute_dims1067: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_fc2_weight5, axes=None) + matmul1066: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu134, permute_dims1067, out_dtype="void") + add1269: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1066, 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) + permute_dims1068: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_q_proj_weight5, axes=None) + matmul1067: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm371, permute_dims1068, out_dtype="void") + add1271: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1067, 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])) + permute_dims1069: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_k_proj_weight5, axes=None) + matmul1068: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm371, permute_dims1069, out_dtype="void") + reshape1407: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1068, R.shape([1, 1, 20, 64])) + permute_dims1070: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_v_proj_weight5, axes=None) + matmul1069: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm371, permute_dims1070, out_dtype="void") + add1272: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1069, 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])) + permute_dims1071: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_out_proj_weight5, axes=None) + matmul1070: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1411, permute_dims1071, out_dtype="void") + add1273: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1070, 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) + permute_dims1072: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_encoder_attn_q_proj_weight5, axes=None) + matmul1071: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm372, permute_dims1072, out_dtype="void") + add1275: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1071, 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])) + permute_dims1073: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_encoder_attn_out_proj_weight5, axes=None) + matmul1072: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1415, permute_dims1073, out_dtype="void") + add1276: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1072, 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) + permute_dims1074: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_5_fc1_weight5, axes=None) + matmul1073: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm373, permute_dims1074, out_dtype="void") + add1278: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1073, model_decoder_layers_5_fc1_bias5) + gelu135: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1278) + permute_dims1075: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_fc2_weight5, axes=None) + matmul1074: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu135, permute_dims1075, out_dtype="void") + add1279: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1074, 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) + permute_dims1076: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_q_proj_weight5, axes=None) + matmul1075: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm374, permute_dims1076, out_dtype="void") + add1281: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1075, 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])) + permute_dims1077: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_k_proj_weight5, axes=None) + matmul1076: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm374, permute_dims1077, out_dtype="void") + reshape1417: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1076, R.shape([1, 1, 20, 64])) + permute_dims1078: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_v_proj_weight5, axes=None) + matmul1077: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm374, permute_dims1078, out_dtype="void") + add1282: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1077, 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])) + permute_dims1079: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_out_proj_weight5, axes=None) + matmul1078: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1421, permute_dims1079, out_dtype="void") + add1283: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1078, 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) + permute_dims1080: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_encoder_attn_q_proj_weight5, axes=None) + matmul1079: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm375, permute_dims1080, out_dtype="void") + add1285: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1079, 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])) + permute_dims1081: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_encoder_attn_out_proj_weight5, axes=None) + matmul1080: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1425, permute_dims1081, out_dtype="void") + add1286: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1080, 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) + permute_dims1082: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_6_fc1_weight5, axes=None) + matmul1081: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm376, permute_dims1082, out_dtype="void") + add1288: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1081, model_decoder_layers_6_fc1_bias5) + gelu136: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1288) + permute_dims1083: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_fc2_weight5, axes=None) + matmul1082: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu136, permute_dims1083, out_dtype="void") + add1289: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1082, 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) + permute_dims1084: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_q_proj_weight5, axes=None) + matmul1083: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm377, permute_dims1084, out_dtype="void") + add1291: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1083, 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])) + permute_dims1085: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_k_proj_weight5, axes=None) + matmul1084: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm377, permute_dims1085, out_dtype="void") + reshape1427: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1084, R.shape([1, 1, 20, 64])) + permute_dims1086: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_v_proj_weight5, axes=None) + matmul1085: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm377, permute_dims1086, out_dtype="void") + add1292: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1085, 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])) + permute_dims1087: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_out_proj_weight5, axes=None) + matmul1086: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1431, permute_dims1087, out_dtype="void") + add1293: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1086, 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) + permute_dims1088: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_encoder_attn_q_proj_weight5, axes=None) + matmul1087: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm378, permute_dims1088, out_dtype="void") + add1295: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1087, 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])) + permute_dims1089: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_encoder_attn_out_proj_weight5, axes=None) + matmul1088: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1435, permute_dims1089, out_dtype="void") + add1296: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1088, 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) + permute_dims1090: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_7_fc1_weight5, axes=None) + matmul1089: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm379, permute_dims1090, out_dtype="void") + add1298: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1089, model_decoder_layers_7_fc1_bias5) + gelu137: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1298) + permute_dims1091: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_fc2_weight5, axes=None) + matmul1090: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu137, permute_dims1091, out_dtype="void") + add1299: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1090, 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) + permute_dims1092: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_q_proj_weight5, axes=None) + matmul1091: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm380, permute_dims1092, out_dtype="void") + add1301: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1091, 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])) + permute_dims1093: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_k_proj_weight5, axes=None) + matmul1092: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm380, permute_dims1093, out_dtype="void") + reshape1437: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1092, R.shape([1, 1, 20, 64])) + permute_dims1094: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_v_proj_weight5, axes=None) + matmul1093: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm380, permute_dims1094, out_dtype="void") + add1302: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1093, 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])) + permute_dims1095: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_out_proj_weight5, axes=None) + matmul1094: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1441, permute_dims1095, out_dtype="void") + add1303: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1094, 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) + permute_dims1096: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_encoder_attn_q_proj_weight5, axes=None) + matmul1095: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm381, permute_dims1096, out_dtype="void") + add1305: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1095, 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])) + permute_dims1097: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_encoder_attn_out_proj_weight5, axes=None) + matmul1096: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1445, permute_dims1097, out_dtype="void") + add1306: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1096, 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) + permute_dims1098: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_8_fc1_weight5, axes=None) + matmul1097: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm382, permute_dims1098, out_dtype="void") + add1308: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1097, model_decoder_layers_8_fc1_bias5) + gelu138: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1308) + permute_dims1099: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_fc2_weight5, axes=None) + matmul1098: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu138, permute_dims1099, out_dtype="void") + add1309: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1098, 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) + permute_dims1100: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_q_proj_weight5, axes=None) + matmul1099: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm383, permute_dims1100, out_dtype="void") + add1311: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1099, 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])) + permute_dims1101: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_k_proj_weight5, axes=None) + matmul1100: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm383, permute_dims1101, out_dtype="void") + reshape1447: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1100, R.shape([1, 1, 20, 64])) + permute_dims1102: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_v_proj_weight5, axes=None) + matmul1101: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm383, permute_dims1102, out_dtype="void") + add1312: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1101, 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])) + permute_dims1103: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_out_proj_weight5, axes=None) + matmul1102: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1451, permute_dims1103, out_dtype="void") + add1313: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1102, 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) + permute_dims1104: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_encoder_attn_q_proj_weight5, axes=None) + matmul1103: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm384, permute_dims1104, out_dtype="void") + add1315: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1103, 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])) + permute_dims1105: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_encoder_attn_out_proj_weight5, axes=None) + matmul1104: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1455, permute_dims1105, out_dtype="void") + add1316: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1104, 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) + permute_dims1106: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_9_fc1_weight5, axes=None) + matmul1105: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm385, permute_dims1106, out_dtype="void") + add1318: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1105, model_decoder_layers_9_fc1_bias5) + gelu139: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1318) + permute_dims1107: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_fc2_weight5, axes=None) + matmul1106: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu139, permute_dims1107, out_dtype="void") + add1319: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1106, 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) + permute_dims1108: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_q_proj_weight5, axes=None) + matmul1107: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm386, permute_dims1108, out_dtype="void") + add1321: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1107, 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])) + permute_dims1109: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_k_proj_weight5, axes=None) + matmul1108: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm386, permute_dims1109, out_dtype="void") + reshape1457: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1108, R.shape([1, 1, 20, 64])) + permute_dims1110: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_v_proj_weight5, axes=None) + matmul1109: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm386, permute_dims1110, out_dtype="void") + add1322: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1109, 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])) + permute_dims1111: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_out_proj_weight5, axes=None) + matmul1110: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1461, permute_dims1111, out_dtype="void") + add1323: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1110, 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) + permute_dims1112: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_encoder_attn_q_proj_weight5, axes=None) + matmul1111: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm387, permute_dims1112, out_dtype="void") + add1325: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1111, 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])) + permute_dims1113: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_encoder_attn_out_proj_weight5, axes=None) + matmul1112: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1465, permute_dims1113, out_dtype="void") + add1326: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1112, 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) + permute_dims1114: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_10_fc1_weight5, axes=None) + matmul1113: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm388, permute_dims1114, out_dtype="void") + add1328: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1113, model_decoder_layers_10_fc1_bias5) + gelu140: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1328) + permute_dims1115: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_fc2_weight5, axes=None) + matmul1114: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu140, permute_dims1115, out_dtype="void") + add1329: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1114, 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) + permute_dims1116: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_q_proj_weight5, axes=None) + matmul1115: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm389, permute_dims1116, out_dtype="void") + add1331: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1115, 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])) + permute_dims1117: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_k_proj_weight5, axes=None) + matmul1116: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm389, permute_dims1117, out_dtype="void") + reshape1467: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1116, R.shape([1, 1, 20, 64])) + permute_dims1118: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_v_proj_weight5, axes=None) + matmul1117: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm389, permute_dims1118, out_dtype="void") + add1332: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1117, 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])) + permute_dims1119: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_out_proj_weight5, axes=None) + matmul1118: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1471, permute_dims1119, out_dtype="void") + add1333: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1118, 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) + permute_dims1120: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_encoder_attn_q_proj_weight5, axes=None) + matmul1119: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm390, permute_dims1120, out_dtype="void") + add1335: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1119, 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])) + permute_dims1121: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_encoder_attn_out_proj_weight5, axes=None) + matmul1120: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1475, permute_dims1121, out_dtype="void") + add1336: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1120, 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) + permute_dims1122: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_11_fc1_weight5, axes=None) + matmul1121: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm391, permute_dims1122, out_dtype="void") + add1338: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1121, model_decoder_layers_11_fc1_bias5) + gelu141: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1338) + permute_dims1123: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_fc2_weight5, axes=None) + matmul1122: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu141, permute_dims1123, out_dtype="void") + add1339: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1122, 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) + permute_dims1124: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_q_proj_weight5, axes=None) + matmul1123: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm392, permute_dims1124, out_dtype="void") + add1341: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1123, 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])) + permute_dims1125: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_k_proj_weight5, axes=None) + matmul1124: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm392, permute_dims1125, out_dtype="void") + reshape1477: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1124, R.shape([1, 1, 20, 64])) + permute_dims1126: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_v_proj_weight5, axes=None) + matmul1125: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm392, permute_dims1126, out_dtype="void") + add1342: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1125, 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])) + permute_dims1127: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_out_proj_weight5, axes=None) + matmul1126: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1481, permute_dims1127, out_dtype="void") + add1343: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1126, 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) + permute_dims1128: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_encoder_attn_q_proj_weight5, axes=None) + matmul1127: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm393, permute_dims1128, out_dtype="void") + add1345: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1127, 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])) + permute_dims1129: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_encoder_attn_out_proj_weight5, axes=None) + matmul1128: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1485, permute_dims1129, out_dtype="void") + add1346: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1128, 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) + permute_dims1130: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_12_fc1_weight5, axes=None) + matmul1129: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm394, permute_dims1130, out_dtype="void") + add1348: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1129, model_decoder_layers_12_fc1_bias5) + gelu142: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1348) + permute_dims1131: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_fc2_weight5, axes=None) + matmul1130: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu142, permute_dims1131, out_dtype="void") + add1349: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1130, 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) + permute_dims1132: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_q_proj_weight5, axes=None) + matmul1131: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm395, permute_dims1132, out_dtype="void") + add1351: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1131, 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])) + permute_dims1133: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_k_proj_weight5, axes=None) + matmul1132: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm395, permute_dims1133, out_dtype="void") + reshape1487: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1132, R.shape([1, 1, 20, 64])) + permute_dims1134: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_v_proj_weight5, axes=None) + matmul1133: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm395, permute_dims1134, out_dtype="void") + add1352: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1133, 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])) + permute_dims1135: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_out_proj_weight5, axes=None) + matmul1134: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1491, permute_dims1135, out_dtype="void") + add1353: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1134, 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) + permute_dims1136: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_encoder_attn_q_proj_weight5, axes=None) + matmul1135: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm396, permute_dims1136, out_dtype="void") + add1355: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1135, 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])) + permute_dims1137: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_encoder_attn_out_proj_weight5, axes=None) + matmul1136: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1495, permute_dims1137, out_dtype="void") + add1356: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1136, 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) + permute_dims1138: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_13_fc1_weight5, axes=None) + matmul1137: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm397, permute_dims1138, out_dtype="void") + add1358: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1137, model_decoder_layers_13_fc1_bias5) + gelu143: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1358) + permute_dims1139: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_fc2_weight5, axes=None) + matmul1138: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu143, permute_dims1139, out_dtype="void") + add1359: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1138, 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) + permute_dims1140: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_q_proj_weight5, axes=None) + matmul1139: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm398, permute_dims1140, out_dtype="void") + add1361: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1139, 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])) + permute_dims1141: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_k_proj_weight5, axes=None) + matmul1140: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm398, permute_dims1141, out_dtype="void") + reshape1497: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1140, R.shape([1, 1, 20, 64])) + permute_dims1142: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_v_proj_weight5, axes=None) + matmul1141: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm398, permute_dims1142, out_dtype="void") + add1362: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1141, 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])) + permute_dims1143: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_out_proj_weight5, axes=None) + matmul1142: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1501, permute_dims1143, out_dtype="void") + add1363: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1142, 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) + permute_dims1144: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_encoder_attn_q_proj_weight5, axes=None) + matmul1143: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm399, permute_dims1144, out_dtype="void") + add1365: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1143, 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])) + permute_dims1145: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_encoder_attn_out_proj_weight5, axes=None) + matmul1144: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1505, permute_dims1145, out_dtype="void") + add1366: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1144, 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) + permute_dims1146: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_14_fc1_weight5, axes=None) + matmul1145: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm400, permute_dims1146, out_dtype="void") + add1368: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1145, model_decoder_layers_14_fc1_bias5) + gelu144: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1368) + permute_dims1147: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_fc2_weight5, axes=None) + matmul1146: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu144, permute_dims1147, out_dtype="void") + add1369: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1146, 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) + permute_dims1148: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_q_proj_weight5, axes=None) + matmul1147: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm401, permute_dims1148, out_dtype="void") + add1371: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1147, 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])) + permute_dims1149: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_k_proj_weight5, axes=None) + matmul1148: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm401, permute_dims1149, out_dtype="void") + reshape1507: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1148, R.shape([1, 1, 20, 64])) + permute_dims1150: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_v_proj_weight5, axes=None) + matmul1149: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm401, permute_dims1150, out_dtype="void") + add1372: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1149, 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])) + permute_dims1151: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_out_proj_weight5, axes=None) + matmul1150: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1511, permute_dims1151, out_dtype="void") + add1373: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1150, 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) + permute_dims1152: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_encoder_attn_q_proj_weight5, axes=None) + matmul1151: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm402, permute_dims1152, out_dtype="void") + add1375: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1151, 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])) + permute_dims1153: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_encoder_attn_out_proj_weight5, axes=None) + matmul1152: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1515, permute_dims1153, out_dtype="void") + add1376: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1152, 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) + permute_dims1154: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_15_fc1_weight5, axes=None) + matmul1153: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm403, permute_dims1154, out_dtype="void") + add1378: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1153, model_decoder_layers_15_fc1_bias5) + gelu145: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1378) + permute_dims1155: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_fc2_weight5, axes=None) + matmul1154: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu145, permute_dims1155, out_dtype="void") + add1379: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1154, 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) + permute_dims1156: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_q_proj_weight5, axes=None) + matmul1155: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm404, permute_dims1156, out_dtype="void") + add1381: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1155, 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])) + permute_dims1157: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_k_proj_weight5, axes=None) + matmul1156: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm404, permute_dims1157, out_dtype="void") + reshape1517: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1156, R.shape([1, 1, 20, 64])) + permute_dims1158: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_v_proj_weight5, axes=None) + matmul1157: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm404, permute_dims1158, out_dtype="void") + add1382: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1157, 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])) + permute_dims1159: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_out_proj_weight5, axes=None) + matmul1158: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1521, permute_dims1159, out_dtype="void") + add1383: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1158, 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) + permute_dims1160: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_encoder_attn_q_proj_weight5, axes=None) + matmul1159: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm405, permute_dims1160, out_dtype="void") + add1385: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1159, 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])) + permute_dims1161: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_encoder_attn_out_proj_weight5, axes=None) + matmul1160: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1525, permute_dims1161, out_dtype="void") + add1386: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1160, 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) + permute_dims1162: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_16_fc1_weight5, axes=None) + matmul1161: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm406, permute_dims1162, out_dtype="void") + add1388: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1161, model_decoder_layers_16_fc1_bias5) + gelu146: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1388) + permute_dims1163: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_fc2_weight5, axes=None) + matmul1162: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu146, permute_dims1163, out_dtype="void") + add1389: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1162, 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) + permute_dims1164: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_q_proj_weight5, axes=None) + matmul1163: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm407, permute_dims1164, out_dtype="void") + add1391: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1163, 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])) + permute_dims1165: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_k_proj_weight5, axes=None) + matmul1164: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm407, permute_dims1165, out_dtype="void") + reshape1527: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1164, R.shape([1, 1, 20, 64])) + permute_dims1166: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_v_proj_weight5, axes=None) + matmul1165: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm407, permute_dims1166, out_dtype="void") + add1392: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1165, 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])) + permute_dims1167: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_out_proj_weight5, axes=None) + matmul1166: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1531, permute_dims1167, out_dtype="void") + add1393: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1166, 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) + permute_dims1168: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_encoder_attn_q_proj_weight5, axes=None) + matmul1167: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm408, permute_dims1168, out_dtype="void") + add1395: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1167, 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])) + permute_dims1169: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_encoder_attn_out_proj_weight5, axes=None) + matmul1168: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1535, permute_dims1169, out_dtype="void") + add1396: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1168, 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) + permute_dims1170: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_17_fc1_weight5, axes=None) + matmul1169: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm409, permute_dims1170, out_dtype="void") + add1398: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1169, model_decoder_layers_17_fc1_bias5) + gelu147: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1398) + permute_dims1171: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_fc2_weight5, axes=None) + matmul1170: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu147, permute_dims1171, out_dtype="void") + add1399: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1170, 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) + permute_dims1172: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_q_proj_weight5, axes=None) + matmul1171: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm410, permute_dims1172, out_dtype="void") + add1401: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1171, 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])) + permute_dims1173: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_k_proj_weight5, axes=None) + matmul1172: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm410, permute_dims1173, out_dtype="void") + reshape1537: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1172, R.shape([1, 1, 20, 64])) + permute_dims1174: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_v_proj_weight5, axes=None) + matmul1173: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm410, permute_dims1174, out_dtype="void") + add1402: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1173, 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])) + permute_dims1175: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_out_proj_weight5, axes=None) + matmul1174: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1541, permute_dims1175, out_dtype="void") + add1403: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1174, 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) + permute_dims1176: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_encoder_attn_q_proj_weight5, axes=None) + matmul1175: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm411, permute_dims1176, out_dtype="void") + add1405: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1175, 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])) + permute_dims1177: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_encoder_attn_out_proj_weight5, axes=None) + matmul1176: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1545, permute_dims1177, out_dtype="void") + add1406: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1176, 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) + permute_dims1178: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_18_fc1_weight5, axes=None) + matmul1177: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm412, permute_dims1178, out_dtype="void") + add1408: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1177, model_decoder_layers_18_fc1_bias5) + gelu148: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1408) + permute_dims1179: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_fc2_weight5, axes=None) + matmul1178: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu148, permute_dims1179, out_dtype="void") + add1409: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1178, 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) + permute_dims1180: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_q_proj_weight5, axes=None) + matmul1179: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm413, permute_dims1180, out_dtype="void") + add1411: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1179, 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])) + permute_dims1181: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_k_proj_weight5, axes=None) + matmul1180: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm413, permute_dims1181, out_dtype="void") + reshape1547: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1180, R.shape([1, 1, 20, 64])) + permute_dims1182: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_v_proj_weight5, axes=None) + matmul1181: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm413, permute_dims1182, out_dtype="void") + add1412: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1181, 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])) + permute_dims1183: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_out_proj_weight5, axes=None) + matmul1182: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1551, permute_dims1183, out_dtype="void") + add1413: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1182, 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) + permute_dims1184: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_encoder_attn_q_proj_weight5, axes=None) + matmul1183: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm414, permute_dims1184, out_dtype="void") + add1415: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1183, 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])) + permute_dims1185: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_encoder_attn_out_proj_weight5, axes=None) + matmul1184: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1555, permute_dims1185, out_dtype="void") + add1416: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1184, 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) + permute_dims1186: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_19_fc1_weight5, axes=None) + matmul1185: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm415, permute_dims1186, out_dtype="void") + add1418: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1185, model_decoder_layers_19_fc1_bias5) + gelu149: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1418) + permute_dims1187: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_fc2_weight5, axes=None) + matmul1186: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu149, permute_dims1187, out_dtype="void") + add1419: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1186, 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) + permute_dims1188: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_q_proj_weight5, axes=None) + matmul1187: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm416, permute_dims1188, out_dtype="void") + add1421: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1187, 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])) + permute_dims1189: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_k_proj_weight5, axes=None) + matmul1188: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm416, permute_dims1189, out_dtype="void") + reshape1557: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1188, R.shape([1, 1, 20, 64])) + permute_dims1190: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_v_proj_weight5, axes=None) + matmul1189: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm416, permute_dims1190, out_dtype="void") + add1422: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1189, 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])) + permute_dims1191: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_out_proj_weight5, axes=None) + matmul1190: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1561, permute_dims1191, out_dtype="void") + add1423: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1190, 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) + permute_dims1192: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_encoder_attn_q_proj_weight5, axes=None) + matmul1191: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm417, permute_dims1192, out_dtype="void") + add1425: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1191, 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])) + permute_dims1193: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_encoder_attn_out_proj_weight5, axes=None) + matmul1192: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1565, permute_dims1193, out_dtype="void") + add1426: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1192, 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) + permute_dims1194: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_20_fc1_weight5, axes=None) + matmul1193: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm418, permute_dims1194, out_dtype="void") + add1428: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1193, model_decoder_layers_20_fc1_bias5) + gelu150: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1428) + permute_dims1195: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_fc2_weight5, axes=None) + matmul1194: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu150, permute_dims1195, out_dtype="void") + add1429: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1194, 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) + permute_dims1196: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_q_proj_weight5, axes=None) + matmul1195: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm419, permute_dims1196, out_dtype="void") + add1431: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1195, 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])) + permute_dims1197: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_k_proj_weight5, axes=None) + matmul1196: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm419, permute_dims1197, out_dtype="void") + reshape1567: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1196, R.shape([1, 1, 20, 64])) + permute_dims1198: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_v_proj_weight5, axes=None) + matmul1197: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm419, permute_dims1198, out_dtype="void") + add1432: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1197, 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])) + permute_dims1199: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_out_proj_weight5, axes=None) + matmul1198: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1571, permute_dims1199, out_dtype="void") + add1433: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1198, 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) + permute_dims1200: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_encoder_attn_q_proj_weight5, axes=None) + matmul1199: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm420, permute_dims1200, out_dtype="void") + add1435: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1199, 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])) + permute_dims1201: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_encoder_attn_out_proj_weight5, axes=None) + matmul1200: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1575, permute_dims1201, out_dtype="void") + add1436: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1200, 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) + permute_dims1202: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_21_fc1_weight5, axes=None) + matmul1201: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm421, permute_dims1202, out_dtype="void") + add1438: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1201, model_decoder_layers_21_fc1_bias5) + gelu151: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1438) + permute_dims1203: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_fc2_weight5, axes=None) + matmul1202: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu151, permute_dims1203, out_dtype="void") + add1439: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1202, 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) + permute_dims1204: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_q_proj_weight5, axes=None) + matmul1203: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm422, permute_dims1204, out_dtype="void") + add1441: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1203, 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])) + permute_dims1205: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_k_proj_weight5, axes=None) + matmul1204: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm422, permute_dims1205, out_dtype="void") + reshape1577: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1204, R.shape([1, 1, 20, 64])) + permute_dims1206: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_v_proj_weight5, axes=None) + matmul1205: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm422, permute_dims1206, out_dtype="void") + add1442: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1205, 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])) + permute_dims1207: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_out_proj_weight5, axes=None) + matmul1206: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1581, permute_dims1207, out_dtype="void") + add1443: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1206, 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) + permute_dims1208: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_encoder_attn_q_proj_weight5, axes=None) + matmul1207: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm423, permute_dims1208, out_dtype="void") + add1445: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1207, 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])) + permute_dims1209: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_encoder_attn_out_proj_weight5, axes=None) + matmul1208: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1585, permute_dims1209, out_dtype="void") + add1446: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1208, 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) + permute_dims1210: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_22_fc1_weight5, axes=None) + matmul1209: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm424, permute_dims1210, out_dtype="void") + add1448: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1209, model_decoder_layers_22_fc1_bias5) + gelu152: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1448) + permute_dims1211: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_fc2_weight5, axes=None) + matmul1210: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu152, permute_dims1211, out_dtype="void") + add1449: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1210, 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) + permute_dims1212: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_q_proj_weight5, axes=None) + matmul1211: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm425, permute_dims1212, out_dtype="void") + add1451: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1211, 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])) + permute_dims1213: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_k_proj_weight5, axes=None) + matmul1212: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm425, permute_dims1213, out_dtype="void") + reshape1587: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1212, R.shape([1, 1, 20, 64])) + permute_dims1214: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_v_proj_weight5, axes=None) + matmul1213: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm425, permute_dims1214, out_dtype="void") + add1452: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1213, 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])) + permute_dims1215: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_out_proj_weight5, axes=None) + matmul1214: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1591, permute_dims1215, out_dtype="void") + add1453: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1214, 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) + permute_dims1216: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_encoder_attn_q_proj_weight5, axes=None) + matmul1215: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm426, permute_dims1216, out_dtype="void") + add1455: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1215, 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])) + permute_dims1217: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_encoder_attn_out_proj_weight5, axes=None) + matmul1216: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1595, permute_dims1217, out_dtype="void") + add1456: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1216, 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) + permute_dims1218: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_23_fc1_weight5, axes=None) + matmul1217: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm427, permute_dims1218, out_dtype="void") + add1458: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1217, model_decoder_layers_23_fc1_bias5) + gelu153: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1458) + permute_dims1219: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_fc2_weight5, axes=None) + matmul1218: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu153, permute_dims1219, out_dtype="void") + add1459: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1218, 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) + permute_dims1220: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_q_proj_weight5, axes=None) + matmul1219: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm428, permute_dims1220, out_dtype="void") + add1461: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1219, 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])) + permute_dims1221: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_k_proj_weight5, axes=None) + matmul1220: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm428, permute_dims1221, out_dtype="void") + reshape1597: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1220, R.shape([1, 1, 20, 64])) + permute_dims1222: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_v_proj_weight5, axes=None) + matmul1221: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm428, permute_dims1222, out_dtype="void") + add1462: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1221, 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])) + permute_dims1223: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_out_proj_weight5, axes=None) + matmul1222: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1601, permute_dims1223, out_dtype="void") + add1463: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1222, 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) + permute_dims1224: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_encoder_attn_q_proj_weight5, axes=None) + matmul1223: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm429, permute_dims1224, out_dtype="void") + add1465: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1223, 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])) + permute_dims1225: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_encoder_attn_out_proj_weight5, axes=None) + matmul1224: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1605, permute_dims1225, out_dtype="void") + add1466: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1224, 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) + permute_dims1226: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_24_fc1_weight5, axes=None) + matmul1225: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm430, permute_dims1226, out_dtype="void") + add1468: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1225, model_decoder_layers_24_fc1_bias5) + gelu154: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1468) + permute_dims1227: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_fc2_weight5, axes=None) + matmul1226: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu154, permute_dims1227, out_dtype="void") + add1469: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1226, 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) + permute_dims1228: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_q_proj_weight5, axes=None) + matmul1227: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm431, permute_dims1228, out_dtype="void") + add1471: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1227, 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])) + permute_dims1229: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_k_proj_weight5, axes=None) + matmul1228: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm431, permute_dims1229, out_dtype="void") + reshape1607: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1228, R.shape([1, 1, 20, 64])) + permute_dims1230: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_v_proj_weight5, axes=None) + matmul1229: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm431, permute_dims1230, out_dtype="void") + add1472: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1229, 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])) + permute_dims1231: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_out_proj_weight5, axes=None) + matmul1230: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1611, permute_dims1231, out_dtype="void") + add1473: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1230, 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) + permute_dims1232: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_encoder_attn_q_proj_weight5, axes=None) + matmul1231: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm432, permute_dims1232, out_dtype="void") + add1475: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1231, 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])) + permute_dims1233: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_encoder_attn_out_proj_weight5, axes=None) + matmul1232: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1615, permute_dims1233, out_dtype="void") + add1476: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1232, 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) + permute_dims1234: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_25_fc1_weight5, axes=None) + matmul1233: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm433, permute_dims1234, out_dtype="void") + add1478: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1233, model_decoder_layers_25_fc1_bias5) + gelu155: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1478) + permute_dims1235: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_fc2_weight5, axes=None) + matmul1234: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu155, permute_dims1235, out_dtype="void") + add1479: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1234, 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) + permute_dims1236: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_q_proj_weight5, axes=None) + matmul1235: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm434, permute_dims1236, out_dtype="void") + add1481: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1235, 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])) + permute_dims1237: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_k_proj_weight5, axes=None) + matmul1236: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm434, permute_dims1237, out_dtype="void") + reshape1617: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1236, R.shape([1, 1, 20, 64])) + permute_dims1238: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_v_proj_weight5, axes=None) + matmul1237: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm434, permute_dims1238, out_dtype="void") + add1482: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1237, 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])) + permute_dims1239: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_out_proj_weight5, axes=None) + matmul1238: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1621, permute_dims1239, out_dtype="void") + add1483: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1238, 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) + permute_dims1240: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_encoder_attn_q_proj_weight5, axes=None) + matmul1239: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm435, permute_dims1240, out_dtype="void") + add1485: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1239, 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])) + permute_dims1241: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_encoder_attn_out_proj_weight5, axes=None) + matmul1240: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1625, permute_dims1241, out_dtype="void") + add1486: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1240, 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) + permute_dims1242: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_26_fc1_weight5, axes=None) + matmul1241: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm436, permute_dims1242, out_dtype="void") + add1488: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1241, model_decoder_layers_26_fc1_bias5) + gelu156: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1488) + permute_dims1243: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_fc2_weight5, axes=None) + matmul1242: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu156, permute_dims1243, out_dtype="void") + add1489: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1242, 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) + permute_dims1244: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_q_proj_weight5, axes=None) + matmul1243: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm437, permute_dims1244, out_dtype="void") + add1491: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1243, 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])) + permute_dims1245: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_k_proj_weight5, axes=None) + matmul1244: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm437, permute_dims1245, out_dtype="void") + reshape1627: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1244, R.shape([1, 1, 20, 64])) + permute_dims1246: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_v_proj_weight5, axes=None) + matmul1245: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm437, permute_dims1246, out_dtype="void") + add1492: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1245, 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])) + permute_dims1247: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_out_proj_weight5, axes=None) + matmul1246: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1631, permute_dims1247, out_dtype="void") + add1493: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1246, 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) + permute_dims1248: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_encoder_attn_q_proj_weight5, axes=None) + matmul1247: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm438, permute_dims1248, out_dtype="void") + add1495: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1247, 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])) + permute_dims1249: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_encoder_attn_out_proj_weight5, axes=None) + matmul1248: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1635, permute_dims1249, out_dtype="void") + add1496: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1248, 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) + permute_dims1250: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_27_fc1_weight5, axes=None) + matmul1249: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm439, permute_dims1250, out_dtype="void") + add1498: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1249, model_decoder_layers_27_fc1_bias5) + gelu157: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1498) + permute_dims1251: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_fc2_weight5, axes=None) + matmul1250: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu157, permute_dims1251, out_dtype="void") + add1499: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1250, 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) + permute_dims1252: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_q_proj_weight5, axes=None) + matmul1251: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm440, permute_dims1252, out_dtype="void") + add1501: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1251, 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])) + permute_dims1253: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_k_proj_weight5, axes=None) + matmul1252: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm440, permute_dims1253, out_dtype="void") + reshape1637: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1252, R.shape([1, 1, 20, 64])) + permute_dims1254: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_v_proj_weight5, axes=None) + matmul1253: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm440, permute_dims1254, out_dtype="void") + add1502: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1253, 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])) + permute_dims1255: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_out_proj_weight5, axes=None) + matmul1254: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1641, permute_dims1255, out_dtype="void") + add1503: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1254, 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) + permute_dims1256: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_encoder_attn_q_proj_weight5, axes=None) + matmul1255: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm441, permute_dims1256, out_dtype="void") + add1505: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1255, 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])) + permute_dims1257: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_encoder_attn_out_proj_weight5, axes=None) + matmul1256: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1645, permute_dims1257, out_dtype="void") + add1506: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1256, 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) + permute_dims1258: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_28_fc1_weight5, axes=None) + matmul1257: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm442, permute_dims1258, out_dtype="void") + add1508: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1257, model_decoder_layers_28_fc1_bias5) + gelu158: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1508) + permute_dims1259: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_fc2_weight5, axes=None) + matmul1258: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu158, permute_dims1259, out_dtype="void") + add1509: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1258, 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) + permute_dims1260: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_q_proj_weight5, axes=None) + matmul1259: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm443, permute_dims1260, out_dtype="void") + add1511: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1259, 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])) + permute_dims1261: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_k_proj_weight5, axes=None) + matmul1260: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm443, permute_dims1261, out_dtype="void") + reshape1647: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1260, R.shape([1, 1, 20, 64])) + permute_dims1262: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_v_proj_weight5, axes=None) + matmul1261: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm443, permute_dims1262, out_dtype="void") + add1512: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1261, 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])) + permute_dims1263: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_out_proj_weight5, axes=None) + matmul1262: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1651, permute_dims1263, out_dtype="void") + add1513: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1262, 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) + permute_dims1264: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_encoder_attn_q_proj_weight5, axes=None) + matmul1263: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm444, permute_dims1264, out_dtype="void") + add1515: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1263, 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])) + permute_dims1265: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_encoder_attn_out_proj_weight5, axes=None) + matmul1264: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1655, permute_dims1265, out_dtype="void") + add1516: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1264, 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) + permute_dims1266: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_29_fc1_weight5, axes=None) + matmul1265: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm445, permute_dims1266, out_dtype="void") + add1518: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1265, model_decoder_layers_29_fc1_bias5) + gelu159: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1518) + permute_dims1267: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_fc2_weight5, axes=None) + matmul1266: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu159, permute_dims1267, out_dtype="void") + add1519: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1266, 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) + permute_dims1268: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_q_proj_weight5, axes=None) + matmul1267: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm446, permute_dims1268, out_dtype="void") + add1521: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1267, 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])) + permute_dims1269: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_k_proj_weight5, axes=None) + matmul1268: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm446, permute_dims1269, out_dtype="void") + reshape1657: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1268, R.shape([1, 1, 20, 64])) + permute_dims1270: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_v_proj_weight5, axes=None) + matmul1269: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm446, permute_dims1270, out_dtype="void") + add1522: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1269, 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])) + permute_dims1271: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_out_proj_weight5, axes=None) + matmul1270: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1661, permute_dims1271, out_dtype="void") + add1523: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1270, 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) + permute_dims1272: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_encoder_attn_q_proj_weight5, axes=None) + matmul1271: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm447, permute_dims1272, out_dtype="void") + add1525: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1271, 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])) + permute_dims1273: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_encoder_attn_out_proj_weight5, axes=None) + matmul1272: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1665, permute_dims1273, out_dtype="void") + add1526: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1272, 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) + permute_dims1274: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_30_fc1_weight5, axes=None) + matmul1273: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm448, permute_dims1274, out_dtype="void") + add1528: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1273, model_decoder_layers_30_fc1_bias5) + gelu160: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1528) + permute_dims1275: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_fc2_weight5, axes=None) + matmul1274: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu160, permute_dims1275, out_dtype="void") + add1529: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1274, 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) + permute_dims1276: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_q_proj_weight5, axes=None) + matmul1275: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm449, permute_dims1276, out_dtype="void") + add1531: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1275, 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])) + permute_dims1277: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_k_proj_weight5, axes=None) + matmul1276: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm449, permute_dims1277, out_dtype="void") + reshape1667: R.Tensor((1, 1, 20, 64), dtype="float16") = R.reshape(matmul1276, R.shape([1, 1, 20, 64])) + permute_dims1278: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_v_proj_weight5, axes=None) + matmul1277: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm449, permute_dims1278, out_dtype="void") + add1532: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1277, 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])) + permute_dims1279: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_out_proj_weight5, axes=None) + matmul1278: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1671, permute_dims1279, out_dtype="void") + add1533: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1278, 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) + permute_dims1280: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_encoder_attn_q_proj_weight5, axes=None) + matmul1279: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(layer_norm450, permute_dims1280, out_dtype="void") + add1535: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1279, 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])) + permute_dims1281: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_encoder_attn_out_proj_weight5, axes=None) + matmul1280: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(reshape1675, permute_dims1281, out_dtype="void") + add1536: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1280, 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) + permute_dims1282: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_31_fc1_weight5, axes=None) + matmul1281: R.Tensor((1, 1, 5120), dtype="float16") = R.matmul(layer_norm451, permute_dims1282, out_dtype="void") + add1538: R.Tensor((1, 1, 5120), dtype="float16") = R.add(matmul1281, model_decoder_layers_31_fc1_bias5) + gelu161: R.Tensor((1, 1, 5120), dtype="float16") = R.nn.gelu(add1538) + permute_dims1283: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_fc2_weight5, axes=None) + matmul1282: R.Tensor((1, 1, 1280), dtype="float16") = R.matmul(gelu161, permute_dims1283, out_dtype="void") + add1539: R.Tensor((1, 1, 1280), dtype="float16") = R.add(matmul1282, 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) + permute_dims1284: R.Tensor((1280, 51866), dtype="float16") = R.permute_dims(model_decoder_embed_tokens_weight5, axes=None) + matmul1283: R.Tensor((1, 1, 51866), dtype="float32") = R.matmul(layer_norm452, permute_dims1284, out_dtype="float32") + gv5: R.Tensor((1, 1, 51866), dtype="float32") = matmul1283 + R.output(gv5) + return gv5 + + @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_encoder_conv1_weight4: R.Tensor((1280, 128, 3), dtype="float16") = packed_params[0] + model_encoder_conv1_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1] + model_encoder_conv2_weight4: R.Tensor((1280, 1280, 3), dtype="float16") = packed_params[2] + model_encoder_conv2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[3] + model_encoder_embed_positions_weight4: R.Tensor((1500, 1280), dtype="float16") = packed_params[4] + model_encoder_layers_0_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[5] + model_encoder_layers_0_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[6] + model_encoder_layers_0_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[7] + model_encoder_layers_0_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[8] + model_encoder_layers_0_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[9] + model_encoder_layers_0_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[10] + model_encoder_layers_0_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[11] + model_encoder_layers_0_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[12] + model_encoder_layers_0_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[13] + model_encoder_layers_0_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[14] + model_encoder_layers_0_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[15] + model_encoder_layers_0_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[16] + model_encoder_layers_0_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[17] + model_encoder_layers_0_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[18] + model_encoder_layers_0_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[19] + model_encoder_layers_1_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[20] + model_encoder_layers_1_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[21] + model_encoder_layers_1_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[22] + model_encoder_layers_1_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[23] + model_encoder_layers_1_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[24] + model_encoder_layers_1_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[25] + model_encoder_layers_1_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[26] + model_encoder_layers_1_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[27] + model_encoder_layers_1_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[28] + model_encoder_layers_1_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[29] + model_encoder_layers_1_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[30] + model_encoder_layers_1_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[31] + model_encoder_layers_1_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[32] + model_encoder_layers_1_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[33] + model_encoder_layers_1_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[34] + model_encoder_layers_2_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[35] + model_encoder_layers_2_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[36] + model_encoder_layers_2_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[37] + model_encoder_layers_2_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[38] + model_encoder_layers_2_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[39] + model_encoder_layers_2_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[40] + model_encoder_layers_2_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[41] + model_encoder_layers_2_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[42] + model_encoder_layers_2_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[43] + model_encoder_layers_2_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[44] + model_encoder_layers_2_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[45] + model_encoder_layers_2_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[46] + model_encoder_layers_2_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[47] + model_encoder_layers_2_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[48] + model_encoder_layers_2_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[49] + model_encoder_layers_3_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[50] + model_encoder_layers_3_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[51] + model_encoder_layers_3_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[52] + model_encoder_layers_3_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[53] + model_encoder_layers_3_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[54] + model_encoder_layers_3_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[55] + model_encoder_layers_3_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[56] + model_encoder_layers_3_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[57] + model_encoder_layers_3_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[58] + model_encoder_layers_3_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[59] + model_encoder_layers_3_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[60] + model_encoder_layers_3_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[61] + model_encoder_layers_3_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[62] + model_encoder_layers_3_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[63] + model_encoder_layers_3_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[64] + model_encoder_layers_4_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[65] + model_encoder_layers_4_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[66] + model_encoder_layers_4_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[67] + model_encoder_layers_4_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[68] + model_encoder_layers_4_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[69] + model_encoder_layers_4_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[70] + model_encoder_layers_4_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[71] + model_encoder_layers_4_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[72] + model_encoder_layers_4_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[73] + model_encoder_layers_4_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[74] + model_encoder_layers_4_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[75] + model_encoder_layers_4_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[76] + model_encoder_layers_4_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[77] + model_encoder_layers_4_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[78] + model_encoder_layers_4_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[79] + model_encoder_layers_5_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[80] + model_encoder_layers_5_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[81] + model_encoder_layers_5_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[82] + model_encoder_layers_5_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[83] + model_encoder_layers_5_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[84] + model_encoder_layers_5_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[85] + model_encoder_layers_5_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[86] + model_encoder_layers_5_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[87] + model_encoder_layers_5_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[88] + model_encoder_layers_5_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[89] + model_encoder_layers_5_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[90] + model_encoder_layers_5_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[91] + model_encoder_layers_5_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[92] + model_encoder_layers_5_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[93] + model_encoder_layers_5_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[94] + model_encoder_layers_6_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[95] + model_encoder_layers_6_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[96] + model_encoder_layers_6_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[97] + model_encoder_layers_6_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[98] + model_encoder_layers_6_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[99] + model_encoder_layers_6_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[100] + model_encoder_layers_6_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[101] + model_encoder_layers_6_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[102] + model_encoder_layers_6_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[103] + model_encoder_layers_6_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[104] + model_encoder_layers_6_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[105] + model_encoder_layers_6_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[106] + model_encoder_layers_6_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[107] + model_encoder_layers_6_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[108] + model_encoder_layers_6_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[109] + model_encoder_layers_7_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[110] + model_encoder_layers_7_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[111] + model_encoder_layers_7_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[112] + model_encoder_layers_7_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[113] + model_encoder_layers_7_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[114] + model_encoder_layers_7_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[115] + model_encoder_layers_7_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[116] + model_encoder_layers_7_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[117] + model_encoder_layers_7_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[118] + model_encoder_layers_7_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[119] + model_encoder_layers_7_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[120] + model_encoder_layers_7_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[121] + model_encoder_layers_7_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[122] + model_encoder_layers_7_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[123] + model_encoder_layers_7_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[124] + model_encoder_layers_8_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[125] + model_encoder_layers_8_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[126] + model_encoder_layers_8_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[127] + model_encoder_layers_8_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[128] + model_encoder_layers_8_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[129] + model_encoder_layers_8_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[130] + model_encoder_layers_8_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[131] + model_encoder_layers_8_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[132] + model_encoder_layers_8_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[133] + model_encoder_layers_8_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[134] + model_encoder_layers_8_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[135] + model_encoder_layers_8_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[136] + model_encoder_layers_8_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[137] + model_encoder_layers_8_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[138] + model_encoder_layers_8_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[139] + model_encoder_layers_9_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[140] + model_encoder_layers_9_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[141] + model_encoder_layers_9_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[142] + model_encoder_layers_9_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[143] + model_encoder_layers_9_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[144] + model_encoder_layers_9_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[145] + model_encoder_layers_9_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[146] + model_encoder_layers_9_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[147] + model_encoder_layers_9_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[148] + model_encoder_layers_9_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[149] + model_encoder_layers_9_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[150] + model_encoder_layers_9_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[151] + model_encoder_layers_9_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[152] + model_encoder_layers_9_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[153] + model_encoder_layers_9_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[154] + model_encoder_layers_10_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[155] + model_encoder_layers_10_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[156] + model_encoder_layers_10_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[157] + model_encoder_layers_10_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[158] + model_encoder_layers_10_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[159] + model_encoder_layers_10_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[160] + model_encoder_layers_10_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[161] + model_encoder_layers_10_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[162] + model_encoder_layers_10_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[163] + model_encoder_layers_10_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[164] + model_encoder_layers_10_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[165] + model_encoder_layers_10_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[166] + model_encoder_layers_10_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[167] + model_encoder_layers_10_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[168] + model_encoder_layers_10_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[169] + model_encoder_layers_11_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[170] + model_encoder_layers_11_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[171] + model_encoder_layers_11_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[172] + model_encoder_layers_11_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[173] + model_encoder_layers_11_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[174] + model_encoder_layers_11_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[175] + model_encoder_layers_11_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[176] + model_encoder_layers_11_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[177] + model_encoder_layers_11_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[178] + model_encoder_layers_11_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[179] + model_encoder_layers_11_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[180] + model_encoder_layers_11_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[181] + model_encoder_layers_11_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[182] + model_encoder_layers_11_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[183] + model_encoder_layers_11_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[184] + model_encoder_layers_12_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[185] + model_encoder_layers_12_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[186] + model_encoder_layers_12_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[187] + model_encoder_layers_12_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[188] + model_encoder_layers_12_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[189] + model_encoder_layers_12_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[190] + model_encoder_layers_12_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[191] + model_encoder_layers_12_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[192] + model_encoder_layers_12_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[193] + model_encoder_layers_12_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[194] + model_encoder_layers_12_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[195] + model_encoder_layers_12_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[196] + model_encoder_layers_12_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[197] + model_encoder_layers_12_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[198] + model_encoder_layers_12_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[199] + model_encoder_layers_13_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[200] + model_encoder_layers_13_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[201] + model_encoder_layers_13_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[202] + model_encoder_layers_13_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[203] + model_encoder_layers_13_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[204] + model_encoder_layers_13_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[205] + model_encoder_layers_13_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[206] + model_encoder_layers_13_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[207] + model_encoder_layers_13_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[208] + model_encoder_layers_13_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[209] + model_encoder_layers_13_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[210] + model_encoder_layers_13_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[211] + model_encoder_layers_13_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[212] + model_encoder_layers_13_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[213] + model_encoder_layers_13_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[214] + model_encoder_layers_14_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[215] + model_encoder_layers_14_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[216] + model_encoder_layers_14_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[217] + model_encoder_layers_14_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[218] + model_encoder_layers_14_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[219] + model_encoder_layers_14_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[220] + model_encoder_layers_14_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[221] + model_encoder_layers_14_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[222] + model_encoder_layers_14_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[223] + model_encoder_layers_14_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[224] + model_encoder_layers_14_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[225] + model_encoder_layers_14_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[226] + model_encoder_layers_14_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[227] + model_encoder_layers_14_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[228] + model_encoder_layers_14_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[229] + model_encoder_layers_15_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[230] + model_encoder_layers_15_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[231] + model_encoder_layers_15_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[232] + model_encoder_layers_15_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[233] + model_encoder_layers_15_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[234] + model_encoder_layers_15_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[235] + model_encoder_layers_15_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[236] + model_encoder_layers_15_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[237] + model_encoder_layers_15_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[238] + model_encoder_layers_15_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[239] + model_encoder_layers_15_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[240] + model_encoder_layers_15_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[241] + model_encoder_layers_15_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[242] + model_encoder_layers_15_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[243] + model_encoder_layers_15_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[244] + model_encoder_layers_16_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[245] + model_encoder_layers_16_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[246] + model_encoder_layers_16_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[247] + model_encoder_layers_16_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[248] + model_encoder_layers_16_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[249] + model_encoder_layers_16_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[250] + model_encoder_layers_16_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[251] + model_encoder_layers_16_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[252] + model_encoder_layers_16_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[253] + model_encoder_layers_16_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[254] + model_encoder_layers_16_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[255] + model_encoder_layers_16_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[256] + model_encoder_layers_16_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[257] + model_encoder_layers_16_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[258] + model_encoder_layers_16_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[259] + model_encoder_layers_17_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[260] + model_encoder_layers_17_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[261] + model_encoder_layers_17_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[262] + model_encoder_layers_17_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[263] + model_encoder_layers_17_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[264] + model_encoder_layers_17_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[265] + model_encoder_layers_17_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[266] + model_encoder_layers_17_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[267] + model_encoder_layers_17_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[268] + model_encoder_layers_17_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[269] + model_encoder_layers_17_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[270] + model_encoder_layers_17_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[271] + model_encoder_layers_17_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[272] + model_encoder_layers_17_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[273] + model_encoder_layers_17_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[274] + model_encoder_layers_18_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[275] + model_encoder_layers_18_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[276] + model_encoder_layers_18_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[277] + model_encoder_layers_18_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[278] + model_encoder_layers_18_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[279] + model_encoder_layers_18_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[280] + model_encoder_layers_18_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[281] + model_encoder_layers_18_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[282] + model_encoder_layers_18_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[283] + model_encoder_layers_18_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[284] + model_encoder_layers_18_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[285] + model_encoder_layers_18_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[286] + model_encoder_layers_18_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[287] + model_encoder_layers_18_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[288] + model_encoder_layers_18_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[289] + model_encoder_layers_19_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[290] + model_encoder_layers_19_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[291] + model_encoder_layers_19_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[292] + model_encoder_layers_19_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[293] + model_encoder_layers_19_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[294] + model_encoder_layers_19_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[295] + model_encoder_layers_19_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[296] + model_encoder_layers_19_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[297] + model_encoder_layers_19_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[298] + model_encoder_layers_19_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[299] + model_encoder_layers_19_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[300] + model_encoder_layers_19_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[301] + model_encoder_layers_19_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[302] + model_encoder_layers_19_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[303] + model_encoder_layers_19_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[304] + model_encoder_layers_20_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[305] + model_encoder_layers_20_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[306] + model_encoder_layers_20_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[307] + model_encoder_layers_20_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[308] + model_encoder_layers_20_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[309] + model_encoder_layers_20_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[310] + model_encoder_layers_20_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[311] + model_encoder_layers_20_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[312] + model_encoder_layers_20_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[313] + model_encoder_layers_20_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[314] + model_encoder_layers_20_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[315] + model_encoder_layers_20_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[316] + model_encoder_layers_20_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[317] + model_encoder_layers_20_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[318] + model_encoder_layers_20_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[319] + model_encoder_layers_21_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[320] + model_encoder_layers_21_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[321] + model_encoder_layers_21_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[322] + model_encoder_layers_21_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[323] + model_encoder_layers_21_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[324] + model_encoder_layers_21_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[325] + model_encoder_layers_21_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[326] + model_encoder_layers_21_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[327] + model_encoder_layers_21_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[328] + model_encoder_layers_21_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[329] + model_encoder_layers_21_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[330] + model_encoder_layers_21_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[331] + model_encoder_layers_21_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[332] + model_encoder_layers_21_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[333] + model_encoder_layers_21_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[334] + model_encoder_layers_22_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[335] + model_encoder_layers_22_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[336] + model_encoder_layers_22_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[337] + model_encoder_layers_22_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[338] + model_encoder_layers_22_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[339] + model_encoder_layers_22_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[340] + model_encoder_layers_22_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[341] + model_encoder_layers_22_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[342] + model_encoder_layers_22_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[343] + model_encoder_layers_22_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[344] + model_encoder_layers_22_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[345] + model_encoder_layers_22_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[346] + model_encoder_layers_22_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[347] + model_encoder_layers_22_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[348] + model_encoder_layers_22_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[349] + model_encoder_layers_23_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[350] + model_encoder_layers_23_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[351] + model_encoder_layers_23_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[352] + model_encoder_layers_23_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[353] + model_encoder_layers_23_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[354] + model_encoder_layers_23_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[355] + model_encoder_layers_23_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[356] + model_encoder_layers_23_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[357] + model_encoder_layers_23_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[358] + model_encoder_layers_23_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[359] + model_encoder_layers_23_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[360] + model_encoder_layers_23_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[361] + model_encoder_layers_23_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[362] + model_encoder_layers_23_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[363] + model_encoder_layers_23_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[364] + model_encoder_layers_24_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[365] + model_encoder_layers_24_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[366] + model_encoder_layers_24_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[367] + model_encoder_layers_24_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[368] + model_encoder_layers_24_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[369] + model_encoder_layers_24_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[370] + model_encoder_layers_24_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[371] + model_encoder_layers_24_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[372] + model_encoder_layers_24_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[373] + model_encoder_layers_24_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[374] + model_encoder_layers_24_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[375] + model_encoder_layers_24_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[376] + model_encoder_layers_24_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[377] + model_encoder_layers_24_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[378] + model_encoder_layers_24_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[379] + model_encoder_layers_25_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[380] + model_encoder_layers_25_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[381] + model_encoder_layers_25_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[382] + model_encoder_layers_25_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[383] + model_encoder_layers_25_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[384] + model_encoder_layers_25_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[385] + model_encoder_layers_25_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[386] + model_encoder_layers_25_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[387] + model_encoder_layers_25_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[388] + model_encoder_layers_25_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[389] + model_encoder_layers_25_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[390] + model_encoder_layers_25_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[391] + model_encoder_layers_25_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[392] + model_encoder_layers_25_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[393] + model_encoder_layers_25_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[394] + model_encoder_layers_26_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[395] + model_encoder_layers_26_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[396] + model_encoder_layers_26_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[397] + model_encoder_layers_26_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[398] + model_encoder_layers_26_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[399] + model_encoder_layers_26_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[400] + model_encoder_layers_26_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[401] + model_encoder_layers_26_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[402] + model_encoder_layers_26_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[403] + model_encoder_layers_26_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[404] + model_encoder_layers_26_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[405] + model_encoder_layers_26_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[406] + model_encoder_layers_26_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[407] + model_encoder_layers_26_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[408] + model_encoder_layers_26_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[409] + model_encoder_layers_27_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[410] + model_encoder_layers_27_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[411] + model_encoder_layers_27_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[412] + model_encoder_layers_27_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[413] + model_encoder_layers_27_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[414] + model_encoder_layers_27_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[415] + model_encoder_layers_27_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[416] + model_encoder_layers_27_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[417] + model_encoder_layers_27_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[418] + model_encoder_layers_27_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[419] + model_encoder_layers_27_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[420] + model_encoder_layers_27_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[421] + model_encoder_layers_27_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[422] + model_encoder_layers_27_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[423] + model_encoder_layers_27_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[424] + model_encoder_layers_28_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[425] + model_encoder_layers_28_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[426] + model_encoder_layers_28_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[427] + model_encoder_layers_28_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[428] + model_encoder_layers_28_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[429] + model_encoder_layers_28_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[430] + model_encoder_layers_28_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[431] + model_encoder_layers_28_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[432] + model_encoder_layers_28_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[433] + model_encoder_layers_28_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[434] + model_encoder_layers_28_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[435] + model_encoder_layers_28_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[436] + model_encoder_layers_28_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[437] + model_encoder_layers_28_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[438] + model_encoder_layers_28_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[439] + model_encoder_layers_29_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[440] + model_encoder_layers_29_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[441] + model_encoder_layers_29_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[442] + model_encoder_layers_29_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[443] + model_encoder_layers_29_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[444] + model_encoder_layers_29_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[445] + model_encoder_layers_29_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[446] + model_encoder_layers_29_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[447] + model_encoder_layers_29_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[448] + model_encoder_layers_29_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[449] + model_encoder_layers_29_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[450] + model_encoder_layers_29_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[451] + model_encoder_layers_29_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[452] + model_encoder_layers_29_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[453] + model_encoder_layers_29_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[454] + model_encoder_layers_30_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[455] + model_encoder_layers_30_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[456] + model_encoder_layers_30_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[457] + model_encoder_layers_30_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[458] + model_encoder_layers_30_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[459] + model_encoder_layers_30_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[460] + model_encoder_layers_30_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[461] + model_encoder_layers_30_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[462] + model_encoder_layers_30_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[463] + model_encoder_layers_30_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[464] + model_encoder_layers_30_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[465] + model_encoder_layers_30_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[466] + model_encoder_layers_30_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[467] + model_encoder_layers_30_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[468] + model_encoder_layers_30_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[469] + model_encoder_layers_31_self_attn_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[470] + model_encoder_layers_31_self_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[471] + model_encoder_layers_31_self_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[472] + model_encoder_layers_31_self_attn_q_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[473] + model_encoder_layers_31_self_attn_q_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[474] + model_encoder_layers_31_self_attn_out_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[475] + model_encoder_layers_31_self_attn_out_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[476] + model_encoder_layers_31_self_attn_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[477] + model_encoder_layers_31_self_attn_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[478] + model_encoder_layers_31_fc1_weight4: R.Tensor((5120, 1280), dtype="float16") = packed_params[479] + model_encoder_layers_31_fc1_bias4: R.Tensor((5120,), dtype="float16") = packed_params[480] + model_encoder_layers_31_fc2_weight4: R.Tensor((1280, 5120), dtype="float16") = packed_params[481] + model_encoder_layers_31_fc2_bias4: R.Tensor((1280,), dtype="float16") = packed_params[482] + model_encoder_layers_31_final_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[483] + model_encoder_layers_31_final_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[484] + model_encoder_layer_norm_weight4: R.Tensor((1280,), dtype="float16") = packed_params[485] + model_encoder_layer_norm_bias4: R.Tensor((1280,), dtype="float16") = packed_params[486] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[498] + model_decoder_layers_0_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[499] + model_decoder_layers_0_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[500] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[522] + model_decoder_layers_1_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[523] + model_decoder_layers_1_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[524] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[546] + model_decoder_layers_2_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[547] + model_decoder_layers_2_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[548] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[570] + model_decoder_layers_3_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[571] + model_decoder_layers_3_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[572] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[594] + model_decoder_layers_4_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[595] + model_decoder_layers_4_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[596] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[618] + model_decoder_layers_5_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[619] + model_decoder_layers_5_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[620] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[642] + model_decoder_layers_6_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[643] + model_decoder_layers_6_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[644] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[666] + model_decoder_layers_7_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[667] + model_decoder_layers_7_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[668] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[690] + model_decoder_layers_8_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[691] + model_decoder_layers_8_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[692] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[714] + model_decoder_layers_9_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[715] + model_decoder_layers_9_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[716] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[738] + model_decoder_layers_10_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[739] + model_decoder_layers_10_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[740] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[762] + model_decoder_layers_11_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[763] + model_decoder_layers_11_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[764] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[786] + model_decoder_layers_12_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[787] + model_decoder_layers_12_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[788] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[810] + model_decoder_layers_13_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[811] + model_decoder_layers_13_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[812] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[834] + model_decoder_layers_14_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[835] + model_decoder_layers_14_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[836] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[858] + model_decoder_layers_15_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[859] + model_decoder_layers_15_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[860] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[882] + model_decoder_layers_16_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[883] + model_decoder_layers_16_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[884] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[906] + model_decoder_layers_17_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[907] + model_decoder_layers_17_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[908] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[930] + model_decoder_layers_18_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[931] + model_decoder_layers_18_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[932] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[954] + model_decoder_layers_19_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[955] + model_decoder_layers_19_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[956] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[978] + model_decoder_layers_20_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[979] + model_decoder_layers_20_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[980] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1002] + model_decoder_layers_21_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1003] + model_decoder_layers_21_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1004] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1026] + model_decoder_layers_22_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1027] + model_decoder_layers_22_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1028] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1050] + model_decoder_layers_23_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1051] + model_decoder_layers_23_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1052] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1074] + model_decoder_layers_24_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1075] + model_decoder_layers_24_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1076] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1098] + model_decoder_layers_25_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1099] + model_decoder_layers_25_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1100] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1122] + model_decoder_layers_26_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1123] + model_decoder_layers_26_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1124] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1146] + model_decoder_layers_27_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1147] + model_decoder_layers_27_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1148] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1170] + model_decoder_layers_28_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1171] + model_decoder_layers_28_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1172] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1194] + model_decoder_layers_29_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1195] + model_decoder_layers_29_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1196] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1218] + model_decoder_layers_30_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1219] + model_decoder_layers_30_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1220] + 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_k_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1242] + model_decoder_layers_31_encoder_attn_v_proj_weight4: R.Tensor((1280, 1280), dtype="float16") = packed_params[1243] + model_decoder_layers_31_encoder_attn_v_proj_bias4: R.Tensor((1280,), dtype="float16") = packed_params[1244] + 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) + permute_dims771: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_q_proj_weight4, axes=None) + matmul770: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm259, permute_dims771, out_dtype="void") + add900: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul770, model_decoder_layers_0_self_attn_q_proj_bias4) + reshape1033: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add900, R.shape([1, seq_len, 20, 64])) + permute_dims772: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_k_proj_weight4, axes=None) + matmul771: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm259, permute_dims772, out_dtype="void") + reshape1034: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul771, R.shape([1, seq_len, 20, 64])) + permute_dims773: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_v_proj_weight4, axes=None) + matmul772: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm259, permute_dims773, out_dtype="void") + add901: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul772, model_decoder_layers_0_self_attn_v_proj_bias4) + reshape1035: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add901, 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])) + permute_dims774: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_self_attn_out_proj_weight4, axes=None) + matmul773: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1038, permute_dims774, out_dtype="void") + add902: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul773, model_decoder_layers_0_self_attn_out_proj_bias4) + add903: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add899, add902) + 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) + permute_dims775: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_encoder_attn_q_proj_weight4, axes=None) + matmul774: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm260, permute_dims775, out_dtype="void") + add904: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul774, model_decoder_layers_0_encoder_attn_q_proj_bias4) + reshape1039: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add904, 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])) + permute_dims776: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_encoder_attn_out_proj_weight4, axes=None) + matmul775: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1042, permute_dims776, out_dtype="void") + add905: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul775, model_decoder_layers_0_encoder_attn_out_proj_bias4) + add906: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add903, add905) + 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) + permute_dims777: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_0_fc1_weight4, axes=None) + matmul776: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm261, permute_dims777, out_dtype="void") + add907: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul776, model_decoder_layers_0_fc1_bias4) + gelu98: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add907) + permute_dims778: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_0_fc2_weight4, axes=None) + matmul777: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu98, permute_dims778, out_dtype="void") + add908: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul777, model_decoder_layers_0_fc2_bias4) + add909: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add906, add908) + 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) + permute_dims779: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_q_proj_weight4, axes=None) + matmul778: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm262, permute_dims779, out_dtype="void") + add910: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul778, model_decoder_layers_1_self_attn_q_proj_bias4) + reshape1043: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add910, R.shape([1, seq_len, 20, 64])) + permute_dims780: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_k_proj_weight4, axes=None) + matmul779: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm262, permute_dims780, out_dtype="void") + reshape1044: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul779, R.shape([1, seq_len, 20, 64])) + permute_dims781: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_v_proj_weight4, axes=None) + matmul780: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm262, permute_dims781, out_dtype="void") + add911: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul780, model_decoder_layers_1_self_attn_v_proj_bias4) + reshape1045: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add911, 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])) + permute_dims782: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_self_attn_out_proj_weight4, axes=None) + matmul781: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1048, permute_dims782, out_dtype="void") + add912: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul781, model_decoder_layers_1_self_attn_out_proj_bias4) + add913: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add909, add912) + 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) + permute_dims783: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_encoder_attn_q_proj_weight4, axes=None) + matmul782: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm263, permute_dims783, out_dtype="void") + add914: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul782, model_decoder_layers_1_encoder_attn_q_proj_bias4) + reshape1049: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add914, 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])) + permute_dims784: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_encoder_attn_out_proj_weight4, axes=None) + matmul783: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1052, permute_dims784, out_dtype="void") + add915: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul783, model_decoder_layers_1_encoder_attn_out_proj_bias4) + add916: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add913, add915) + 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) + permute_dims785: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_1_fc1_weight4, axes=None) + matmul784: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm264, permute_dims785, out_dtype="void") + add917: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul784, model_decoder_layers_1_fc1_bias4) + gelu99: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add917) + permute_dims786: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_1_fc2_weight4, axes=None) + matmul785: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu99, permute_dims786, out_dtype="void") + add918: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul785, model_decoder_layers_1_fc2_bias4) + add919: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add916, add918) + 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) + permute_dims787: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_q_proj_weight4, axes=None) + matmul786: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm265, permute_dims787, out_dtype="void") + add920: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul786, model_decoder_layers_2_self_attn_q_proj_bias4) + reshape1053: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add920, R.shape([1, seq_len, 20, 64])) + permute_dims788: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_k_proj_weight4, axes=None) + matmul787: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm265, permute_dims788, out_dtype="void") + reshape1054: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul787, R.shape([1, seq_len, 20, 64])) + permute_dims789: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_v_proj_weight4, axes=None) + matmul788: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm265, permute_dims789, out_dtype="void") + add921: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul788, model_decoder_layers_2_self_attn_v_proj_bias4) + reshape1055: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add921, 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])) + permute_dims790: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_self_attn_out_proj_weight4, axes=None) + matmul789: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1058, permute_dims790, out_dtype="void") + add922: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul789, model_decoder_layers_2_self_attn_out_proj_bias4) + add923: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add919, add922) + 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) + permute_dims791: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_encoder_attn_q_proj_weight4, axes=None) + matmul790: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm266, permute_dims791, out_dtype="void") + add924: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul790, model_decoder_layers_2_encoder_attn_q_proj_bias4) + reshape1059: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add924, 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])) + permute_dims792: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_encoder_attn_out_proj_weight4, axes=None) + matmul791: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1062, permute_dims792, out_dtype="void") + add925: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul791, model_decoder_layers_2_encoder_attn_out_proj_bias4) + add926: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add923, add925) + 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) + permute_dims793: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_2_fc1_weight4, axes=None) + matmul792: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm267, permute_dims793, out_dtype="void") + add927: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul792, model_decoder_layers_2_fc1_bias4) + gelu100: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add927) + permute_dims794: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_2_fc2_weight4, axes=None) + matmul793: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu100, permute_dims794, out_dtype="void") + add928: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul793, model_decoder_layers_2_fc2_bias4) + add929: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add926, add928) + 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) + permute_dims795: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_q_proj_weight4, axes=None) + matmul794: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm268, permute_dims795, out_dtype="void") + add930: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul794, model_decoder_layers_3_self_attn_q_proj_bias4) + reshape1063: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add930, R.shape([1, seq_len, 20, 64])) + permute_dims796: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_k_proj_weight4, axes=None) + matmul795: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm268, permute_dims796, out_dtype="void") + reshape1064: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul795, R.shape([1, seq_len, 20, 64])) + permute_dims797: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_v_proj_weight4, axes=None) + matmul796: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm268, permute_dims797, out_dtype="void") + add931: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul796, model_decoder_layers_3_self_attn_v_proj_bias4) + reshape1065: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add931, 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])) + permute_dims798: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_self_attn_out_proj_weight4, axes=None) + matmul797: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1068, permute_dims798, out_dtype="void") + add932: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul797, model_decoder_layers_3_self_attn_out_proj_bias4) + add933: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add929, add932) + 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) + permute_dims799: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_encoder_attn_q_proj_weight4, axes=None) + matmul798: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm269, permute_dims799, out_dtype="void") + add934: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul798, model_decoder_layers_3_encoder_attn_q_proj_bias4) + reshape1069: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add934, 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])) + permute_dims800: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_encoder_attn_out_proj_weight4, axes=None) + matmul799: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1072, permute_dims800, out_dtype="void") + add935: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul799, model_decoder_layers_3_encoder_attn_out_proj_bias4) + add936: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add933, add935) + 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) + permute_dims801: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_3_fc1_weight4, axes=None) + matmul800: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm270, permute_dims801, out_dtype="void") + add937: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul800, model_decoder_layers_3_fc1_bias4) + gelu101: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add937) + permute_dims802: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_3_fc2_weight4, axes=None) + matmul801: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu101, permute_dims802, out_dtype="void") + add938: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul801, model_decoder_layers_3_fc2_bias4) + add939: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add936, add938) + 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) + permute_dims803: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_q_proj_weight4, axes=None) + matmul802: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm271, permute_dims803, out_dtype="void") + add940: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul802, model_decoder_layers_4_self_attn_q_proj_bias4) + reshape1073: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add940, R.shape([1, seq_len, 20, 64])) + permute_dims804: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_k_proj_weight4, axes=None) + matmul803: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm271, permute_dims804, out_dtype="void") + reshape1074: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul803, R.shape([1, seq_len, 20, 64])) + permute_dims805: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_v_proj_weight4, axes=None) + matmul804: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm271, permute_dims805, out_dtype="void") + add941: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul804, model_decoder_layers_4_self_attn_v_proj_bias4) + reshape1075: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add941, 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])) + permute_dims806: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_self_attn_out_proj_weight4, axes=None) + matmul805: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1078, permute_dims806, out_dtype="void") + add942: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul805, model_decoder_layers_4_self_attn_out_proj_bias4) + add943: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add939, add942) + 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) + permute_dims807: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_encoder_attn_q_proj_weight4, axes=None) + matmul806: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm272, permute_dims807, out_dtype="void") + add944: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul806, model_decoder_layers_4_encoder_attn_q_proj_bias4) + reshape1079: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add944, 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])) + permute_dims808: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_encoder_attn_out_proj_weight4, axes=None) + matmul807: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1082, permute_dims808, out_dtype="void") + add945: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul807, model_decoder_layers_4_encoder_attn_out_proj_bias4) + add946: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add943, add945) + 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) + permute_dims809: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_4_fc1_weight4, axes=None) + matmul808: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm273, permute_dims809, out_dtype="void") + add947: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul808, model_decoder_layers_4_fc1_bias4) + gelu102: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add947) + permute_dims810: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_4_fc2_weight4, axes=None) + matmul809: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu102, permute_dims810, out_dtype="void") + add948: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul809, model_decoder_layers_4_fc2_bias4) + add949: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add946, add948) + 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) + permute_dims811: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_q_proj_weight4, axes=None) + matmul810: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm274, permute_dims811, out_dtype="void") + add950: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul810, model_decoder_layers_5_self_attn_q_proj_bias4) + reshape1083: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add950, R.shape([1, seq_len, 20, 64])) + permute_dims812: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_k_proj_weight4, axes=None) + matmul811: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm274, permute_dims812, out_dtype="void") + reshape1084: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul811, R.shape([1, seq_len, 20, 64])) + permute_dims813: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_v_proj_weight4, axes=None) + matmul812: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm274, permute_dims813, out_dtype="void") + add951: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul812, model_decoder_layers_5_self_attn_v_proj_bias4) + reshape1085: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add951, 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])) + permute_dims814: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_self_attn_out_proj_weight4, axes=None) + matmul813: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1088, permute_dims814, out_dtype="void") + add952: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul813, model_decoder_layers_5_self_attn_out_proj_bias4) + add953: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add949, add952) + 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) + permute_dims815: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_encoder_attn_q_proj_weight4, axes=None) + matmul814: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm275, permute_dims815, out_dtype="void") + add954: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul814, model_decoder_layers_5_encoder_attn_q_proj_bias4) + reshape1089: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add954, 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])) + permute_dims816: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_encoder_attn_out_proj_weight4, axes=None) + matmul815: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1092, permute_dims816, out_dtype="void") + add955: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul815, model_decoder_layers_5_encoder_attn_out_proj_bias4) + add956: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add953, add955) + 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) + permute_dims817: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_5_fc1_weight4, axes=None) + matmul816: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm276, permute_dims817, out_dtype="void") + add957: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul816, model_decoder_layers_5_fc1_bias4) + gelu103: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add957) + permute_dims818: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_5_fc2_weight4, axes=None) + matmul817: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu103, permute_dims818, out_dtype="void") + add958: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul817, model_decoder_layers_5_fc2_bias4) + add959: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add956, add958) + 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) + permute_dims819: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_q_proj_weight4, axes=None) + matmul818: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm277, permute_dims819, out_dtype="void") + add960: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul818, model_decoder_layers_6_self_attn_q_proj_bias4) + reshape1093: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add960, R.shape([1, seq_len, 20, 64])) + permute_dims820: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_k_proj_weight4, axes=None) + matmul819: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm277, permute_dims820, out_dtype="void") + reshape1094: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul819, R.shape([1, seq_len, 20, 64])) + permute_dims821: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_v_proj_weight4, axes=None) + matmul820: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm277, permute_dims821, out_dtype="void") + add961: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul820, model_decoder_layers_6_self_attn_v_proj_bias4) + reshape1095: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add961, 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])) + permute_dims822: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_self_attn_out_proj_weight4, axes=None) + matmul821: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1098, permute_dims822, out_dtype="void") + add962: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul821, model_decoder_layers_6_self_attn_out_proj_bias4) + add963: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add959, add962) + 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) + permute_dims823: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_encoder_attn_q_proj_weight4, axes=None) + matmul822: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm278, permute_dims823, out_dtype="void") + add964: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul822, model_decoder_layers_6_encoder_attn_q_proj_bias4) + reshape1099: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add964, 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])) + permute_dims824: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_encoder_attn_out_proj_weight4, axes=None) + matmul823: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1102, permute_dims824, out_dtype="void") + add965: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul823, model_decoder_layers_6_encoder_attn_out_proj_bias4) + add966: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add963, add965) + 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) + permute_dims825: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_6_fc1_weight4, axes=None) + matmul824: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm279, permute_dims825, out_dtype="void") + add967: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul824, model_decoder_layers_6_fc1_bias4) + gelu104: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add967) + permute_dims826: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_6_fc2_weight4, axes=None) + matmul825: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu104, permute_dims826, out_dtype="void") + add968: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul825, model_decoder_layers_6_fc2_bias4) + add969: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add966, add968) + 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) + permute_dims827: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_q_proj_weight4, axes=None) + matmul826: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm280, permute_dims827, out_dtype="void") + add970: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul826, model_decoder_layers_7_self_attn_q_proj_bias4) + reshape1103: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add970, R.shape([1, seq_len, 20, 64])) + permute_dims828: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_k_proj_weight4, axes=None) + matmul827: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm280, permute_dims828, out_dtype="void") + reshape1104: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul827, R.shape([1, seq_len, 20, 64])) + permute_dims829: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_v_proj_weight4, axes=None) + matmul828: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm280, permute_dims829, out_dtype="void") + add971: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul828, model_decoder_layers_7_self_attn_v_proj_bias4) + reshape1105: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add971, 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])) + permute_dims830: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_self_attn_out_proj_weight4, axes=None) + matmul829: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1108, permute_dims830, out_dtype="void") + add972: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul829, model_decoder_layers_7_self_attn_out_proj_bias4) + add973: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add969, add972) + 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) + permute_dims831: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_encoder_attn_q_proj_weight4, axes=None) + matmul830: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm281, permute_dims831, out_dtype="void") + add974: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul830, model_decoder_layers_7_encoder_attn_q_proj_bias4) + reshape1109: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add974, 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])) + permute_dims832: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_encoder_attn_out_proj_weight4, axes=None) + matmul831: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1112, permute_dims832, out_dtype="void") + add975: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul831, model_decoder_layers_7_encoder_attn_out_proj_bias4) + add976: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add973, add975) + 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) + permute_dims833: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_7_fc1_weight4, axes=None) + matmul832: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm282, permute_dims833, out_dtype="void") + add977: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul832, model_decoder_layers_7_fc1_bias4) + gelu105: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add977) + permute_dims834: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_7_fc2_weight4, axes=None) + matmul833: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu105, permute_dims834, out_dtype="void") + add978: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul833, model_decoder_layers_7_fc2_bias4) + add979: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add976, add978) + 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) + permute_dims835: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_q_proj_weight4, axes=None) + matmul834: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm283, permute_dims835, out_dtype="void") + add980: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul834, model_decoder_layers_8_self_attn_q_proj_bias4) + reshape1113: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add980, R.shape([1, seq_len, 20, 64])) + permute_dims836: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_k_proj_weight4, axes=None) + matmul835: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm283, permute_dims836, out_dtype="void") + reshape1114: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul835, R.shape([1, seq_len, 20, 64])) + permute_dims837: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_v_proj_weight4, axes=None) + matmul836: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm283, permute_dims837, out_dtype="void") + add981: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul836, model_decoder_layers_8_self_attn_v_proj_bias4) + reshape1115: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add981, 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])) + permute_dims838: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_self_attn_out_proj_weight4, axes=None) + matmul837: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1118, permute_dims838, out_dtype="void") + add982: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul837, model_decoder_layers_8_self_attn_out_proj_bias4) + add983: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add979, add982) + 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) + permute_dims839: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_encoder_attn_q_proj_weight4, axes=None) + matmul838: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm284, permute_dims839, out_dtype="void") + add984: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul838, model_decoder_layers_8_encoder_attn_q_proj_bias4) + reshape1119: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add984, 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])) + permute_dims840: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_encoder_attn_out_proj_weight4, axes=None) + matmul839: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1122, permute_dims840, out_dtype="void") + add985: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul839, model_decoder_layers_8_encoder_attn_out_proj_bias4) + add986: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add983, add985) + 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) + permute_dims841: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_8_fc1_weight4, axes=None) + matmul840: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm285, permute_dims841, out_dtype="void") + add987: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul840, model_decoder_layers_8_fc1_bias4) + gelu106: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add987) + permute_dims842: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_8_fc2_weight4, axes=None) + matmul841: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu106, permute_dims842, out_dtype="void") + add988: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul841, model_decoder_layers_8_fc2_bias4) + add989: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add986, add988) + 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) + permute_dims843: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_q_proj_weight4, axes=None) + matmul842: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm286, permute_dims843, out_dtype="void") + add990: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul842, model_decoder_layers_9_self_attn_q_proj_bias4) + reshape1123: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add990, R.shape([1, seq_len, 20, 64])) + permute_dims844: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_k_proj_weight4, axes=None) + matmul843: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm286, permute_dims844, out_dtype="void") + reshape1124: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul843, R.shape([1, seq_len, 20, 64])) + permute_dims845: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_v_proj_weight4, axes=None) + matmul844: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm286, permute_dims845, out_dtype="void") + add991: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul844, model_decoder_layers_9_self_attn_v_proj_bias4) + reshape1125: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add991, 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])) + permute_dims846: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_self_attn_out_proj_weight4, axes=None) + matmul845: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1128, permute_dims846, out_dtype="void") + add992: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul845, model_decoder_layers_9_self_attn_out_proj_bias4) + add993: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add989, add992) + 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) + permute_dims847: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_encoder_attn_q_proj_weight4, axes=None) + matmul846: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm287, permute_dims847, out_dtype="void") + add994: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul846, model_decoder_layers_9_encoder_attn_q_proj_bias4) + reshape1129: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add994, 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])) + permute_dims848: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_encoder_attn_out_proj_weight4, axes=None) + matmul847: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1132, permute_dims848, out_dtype="void") + add995: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul847, model_decoder_layers_9_encoder_attn_out_proj_bias4) + add996: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add993, add995) + 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) + permute_dims849: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_9_fc1_weight4, axes=None) + matmul848: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm288, permute_dims849, out_dtype="void") + add997: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul848, model_decoder_layers_9_fc1_bias4) + gelu107: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add997) + permute_dims850: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_9_fc2_weight4, axes=None) + matmul849: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu107, permute_dims850, out_dtype="void") + add998: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul849, model_decoder_layers_9_fc2_bias4) + add999: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add996, add998) + 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) + permute_dims851: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_q_proj_weight4, axes=None) + matmul850: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm289, permute_dims851, out_dtype="void") + add1000: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul850, model_decoder_layers_10_self_attn_q_proj_bias4) + reshape1133: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1000, R.shape([1, seq_len, 20, 64])) + permute_dims852: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_k_proj_weight4, axes=None) + matmul851: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm289, permute_dims852, out_dtype="void") + reshape1134: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul851, R.shape([1, seq_len, 20, 64])) + permute_dims853: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_v_proj_weight4, axes=None) + matmul852: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm289, permute_dims853, out_dtype="void") + add1001: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul852, model_decoder_layers_10_self_attn_v_proj_bias4) + reshape1135: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1001, 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])) + permute_dims854: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_self_attn_out_proj_weight4, axes=None) + matmul853: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1138, permute_dims854, out_dtype="void") + add1002: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul853, model_decoder_layers_10_self_attn_out_proj_bias4) + add1003: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add999, add1002) + 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) + permute_dims855: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_encoder_attn_q_proj_weight4, axes=None) + matmul854: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm290, permute_dims855, out_dtype="void") + add1004: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul854, model_decoder_layers_10_encoder_attn_q_proj_bias4) + reshape1139: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1004, 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])) + permute_dims856: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_encoder_attn_out_proj_weight4, axes=None) + matmul855: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1142, permute_dims856, out_dtype="void") + add1005: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul855, model_decoder_layers_10_encoder_attn_out_proj_bias4) + add1006: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1003, add1005) + 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) + permute_dims857: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_10_fc1_weight4, axes=None) + matmul856: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm291, permute_dims857, out_dtype="void") + add1007: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul856, model_decoder_layers_10_fc1_bias4) + gelu108: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1007) + permute_dims858: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_10_fc2_weight4, axes=None) + matmul857: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu108, permute_dims858, out_dtype="void") + add1008: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul857, model_decoder_layers_10_fc2_bias4) + add1009: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1006, add1008) + 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) + permute_dims859: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_q_proj_weight4, axes=None) + matmul858: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm292, permute_dims859, out_dtype="void") + add1010: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul858, model_decoder_layers_11_self_attn_q_proj_bias4) + reshape1143: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1010, R.shape([1, seq_len, 20, 64])) + permute_dims860: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_k_proj_weight4, axes=None) + matmul859: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm292, permute_dims860, out_dtype="void") + reshape1144: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul859, R.shape([1, seq_len, 20, 64])) + permute_dims861: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_v_proj_weight4, axes=None) + matmul860: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm292, permute_dims861, out_dtype="void") + add1011: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul860, model_decoder_layers_11_self_attn_v_proj_bias4) + reshape1145: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1011, 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])) + permute_dims862: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_self_attn_out_proj_weight4, axes=None) + matmul861: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1148, permute_dims862, out_dtype="void") + add1012: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul861, model_decoder_layers_11_self_attn_out_proj_bias4) + add1013: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1009, add1012) + 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) + permute_dims863: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_encoder_attn_q_proj_weight4, axes=None) + matmul862: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm293, permute_dims863, out_dtype="void") + add1014: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul862, model_decoder_layers_11_encoder_attn_q_proj_bias4) + reshape1149: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1014, 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])) + permute_dims864: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_encoder_attn_out_proj_weight4, axes=None) + matmul863: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1152, permute_dims864, out_dtype="void") + add1015: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul863, model_decoder_layers_11_encoder_attn_out_proj_bias4) + add1016: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1013, add1015) + 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) + permute_dims865: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_11_fc1_weight4, axes=None) + matmul864: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm294, permute_dims865, out_dtype="void") + add1017: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul864, model_decoder_layers_11_fc1_bias4) + gelu109: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1017) + permute_dims866: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_11_fc2_weight4, axes=None) + matmul865: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu109, permute_dims866, out_dtype="void") + add1018: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul865, model_decoder_layers_11_fc2_bias4) + add1019: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1016, add1018) + 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) + permute_dims867: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_q_proj_weight4, axes=None) + matmul866: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm295, permute_dims867, out_dtype="void") + add1020: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul866, model_decoder_layers_12_self_attn_q_proj_bias4) + reshape1153: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1020, R.shape([1, seq_len, 20, 64])) + permute_dims868: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_k_proj_weight4, axes=None) + matmul867: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm295, permute_dims868, out_dtype="void") + reshape1154: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul867, R.shape([1, seq_len, 20, 64])) + permute_dims869: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_v_proj_weight4, axes=None) + matmul868: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm295, permute_dims869, out_dtype="void") + add1021: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul868, model_decoder_layers_12_self_attn_v_proj_bias4) + reshape1155: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1021, 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])) + permute_dims870: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_self_attn_out_proj_weight4, axes=None) + matmul869: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1158, permute_dims870, out_dtype="void") + add1022: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul869, model_decoder_layers_12_self_attn_out_proj_bias4) + add1023: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1019, add1022) + 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) + permute_dims871: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_encoder_attn_q_proj_weight4, axes=None) + matmul870: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm296, permute_dims871, out_dtype="void") + add1024: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul870, model_decoder_layers_12_encoder_attn_q_proj_bias4) + reshape1159: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1024, 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])) + permute_dims872: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_encoder_attn_out_proj_weight4, axes=None) + matmul871: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1162, permute_dims872, out_dtype="void") + add1025: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul871, model_decoder_layers_12_encoder_attn_out_proj_bias4) + add1026: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1023, add1025) + 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) + permute_dims873: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_12_fc1_weight4, axes=None) + matmul872: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm297, permute_dims873, out_dtype="void") + add1027: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul872, model_decoder_layers_12_fc1_bias4) + gelu110: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1027) + permute_dims874: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_12_fc2_weight4, axes=None) + matmul873: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu110, permute_dims874, out_dtype="void") + add1028: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul873, model_decoder_layers_12_fc2_bias4) + add1029: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1026, add1028) + 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) + permute_dims875: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_q_proj_weight4, axes=None) + matmul874: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm298, permute_dims875, out_dtype="void") + add1030: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul874, model_decoder_layers_13_self_attn_q_proj_bias4) + reshape1163: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1030, R.shape([1, seq_len, 20, 64])) + permute_dims876: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_k_proj_weight4, axes=None) + matmul875: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm298, permute_dims876, out_dtype="void") + reshape1164: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul875, R.shape([1, seq_len, 20, 64])) + permute_dims877: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_v_proj_weight4, axes=None) + matmul876: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm298, permute_dims877, out_dtype="void") + add1031: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul876, model_decoder_layers_13_self_attn_v_proj_bias4) + reshape1165: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1031, 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])) + permute_dims878: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_self_attn_out_proj_weight4, axes=None) + matmul877: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1168, permute_dims878, out_dtype="void") + add1032: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul877, model_decoder_layers_13_self_attn_out_proj_bias4) + add1033: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1029, add1032) + 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) + permute_dims879: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_encoder_attn_q_proj_weight4, axes=None) + matmul878: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm299, permute_dims879, out_dtype="void") + add1034: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul878, model_decoder_layers_13_encoder_attn_q_proj_bias4) + reshape1169: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1034, 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])) + permute_dims880: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_encoder_attn_out_proj_weight4, axes=None) + matmul879: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1172, permute_dims880, out_dtype="void") + add1035: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul879, model_decoder_layers_13_encoder_attn_out_proj_bias4) + add1036: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1033, add1035) + 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) + permute_dims881: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_13_fc1_weight4, axes=None) + matmul880: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm300, permute_dims881, out_dtype="void") + add1037: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul880, model_decoder_layers_13_fc1_bias4) + gelu111: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1037) + permute_dims882: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_13_fc2_weight4, axes=None) + matmul881: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu111, permute_dims882, out_dtype="void") + add1038: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul881, model_decoder_layers_13_fc2_bias4) + add1039: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1036, add1038) + 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) + permute_dims883: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_q_proj_weight4, axes=None) + matmul882: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm301, permute_dims883, out_dtype="void") + add1040: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul882, model_decoder_layers_14_self_attn_q_proj_bias4) + reshape1173: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1040, R.shape([1, seq_len, 20, 64])) + permute_dims884: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_k_proj_weight4, axes=None) + matmul883: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm301, permute_dims884, out_dtype="void") + reshape1174: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul883, R.shape([1, seq_len, 20, 64])) + permute_dims885: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_v_proj_weight4, axes=None) + matmul884: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm301, permute_dims885, out_dtype="void") + add1041: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul884, model_decoder_layers_14_self_attn_v_proj_bias4) + reshape1175: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1041, 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])) + permute_dims886: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_self_attn_out_proj_weight4, axes=None) + matmul885: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1178, permute_dims886, out_dtype="void") + add1042: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul885, model_decoder_layers_14_self_attn_out_proj_bias4) + add1043: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1039, add1042) + 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) + permute_dims887: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_encoder_attn_q_proj_weight4, axes=None) + matmul886: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm302, permute_dims887, out_dtype="void") + add1044: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul886, model_decoder_layers_14_encoder_attn_q_proj_bias4) + reshape1179: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1044, 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])) + permute_dims888: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_encoder_attn_out_proj_weight4, axes=None) + matmul887: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1182, permute_dims888, out_dtype="void") + add1045: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul887, model_decoder_layers_14_encoder_attn_out_proj_bias4) + add1046: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1043, add1045) + 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) + permute_dims889: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_14_fc1_weight4, axes=None) + matmul888: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm303, permute_dims889, out_dtype="void") + add1047: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul888, model_decoder_layers_14_fc1_bias4) + gelu112: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1047) + permute_dims890: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_14_fc2_weight4, axes=None) + matmul889: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu112, permute_dims890, out_dtype="void") + add1048: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul889, model_decoder_layers_14_fc2_bias4) + add1049: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1046, add1048) + 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) + permute_dims891: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_q_proj_weight4, axes=None) + matmul890: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm304, permute_dims891, out_dtype="void") + add1050: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul890, model_decoder_layers_15_self_attn_q_proj_bias4) + reshape1183: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1050, R.shape([1, seq_len, 20, 64])) + permute_dims892: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_k_proj_weight4, axes=None) + matmul891: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm304, permute_dims892, out_dtype="void") + reshape1184: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul891, R.shape([1, seq_len, 20, 64])) + permute_dims893: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_v_proj_weight4, axes=None) + matmul892: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm304, permute_dims893, out_dtype="void") + add1051: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul892, model_decoder_layers_15_self_attn_v_proj_bias4) + reshape1185: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1051, 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])) + permute_dims894: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_self_attn_out_proj_weight4, axes=None) + matmul893: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1188, permute_dims894, out_dtype="void") + add1052: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul893, model_decoder_layers_15_self_attn_out_proj_bias4) + add1053: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1049, add1052) + 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) + permute_dims895: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_encoder_attn_q_proj_weight4, axes=None) + matmul894: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm305, permute_dims895, out_dtype="void") + add1054: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul894, model_decoder_layers_15_encoder_attn_q_proj_bias4) + reshape1189: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1054, 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])) + permute_dims896: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_encoder_attn_out_proj_weight4, axes=None) + matmul895: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1192, permute_dims896, out_dtype="void") + add1055: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul895, model_decoder_layers_15_encoder_attn_out_proj_bias4) + add1056: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1053, add1055) + 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) + permute_dims897: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_15_fc1_weight4, axes=None) + matmul896: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm306, permute_dims897, out_dtype="void") + add1057: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul896, model_decoder_layers_15_fc1_bias4) + gelu113: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1057) + permute_dims898: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_15_fc2_weight4, axes=None) + matmul897: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu113, permute_dims898, out_dtype="void") + add1058: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul897, model_decoder_layers_15_fc2_bias4) + add1059: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1056, add1058) + 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) + permute_dims899: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_q_proj_weight4, axes=None) + matmul898: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm307, permute_dims899, out_dtype="void") + add1060: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul898, model_decoder_layers_16_self_attn_q_proj_bias4) + reshape1193: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1060, R.shape([1, seq_len, 20, 64])) + permute_dims900: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_k_proj_weight4, axes=None) + matmul899: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm307, permute_dims900, out_dtype="void") + reshape1194: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul899, R.shape([1, seq_len, 20, 64])) + permute_dims901: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_v_proj_weight4, axes=None) + matmul900: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm307, permute_dims901, out_dtype="void") + add1061: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul900, model_decoder_layers_16_self_attn_v_proj_bias4) + reshape1195: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1061, 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])) + permute_dims902: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_self_attn_out_proj_weight4, axes=None) + matmul901: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1198, permute_dims902, out_dtype="void") + add1062: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul901, model_decoder_layers_16_self_attn_out_proj_bias4) + add1063: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1059, add1062) + 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) + permute_dims903: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_encoder_attn_q_proj_weight4, axes=None) + matmul902: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm308, permute_dims903, out_dtype="void") + add1064: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul902, model_decoder_layers_16_encoder_attn_q_proj_bias4) + reshape1199: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1064, 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])) + permute_dims904: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_encoder_attn_out_proj_weight4, axes=None) + matmul903: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1202, permute_dims904, out_dtype="void") + add1065: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul903, model_decoder_layers_16_encoder_attn_out_proj_bias4) + add1066: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1063, add1065) + 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) + permute_dims905: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_16_fc1_weight4, axes=None) + matmul904: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm309, permute_dims905, out_dtype="void") + add1067: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul904, model_decoder_layers_16_fc1_bias4) + gelu114: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1067) + permute_dims906: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_16_fc2_weight4, axes=None) + matmul905: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu114, permute_dims906, out_dtype="void") + add1068: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul905, model_decoder_layers_16_fc2_bias4) + add1069: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1066, add1068) + 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) + permute_dims907: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_q_proj_weight4, axes=None) + matmul906: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm310, permute_dims907, out_dtype="void") + add1070: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul906, model_decoder_layers_17_self_attn_q_proj_bias4) + reshape1203: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1070, R.shape([1, seq_len, 20, 64])) + permute_dims908: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_k_proj_weight4, axes=None) + matmul907: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm310, permute_dims908, out_dtype="void") + reshape1204: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul907, R.shape([1, seq_len, 20, 64])) + permute_dims909: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_v_proj_weight4, axes=None) + matmul908: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm310, permute_dims909, out_dtype="void") + add1071: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul908, model_decoder_layers_17_self_attn_v_proj_bias4) + reshape1205: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1071, 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])) + permute_dims910: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_self_attn_out_proj_weight4, axes=None) + matmul909: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1208, permute_dims910, out_dtype="void") + add1072: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul909, model_decoder_layers_17_self_attn_out_proj_bias4) + add1073: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1069, add1072) + 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) + permute_dims911: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_encoder_attn_q_proj_weight4, axes=None) + matmul910: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm311, permute_dims911, out_dtype="void") + add1074: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul910, model_decoder_layers_17_encoder_attn_q_proj_bias4) + reshape1209: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1074, 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])) + permute_dims912: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_encoder_attn_out_proj_weight4, axes=None) + matmul911: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1212, permute_dims912, out_dtype="void") + add1075: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul911, model_decoder_layers_17_encoder_attn_out_proj_bias4) + add1076: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1073, add1075) + 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) + permute_dims913: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_17_fc1_weight4, axes=None) + matmul912: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm312, permute_dims913, out_dtype="void") + add1077: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul912, model_decoder_layers_17_fc1_bias4) + gelu115: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1077) + permute_dims914: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_17_fc2_weight4, axes=None) + matmul913: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu115, permute_dims914, out_dtype="void") + add1078: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul913, model_decoder_layers_17_fc2_bias4) + add1079: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1076, add1078) + 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) + permute_dims915: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_q_proj_weight4, axes=None) + matmul914: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm313, permute_dims915, out_dtype="void") + add1080: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul914, model_decoder_layers_18_self_attn_q_proj_bias4) + reshape1213: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1080, R.shape([1, seq_len, 20, 64])) + permute_dims916: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_k_proj_weight4, axes=None) + matmul915: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm313, permute_dims916, out_dtype="void") + reshape1214: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul915, R.shape([1, seq_len, 20, 64])) + permute_dims917: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_v_proj_weight4, axes=None) + matmul916: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm313, permute_dims917, out_dtype="void") + add1081: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul916, model_decoder_layers_18_self_attn_v_proj_bias4) + reshape1215: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1081, 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])) + permute_dims918: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_self_attn_out_proj_weight4, axes=None) + matmul917: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1218, permute_dims918, out_dtype="void") + add1082: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul917, model_decoder_layers_18_self_attn_out_proj_bias4) + add1083: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1079, add1082) + 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) + permute_dims919: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_encoder_attn_q_proj_weight4, axes=None) + matmul918: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm314, permute_dims919, out_dtype="void") + add1084: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul918, model_decoder_layers_18_encoder_attn_q_proj_bias4) + reshape1219: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1084, 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])) + permute_dims920: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_encoder_attn_out_proj_weight4, axes=None) + matmul919: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1222, permute_dims920, out_dtype="void") + add1085: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul919, model_decoder_layers_18_encoder_attn_out_proj_bias4) + add1086: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1083, add1085) + 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) + permute_dims921: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_18_fc1_weight4, axes=None) + matmul920: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm315, permute_dims921, out_dtype="void") + add1087: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul920, model_decoder_layers_18_fc1_bias4) + gelu116: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1087) + permute_dims922: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_18_fc2_weight4, axes=None) + matmul921: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu116, permute_dims922, out_dtype="void") + add1088: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul921, model_decoder_layers_18_fc2_bias4) + add1089: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1086, add1088) + 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) + permute_dims923: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_q_proj_weight4, axes=None) + matmul922: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm316, permute_dims923, out_dtype="void") + add1090: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul922, model_decoder_layers_19_self_attn_q_proj_bias4) + reshape1223: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1090, R.shape([1, seq_len, 20, 64])) + permute_dims924: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_k_proj_weight4, axes=None) + matmul923: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm316, permute_dims924, out_dtype="void") + reshape1224: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul923, R.shape([1, seq_len, 20, 64])) + permute_dims925: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_v_proj_weight4, axes=None) + matmul924: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm316, permute_dims925, out_dtype="void") + add1091: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul924, model_decoder_layers_19_self_attn_v_proj_bias4) + reshape1225: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1091, 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])) + permute_dims926: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_self_attn_out_proj_weight4, axes=None) + matmul925: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1228, permute_dims926, out_dtype="void") + add1092: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul925, model_decoder_layers_19_self_attn_out_proj_bias4) + add1093: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1089, add1092) + 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) + permute_dims927: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_encoder_attn_q_proj_weight4, axes=None) + matmul926: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm317, permute_dims927, out_dtype="void") + add1094: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul926, model_decoder_layers_19_encoder_attn_q_proj_bias4) + reshape1229: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1094, 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])) + permute_dims928: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_encoder_attn_out_proj_weight4, axes=None) + matmul927: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1232, permute_dims928, out_dtype="void") + add1095: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul927, model_decoder_layers_19_encoder_attn_out_proj_bias4) + add1096: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1093, add1095) + 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) + permute_dims929: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_19_fc1_weight4, axes=None) + matmul928: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm318, permute_dims929, out_dtype="void") + add1097: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul928, model_decoder_layers_19_fc1_bias4) + gelu117: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1097) + permute_dims930: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_19_fc2_weight4, axes=None) + matmul929: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu117, permute_dims930, out_dtype="void") + add1098: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul929, model_decoder_layers_19_fc2_bias4) + add1099: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1096, add1098) + 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) + permute_dims931: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_q_proj_weight4, axes=None) + matmul930: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm319, permute_dims931, out_dtype="void") + add1100: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul930, model_decoder_layers_20_self_attn_q_proj_bias4) + reshape1233: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1100, R.shape([1, seq_len, 20, 64])) + permute_dims932: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_k_proj_weight4, axes=None) + matmul931: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm319, permute_dims932, out_dtype="void") + reshape1234: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul931, R.shape([1, seq_len, 20, 64])) + permute_dims933: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_v_proj_weight4, axes=None) + matmul932: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm319, permute_dims933, out_dtype="void") + add1101: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul932, model_decoder_layers_20_self_attn_v_proj_bias4) + reshape1235: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1101, 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])) + permute_dims934: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_self_attn_out_proj_weight4, axes=None) + matmul933: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1238, permute_dims934, out_dtype="void") + add1102: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul933, model_decoder_layers_20_self_attn_out_proj_bias4) + add1103: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1099, add1102) + 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) + permute_dims935: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_encoder_attn_q_proj_weight4, axes=None) + matmul934: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm320, permute_dims935, out_dtype="void") + add1104: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul934, model_decoder_layers_20_encoder_attn_q_proj_bias4) + reshape1239: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1104, 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])) + permute_dims936: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_encoder_attn_out_proj_weight4, axes=None) + matmul935: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1242, permute_dims936, out_dtype="void") + add1105: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul935, model_decoder_layers_20_encoder_attn_out_proj_bias4) + add1106: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1103, add1105) + 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) + permute_dims937: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_20_fc1_weight4, axes=None) + matmul936: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm321, permute_dims937, out_dtype="void") + add1107: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul936, model_decoder_layers_20_fc1_bias4) + gelu118: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1107) + permute_dims938: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_20_fc2_weight4, axes=None) + matmul937: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu118, permute_dims938, out_dtype="void") + add1108: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul937, model_decoder_layers_20_fc2_bias4) + add1109: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1106, add1108) + 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) + permute_dims939: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_q_proj_weight4, axes=None) + matmul938: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm322, permute_dims939, out_dtype="void") + add1110: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul938, model_decoder_layers_21_self_attn_q_proj_bias4) + reshape1243: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1110, R.shape([1, seq_len, 20, 64])) + permute_dims940: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_k_proj_weight4, axes=None) + matmul939: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm322, permute_dims940, out_dtype="void") + reshape1244: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul939, R.shape([1, seq_len, 20, 64])) + permute_dims941: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_v_proj_weight4, axes=None) + matmul940: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm322, permute_dims941, out_dtype="void") + add1111: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul940, model_decoder_layers_21_self_attn_v_proj_bias4) + reshape1245: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1111, 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])) + permute_dims942: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_self_attn_out_proj_weight4, axes=None) + matmul941: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1248, permute_dims942, out_dtype="void") + add1112: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul941, model_decoder_layers_21_self_attn_out_proj_bias4) + add1113: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1109, add1112) + 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) + permute_dims943: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_encoder_attn_q_proj_weight4, axes=None) + matmul942: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm323, permute_dims943, out_dtype="void") + add1114: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul942, model_decoder_layers_21_encoder_attn_q_proj_bias4) + reshape1249: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1114, 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])) + permute_dims944: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_encoder_attn_out_proj_weight4, axes=None) + matmul943: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1252, permute_dims944, out_dtype="void") + add1115: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul943, model_decoder_layers_21_encoder_attn_out_proj_bias4) + add1116: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1113, add1115) + 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) + permute_dims945: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_21_fc1_weight4, axes=None) + matmul944: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm324, permute_dims945, out_dtype="void") + add1117: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul944, model_decoder_layers_21_fc1_bias4) + gelu119: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1117) + permute_dims946: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_21_fc2_weight4, axes=None) + matmul945: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu119, permute_dims946, out_dtype="void") + add1118: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul945, model_decoder_layers_21_fc2_bias4) + add1119: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1116, add1118) + 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) + permute_dims947: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_q_proj_weight4, axes=None) + matmul946: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm325, permute_dims947, out_dtype="void") + add1120: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul946, model_decoder_layers_22_self_attn_q_proj_bias4) + reshape1253: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1120, R.shape([1, seq_len, 20, 64])) + permute_dims948: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_k_proj_weight4, axes=None) + matmul947: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm325, permute_dims948, out_dtype="void") + reshape1254: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul947, R.shape([1, seq_len, 20, 64])) + permute_dims949: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_v_proj_weight4, axes=None) + matmul948: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm325, permute_dims949, out_dtype="void") + add1121: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul948, model_decoder_layers_22_self_attn_v_proj_bias4) + reshape1255: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1121, 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])) + permute_dims950: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_self_attn_out_proj_weight4, axes=None) + matmul949: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1258, permute_dims950, out_dtype="void") + add1122: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul949, model_decoder_layers_22_self_attn_out_proj_bias4) + add1123: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1119, add1122) + 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) + permute_dims951: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_encoder_attn_q_proj_weight4, axes=None) + matmul950: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm326, permute_dims951, out_dtype="void") + add1124: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul950, model_decoder_layers_22_encoder_attn_q_proj_bias4) + reshape1259: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1124, 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])) + permute_dims952: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_encoder_attn_out_proj_weight4, axes=None) + matmul951: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1262, permute_dims952, out_dtype="void") + add1125: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul951, model_decoder_layers_22_encoder_attn_out_proj_bias4) + add1126: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1123, add1125) + 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) + permute_dims953: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_22_fc1_weight4, axes=None) + matmul952: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm327, permute_dims953, out_dtype="void") + add1127: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul952, model_decoder_layers_22_fc1_bias4) + gelu120: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1127) + permute_dims954: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_22_fc2_weight4, axes=None) + matmul953: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu120, permute_dims954, out_dtype="void") + add1128: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul953, model_decoder_layers_22_fc2_bias4) + add1129: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1126, add1128) + 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) + permute_dims955: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_q_proj_weight4, axes=None) + matmul954: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm328, permute_dims955, out_dtype="void") + add1130: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul954, model_decoder_layers_23_self_attn_q_proj_bias4) + reshape1263: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1130, R.shape([1, seq_len, 20, 64])) + permute_dims956: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_k_proj_weight4, axes=None) + matmul955: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm328, permute_dims956, out_dtype="void") + reshape1264: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul955, R.shape([1, seq_len, 20, 64])) + permute_dims957: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_v_proj_weight4, axes=None) + matmul956: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm328, permute_dims957, out_dtype="void") + add1131: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul956, model_decoder_layers_23_self_attn_v_proj_bias4) + reshape1265: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1131, 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])) + permute_dims958: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_self_attn_out_proj_weight4, axes=None) + matmul957: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1268, permute_dims958, out_dtype="void") + add1132: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul957, model_decoder_layers_23_self_attn_out_proj_bias4) + add1133: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1129, add1132) + 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) + permute_dims959: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_encoder_attn_q_proj_weight4, axes=None) + matmul958: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm329, permute_dims959, out_dtype="void") + add1134: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul958, model_decoder_layers_23_encoder_attn_q_proj_bias4) + reshape1269: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1134, 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])) + permute_dims960: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_encoder_attn_out_proj_weight4, axes=None) + matmul959: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1272, permute_dims960, out_dtype="void") + add1135: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul959, model_decoder_layers_23_encoder_attn_out_proj_bias4) + add1136: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1133, add1135) + 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) + permute_dims961: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_23_fc1_weight4, axes=None) + matmul960: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm330, permute_dims961, out_dtype="void") + add1137: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul960, model_decoder_layers_23_fc1_bias4) + gelu121: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1137) + permute_dims962: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_23_fc2_weight4, axes=None) + matmul961: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu121, permute_dims962, out_dtype="void") + add1138: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul961, model_decoder_layers_23_fc2_bias4) + add1139: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1136, add1138) + 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) + permute_dims963: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_q_proj_weight4, axes=None) + matmul962: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm331, permute_dims963, out_dtype="void") + add1140: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul962, model_decoder_layers_24_self_attn_q_proj_bias4) + reshape1273: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1140, R.shape([1, seq_len, 20, 64])) + permute_dims964: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_k_proj_weight4, axes=None) + matmul963: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm331, permute_dims964, out_dtype="void") + reshape1274: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul963, R.shape([1, seq_len, 20, 64])) + permute_dims965: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_v_proj_weight4, axes=None) + matmul964: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm331, permute_dims965, out_dtype="void") + add1141: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul964, model_decoder_layers_24_self_attn_v_proj_bias4) + reshape1275: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1141, 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])) + permute_dims966: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_self_attn_out_proj_weight4, axes=None) + matmul965: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1278, permute_dims966, out_dtype="void") + add1142: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul965, model_decoder_layers_24_self_attn_out_proj_bias4) + add1143: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1139, add1142) + 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) + permute_dims967: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_encoder_attn_q_proj_weight4, axes=None) + matmul966: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm332, permute_dims967, out_dtype="void") + add1144: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul966, model_decoder_layers_24_encoder_attn_q_proj_bias4) + reshape1279: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1144, 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])) + permute_dims968: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_encoder_attn_out_proj_weight4, axes=None) + matmul967: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1282, permute_dims968, out_dtype="void") + add1145: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul967, model_decoder_layers_24_encoder_attn_out_proj_bias4) + add1146: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1143, add1145) + 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) + permute_dims969: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_24_fc1_weight4, axes=None) + matmul968: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm333, permute_dims969, out_dtype="void") + add1147: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul968, model_decoder_layers_24_fc1_bias4) + gelu122: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1147) + permute_dims970: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_24_fc2_weight4, axes=None) + matmul969: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu122, permute_dims970, out_dtype="void") + add1148: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul969, model_decoder_layers_24_fc2_bias4) + add1149: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1146, add1148) + 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) + permute_dims971: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_q_proj_weight4, axes=None) + matmul970: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm334, permute_dims971, out_dtype="void") + add1150: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul970, model_decoder_layers_25_self_attn_q_proj_bias4) + reshape1283: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1150, R.shape([1, seq_len, 20, 64])) + permute_dims972: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_k_proj_weight4, axes=None) + matmul971: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm334, permute_dims972, out_dtype="void") + reshape1284: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul971, R.shape([1, seq_len, 20, 64])) + permute_dims973: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_v_proj_weight4, axes=None) + matmul972: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm334, permute_dims973, out_dtype="void") + add1151: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul972, model_decoder_layers_25_self_attn_v_proj_bias4) + reshape1285: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1151, 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])) + permute_dims974: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_self_attn_out_proj_weight4, axes=None) + matmul973: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1288, permute_dims974, out_dtype="void") + add1152: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul973, model_decoder_layers_25_self_attn_out_proj_bias4) + add1153: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1149, add1152) + 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) + permute_dims975: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_encoder_attn_q_proj_weight4, axes=None) + matmul974: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm335, permute_dims975, out_dtype="void") + add1154: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul974, model_decoder_layers_25_encoder_attn_q_proj_bias4) + reshape1289: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1154, 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])) + permute_dims976: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_encoder_attn_out_proj_weight4, axes=None) + matmul975: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1292, permute_dims976, out_dtype="void") + add1155: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul975, model_decoder_layers_25_encoder_attn_out_proj_bias4) + add1156: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1153, add1155) + 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) + permute_dims977: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_25_fc1_weight4, axes=None) + matmul976: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm336, permute_dims977, out_dtype="void") + add1157: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul976, model_decoder_layers_25_fc1_bias4) + gelu123: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1157) + permute_dims978: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_25_fc2_weight4, axes=None) + matmul977: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu123, permute_dims978, out_dtype="void") + add1158: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul977, model_decoder_layers_25_fc2_bias4) + add1159: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1156, add1158) + 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) + permute_dims979: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_q_proj_weight4, axes=None) + matmul978: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm337, permute_dims979, out_dtype="void") + add1160: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul978, model_decoder_layers_26_self_attn_q_proj_bias4) + reshape1293: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1160, R.shape([1, seq_len, 20, 64])) + permute_dims980: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_k_proj_weight4, axes=None) + matmul979: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm337, permute_dims980, out_dtype="void") + reshape1294: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul979, R.shape([1, seq_len, 20, 64])) + permute_dims981: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_v_proj_weight4, axes=None) + matmul980: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm337, permute_dims981, out_dtype="void") + add1161: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul980, model_decoder_layers_26_self_attn_v_proj_bias4) + reshape1295: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1161, 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])) + permute_dims982: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_self_attn_out_proj_weight4, axes=None) + matmul981: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1298, permute_dims982, out_dtype="void") + add1162: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul981, model_decoder_layers_26_self_attn_out_proj_bias4) + add1163: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1159, add1162) + 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) + permute_dims983: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_encoder_attn_q_proj_weight4, axes=None) + matmul982: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm338, permute_dims983, out_dtype="void") + add1164: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul982, model_decoder_layers_26_encoder_attn_q_proj_bias4) + reshape1299: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1164, 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])) + permute_dims984: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_encoder_attn_out_proj_weight4, axes=None) + matmul983: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1302, permute_dims984, out_dtype="void") + add1165: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul983, model_decoder_layers_26_encoder_attn_out_proj_bias4) + add1166: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1163, add1165) + 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) + permute_dims985: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_26_fc1_weight4, axes=None) + matmul984: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm339, permute_dims985, out_dtype="void") + add1167: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul984, model_decoder_layers_26_fc1_bias4) + gelu124: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1167) + permute_dims986: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_26_fc2_weight4, axes=None) + matmul985: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu124, permute_dims986, out_dtype="void") + add1168: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul985, model_decoder_layers_26_fc2_bias4) + add1169: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1166, add1168) + 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) + permute_dims987: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_q_proj_weight4, axes=None) + matmul986: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm340, permute_dims987, out_dtype="void") + add1170: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul986, model_decoder_layers_27_self_attn_q_proj_bias4) + reshape1303: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1170, R.shape([1, seq_len, 20, 64])) + permute_dims988: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_k_proj_weight4, axes=None) + matmul987: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm340, permute_dims988, out_dtype="void") + reshape1304: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul987, R.shape([1, seq_len, 20, 64])) + permute_dims989: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_v_proj_weight4, axes=None) + matmul988: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm340, permute_dims989, out_dtype="void") + add1171: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul988, model_decoder_layers_27_self_attn_v_proj_bias4) + reshape1305: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1171, 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])) + permute_dims990: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_self_attn_out_proj_weight4, axes=None) + matmul989: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1308, permute_dims990, out_dtype="void") + add1172: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul989, model_decoder_layers_27_self_attn_out_proj_bias4) + add1173: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1169, add1172) + 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) + permute_dims991: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_encoder_attn_q_proj_weight4, axes=None) + matmul990: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm341, permute_dims991, out_dtype="void") + add1174: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul990, model_decoder_layers_27_encoder_attn_q_proj_bias4) + reshape1309: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1174, 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])) + permute_dims992: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_encoder_attn_out_proj_weight4, axes=None) + matmul991: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1312, permute_dims992, out_dtype="void") + add1175: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul991, model_decoder_layers_27_encoder_attn_out_proj_bias4) + add1176: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1173, add1175) + 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) + permute_dims993: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_27_fc1_weight4, axes=None) + matmul992: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm342, permute_dims993, out_dtype="void") + add1177: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul992, model_decoder_layers_27_fc1_bias4) + gelu125: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1177) + permute_dims994: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_27_fc2_weight4, axes=None) + matmul993: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu125, permute_dims994, out_dtype="void") + add1178: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul993, model_decoder_layers_27_fc2_bias4) + add1179: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1176, add1178) + 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) + permute_dims995: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_q_proj_weight4, axes=None) + matmul994: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm343, permute_dims995, out_dtype="void") + add1180: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul994, model_decoder_layers_28_self_attn_q_proj_bias4) + reshape1313: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1180, R.shape([1, seq_len, 20, 64])) + permute_dims996: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_k_proj_weight4, axes=None) + matmul995: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm343, permute_dims996, out_dtype="void") + reshape1314: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul995, R.shape([1, seq_len, 20, 64])) + permute_dims997: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_v_proj_weight4, axes=None) + matmul996: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm343, permute_dims997, out_dtype="void") + add1181: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul996, model_decoder_layers_28_self_attn_v_proj_bias4) + reshape1315: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1181, 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])) + permute_dims998: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_self_attn_out_proj_weight4, axes=None) + matmul997: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1318, permute_dims998, out_dtype="void") + add1182: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul997, model_decoder_layers_28_self_attn_out_proj_bias4) + add1183: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1179, add1182) + 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) + permute_dims999: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_encoder_attn_q_proj_weight4, axes=None) + matmul998: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm344, permute_dims999, out_dtype="void") + add1184: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul998, model_decoder_layers_28_encoder_attn_q_proj_bias4) + reshape1319: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1184, 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])) + permute_dims1000: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_encoder_attn_out_proj_weight4, axes=None) + matmul999: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1322, permute_dims1000, out_dtype="void") + add1185: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul999, model_decoder_layers_28_encoder_attn_out_proj_bias4) + add1186: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1183, add1185) + 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) + permute_dims1001: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_28_fc1_weight4, axes=None) + matmul1000: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm345, permute_dims1001, out_dtype="void") + add1187: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul1000, model_decoder_layers_28_fc1_bias4) + gelu126: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1187) + permute_dims1002: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_28_fc2_weight4, axes=None) + matmul1001: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu126, permute_dims1002, out_dtype="void") + add1188: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1001, model_decoder_layers_28_fc2_bias4) + add1189: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1186, add1188) + 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) + permute_dims1003: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_q_proj_weight4, axes=None) + matmul1002: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm346, permute_dims1003, out_dtype="void") + add1190: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1002, model_decoder_layers_29_self_attn_q_proj_bias4) + reshape1323: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1190, R.shape([1, seq_len, 20, 64])) + permute_dims1004: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_k_proj_weight4, axes=None) + matmul1003: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm346, permute_dims1004, out_dtype="void") + reshape1324: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul1003, R.shape([1, seq_len, 20, 64])) + permute_dims1005: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_v_proj_weight4, axes=None) + matmul1004: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm346, permute_dims1005, out_dtype="void") + add1191: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1004, model_decoder_layers_29_self_attn_v_proj_bias4) + reshape1325: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1191, 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])) + permute_dims1006: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_self_attn_out_proj_weight4, axes=None) + matmul1005: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1328, permute_dims1006, out_dtype="void") + add1192: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1005, model_decoder_layers_29_self_attn_out_proj_bias4) + add1193: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1189, add1192) + 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) + permute_dims1007: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_encoder_attn_q_proj_weight4, axes=None) + matmul1006: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm347, permute_dims1007, out_dtype="void") + add1194: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1006, model_decoder_layers_29_encoder_attn_q_proj_bias4) + reshape1329: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1194, 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])) + permute_dims1008: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_encoder_attn_out_proj_weight4, axes=None) + matmul1007: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1332, permute_dims1008, out_dtype="void") + add1195: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1007, model_decoder_layers_29_encoder_attn_out_proj_bias4) + add1196: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1193, add1195) + 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) + permute_dims1009: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_29_fc1_weight4, axes=None) + matmul1008: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm348, permute_dims1009, out_dtype="void") + add1197: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul1008, model_decoder_layers_29_fc1_bias4) + gelu127: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1197) + permute_dims1010: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_29_fc2_weight4, axes=None) + matmul1009: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu127, permute_dims1010, out_dtype="void") + add1198: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1009, model_decoder_layers_29_fc2_bias4) + add1199: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1196, add1198) + 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) + permute_dims1011: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_q_proj_weight4, axes=None) + matmul1010: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm349, permute_dims1011, out_dtype="void") + add1200: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1010, model_decoder_layers_30_self_attn_q_proj_bias4) + reshape1333: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1200, R.shape([1, seq_len, 20, 64])) + permute_dims1012: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_k_proj_weight4, axes=None) + matmul1011: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm349, permute_dims1012, out_dtype="void") + reshape1334: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul1011, R.shape([1, seq_len, 20, 64])) + permute_dims1013: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_v_proj_weight4, axes=None) + matmul1012: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm349, permute_dims1013, out_dtype="void") + add1201: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1012, model_decoder_layers_30_self_attn_v_proj_bias4) + reshape1335: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1201, 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])) + permute_dims1014: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_self_attn_out_proj_weight4, axes=None) + matmul1013: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1338, permute_dims1014, out_dtype="void") + add1202: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1013, model_decoder_layers_30_self_attn_out_proj_bias4) + add1203: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1199, add1202) + 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) + permute_dims1015: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_encoder_attn_q_proj_weight4, axes=None) + matmul1014: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm350, permute_dims1015, out_dtype="void") + add1204: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1014, model_decoder_layers_30_encoder_attn_q_proj_bias4) + reshape1339: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1204, 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])) + permute_dims1016: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_encoder_attn_out_proj_weight4, axes=None) + matmul1015: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1342, permute_dims1016, out_dtype="void") + add1205: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1015, model_decoder_layers_30_encoder_attn_out_proj_bias4) + add1206: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1203, add1205) + 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) + permute_dims1017: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_30_fc1_weight4, axes=None) + matmul1016: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm351, permute_dims1017, out_dtype="void") + add1207: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul1016, model_decoder_layers_30_fc1_bias4) + gelu128: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1207) + permute_dims1018: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_30_fc2_weight4, axes=None) + matmul1017: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu128, permute_dims1018, out_dtype="void") + add1208: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1017, model_decoder_layers_30_fc2_bias4) + add1209: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1206, add1208) + 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) + permute_dims1019: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_q_proj_weight4, axes=None) + matmul1018: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm352, permute_dims1019, out_dtype="void") + add1210: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1018, model_decoder_layers_31_self_attn_q_proj_bias4) + reshape1343: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1210, R.shape([1, seq_len, 20, 64])) + permute_dims1020: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_k_proj_weight4, axes=None) + matmul1019: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm352, permute_dims1020, out_dtype="void") + reshape1344: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(matmul1019, R.shape([1, seq_len, 20, 64])) + permute_dims1021: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_v_proj_weight4, axes=None) + matmul1020: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm352, permute_dims1021, out_dtype="void") + add1211: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1020, model_decoder_layers_31_self_attn_v_proj_bias4) + reshape1345: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1211, 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])) + permute_dims1022: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_self_attn_out_proj_weight4, axes=None) + matmul1021: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1348, permute_dims1022, out_dtype="void") + add1212: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1021, model_decoder_layers_31_self_attn_out_proj_bias4) + add1213: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1209, add1212) + 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) + permute_dims1023: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_encoder_attn_q_proj_weight4, axes=None) + matmul1022: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(layer_norm353, permute_dims1023, out_dtype="void") + add1214: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1022, model_decoder_layers_31_encoder_attn_q_proj_bias4) + reshape1349: R.Tensor((1, seq_len, 20, 64), dtype="float16") = R.reshape(add1214, 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])) + permute_dims1024: R.Tensor((1280, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_encoder_attn_out_proj_weight4, axes=None) + matmul1023: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(reshape1352, permute_dims1024, out_dtype="void") + add1215: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1023, model_decoder_layers_31_encoder_attn_out_proj_bias4) + add1216: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1213, add1215) + 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) + permute_dims1025: R.Tensor((1280, 5120), dtype="float16") = R.permute_dims(model_decoder_layers_31_fc1_weight4, axes=None) + matmul1024: R.Tensor((1, seq_len, 5120), dtype="float16") = R.matmul(layer_norm354, permute_dims1025, out_dtype="void") + add1217: R.Tensor((1, seq_len, 5120), dtype="float16") = R.add(matmul1024, model_decoder_layers_31_fc1_bias4) + gelu129: R.Tensor((1, seq_len, 5120), dtype="float16") = R.nn.gelu(add1217) + permute_dims1026: R.Tensor((5120, 1280), dtype="float16") = R.permute_dims(model_decoder_layers_31_fc2_weight4, axes=None) + matmul1025: R.Tensor((1, seq_len, 1280), dtype="float16") = R.matmul(gelu129, permute_dims1026, out_dtype="void") + add1218: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(matmul1025, model_decoder_layers_31_fc2_bias4) + add1219: R.Tensor((1, seq_len, 1280), dtype="float16") = R.add(add1216, add1218) + 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")) + permute_dims1027: R.Tensor((1280, 51866), dtype="float16") = R.permute_dims(model_decoder_embed_tokens_weight4, axes=None) + matmul1026: R.Tensor((1, 1, 51866), dtype="float32") = R.matmul(lv263, permute_dims1027, out_dtype="float32") + gv4: R.Tensor((1, 1, 51866), dtype="float32") = matmul1026 + 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 \ No newline at end of file