===== embedder/qwen3-embedding-0.6b-autorag (qwen3, /home/jovyan/Uday_DB_Ins_PoC_Prep/model/Qwen3-Embedding-0.6B) -> /home/jovyan/Uday_DB_Ins_PoC_Prep/qwen3-autorag-compile/qwen3_autorag_compile_package/mblt-rag/mxq/aries-rb/embedder/qwen3-embedding-0.6b-autorag [recipe] calib present: /home/jovyan/Uday_DB_Ins_PoC_Prep/qwen3-autorag-compile/qwen3_autorag_compile_package/mblt-rag/calib_data/qwen3-embedding-0.6b-autorag (--force to rebuild) + /home/jovyan/Uday_DB_Ins_PoC_Prep/qbcompiler-work/venv-qbcompiler/bin/python /home/jovyan/Uday_DB_Ins_PoC_Prep/qwen3-autorag-compile/qwen3_autorag_compile_package/mblt-rag/compile/lib/qwen3_compile.py --model-id /home/jovyan/Uday_DB_Ins_PoC_Prep/model/Qwen3-Embedding-0.6B --bits w8v8 --preset R1R2 --calib-data-path /home/jovyan/Uday_DB_Ins_PoC_Prep/qwen3-autorag-compile/qwen3_autorag_compile_package/mblt-rag/calib_data/qwen3-embedding-0.6b-autorag --out-dir /home/jovyan/Uday_DB_Ins_PoC_Prep/qwen3-autorag-compile/qwen3_autorag_compile_package/mblt-rag/mxq/aries-rb/embedder/qwen3-embedding-0.6b-autorag --target-device aries-rb --inference-scheme all --max-seq-length 2048 --weight-dtype bfloat16 --optq --tag autorag [2026-09-17 01:30:08] qbcompiler.model_dict.common._reshape_util.reshape_check : [INFO] Parser is configured to use reshape_check_util (Python implementation). [surgery] embedder: lm_head -> identity Linear(1024,1024); the graph returns the last hidden state and stays classifiable for SpinR1 [compile] weight rounding: optq (apply=True) === mxq_compile === Qwen3-Embedding-0.6B_w8v8_R1R2_autorag [⣾] Parsing deep learning model....[2026-09-17 01:31:33] qbcompiler.compiler.compiler : [WARNING] Trying to parse HF-based LLMs and multimodal generative models. If the Torch model isn’t an LLM/VLM, please don't use hf_config. [⣽] Parsing deep learning model.....Setting `pad_token_id` to `eos_token_id`:151643 for open-end generation. [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model...[2026-09-17 01:31:36] qbcompiler.model_dict.parser.backend.fx_hf_extensions : [INFO] No module named 'qwen_asr' [2026-09-17 01:31:36] qbcompiler.model_dict.parser.backend.fx_hf_extensions : [INFO] failed to import qwen3asr [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model.....'(MaxRetryError('HTTPSConnectionPool(host=\'huggingface.co\', port=443): Max retries exceeded with url: /openvla/openvla-7b/resolve/main/modeling_prismatic.py (Caused by NewConnectionError("HTTPSConnection(host=\'huggingface.co\', port=443): Failed to establish a new connection: [Errno 101] Network is unreachable"))'), '(Request ID: 78d0802c-e8e8-4ccf-9e6e-09e862d54eb6)')' thrown while requesting HEAD https://huggingface.co/openvla/openvla-7b/resolve/main/modeling_prismatic.py Retrying in 1s [Retry 1/5]. [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model......'(MaxRetryError('HTTPSConnectionPool(host=\'huggingface.co\', port=443): Max retries exceeded with url: /openvla/openvla-7b/resolve/main/modeling_prismatic.py (Caused by NewConnectionError("HTTPSConnection(host=\'huggingface.co\', port=443): Failed to establish a new connection: [Errno 101] Network is unreachable"))'), '(Request ID: 8814a161-a248-4e73-ab34-97c66a1d04a4)')' thrown while requesting HEAD https://huggingface.co/openvla/openvla-7b/resolve/main/modeling_prismatic.py Retrying in 2s [Retry 2/5]. [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model....'(MaxRetryError('HTTPSConnectionPool(host=\'huggingface.co\', port=443): Max retries exceeded with url: /openvla/openvla-7b/resolve/main/modeling_prismatic.py (Caused by NewConnectionError("HTTPSConnection(host=\'huggingface.co\', port=443): Failed to establish a new connection: [Errno 101] Network is unreachable"))'), '(Request ID: 5db39612-0ac3-4e71-8780-bfbfd8bde486)')' thrown while requesting HEAD https://huggingface.co/openvla/openvla-7b/resolve/main/modeling_prismatic.py Retrying in 4s [Retry 3/5]. [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model.....'(MaxRetryError('HTTPSConnectionPool(host=\'huggingface.co\', port=443): Max retries exceeded with url: /openvla/openvla-7b/resolve/main/modeling_prismatic.py (Caused by NewConnectionError("HTTPSConnection(host=\'huggingface.co\', port=443): Failed to establish a new connection: [Errno 101] Network is unreachable"))'), '(Request ID: cd2cd097-2c28-4f55-8328-fc9d283f9ab8)')' thrown while requesting HEAD https://huggingface.co/openvla/openvla-7b/resolve/main/modeling_prismatic.py Retrying in 8s [Retry 4/5]. [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model.....'(MaxRetryError('HTTPSConnectionPool(host=\'huggingface.co\', port=443): Max retries exceeded with url: /openvla/openvla-7b/resolve/main/modeling_prismatic.py (Caused by NewConnectionError("HTTPSConnection(host=\'huggingface.co\', port=443): Failed to establish a new connection: [Errno 101] Network is unreachable"))'), '(Request ID: 3e573225-ac43-4dbd-b3ad-9b5e5db564ec)')' thrown while requesting HEAD https://huggingface.co/openvla/openvla-7b/resolve/main/modeling_prismatic.py Retrying in 8s [Retry 5/5]. [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model....'(MaxRetryError('HTTPSConnectionPool(host=\'huggingface.co\', port=443): Max retries exceeded with url: /openvla/openvla-7b/resolve/main/modeling_prismatic.py (Caused by NewConnectionError("HTTPSConnection(host=\'huggingface.co\', port=443): Failed to establish a new connection: [Errno 101] Network is unreachable"))'), '(Request ID: cbd91d06-2858-4e3c-a6e9-a077548384b0)')' thrown while requesting HEAD https://huggingface.co/openvla/openvla-7b/resolve/main/modeling_prismatic.py [2026-09-17 01:32:00] qbcompiler.model_dict.parser.backend.fx_hf_extensions : [INFO] We couldn't connect to 'https://huggingface.co' to load the files, and couldn't find them in the cached files. Check your internet connection or see how to run the library in offline mode at 'https://huggingface.co/docs/transformers/installation#offline-mode'. [2026-09-17 01:32:00] qbcompiler.model_dict.parser.backend.fx_hf_extensions : [INFO] failed to import openvla [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model......[2026-09-17 01:32:01] qbcompiler.model_dict.parser.backend.fx_hf_extensions : [INFO] No module named 'timm' [2026-09-17 01:32:01] qbcompiler.model_dict.parser.backend.fx_hf_extensions : [INFO] failed to import edgenext [⣷] Parsing deep learning model... [⣾] Parsing deep learning model....[2026-09-17 01:32:01] qbcompiler.model_dict.parser.backend.torch.fx._trace : [INFO] input_names={'kwargs', 'input_ids', 'labels', 'cache_position', 'attention_mask', 'output_hidden_states', 'position_ids', 'output_attentions', 'past_key_values'} [2026-09-17 01:32:01] qbcompiler.model_dict.parser.backend.torch.fx._trace : [INFO] concrete_args={'inputs_embeds': None, 'logits_to_keep': 1, 'use_cache': True, 'kwargs': {'return_dict': True}}) [2026-09-17 01:32:01] qbcompiler.model_dict.parser.backend.torch.fx._trace : [INFO] default_args={'inputs_embeds': None, 'labels': None, 'use_cache': True, 'output_attentions': None, 'output_hidden_states': None, 'logits_to_keep': 1} [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... 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[⡿] Parsing deep learning model.... =============================================================== Input placeholders: [input_ids, position_ids, past_key_values, cache_position] Outputs: ([language_model_lm_head, past_key_values],) graph(): %input_ids : typing.Optional[torch.LongTensor] [num_users=1] = placeholder[target=input_ids](default=None) %position_ids : typing.Optional[torch.LongTensor] [num_users=2] = placeholder[target=position_ids](default=None) %past_key_values : [num_users=29] = placeholder[target=past_key_values] %cache_position : typing.Optional[torch.LongTensor] [num_users=28] = placeholder[target=cache_position](default=None) %language_model_model_embed_tokens : [num_users=3] = call_module[target=language_model.model.embed_tokens](args = (%input_ids,), kwargs = {}) %language_model_model_rotary_emb_cos : [num_users=1] = get_attr[target=language_model.model.rotary_emb.cos] %reshape : [num_users=1] = call_method[target=reshape](args = (%position_ids, -1), kwargs = {}) %getitem : [num_users=1] = call_function[target=operator.getitem](args = (%language_model_model_rotary_emb_cos, (slice(None, None, None), %reshape, slice(None, None, None))), kwargs = {}) %getattr_3 : [num_users=1] = call_function[target=builtins.getattr](args = (%language_model_model_embed_tokens, dtype), kwargs = {}) %to : [num_users=56] = call_method[target=to](args = (%getitem,), kwargs = {dtype: %getattr_3}) %language_model_model_rotary_emb_sin : [num_users=1] = get_attr[target=language_model.model.rotary_emb.sin] %reshape_1 : [num_users=1] = call_method[target=reshape](args = (%position_ids, -1), kwargs = {}) %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%language_model_model_rotary_emb_sin, (slice(None, None, None), %reshape_1, slice(None, None, None))), kwargs = {}) %to_1 : [num_users=56] = call_method[target=to](args = (%getitem_1,), kwargs = {dtype: torch.float32}) %to_2 : [num_users=2] = call_method[target=to](args = (%language_model_model_embed_tokens, torch.float32), kwargs = {}) %pow_1 : [num_users=1] = call_method[target=pow](args = (%to_2, 2), kwargs = {}) %mean : [num_users=1] = call_method[target=mean](args = (%pow_1, -1), kwargs = {keepdim: True}) %add : [num_users=1] = call_function[target=operator.add](args = (%mean, 1e-06), kwargs = {}) %rsqrt : [num_users=1] = call_function[target=torch.rsqrt](args = (%add,), kwargs = {}) %mul : [num_users=1] = call_function[target=operator.mul](args = (%to_2, %rsqrt), kwargs = {}) %language_model_model_layers_0_input_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.0.input_layernorm.weight] %to_3 : [num_users=1] = call_method[target=to](args = (%mul, torch.float32), kwargs = {}) %mul_1 : [num_users=3] = call_function[target=operator.mul](args = (%language_model_model_layers_0_input_layernorm_weight, %to_3), kwargs = {}) %language_model_model_layers_0_self_attn_q_proj : [num_users=1] = call_module[target=language_model.model.layers.0.self_attn.q_proj](args = (%mul_1,), kwargs = {}) %view : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_0_self_attn_q_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_11 : [num_users=1] = call_function[target=builtins.getattr](args = (%view, dtype), kwargs = {}) %to_4 : [num_users=2] = call_method[target=to](args = (%view, torch.float32), kwargs = {}) %pow_2 : [num_users=1] = call_method[target=pow](args = (%to_4, 2), kwargs = {}) %mean_1 : [num_users=1] = call_method[target=mean](args = (%pow_2, -1), kwargs = {keepdim: True}) %add_1 : [num_users=1] = call_function[target=operator.add](args = (%mean_1, 1e-06), kwargs = {}) %rsqrt_1 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_1,), kwargs = {}) %mul_2 : [num_users=1] = call_function[target=operator.mul](args = (%to_4, %rsqrt_1), kwargs = {}) %language_model_model_layers_0_self_attn_q_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.0.self_attn.q_norm.weight] %to_5 : [num_users=1] = call_method[target=to](args = (%mul_2, %getattr_11), kwargs = {}) %mul_3 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_0_self_attn_q_norm_weight, %to_5), kwargs = {}) %transpose : [num_users=1] = call_method[target=transpose](args = (%mul_3, 1, 2), kwargs = {}) %language_model_model_layers_0_self_attn_k_proj : [num_users=1] = call_module[target=language_model.model.layers.0.self_attn.k_proj](args = (%mul_1,), kwargs = {}) %view_1 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_0_self_attn_k_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_18 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_1, dtype), kwargs = {}) %to_6 : [num_users=2] = call_method[target=to](args = (%view_1, torch.float32), kwargs = {}) %pow_3 : [num_users=1] = call_method[target=pow](args = (%to_6, 2), kwargs = {}) %mean_2 : [num_users=1] = call_method[target=mean](args = (%pow_3, -1), kwargs = {keepdim: True}) %add_2 : [num_users=1] = call_function[target=operator.add](args = (%mean_2, 1e-06), kwargs = {}) %rsqrt_2 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_2,), kwargs = {}) %mul_4 : [num_users=1] = call_function[target=operator.mul](args = (%to_6, %rsqrt_2), kwargs = {}) %language_model_model_layers_0_self_attn_k_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.0.self_attn.k_norm.weight] %to_7 : [num_users=1] = call_method[target=to](args = (%mul_4, %getattr_18), kwargs = {}) %mul_5 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_0_self_attn_k_norm_weight, %to_7), kwargs = {}) %transpose_1 : [num_users=1] = call_method[target=transpose](args = (%mul_5, 1, 2), kwargs = {}) %language_model_model_layers_0_self_attn_v_proj : [num_users=1] = call_module[target=language_model.model.layers.0.self_attn.v_proj](args = (%mul_1,), kwargs = {}) %view_2 : [num_users=1] = call_method[target=view](args = (%language_model_model_layers_0_self_attn_v_proj, (1, 29, -1, 128)), kwargs = {}) %transpose_2 : [num_users=1] = call_method[target=transpose](args = (%view_2, 1, 2), kwargs = {}) %apply_rotary_pos_emb : [num_users=2] = call_function[target=transformers.models.qwen3.modeling_qwen3.apply_rotary_pos_emb](args = (%transpose, %transpose_1, %to, %to_1), kwargs = {}) %getitem_2 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb, 0), kwargs = {}) %getitem_3 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb, 1), kwargs = {}) %update : [num_users=2] = call_method[target=update](args = (%past_key_values, %getitem_3, %transpose_2, 0, {sin: %to_1, cos: %to, cache_position: %cache_position}), kwargs = {}) %getitem_4 : [num_users=1] = call_function[target=operator.getitem](args = (%update, 0), kwargs = {}) %getitem_5 : [num_users=1] = call_function[target=operator.getitem](args = (%update, 1), kwargs = {}) %getitem_6 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_4, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand : [num_users=1] = call_method[target=expand](args = (%getitem_6, 1, 8, 2, 29, 128), kwargs = {}) %reshape_2 : [num_users=1] = call_method[target=reshape](args = (%expand, 1, 16, 29, 128), kwargs = {}) %getitem_7 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_5, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_1 : [num_users=1] = call_method[target=expand](args = (%getitem_7, 1, 8, 2, 29, 128), kwargs = {}) %reshape_3 : [num_users=1] = call_method[target=reshape](args = (%expand_1, 1, 16, 29, 128), kwargs = {}) %scaled_dot_product_attention : [num_users=1] = call_function[target=torch._C._nn.scaled_dot_product_attention](args = (%getitem_2, %reshape_2, %reshape_3), kwargs = {attn_mask: None, dropout_p: 0.0, scale: 0.08838834764831845, is_causal: True}) %transpose_3 : [num_users=1] = call_method[target=transpose](args = (%scaled_dot_product_attention, 1, 2), kwargs = {}) %contiguous : [num_users=1] = call_method[target=contiguous](args = (%transpose_3,), kwargs = {}) %reshape_4 : [num_users=1] = call_method[target=reshape](args = (%contiguous, 1, 29, -1), kwargs = {}) %contiguous_1 : [num_users=1] = call_method[target=contiguous](args = (%reshape_4,), kwargs = {}) %language_model_model_layers_0_self_attn_o_proj : [num_users=1] = call_module[target=language_model.model.layers.0.self_attn.o_proj](args = (%contiguous_1,), kwargs = {}) %add_3 : [num_users=3] = call_function[target=operator.add](args = (%language_model_model_embed_tokens, %language_model_model_layers_0_self_attn_o_proj), kwargs = {}) %getattr_38 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_3, dtype), kwargs = {}) %to_8 : [num_users=2] = call_method[target=to](args = (%add_3, torch.float32), kwargs = {}) %pow_4 : [num_users=1] = call_method[target=pow](args = (%to_8, 2), kwargs = {}) %mean_3 : [num_users=1] = call_method[target=mean](args = (%pow_4, -1), kwargs = {keepdim: True}) %add_4 : [num_users=1] = call_function[target=operator.add](args = (%mean_3, 1e-06), kwargs = {}) %rsqrt_3 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_4,), kwargs = {}) %mul_6 : [num_users=1] = call_function[target=operator.mul](args = (%to_8, %rsqrt_3), kwargs = {}) %language_model_model_layers_0_post_attention_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.0.post_attention_layernorm.weight] %to_9 : [num_users=1] = call_method[target=to](args = (%mul_6, %getattr_38), kwargs = {}) %mul_7 : [num_users=2] = call_function[target=operator.mul](args = (%language_model_model_layers_0_post_attention_layernorm_weight, %to_9), kwargs = {}) %language_model_model_layers_0_mlp_gate_proj : [num_users=1] = call_module[target=language_model.model.layers.0.mlp.gate_proj](args = (%mul_7,), kwargs = {}) %silu : [num_users=1] = call_function[target=torch.nn.functional.silu](args = (%language_model_model_layers_0_mlp_gate_proj,), kwargs = {inplace: False}) %language_model_model_layers_0_mlp_up_proj : [num_users=1] = call_module[target=language_model.model.layers.0.mlp.up_proj](args = (%mul_7,), kwargs = {}) %mul_8 : [num_users=1] = call_function[target=operator.mul](args = (%silu, %language_model_model_layers_0_mlp_up_proj), kwargs = {}) %language_model_model_layers_0_mlp_down_proj : [num_users=1] = call_module[target=language_model.model.layers.0.mlp.down_proj](args = (%mul_8,), kwargs = {}) %add_5 : [num_users=3] = call_function[target=operator.add](args = (%add_3, %language_model_model_layers_0_mlp_down_proj), kwargs = {}) %getattr_43 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_5, dtype), kwargs = {}) %to_10 : [num_users=2] = call_method[target=to](args = (%add_5, torch.float32), kwargs = {}) %pow_5 : [num_users=1] = call_method[target=pow](args = (%to_10, 2), kwargs = {}) %mean_4 : [num_users=1] = call_method[target=mean](args = (%pow_5, -1), kwargs = {keepdim: True}) %add_6 : [num_users=1] = call_function[target=operator.add](args = (%mean_4, 1e-06), kwargs = {}) %rsqrt_4 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_6,), kwargs = {}) %mul_9 : [num_users=1] = call_function[target=operator.mul](args = (%to_10, %rsqrt_4), kwargs = {}) %language_model_model_layers_1_input_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.1.input_layernorm.weight] %to_11 : [num_users=1] = call_method[target=to](args = (%mul_9, %getattr_43), kwargs = {}) %mul_10 : [num_users=3] = call_function[target=operator.mul](args = (%language_model_model_layers_1_input_layernorm_weight, %to_11), kwargs = {}) %language_model_model_layers_1_self_attn_q_proj : [num_users=1] = call_module[target=language_model.model.layers.1.self_attn.q_proj](args = (%mul_10,), kwargs = {}) %view_3 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_1_self_attn_q_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_50 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_3, dtype), kwargs = {}) %to_12 : [num_users=2] = call_method[target=to](args = (%view_3, torch.float32), kwargs = {}) %pow_6 : [num_users=1] = call_method[target=pow](args = (%to_12, 2), kwargs = {}) %mean_5 : [num_users=1] = call_method[target=mean](args = (%pow_6, -1), kwargs = {keepdim: True}) %add_7 : [num_users=1] = call_function[target=operator.add](args = (%mean_5, 1e-06), kwargs = {}) %rsqrt_5 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_7,), kwargs = {}) %mul_11 : [num_users=1] = call_function[target=operator.mul](args = (%to_12, %rsqrt_5), kwargs = {}) %language_model_model_layers_1_self_attn_q_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.1.self_attn.q_norm.weight] %to_13 : [num_users=1] = call_method[target=to](args = (%mul_11, %getattr_50), kwargs = {}) %mul_12 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_1_self_attn_q_norm_weight, %to_13), kwargs = {}) %transpose_4 : [num_users=1] = call_method[target=transpose](args = (%mul_12, 1, 2), kwargs = {}) %language_model_model_layers_1_self_attn_k_proj : [num_users=1] = call_module[target=language_model.model.layers.1.self_attn.k_proj](args = (%mul_10,), kwargs = {}) %view_4 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_1_self_attn_k_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_57 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_4, dtype), kwargs = {}) %to_14 : [num_users=2] = call_method[target=to](args = (%view_4, torch.float32), kwargs = {}) %pow_7 : [num_users=1] = call_method[target=pow](args = (%to_14, 2), kwargs = {}) %mean_6 : [num_users=1] = call_method[target=mean](args = (%pow_7, -1), kwargs = {keepdim: True}) %add_8 : [num_users=1] = call_function[target=operator.add](args = (%mean_6, 1e-06), kwargs = {}) %rsqrt_6 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_8,), kwargs = {}) %mul_13 : [num_users=1] = call_function[target=operator.mul](args = (%to_14, %rsqrt_6), kwargs = {}) %language_model_model_layers_1_self_attn_k_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.1.self_attn.k_norm.weight] %to_15 : [num_users=1] = call_method[target=to](args = (%mul_13, %getattr_57), kwargs = {}) %mul_14 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_1_self_attn_k_norm_weight, %to_15), kwargs = {}) %transpose_5 : [num_users=1] = call_method[target=transpose](args = (%mul_14, 1, 2), kwargs = {}) %language_model_model_layers_1_self_attn_v_proj : [num_users=1] = call_module[target=language_model.model.layers.1.self_attn.v_proj](args = (%mul_10,), kwargs = {}) %view_5 : [num_users=1] = call_method[target=view](args = (%language_model_model_layers_1_self_attn_v_proj, (1, 29, -1, 128)), kwargs = {}) %transpose_6 : [num_users=1] = call_method[target=transpose](args = (%view_5, 1, 2), kwargs = {}) %apply_rotary_pos_emb_1 : [num_users=2] = call_function[target=transformers.models.qwen3.modeling_qwen3.apply_rotary_pos_emb](args = (%transpose_4, %transpose_5, %to, %to_1), kwargs = {}) %getitem_8 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_1, 0), kwargs = {}) %getitem_9 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_1, 1), kwargs = {}) %update_1 : [num_users=2] = call_method[target=update](args = (%past_key_values, %getitem_9, %transpose_6, 1, {sin: %to_1, cos: %to, cache_position: %cache_position}), kwargs = {}) %getitem_10 : [num_users=1] = call_function[target=operator.getitem](args = (%update_1, 0), kwargs = {}) %getitem_11 : [num_users=1] = call_function[target=operator.getitem](args = (%update_1, 1), kwargs = {}) %getitem_12 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_10, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_2 : [num_users=1] = call_method[target=expand](args = (%getitem_12, 1, 8, 2, 29, 128), kwargs = {}) %reshape_5 : [num_users=1] = call_method[target=reshape](args = (%expand_2, 1, 16, 29, 128), kwargs = {}) %getitem_13 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_11, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_3 : [num_users=1] = call_method[target=expand](args = (%getitem_13, 1, 8, 2, 29, 128), kwargs = {}) %reshape_6 : [num_users=1] = call_method[target=reshape](args = (%expand_3, 1, 16, 29, 128), kwargs = {}) %scaled_dot_product_attention_1 : [num_users=1] = call_function[target=torch._C._nn.scaled_dot_product_attention](args = (%getitem_8, %reshape_5, %reshape_6), kwargs = {attn_mask: None, dropout_p: 0.0, scale: 0.08838834764831845, is_causal: True}) %transpose_7 : [num_users=1] = call_method[target=transpose](args = (%scaled_dot_product_attention_1, 1, 2), kwargs = {}) %contiguous_2 : [num_users=1] = call_method[target=contiguous](args = (%transpose_7,), kwargs = {}) %reshape_7 : [num_users=1] = call_method[target=reshape](args = (%contiguous_2, 1, 29, -1), kwargs = {}) %contiguous_3 : [num_users=1] = call_method[target=contiguous](args = (%reshape_7,), kwargs = {}) %language_model_model_layers_1_self_attn_o_proj : [num_users=1] = call_module[target=language_model.model.layers.1.self_attn.o_proj](args = (%contiguous_3,), kwargs = {}) %add_9 : [num_users=3] = call_function[target=operator.add](args = (%add_5, %language_model_model_layers_1_self_attn_o_proj), kwargs = {}) %getattr_76 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_9, dtype), kwargs = {}) %to_16 : [num_users=2] = call_method[target=to](args = (%add_9, torch.float32), kwargs = {}) %pow_8 : [num_users=1] = call_method[target=pow](args = (%to_16, 2), kwargs = {}) %mean_7 : [num_users=1] = call_method[target=mean](args = (%pow_8, -1), kwargs = {keepdim: True}) %add_10 : [num_users=1] = call_function[target=operator.add](args = (%mean_7, 1e-06), kwargs = {}) %rsqrt_7 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_10,), kwargs = {}) %mul_15 : [num_users=1] = call_function[target=operator.mul](args = (%to_16, %rsqrt_7), kwargs = {}) %language_model_model_layers_1_post_attention_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.1.post_attention_layernorm.weight] %to_17 : [num_users=1] = call_method[target=to](args = (%mul_15, %getattr_76), kwargs = {}) %mul_16 : [num_users=2] = call_function[target=operator.mul](args = (%language_model_model_layers_1_post_attention_layernorm_weight, %to_17), kwargs = {}) %language_model_model_layers_1_mlp_gate_proj : [num_users=1] = call_module[target=language_model.model.layers.1.mlp.gate_proj](args = (%mul_16,), kwargs = {}) %silu_1 : [num_users=1] = call_function[target=torch.nn.functional.silu](args = (%language_model_model_layers_1_mlp_gate_proj,), kwargs = {inplace: False}) %language_model_model_layers_1_mlp_up_proj : [num_users=1] = call_module[target=language_model.model.layers.1.mlp.up_proj](args = (%mul_16,), kwargs = {}) %mul_17 : [num_users=1] = call_function[target=operator.mul](args = (%silu_1, %language_model_model_layers_1_mlp_up_proj), kwargs = {}) %language_model_model_layers_1_mlp_down_proj : [num_users=1] = call_module[target=language_model.model.layers.1.mlp.down_proj](args = (%mul_17,), kwargs = {}) %add_11 : [num_users=3] = call_function[target=operator.add](args = (%add_9, %language_model_model_layers_1_mlp_down_proj), kwargs = {}) %getattr_81 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_11, dtype), kwargs = {}) %to_18 : [num_users=2] = call_method[target=to](args = (%add_11, torch.float32), kwargs = {}) %pow_9 : [num_users=1] = call_method[target=pow](args = (%to_18, 2), kwargs = {}) %mean_8 : [num_users=1] = call_method[target=mean](args = (%pow_9, -1), kwargs = {keepdim: True}) %add_12 : [num_users=1] = call_function[target=operator.add](args = (%mean_8, 1e-06), kwargs = {}) %rsqrt_8 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_12,), kwargs = {}) %mul_18 : [num_users=1] = call_function[target=operator.mul](args = (%to_18, %rsqrt_8), kwargs = {}) %language_model_model_layers_2_input_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.2.input_layernorm.weight] %to_19 : [num_users=1] = call_method[target=to](args = (%mul_18, %getattr_81), kwargs = {}) %mul_19 : [num_users=3] = call_function[target=operator.mul](args = (%language_model_model_layers_2_input_layernorm_weight, %to_19), kwargs = {}) %language_model_model_layers_2_self_attn_q_proj : [num_users=1] = call_module[target=language_model.model.layers.2.self_attn.q_proj](args = (%mul_19,), kwargs = {}) %view_6 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_2_self_attn_q_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_88 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_6, dtype), kwargs = {}) %to_20 : [num_users=2] = call_method[target=to](args = (%view_6, torch.float32), kwargs = {}) %pow_10 : [num_users=1] = call_method[target=pow](args = (%to_20, 2), kwargs = {}) %mean_9 : [num_users=1] = call_method[target=mean](args = (%pow_10, -1), kwargs = {keepdim: True}) %add_13 : [num_users=1] = call_function[target=operator.add](args = (%mean_9, 1e-06), kwargs = {}) %rsqrt_9 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_13,), kwargs = {}) %mul_20 : [num_users=1] = call_function[target=operator.mul](args = (%to_20, %rsqrt_9), kwargs = {}) %language_model_model_layers_2_self_attn_q_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.2.self_attn.q_norm.weight] %to_21 : [num_users=1] = call_method[target=to](args = (%mul_20, %getattr_88), kwargs = {}) %mul_21 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_2_self_attn_q_norm_weight, %to_21), kwargs = {}) %transpose_8 : [num_users=1] = call_method[target=transpose](args = (%mul_21, 1, 2), kwargs = {}) %language_model_model_layers_2_self_attn_k_proj : [num_users=1] = call_module[target=language_model.model.layers.2.self_attn.k_proj](args = (%mul_19,), kwargs = {}) %view_7 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_2_self_attn_k_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_95 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_7, dtype), kwargs = {}) %to_22 : [num_users=2] = call_method[target=to](args = (%view_7, torch.float32), kwargs = {}) %pow_11 : [num_users=1] = call_method[target=pow](args = (%to_22, 2), kwargs = {}) %mean_10 : [num_users=1] = call_method[target=mean](args = (%pow_11, -1), kwargs = {keepdim: True}) %add_14 : [num_users=1] = call_function[target=operator.add](args = (%mean_10, 1e-06), kwargs = {}) %rsqrt_10 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_14,), kwargs = {}) %mul_22 : [num_users=1] = call_function[target=operator.mul](args = (%to_22, %rsqrt_10), kwargs = {}) %language_model_model_layers_2_self_attn_k_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.2.self_attn.k_norm.weight] %to_23 : [num_users=1] = call_method[target=to](args = (%mul_22, %getattr_95), kwargs = {}) %mul_23 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_2_self_attn_k_norm_weight, %to_23), kwargs = {}) %transpose_9 : [num_users=1] = call_method[target=transpose](args = (%mul_23, 1, 2), kwargs = {}) %language_model_model_layers_2_self_attn_v_proj : [num_users=1] = call_module[target=language_model.model.layers.2.self_attn.v_proj](args = (%mul_19,), kwargs = {}) %view_8 : [num_users=1] = call_method[target=view](args = (%language_model_model_layers_2_self_attn_v_proj, (1, 29, -1, 128)), kwargs = {}) %transpose_10 : [num_users=1] = call_method[target=transpose](args = (%view_8, 1, 2), kwargs = {}) %apply_rotary_pos_emb_2 : [num_users=2] = call_function[target=transformers.models.qwen3.modeling_qwen3.apply_rotary_pos_emb](args = (%transpose_8, %transpose_9, %to, %to_1), kwargs = {}) %getitem_14 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_2, 0), kwargs = {}) %getitem_15 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_2, 1), kwargs = {}) %update_2 : [num_users=2] = call_method[target=update](args = (%past_key_values, %getitem_15, %transpose_10, 2, {sin: %to_1, cos: %to, cache_position: %cache_position}), kwargs = {}) %getitem_16 : [num_users=1] = call_function[target=operator.getitem](args = (%update_2, 0), kwargs = {}) %getitem_17 : [num_users=1] = call_function[target=operator.getitem](args = (%update_2, 1), kwargs = {}) %getitem_18 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_16, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_4 : [num_users=1] = call_method[target=expand](args = (%getitem_18, 1, 8, 2, 29, 128), kwargs = {}) %reshape_8 : [num_users=1] = call_method[target=reshape](args = (%expand_4, 1, 16, 29, 128), kwargs = {}) %getitem_19 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_17, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_5 : [num_users=1] = call_method[target=expand](args = (%getitem_19, 1, 8, 2, 29, 128), kwargs = {}) %reshape_9 : [num_users=1] = call_method[target=reshape](args = (%expand_5, 1, 16, 29, 128), kwargs = {}) %scaled_dot_product_attention_2 : [num_users=1] = call_function[target=torch._C._nn.scaled_dot_product_attention](args = (%getitem_14, %reshape_8, %reshape_9), kwargs = {attn_mask: None, dropout_p: 0.0, scale: 0.08838834764831845, is_causal: True}) %transpose_11 : [num_users=1] = call_method[target=transpose](args = (%scaled_dot_product_attention_2, 1, 2), kwargs = {}) %contiguous_4 : [num_users=1] = call_method[target=contiguous](args = (%transpose_11,), kwargs = {}) %reshape_10 : [num_users=1] = call_method[target=reshape](args = (%contiguous_4, 1, 29, -1), kwargs = {}) %contiguous_5 : [num_users=1] = call_method[target=contiguous](args = (%reshape_10,), kwargs = {}) %language_model_model_layers_2_self_attn_o_proj : [num_users=1] = call_module[target=language_model.model.layers.2.self_attn.o_proj](args = (%contiguous_5,), kwargs = {}) %add_15 : [num_users=3] = call_function[target=operator.add](args = (%add_11, %language_model_model_layers_2_self_attn_o_proj), kwargs = {}) %getattr_114 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_15, dtype), kwargs = {}) %to_24 : [num_users=2] = call_method[target=to](args = (%add_15, torch.float32), kwargs = {}) %pow_12 : [num_users=1] = call_method[target=pow](args = (%to_24, 2), kwargs = {}) %mean_11 : [num_users=1] = call_method[target=mean](args = (%pow_12, -1), kwargs = {keepdim: True}) %add_16 : [num_users=1] = call_function[target=operator.add](args = (%mean_11, 1e-06), kwargs = {}) %rsqrt_11 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_16,), kwargs = {}) %mul_24 : [num_users=1] = call_function[target=operator.mul](args = (%to_24, %rsqrt_11), kwargs = {}) %language_model_model_layers_2_post_attention_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.2.post_attention_layernorm.weight] %to_25 : [num_users=1] = call_method[target=to](args = (%mul_24, %getattr_114), kwargs = {}) %mul_25 : [num_users=2] = call_function[target=operator.mul](args = (%language_model_model_layers_2_post_attention_layernorm_weight, %to_25), kwargs = {}) %language_model_model_layers_2_mlp_gate_proj : [num_users=1] = call_module[target=language_model.model.layers.2.mlp.gate_proj](args = (%mul_25,), kwargs = {}) %silu_2 : [num_users=1] = call_function[target=torch.nn.functional.silu](args = (%language_model_model_layers_2_mlp_gate_proj,), kwargs = {inplace: False}) %language_model_model_layers_2_mlp_up_proj : [num_users=1] = call_module[target=language_model.model.layers.2.mlp.up_proj](args = (%mul_25,), kwargs = {}) %mul_26 : [num_users=1] = call_function[target=operator.mul](args = (%silu_2, %language_model_model_layers_2_mlp_up_proj), kwargs = {}) %language_model_model_layers_2_mlp_down_proj : [num_users=1] = call_module[target=language_model.model.layers.2.mlp.down_proj](args = (%mul_26,), kwargs = {}) %add_17 : [num_users=3] = call_function[target=operator.add](args = (%add_15, %language_model_model_layers_2_mlp_down_proj), kwargs = {}) %getattr_119 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_17, dtype), kwargs = {}) %to_26 : [num_users=2] = call_method[target=to](args = (%add_17, torch.float32), kwargs = {}) %pow_13 : [num_users=1] = call_method[target=pow](args = (%to_26, 2), kwargs = {}) %mean_12 : [num_users=1] = call_method[target=mean](args = (%pow_13, -1), kwargs = {keepdim: True}) %add_18 : [num_users=1] = call_function[target=operator.add](args = (%mean_12, 1e-06), kwargs = {}) %rsqrt_12 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_18,), kwargs = {}) %mul_27 : [num_users=1] = call_function[target=operator.mul](args = (%to_26, %rsqrt_12), kwargs = {}) %language_model_model_layers_3_input_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.3.input_layernorm.weight] %to_27 : [num_users=1] = call_method[target=to](args = (%mul_27, %getattr_119), kwargs = {}) %mul_28 : [num_users=3] = call_function[target=operator.mul](args = (%language_model_model_layers_3_input_layernorm_weight, %to_27), kwargs = {}) %language_model_model_layers_3_self_attn_q_proj : [num_users=1] = call_module[target=language_model.model.layers.3.self_attn.q_proj](args = (%mul_28,), kwargs = {}) %view_9 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_3_self_attn_q_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_126 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_9, dtype), kwargs = {}) %to_28 : [num_users=2] = call_method[target=to](args = (%view_9, torch.float32), kwargs = {}) %pow_14 : [num_users=1] = call_method[target=pow](args = (%to_28, 2), kwargs = {}) %mean_13 : [num_users=1] = call_method[target=mean](args = (%pow_14, -1), kwargs = {keepdim: True}) %add_19 : [num_users=1] = call_function[target=operator.add](args = (%mean_13, 1e-06), kwargs = {}) %rsqrt_13 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_19,), kwargs = {}) %mul_29 : [num_users=1] = call_function[target=operator.mul](args = (%to_28, %rsqrt_13), kwargs = {}) %language_model_model_layers_3_self_attn_q_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.3.self_attn.q_norm.weight] %to_29 : [num_users=1] = call_method[target=to](args = (%mul_29, %getattr_126), kwargs = {}) %mul_30 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_3_self_attn_q_norm_weight, %to_29), kwargs = {}) %transpose_12 : [num_users=1] = call_method[target=transpose](args = (%mul_30, 1, 2), kwargs = {}) %language_model_model_layers_3_self_attn_k_proj : [num_users=1] = call_module[target=language_model.model.layers.3.self_attn.k_proj](args = (%mul_28,), kwargs = {}) %view_10 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_3_self_attn_k_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_133 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_10, dtype), kwargs = {}) %to_30 : [num_users=2] = call_method[target=to](args = (%view_10, torch.float32), kwargs = {}) %pow_15 : [num_users=1] = call_method[target=pow](args = (%to_30, 2), kwargs = {}) %mean_14 : [num_users=1] = call_method[target=mean](args = (%pow_15, -1), kwargs = {keepdim: True}) %add_20 : [num_users=1] = call_function[target=operator.add](args = (%mean_14, 1e-06), kwargs = {}) %rsqrt_14 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_20,), kwargs = {}) %mul_31 : [num_users=1] = call_function[target=operator.mul](args = (%to_30, %rsqrt_14), kwargs = {}) %language_model_model_layers_3_self_attn_k_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.3.self_attn.k_norm.weight] %to_31 : [num_users=1] = call_method[target=to](args = (%mul_31, %getattr_133), kwargs = {}) %mul_32 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_3_self_attn_k_norm_weight, %to_31), kwargs = {}) %transpose_13 : [num_users=1] = call_method[target=transpose](args = (%mul_32, 1, 2), kwargs = {}) %language_model_model_layers_3_self_attn_v_proj : [num_users=1] = call_module[target=language_model.model.layers.3.self_attn.v_proj](args = (%mul_28,), kwargs = {}) %view_11 : [num_users=1] = call_method[target=view](args = (%language_model_model_layers_3_self_attn_v_proj, (1, 29, -1, 128)), kwargs = {}) %transpose_14 : [num_users=1] = call_method[target=transpose](args = (%view_11, 1, 2), kwargs = {}) %apply_rotary_pos_emb_3 : [num_users=2] = call_function[target=transformers.models.qwen3.modeling_qwen3.apply_rotary_pos_emb](args = (%transpose_12, %transpose_13, %to, %to_1), kwargs = {}) %getitem_20 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_3, 0), kwargs = {}) %getitem_21 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_3, 1), kwargs = {}) %update_3 : [num_users=2] = call_method[target=update](args = (%past_key_values, %getitem_21, %transpose_14, 3, {sin: %to_1, cos: %to, cache_position: %cache_position}), kwargs = {}) %getitem_22 : [num_users=1] = call_function[target=operator.getitem](args = (%update_3, 0), kwargs = {}) %getitem_23 : [num_users=1] = call_function[target=operator.getitem](args = (%update_3, 1), kwargs = {}) %getitem_24 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_22, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_6 : [num_users=1] = call_method[target=expand](args = (%getitem_24, 1, 8, 2, 29, 128), kwargs = {}) %reshape_11 : [num_users=1] = call_method[target=reshape](args = (%expand_6, 1, 16, 29, 128), kwargs = {}) %getitem_25 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_23, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_7 : [num_users=1] = call_method[target=expand](args = (%getitem_25, 1, 8, 2, 29, 128), kwargs = {}) %reshape_12 : [num_users=1] = call_method[target=reshape](args = (%expand_7, 1, 16, 29, 128), kwargs = {}) %scaled_dot_product_attention_3 : [num_users=1] = call_function[target=torch._C._nn.scaled_dot_product_attention](args = (%getitem_20, %reshape_11, %reshape_12), kwargs = {attn_mask: None, dropout_p: 0.0, scale: 0.08838834764831845, is_causal: True}) %transpose_15 : [num_users=1] = call_method[target=transpose](args = (%scaled_dot_product_attention_3, 1, 2), kwargs = {}) %contiguous_6 : [num_users=1] = call_method[target=contiguous](args = (%transpose_15,), kwargs = {}) %reshape_13 : [num_users=1] = call_method[target=reshape](args = (%contiguous_6, 1, 29, -1), kwargs = {}) %contiguous_7 : [num_users=1] = call_method[target=contiguous](args = (%reshape_13,), kwargs = {}) %language_model_model_layers_3_self_attn_o_proj : [num_users=1] = call_module[target=language_model.model.layers.3.self_attn.o_proj](args = (%contiguous_7,), kwargs = {}) %add_21 : [num_users=3] = call_function[target=operator.add](args = (%add_17, %language_model_model_layers_3_self_attn_o_proj), kwargs = {}) %getattr_152 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_21, dtype), kwargs = {}) %to_32 : [num_users=2] = call_method[target=to](args = (%add_21, torch.float32), kwargs = {}) %pow_16 : [num_users=1] = call_method[target=pow](args = (%to_32, 2), kwargs = {}) %mean_15 : [num_users=1] = call_method[target=mean](args = (%pow_16, -1), kwargs = {keepdim: True}) %add_22 : [num_users=1] = call_function[target=operator.add](args = (%mean_15, 1e-06), kwargs = {}) %rsqrt_15 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_22,), kwargs = {}) %mul_33 : [num_users=1] = call_function[target=operator.mul](args = (%to_32, %rsqrt_15), kwargs = {}) %language_model_model_layers_3_post_attention_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.3.post_attention_layernorm.weight] %to_33 : [num_users=1] = call_method[target=to](args = (%mul_33, %getattr_152), kwargs = {}) %mul_34 : [num_users=2] = call_function[target=operator.mul](args = (%language_model_model_layers_3_post_attention_layernorm_weight, %to_33), kwargs = {}) %language_model_model_layers_3_mlp_gate_proj : [num_users=1] = call_module[target=language_model.model.layers.3.mlp.gate_proj](args = (%mul_34,), kwargs = {}) %silu_3 : [num_users=1] = call_function[target=torch.nn.functional.silu](args = (%language_model_model_layers_3_mlp_gate_proj,), kwargs = {inplace: False}) %language_model_model_layers_3_mlp_up_proj : [num_users=1] = call_module[target=language_model.model.layers.3.mlp.up_proj](args = (%mul_34,), kwargs = {}) %mul_35 : [num_users=1] = call_function[target=operator.mul](args = (%silu_3, %language_model_model_layers_3_mlp_up_proj), kwargs = {}) %language_model_model_layers_3_mlp_down_proj : [num_users=1] = call_module[target=language_model.model.layers.3.mlp.down_proj](args = (%mul_35,), kwargs = {}) %add_23 : [num_users=3] = call_function[target=operator.add](args = (%add_21, %language_model_model_layers_3_mlp_down_proj), kwargs = {}) %getattr_157 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_23, dtype), kwargs = {}) %to_34 : [num_users=2] = call_method[target=to](args = (%add_23, torch.float32), kwargs = {}) %pow_17 : [num_users=1] = call_method[target=pow](args = (%to_34, 2), kwargs = {}) %mean_16 : [num_users=1] = call_method[target=mean](args = (%pow_17, -1), kwargs = {keepdim: True}) %add_24 : [num_users=1] = call_function[target=operator.add](args = (%mean_16, 1e-06), kwargs = {}) %rsqrt_16 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_24,), kwargs = {}) %mul_36 : [num_users=1] = call_function[target=operator.mul](args = (%to_34, %rsqrt_16), kwargs = {}) %language_model_model_layers_4_input_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.4.input_layernorm.weight] %to_35 : [num_users=1] = call_method[target=to](args = (%mul_36, %getattr_157), kwargs = {}) %mul_37 : [num_users=3] = call_function[target=operator.mul](args = (%language_model_model_layers_4_input_layernorm_weight, %to_35), kwargs = {}) %language_model_model_layers_4_self_attn_q_proj : [num_users=1] = call_module[target=language_model.model.layers.4.self_attn.q_proj](args = (%mul_37,), kwargs = {}) %view_12 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_4_self_attn_q_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_164 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_12, dtype), kwargs = {}) %to_36 : [num_users=2] = call_method[target=to](args = (%view_12, torch.float32), kwargs = {}) %pow_18 : [num_users=1] = call_method[target=pow](args = (%to_36, 2), kwargs = {}) %mean_17 : [num_users=1] = call_method[target=mean](args = (%pow_18, -1), kwargs = {keepdim: True}) %add_25 : [num_users=1] = call_function[target=operator.add](args = (%mean_17, 1e-06), kwargs = {}) %rsqrt_17 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_25,), kwargs = {}) %mul_38 : [num_users=1] = call_function[target=operator.mul](args = (%to_36, %rsqrt_17), kwargs = {}) %language_model_model_layers_4_self_attn_q_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.4.self_attn.q_norm.weight] %to_37 : [num_users=1] = call_method[target=to](args = (%mul_38, %getattr_164), kwargs = {}) %mul_39 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_4_self_attn_q_norm_weight, %to_37), kwargs = {}) %transpose_16 : [num_users=1] = call_method[target=transpose](args = (%mul_39, 1, 2), kwargs = {}) %language_model_model_layers_4_self_attn_k_proj : [num_users=1] = call_module[target=language_model.model.layers.4.self_attn.k_proj](args = (%mul_37,), kwargs = {}) %view_13 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_4_self_attn_k_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_171 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_13, dtype), kwargs = {}) %to_38 : [num_users=2] = call_method[target=to](args = (%view_13, torch.float32), kwargs = {}) %pow_19 : [num_users=1] = call_method[target=pow](args = (%to_38, 2), kwargs = {}) %mean_18 : [num_users=1] = call_method[target=mean](args = (%pow_19, -1), kwargs = {keepdim: True}) %add_26 : [num_users=1] = call_function[target=operator.add](args = (%mean_18, 1e-06), kwargs = {}) %rsqrt_18 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_26,), kwargs = {}) %mul_40 : [num_users=1] = call_function[target=operator.mul](args = (%to_38, %rsqrt_18), kwargs = {}) %language_model_model_layers_4_self_attn_k_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.4.self_attn.k_norm.weight] %to_39 : [num_users=1] = call_method[target=to](args = (%mul_40, %getattr_171), kwargs = {}) %mul_41 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_4_self_attn_k_norm_weight, %to_39), kwargs = {}) %transpose_17 : [num_users=1] = call_method[target=transpose](args = (%mul_41, 1, 2), kwargs = {}) %language_model_model_layers_4_self_attn_v_proj : [num_users=1] = call_module[target=language_model.model.layers.4.self_attn.v_proj](args = (%mul_37,), kwargs = {}) %view_14 : [num_users=1] = call_method[target=view](args = (%language_model_model_layers_4_self_attn_v_proj, (1, 29, -1, 128)), kwargs = {}) %transpose_18 : [num_users=1] = call_method[target=transpose](args = (%view_14, 1, 2), kwargs = {}) %apply_rotary_pos_emb_4 : [num_users=2] = call_function[target=transformers.models.qwen3.modeling_qwen3.apply_rotary_pos_emb](args = (%transpose_16, %transpose_17, %to, %to_1), kwargs = {}) %getitem_26 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_4, 0), kwargs = {}) %getitem_27 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_4, 1), kwargs = {}) %update_4 : [num_users=2] = call_method[target=update](args = (%past_key_values, %getitem_27, %transpose_18, 4, {sin: %to_1, cos: %to, cache_position: %cache_position}), kwargs = {}) %getitem_28 : [num_users=1] = call_function[target=operator.getitem](args = (%update_4, 0), kwargs = {}) %getitem_29 : [num_users=1] = call_function[target=operator.getitem](args = (%update_4, 1), kwargs = {}) %getitem_30 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_28, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_8 : [num_users=1] = call_method[target=expand](args = (%getitem_30, 1, 8, 2, 29, 128), kwargs = {}) %reshape_14 : [num_users=1] = call_method[target=reshape](args = (%expand_8, 1, 16, 29, 128), kwargs = {}) %getitem_31 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_29, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_9 : [num_users=1] = call_method[target=expand](args = (%getitem_31, 1, 8, 2, 29, 128), kwargs = {}) %reshape_15 : [num_users=1] = call_method[target=reshape](args = (%expand_9, 1, 16, 29, 128), kwargs = {}) %scaled_dot_product_attention_4 : [num_users=1] = call_function[target=torch._C._nn.scaled_dot_product_attention](args = (%getitem_26, %reshape_14, %reshape_15), kwargs = {attn_mask: None, dropout_p: 0.0, scale: 0.08838834764831845, is_causal: True}) %transpose_19 : [num_users=1] = call_method[target=transpose](args = (%scaled_dot_product_attention_4, 1, 2), kwargs = {}) %contiguous_8 : [num_users=1] = call_method[target=contiguous](args = (%transpose_19,), kwargs = {}) %reshape_16 : [num_users=1] = call_method[target=reshape](args = (%contiguous_8, 1, 29, -1), kwargs = {}) %contiguous_9 : [num_users=1] = call_method[target=contiguous](args = (%reshape_16,), kwargs = {}) %language_model_model_layers_4_self_attn_o_proj : [num_users=1] = call_module[target=language_model.model.layers.4.self_attn.o_proj](args = (%contiguous_9,), kwargs = {}) %add_27 : [num_users=3] = call_function[target=operator.add](args = (%add_23, %language_model_model_layers_4_self_attn_o_proj), kwargs = {}) %getattr_190 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_27, dtype), kwargs = {}) %to_40 : [num_users=2] = call_method[target=to](args = (%add_27, torch.float32), kwargs = {}) %pow_20 : [num_users=1] = call_method[target=pow](args = (%to_40, 2), kwargs = {}) %mean_19 : [num_users=1] = call_method[target=mean](args = (%pow_20, -1), kwargs = {keepdim: True}) %add_28 : [num_users=1] = call_function[target=operator.add](args = (%mean_19, 1e-06), kwargs = {}) %rsqrt_19 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_28,), kwargs = {}) %mul_42 : [num_users=1] = call_function[target=operator.mul](args = (%to_40, %rsqrt_19), kwargs = {}) %language_model_model_layers_4_post_attention_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.4.post_attention_layernorm.weight] %to_41 : [num_users=1] = call_method[target=to](args = (%mul_42, %getattr_190), kwargs = {}) %mul_43 : [num_users=2] = call_function[target=operator.mul](args = (%language_model_model_layers_4_post_attention_layernorm_weight, %to_41), kwargs = {}) %language_model_model_layers_4_mlp_gate_proj : [num_users=1] = call_module[target=language_model.model.layers.4.mlp.gate_proj](args = (%mul_43,), kwargs = {}) %silu_4 : [num_users=1] = call_function[target=torch.nn.functional.silu](args = (%language_model_model_layers_4_mlp_gate_proj,), kwargs = {inplace: False}) %language_model_model_layers_4_mlp_up_proj : [num_users=1] = call_module[target=language_model.model.layers.4.mlp.up_proj](args = (%mul_43,), kwargs = {}) %mul_44 : [num_users=1] = call_function[target=operator.mul](args = (%silu_4, %language_model_model_layers_4_mlp_up_proj), kwargs = {}) %language_model_model_layers_4_mlp_down_proj : [num_users=1] = call_module[target=language_model.model.layers.4.mlp.down_proj](args = (%mul_44,), kwargs = {}) %add_29 : [num_users=3] = call_function[target=operator.add](args = (%add_27, %language_model_model_layers_4_mlp_down_proj), kwargs = {}) %getattr_195 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_29, dtype), kwargs = {}) %to_42 : [num_users=2] = call_method[target=to](args = (%add_29, torch.float32), kwargs = {}) %pow_21 : [num_users=1] = call_method[target=pow](args = (%to_42, 2), kwargs = {}) %mean_20 : [num_users=1] = call_method[target=mean](args = (%pow_21, -1), kwargs = {keepdim: True}) %add_30 : [num_users=1] = call_function[target=operator.add](args = (%mean_20, 1e-06), kwargs = {}) %rsqrt_20 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_30,), kwargs = {}) %mul_45 : [num_users=1] = call_function[target=operator.mul](args = (%to_42, %rsqrt_20), kwargs = {}) %language_model_model_layers_5_input_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.5.input_layernorm.weight] %to_43 : [num_users=1] = call_method[target=to](args = (%mul_45, %getattr_195), kwargs = {}) %mul_46 : [num_users=3] = call_function[target=operator.mul](args = (%language_model_model_layers_5_input_layernorm_weight, %to_43), kwargs = {}) %language_model_model_layers_5_self_attn_q_proj : [num_users=1] = call_module[target=language_model.model.layers.5.self_attn.q_proj](args = (%mul_46,), kwargs = {}) %view_15 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_5_self_attn_q_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_202 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_15, dtype), kwargs = {}) %to_44 : [num_users=2] = call_method[target=to](args = (%view_15, torch.float32), kwargs = {}) %pow_22 : [num_users=1] = call_method[target=pow](args = (%to_44, 2), kwargs = {}) %mean_21 : [num_users=1] = call_method[target=mean](args = (%pow_22, -1), kwargs = {keepdim: True}) %add_31 : [num_users=1] = call_function[target=operator.add](args = (%mean_21, 1e-06), kwargs = {}) %rsqrt_21 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_31,), kwargs = {}) %mul_47 : [num_users=1] = call_function[target=operator.mul](args = (%to_44, %rsqrt_21), kwargs = {}) %language_model_model_layers_5_self_attn_q_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.5.self_attn.q_norm.weight] %to_45 : [num_users=1] = call_method[target=to](args = (%mul_47, %getattr_202), kwargs = {}) %mul_48 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_5_self_attn_q_norm_weight, %to_45), kwargs = {}) %transpose_20 : [num_users=1] = call_method[target=transpose](args = (%mul_48, 1, 2), kwargs = {}) %language_model_model_layers_5_self_attn_k_proj : [num_users=1] = call_module[target=language_model.model.layers.5.self_attn.k_proj](args = (%mul_46,), kwargs = {}) %view_16 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_5_self_attn_k_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_209 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_16, dtype), kwargs = {}) %to_46 : [num_users=2] = call_method[target=to](args = (%view_16, torch.float32), kwargs = {}) %pow_23 : [num_users=1] = call_method[target=pow](args = (%to_46, 2), kwargs = {}) %mean_22 : [num_users=1] = call_method[target=mean](args = (%pow_23, -1), kwargs = {keepdim: True}) %add_32 : [num_users=1] = call_function[target=operator.add](args = (%mean_22, 1e-06), kwargs = {}) %rsqrt_22 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_32,), kwargs = {}) %mul_49 : [num_users=1] = call_function[target=operator.mul](args = (%to_46, %rsqrt_22), kwargs = {}) %language_model_model_layers_5_self_attn_k_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.5.self_attn.k_norm.weight] %to_47 : [num_users=1] = call_method[target=to](args = (%mul_49, %getattr_209), kwargs = {}) %mul_50 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_5_self_attn_k_norm_weight, %to_47), kwargs = {}) %transpose_21 : [num_users=1] = call_method[target=transpose](args = (%mul_50, 1, 2), kwargs = {}) %language_model_model_layers_5_self_attn_v_proj : [num_users=1] = call_module[target=language_model.model.layers.5.self_attn.v_proj](args = (%mul_46,), kwargs = {}) %view_17 : [num_users=1] = call_method[target=view](args = (%language_model_model_layers_5_self_attn_v_proj, (1, 29, -1, 128)), kwargs = {}) %transpose_22 : [num_users=1] = call_method[target=transpose](args = (%view_17, 1, 2), kwargs = {}) %apply_rotary_pos_emb_5 : [num_users=2] = call_function[target=transformers.models.qwen3.modeling_qwen3.apply_rotary_pos_emb](args = (%transpose_20, %transpose_21, %to, %to_1), kwargs = {}) %getitem_32 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_5, 0), kwargs = {}) %getitem_33 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_5, 1), kwargs = {}) %update_5 : [num_users=2] = call_method[target=update](args = (%past_key_values, %getitem_33, %transpose_22, 5, {sin: %to_1, cos: %to, cache_position: %cache_position}), kwargs = {}) %getitem_34 : [num_users=1] = call_function[target=operator.getitem](args = (%update_5, 0), kwargs = {}) %getitem_35 : [num_users=1] = call_function[target=operator.getitem](args = (%update_5, 1), kwargs = {}) %getitem_36 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_34, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_10 : [num_users=1] = call_method[target=expand](args = (%getitem_36, 1, 8, 2, 29, 128), kwargs = {}) %reshape_17 : [num_users=1] = call_method[target=reshape](args = (%expand_10, 1, 16, 29, 128), kwargs = {}) %getitem_37 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_35, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_11 : [num_users=1] = call_method[target=expand](args = (%getitem_37, 1, 8, 2, 29, 128), kwargs = {}) %reshape_18 : [num_users=1] = call_method[target=reshape](args = (%expand_11, 1, 16, 29, 128), kwargs = {}) %scaled_dot_product_attention_5 : [num_users=1] = call_function[target=torch._C._nn.scaled_dot_product_attention](args = (%getitem_32, %reshape_17, %reshape_18), kwargs = {attn_mask: None, dropout_p: 0.0, scale: 0.08838834764831845, is_causal: True}) %transpose_23 : [num_users=1] = call_method[target=transpose](args = (%scaled_dot_product_attention_5, 1, 2), kwargs = {}) %contiguous_10 : [num_users=1] = call_method[target=contiguous](args = (%transpose_23,), kwargs = {}) %reshape_19 : [num_users=1] = call_method[target=reshape](args = (%contiguous_10, 1, 29, -1), kwargs = {}) %contiguous_11 : [num_users=1] = call_method[target=contiguous](args = (%reshape_19,), kwargs = {}) %language_model_model_layers_5_self_attn_o_proj : [num_users=1] = call_module[target=language_model.model.layers.5.self_attn.o_proj](args = (%contiguous_11,), kwargs = {}) %add_33 : [num_users=3] = call_function[target=operator.add](args = (%add_29, %language_model_model_layers_5_self_attn_o_proj), kwargs = {}) %getattr_228 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_33, dtype), kwargs = {}) %to_48 : [num_users=2] = call_method[target=to](args = (%add_33, torch.float32), kwargs = {}) %pow_24 : [num_users=1] = call_method[target=pow](args = (%to_48, 2), kwargs = {}) %mean_23 : [num_users=1] = call_method[target=mean](args = (%pow_24, -1), kwargs = {keepdim: True}) %add_34 : [num_users=1] = call_function[target=operator.add](args = (%mean_23, 1e-06), kwargs = {}) %rsqrt_23 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_34,), kwargs = {}) %mul_51 : [num_users=1] = call_function[target=operator.mul](args = (%to_48, %rsqrt_23), kwargs = {}) %language_model_model_layers_5_post_attention_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.5.post_attention_layernorm.weight] %to_49 : [num_users=1] = call_method[target=to](args = (%mul_51, %getattr_228), kwargs = {}) %mul_52 : [num_users=2] = call_function[target=operator.mul](args = (%language_model_model_layers_5_post_attention_layernorm_weight, %to_49), kwargs = {}) %language_model_model_layers_5_mlp_gate_proj : [num_users=1] = call_module[target=language_model.model.layers.5.mlp.gate_proj](args = (%mul_52,), kwargs = {}) %silu_5 : [num_users=1] = call_function[target=torch.nn.functional.silu](args = (%language_model_model_layers_5_mlp_gate_proj,), kwargs = {inplace: False}) %language_model_model_layers_5_mlp_up_proj : [num_users=1] = call_module[target=language_model.model.layers.5.mlp.up_proj](args = (%mul_52,), kwargs = {}) %mul_53 : [num_users=1] = call_function[target=operator.mul](args = (%silu_5, %language_model_model_layers_5_mlp_up_proj), kwargs = {}) %language_model_model_layers_5_mlp_down_proj : [num_users=1] = call_module[target=language_model.model.layers.5.mlp.down_proj](args = (%mul_53,), kwargs = {}) %add_35 : [num_users=3] = call_function[target=operator.add](args = (%add_33, %language_model_model_layers_5_mlp_down_proj), kwargs = {}) %getattr_233 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_35, dtype), kwargs = {}) %to_50 : [num_users=2] = call_method[target=to](args = (%add_35, torch.float32), kwargs = {}) %pow_25 : [num_users=1] = call_method[target=pow](args = (%to_50, 2), kwargs = {}) %mean_24 : [num_users=1] = call_method[target=mean](args = (%pow_25, -1), kwargs = {keepdim: True}) %add_36 : [num_users=1] = call_function[target=operator.add](args = (%mean_24, 1e-06), kwargs = {}) %rsqrt_24 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_36,), kwargs = {}) %mul_54 : [num_users=1] = call_function[target=operator.mul](args = (%to_50, %rsqrt_24), kwargs = {}) %language_model_model_layers_6_input_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.6.input_layernorm.weight] %to_51 : [num_users=1] = call_method[target=to](args = (%mul_54, %getattr_233), kwargs = {}) %mul_55 : [num_users=3] = call_function[target=operator.mul](args = (%language_model_model_layers_6_input_layernorm_weight, %to_51), kwargs = {}) %language_model_model_layers_6_self_attn_q_proj : [num_users=1] = call_module[target=language_model.model.layers.6.self_attn.q_proj](args = (%mul_55,), kwargs = {}) %view_18 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_6_self_attn_q_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_240 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_18, dtype), kwargs = {}) %to_52 : [num_users=2] = call_method[target=to](args = (%view_18, torch.float32), kwargs = {}) %pow_26 : [num_users=1] = call_method[target=pow](args = (%to_52, 2), kwargs = {}) %mean_25 : [num_users=1] = call_method[target=mean](args = (%pow_26, -1), kwargs = {keepdim: True}) %add_37 : [num_users=1] = call_function[target=operator.add](args = (%mean_25, 1e-06), kwargs = {}) %rsqrt_25 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_37,), kwargs = {}) %mul_56 : [num_users=1] = call_function[target=operator.mul](args = (%to_52, %rsqrt_25), kwargs = {}) %language_model_model_layers_6_self_attn_q_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.6.self_attn.q_norm.weight] %to_53 : [num_users=1] = call_method[target=to](args = (%mul_56, %getattr_240), kwargs = {}) %mul_57 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_6_self_attn_q_norm_weight, %to_53), kwargs = {}) %transpose_24 : [num_users=1] = call_method[target=transpose](args = (%mul_57, 1, 2), kwargs = {}) %language_model_model_layers_6_self_attn_k_proj : [num_users=1] = call_module[target=language_model.model.layers.6.self_attn.k_proj](args = (%mul_55,), kwargs = {}) %view_19 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_6_self_attn_k_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_247 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_19, dtype), kwargs = {}) %to_54 : [num_users=2] = call_method[target=to](args = (%view_19, torch.float32), kwargs = {}) %pow_27 : [num_users=1] = call_method[target=pow](args = (%to_54, 2), kwargs = {}) %mean_26 : [num_users=1] = call_method[target=mean](args = (%pow_27, -1), kwargs = {keepdim: True}) %add_38 : [num_users=1] = call_function[target=operator.add](args = (%mean_26, 1e-06), kwargs = {}) %rsqrt_26 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_38,), kwargs = {}) %mul_58 : [num_users=1] = call_function[target=operator.mul](args = (%to_54, %rsqrt_26), kwargs = {}) %language_model_model_layers_6_self_attn_k_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.6.self_attn.k_norm.weight] %to_55 : [num_users=1] = call_method[target=to](args = (%mul_58, %getattr_247), kwargs = {}) %mul_59 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_6_self_attn_k_norm_weight, %to_55), kwargs = {}) %transpose_25 : [num_users=1] = call_method[target=transpose](args = (%mul_59, 1, 2), kwargs = {}) %language_model_model_layers_6_self_attn_v_proj : [num_users=1] = call_module[target=language_model.model.layers.6.self_attn.v_proj](args = (%mul_55,), kwargs = {}) %view_20 : [num_users=1] = call_method[target=view](args = (%language_model_model_layers_6_self_attn_v_proj, (1, 29, -1, 128)), kwargs = {}) %transpose_26 : [num_users=1] = call_method[target=transpose](args = (%view_20, 1, 2), kwargs = {}) %apply_rotary_pos_emb_6 : [num_users=2] = call_function[target=transformers.models.qwen3.modeling_qwen3.apply_rotary_pos_emb](args = (%transpose_24, %transpose_25, %to, %to_1), kwargs = {}) %getitem_38 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_6, 0), kwargs = {}) %getitem_39 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_6, 1), kwargs = {}) %update_6 : [num_users=2] = call_method[target=update](args = (%past_key_values, %getitem_39, %transpose_26, 6, {sin: %to_1, cos: %to, cache_position: %cache_position}), kwargs = {}) %getitem_40 : [num_users=1] = call_function[target=operator.getitem](args = (%update_6, 0), kwargs = {}) %getitem_41 : [num_users=1] = call_function[target=operator.getitem](args = (%update_6, 1), kwargs = {}) %getitem_42 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_40, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_12 : [num_users=1] = call_method[target=expand](args = (%getitem_42, 1, 8, 2, 29, 128), kwargs = {}) %reshape_20 : [num_users=1] = call_method[target=reshape](args = (%expand_12, 1, 16, 29, 128), kwargs = {}) %getitem_43 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_41, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_13 : [num_users=1] = call_method[target=expand](args = (%getitem_43, 1, 8, 2, 29, 128), kwargs = {}) %reshape_21 : [num_users=1] = call_method[target=reshape](args = (%expand_13, 1, 16, 29, 128), kwargs = {}) %scaled_dot_product_attention_6 : [num_users=1] = call_function[target=torch._C._nn.scaled_dot_product_attention](args = (%getitem_38, %reshape_20, %reshape_21), kwargs = {attn_mask: None, dropout_p: 0.0, scale: 0.08838834764831845, is_causal: True}) %transpose_27 : [num_users=1] = call_method[target=transpose](args = (%scaled_dot_product_attention_6, 1, 2), kwargs = {}) %contiguous_12 : [num_users=1] = call_method[target=contiguous](args = (%transpose_27,), kwargs = {}) %reshape_22 : [num_users=1] = call_method[target=reshape](args = (%contiguous_12, 1, 29, -1), kwargs = {}) %contiguous_13 : [num_users=1] = call_method[target=contiguous](args = (%reshape_22,), kwargs = {}) %language_model_model_layers_6_self_attn_o_proj : [num_users=1] = call_module[target=language_model.model.layers.6.self_attn.o_proj](args = (%contiguous_13,), kwargs = {}) %add_39 : [num_users=3] = call_function[target=operator.add](args = (%add_35, %language_model_model_layers_6_self_attn_o_proj), kwargs = {}) %getattr_266 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_39, dtype), kwargs = {}) %to_56 : [num_users=2] = call_method[target=to](args = (%add_39, torch.float32), kwargs = {}) %pow_28 : [num_users=1] = call_method[target=pow](args = (%to_56, 2), kwargs = {}) %mean_27 : [num_users=1] = call_method[target=mean](args = (%pow_28, -1), kwargs = {keepdim: True}) %add_40 : [num_users=1] = call_function[target=operator.add](args = (%mean_27, 1e-06), kwargs = {}) %rsqrt_27 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_40,), kwargs = {}) %mul_60 : [num_users=1] = call_function[target=operator.mul](args = (%to_56, %rsqrt_27), kwargs = {}) %language_model_model_layers_6_post_attention_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.6.post_attention_layernorm.weight] %to_57 : [num_users=1] = call_method[target=to](args = (%mul_60, %getattr_266), kwargs = {}) %mul_61 : [num_users=2] = call_function[target=operator.mul](args = (%language_model_model_layers_6_post_attention_layernorm_weight, %to_57), kwargs = {}) %language_model_model_layers_6_mlp_gate_proj : [num_users=1] = call_module[target=language_model.model.layers.6.mlp.gate_proj](args = (%mul_61,), kwargs = {}) %silu_6 : [num_users=1] = call_function[target=torch.nn.functional.silu](args = (%language_model_model_layers_6_mlp_gate_proj,), kwargs = {inplace: False}) %language_model_model_layers_6_mlp_up_proj : [num_users=1] = call_module[target=language_model.model.layers.6.mlp.up_proj](args = (%mul_61,), kwargs = {}) %mul_62 : [num_users=1] = call_function[target=operator.mul](args = (%silu_6, %language_model_model_layers_6_mlp_up_proj), kwargs = {}) %language_model_model_layers_6_mlp_down_proj : [num_users=1] = call_module[target=language_model.model.layers.6.mlp.down_proj](args = (%mul_62,), kwargs = {}) %add_41 : [num_users=3] = call_function[target=operator.add](args = (%add_39, %language_model_model_layers_6_mlp_down_proj), kwargs = {}) %getattr_271 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_41, dtype), kwargs = {}) %to_58 : [num_users=2] = call_method[target=to](args = (%add_41, torch.float32), kwargs = {}) %pow_29 : [num_users=1] = call_method[target=pow](args = (%to_58, 2), kwargs = {}) %mean_28 : [num_users=1] = call_method[target=mean](args = (%pow_29, -1), kwargs = {keepdim: True}) %add_42 : [num_users=1] = call_function[target=operator.add](args = (%mean_28, 1e-06), kwargs = {}) %rsqrt_28 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_42,), kwargs = {}) %mul_63 : [num_users=1] = call_function[target=operator.mul](args = (%to_58, %rsqrt_28), kwargs = {}) %language_model_model_layers_7_input_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.7.input_layernorm.weight] %to_59 : [num_users=1] = call_method[target=to](args = (%mul_63, %getattr_271), kwargs = {}) %mul_64 : [num_users=3] = call_function[target=operator.mul](args = (%language_model_model_layers_7_input_layernorm_weight, %to_59), kwargs = {}) %language_model_model_layers_7_self_attn_q_proj : [num_users=1] = call_module[target=language_model.model.layers.7.self_attn.q_proj](args = (%mul_64,), kwargs = {}) %view_21 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_7_self_attn_q_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_278 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_21, dtype), kwargs = {}) %to_60 : [num_users=2] = call_method[target=to](args = (%view_21, torch.float32), kwargs = {}) %pow_30 : [num_users=1] = call_method[target=pow](args = (%to_60, 2), kwargs = {}) %mean_29 : [num_users=1] = call_method[target=mean](args = (%pow_30, -1), kwargs = {keepdim: True}) %add_43 : [num_users=1] = call_function[target=operator.add](args = (%mean_29, 1e-06), kwargs = {}) %rsqrt_29 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_43,), kwargs = {}) %mul_65 : [num_users=1] = call_function[target=operator.mul](args = (%to_60, %rsqrt_29), kwargs = {}) %language_model_model_layers_7_self_attn_q_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.7.self_attn.q_norm.weight] %to_61 : [num_users=1] = call_method[target=to](args = (%mul_65, %getattr_278), kwargs = {}) %mul_66 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_7_self_attn_q_norm_weight, %to_61), kwargs = {}) %transpose_28 : [num_users=1] = call_method[target=transpose](args = (%mul_66, 1, 2), kwargs = {}) %language_model_model_layers_7_self_attn_k_proj : [num_users=1] = call_module[target=language_model.model.layers.7.self_attn.k_proj](args = (%mul_64,), kwargs = {}) %view_22 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_7_self_attn_k_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_285 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_22, dtype), kwargs = {}) %to_62 : [num_users=2] = call_method[target=to](args = (%view_22, torch.float32), kwargs = {}) %pow_31 : [num_users=1] = call_method[target=pow](args = (%to_62, 2), kwargs = {}) %mean_30 : [num_users=1] = call_method[target=mean](args = (%pow_31, -1), kwargs = {keepdim: True}) %add_44 : [num_users=1] = call_function[target=operator.add](args = (%mean_30, 1e-06), kwargs = {}) %rsqrt_30 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_44,), kwargs = {}) %mul_67 : [num_users=1] = call_function[target=operator.mul](args = (%to_62, %rsqrt_30), kwargs = {}) %language_model_model_layers_7_self_attn_k_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.7.self_attn.k_norm.weight] %to_63 : [num_users=1] = call_method[target=to](args = (%mul_67, %getattr_285), kwargs = {}) %mul_68 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_7_self_attn_k_norm_weight, %to_63), kwargs = {}) %transpose_29 : [num_users=1] = call_method[target=transpose](args = (%mul_68, 1, 2), kwargs = {}) %language_model_model_layers_7_self_attn_v_proj : [num_users=1] = call_module[target=language_model.model.layers.7.self_attn.v_proj](args = (%mul_64,), kwargs = {}) %view_23 : [num_users=1] = call_method[target=view](args = (%language_model_model_layers_7_self_attn_v_proj, (1, 29, -1, 128)), kwargs = {}) %transpose_30 : [num_users=1] = call_method[target=transpose](args = (%view_23, 1, 2), kwargs = {}) %apply_rotary_pos_emb_7 : [num_users=2] = call_function[target=transformers.models.qwen3.modeling_qwen3.apply_rotary_pos_emb](args = (%transpose_28, %transpose_29, %to, %to_1), kwargs = {}) %getitem_44 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_7, 0), kwargs = {}) %getitem_45 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_7, 1), kwargs = {}) %update_7 : [num_users=2] = call_method[target=update](args = (%past_key_values, %getitem_45, %transpose_30, 7, {sin: %to_1, cos: %to, cache_position: %cache_position}), kwargs = {}) %getitem_46 : [num_users=1] = call_function[target=operator.getitem](args = (%update_7, 0), kwargs = {}) %getitem_47 : [num_users=1] = call_function[target=operator.getitem](args = (%update_7, 1), kwargs = {}) %getitem_48 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_46, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_14 : [num_users=1] = call_method[target=expand](args = (%getitem_48, 1, 8, 2, 29, 128), kwargs = {}) %reshape_23 : [num_users=1] = call_method[target=reshape](args = (%expand_14, 1, 16, 29, 128), kwargs = {}) %getitem_49 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_47, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_15 : [num_users=1] = call_method[target=expand](args = (%getitem_49, 1, 8, 2, 29, 128), kwargs = {}) %reshape_24 : [num_users=1] = call_method[target=reshape](args = (%expand_15, 1, 16, 29, 128), kwargs = {}) %scaled_dot_product_attention_7 : [num_users=1] = call_function[target=torch._C._nn.scaled_dot_product_attention](args = (%getitem_44, %reshape_23, %reshape_24), kwargs = {attn_mask: None, dropout_p: 0.0, scale: 0.08838834764831845, is_causal: True}) %transpose_31 : [num_users=1] = call_method[target=transpose](args = (%scaled_dot_product_attention_7, 1, 2), kwargs = {}) %contiguous_14 : [num_users=1] = call_method[target=contiguous](args = (%transpose_31,), kwargs = {}) %reshape_25 : [num_users=1] = call_method[target=reshape](args = (%contiguous_14, 1, 29, -1), kwargs = {}) %contiguous_15 : [num_users=1] = call_method[target=contiguous](args = (%reshape_25,), kwargs = {}) %language_model_model_layers_7_self_attn_o_proj : [num_users=1] = call_module[target=language_model.model.layers.7.self_attn.o_proj](args = (%contiguous_15,), kwargs = {}) %add_45 : [num_users=3] = call_function[target=operator.add](args = (%add_41, %language_model_model_layers_7_self_attn_o_proj), kwargs = {}) %getattr_304 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_45, dtype), kwargs = {}) %to_64 : [num_users=2] = call_method[target=to](args = (%add_45, torch.float32), kwargs = {}) %pow_32 : [num_users=1] = call_method[target=pow](args = (%to_64, 2), kwargs = {}) %mean_31 : [num_users=1] = call_method[target=mean](args = (%pow_32, -1), kwargs = {keepdim: True}) %add_46 : [num_users=1] = call_function[target=operator.add](args = (%mean_31, 1e-06), kwargs = {}) %rsqrt_31 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_46,), kwargs = {}) %mul_69 : [num_users=1] = call_function[target=operator.mul](args = (%to_64, %rsqrt_31), kwargs = {}) %language_model_model_layers_7_post_attention_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.7.post_attention_layernorm.weight] %to_65 : [num_users=1] = call_method[target=to](args = (%mul_69, %getattr_304), kwargs = {}) %mul_70 : [num_users=2] = call_function[target=operator.mul](args = (%language_model_model_layers_7_post_attention_layernorm_weight, %to_65), kwargs = {}) %language_model_model_layers_7_mlp_gate_proj : [num_users=1] = call_module[target=language_model.model.layers.7.mlp.gate_proj](args = (%mul_70,), kwargs = {}) %silu_7 : [num_users=1] = call_function[target=torch.nn.functional.silu](args = (%language_model_model_layers_7_mlp_gate_proj,), kwargs = {inplace: False}) %language_model_model_layers_7_mlp_up_proj : [num_users=1] = call_module[target=language_model.model.layers.7.mlp.up_proj](args = (%mul_70,), kwargs = {}) %mul_71 : [num_users=1] = call_function[target=operator.mul](args = (%silu_7, %language_model_model_layers_7_mlp_up_proj), kwargs = {}) %language_model_model_layers_7_mlp_down_proj : [num_users=1] = call_module[target=language_model.model.layers.7.mlp.down_proj](args = (%mul_71,), kwargs = {}) %add_47 : [num_users=3] = call_function[target=operator.add](args = (%add_45, %language_model_model_layers_7_mlp_down_proj), kwargs = {}) %getattr_309 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_47, dtype), kwargs = {}) %to_66 : [num_users=2] = call_method[target=to](args = (%add_47, torch.float32), kwargs = {}) %pow_33 : [num_users=1] = call_method[target=pow](args = (%to_66, 2), kwargs = {}) %mean_32 : [num_users=1] = call_method[target=mean](args = (%pow_33, -1), kwargs = {keepdim: True}) %add_48 : [num_users=1] = call_function[target=operator.add](args = (%mean_32, 1e-06), kwargs = {}) %rsqrt_32 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_48,), kwargs = {}) %mul_72 : [num_users=1] = call_function[target=operator.mul](args = (%to_66, %rsqrt_32), kwargs = {}) %language_model_model_layers_8_input_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.8.input_layernorm.weight] %to_67 : [num_users=1] = call_method[target=to](args = (%mul_72, %getattr_309), kwargs = {}) %mul_73 : [num_users=3] = call_function[target=operator.mul](args = (%language_model_model_layers_8_input_layernorm_weight, %to_67), kwargs = {}) %language_model_model_layers_8_self_attn_q_proj : [num_users=1] = call_module[target=language_model.model.layers.8.self_attn.q_proj](args = (%mul_73,), kwargs = {}) %view_24 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_8_self_attn_q_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_316 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_24, dtype), kwargs = {}) %to_68 : [num_users=2] = call_method[target=to](args = (%view_24, torch.float32), kwargs = {}) %pow_34 : [num_users=1] = call_method[target=pow](args = (%to_68, 2), kwargs = {}) %mean_33 : [num_users=1] = call_method[target=mean](args = (%pow_34, -1), kwargs = {keepdim: True}) %add_49 : [num_users=1] = call_function[target=operator.add](args = (%mean_33, 1e-06), kwargs = {}) %rsqrt_33 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_49,), kwargs = {}) %mul_74 : [num_users=1] = call_function[target=operator.mul](args = (%to_68, %rsqrt_33), kwargs = {}) %language_model_model_layers_8_self_attn_q_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.8.self_attn.q_norm.weight] %to_69 : [num_users=1] = call_method[target=to](args = (%mul_74, %getattr_316), kwargs = {}) %mul_75 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_8_self_attn_q_norm_weight, %to_69), kwargs = {}) %transpose_32 : [num_users=1] = call_method[target=transpose](args = (%mul_75, 1, 2), kwargs = {}) %language_model_model_layers_8_self_attn_k_proj : [num_users=1] = call_module[target=language_model.model.layers.8.self_attn.k_proj](args = (%mul_73,), kwargs = {}) %view_25 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_8_self_attn_k_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_323 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_25, dtype), kwargs = {}) %to_70 : [num_users=2] = call_method[target=to](args = (%view_25, torch.float32), kwargs = {}) %pow_35 : [num_users=1] = call_method[target=pow](args = (%to_70, 2), kwargs = {}) %mean_34 : [num_users=1] = call_method[target=mean](args = (%pow_35, -1), kwargs = {keepdim: True}) %add_50 : [num_users=1] = call_function[target=operator.add](args = (%mean_34, 1e-06), kwargs = {}) %rsqrt_34 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_50,), kwargs = {}) %mul_76 : [num_users=1] = call_function[target=operator.mul](args = (%to_70, %rsqrt_34), kwargs = {}) %language_model_model_layers_8_self_attn_k_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.8.self_attn.k_norm.weight] %to_71 : [num_users=1] = call_method[target=to](args = (%mul_76, %getattr_323), kwargs = {}) %mul_77 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_8_self_attn_k_norm_weight, %to_71), kwargs = {}) %transpose_33 : [num_users=1] = call_method[target=transpose](args = (%mul_77, 1, 2), kwargs = {}) %language_model_model_layers_8_self_attn_v_proj : [num_users=1] = call_module[target=language_model.model.layers.8.self_attn.v_proj](args = (%mul_73,), kwargs = {}) %view_26 : [num_users=1] = call_method[target=view](args = (%language_model_model_layers_8_self_attn_v_proj, (1, 29, -1, 128)), kwargs = {}) %transpose_34 : [num_users=1] = call_method[target=transpose](args = (%view_26, 1, 2), kwargs = {}) %apply_rotary_pos_emb_8 : [num_users=2] = call_function[target=transformers.models.qwen3.modeling_qwen3.apply_rotary_pos_emb](args = (%transpose_32, %transpose_33, %to, %to_1), kwargs = {}) %getitem_50 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_8, 0), kwargs = {}) %getitem_51 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_8, 1), kwargs = {}) %update_8 : [num_users=2] = call_method[target=update](args = (%past_key_values, %getitem_51, %transpose_34, 8, {sin: %to_1, cos: %to, cache_position: %cache_position}), kwargs = {}) %getitem_52 : [num_users=1] = call_function[target=operator.getitem](args = (%update_8, 0), kwargs = {}) %getitem_53 : [num_users=1] = call_function[target=operator.getitem](args = (%update_8, 1), kwargs = {}) %getitem_54 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_52, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_16 : [num_users=1] = call_method[target=expand](args = (%getitem_54, 1, 8, 2, 29, 128), kwargs = {}) %reshape_26 : [num_users=1] = call_method[target=reshape](args = (%expand_16, 1, 16, 29, 128), kwargs = {}) %getitem_55 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_53, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_17 : [num_users=1] = call_method[target=expand](args = (%getitem_55, 1, 8, 2, 29, 128), kwargs = {}) %reshape_27 : [num_users=1] = call_method[target=reshape](args = (%expand_17, 1, 16, 29, 128), kwargs = {}) %scaled_dot_product_attention_8 : [num_users=1] = call_function[target=torch._C._nn.scaled_dot_product_attention](args = (%getitem_50, %reshape_26, %reshape_27), kwargs = {attn_mask: None, dropout_p: 0.0, scale: 0.08838834764831845, is_causal: True}) %transpose_35 : [num_users=1] = call_method[target=transpose](args = (%scaled_dot_product_attention_8, 1, 2), kwargs = {}) %contiguous_16 : [num_users=1] = call_method[target=contiguous](args = (%transpose_35,), kwargs = {}) %reshape_28 : [num_users=1] = call_method[target=reshape](args = (%contiguous_16, 1, 29, -1), kwargs = {}) %contiguous_17 : [num_users=1] = call_method[target=contiguous](args = (%reshape_28,), kwargs = {}) %language_model_model_layers_8_self_attn_o_proj : [num_users=1] = call_module[target=language_model.model.layers.8.self_attn.o_proj](args = (%contiguous_17,), kwargs = {}) %add_51 : [num_users=3] = call_function[target=operator.add](args = (%add_47, %language_model_model_layers_8_self_attn_o_proj), kwargs = {}) %getattr_342 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_51, dtype), kwargs = {}) %to_72 : [num_users=2] = call_method[target=to](args = (%add_51, torch.float32), kwargs = {}) %pow_36 : [num_users=1] = call_method[target=pow](args = (%to_72, 2), kwargs = {}) %mean_35 : [num_users=1] = call_method[target=mean](args = (%pow_36, -1), kwargs = {keepdim: True}) %add_52 : [num_users=1] = call_function[target=operator.add](args = (%mean_35, 1e-06), kwargs = {}) %rsqrt_35 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_52,), kwargs = {}) %mul_78 : [num_users=1] = call_function[target=operator.mul](args = (%to_72, %rsqrt_35), kwargs = {}) %language_model_model_layers_8_post_attention_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.8.post_attention_layernorm.weight] %to_73 : [num_users=1] = call_method[target=to](args = (%mul_78, %getattr_342), kwargs = {}) %mul_79 : [num_users=2] = call_function[target=operator.mul](args = (%language_model_model_layers_8_post_attention_layernorm_weight, %to_73), kwargs = {}) %language_model_model_layers_8_mlp_gate_proj : [num_users=1] = call_module[target=language_model.model.layers.8.mlp.gate_proj](args = (%mul_79,), kwargs = {}) %silu_8 : [num_users=1] = call_function[target=torch.nn.functional.silu](args = (%language_model_model_layers_8_mlp_gate_proj,), kwargs = {inplace: False}) %language_model_model_layers_8_mlp_up_proj : [num_users=1] = call_module[target=language_model.model.layers.8.mlp.up_proj](args = (%mul_79,), kwargs = {}) %mul_80 : [num_users=1] = call_function[target=operator.mul](args = (%silu_8, %language_model_model_layers_8_mlp_up_proj), kwargs = {}) %language_model_model_layers_8_mlp_down_proj : [num_users=1] = call_module[target=language_model.model.layers.8.mlp.down_proj](args = (%mul_80,), kwargs = {}) %add_53 : [num_users=3] = call_function[target=operator.add](args = (%add_51, %language_model_model_layers_8_mlp_down_proj), kwargs = {}) %getattr_347 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_53, dtype), kwargs = {}) %to_74 : [num_users=2] = call_method[target=to](args = (%add_53, torch.float32), kwargs = {}) %pow_37 : [num_users=1] = call_method[target=pow](args = (%to_74, 2), kwargs = {}) %mean_36 : [num_users=1] = call_method[target=mean](args = (%pow_37, -1), kwargs = {keepdim: True}) %add_54 : [num_users=1] = call_function[target=operator.add](args = (%mean_36, 1e-06), kwargs = {}) %rsqrt_36 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_54,), kwargs = {}) %mul_81 : [num_users=1] = call_function[target=operator.mul](args = (%to_74, %rsqrt_36), kwargs = {}) %language_model_model_layers_9_input_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.9.input_layernorm.weight] %to_75 : [num_users=1] = call_method[target=to](args = (%mul_81, %getattr_347), kwargs = {}) %mul_82 : [num_users=3] = call_function[target=operator.mul](args = (%language_model_model_layers_9_input_layernorm_weight, %to_75), kwargs = {}) %language_model_model_layers_9_self_attn_q_proj : [num_users=1] = call_module[target=language_model.model.layers.9.self_attn.q_proj](args = (%mul_82,), kwargs = {}) %view_27 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_9_self_attn_q_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_354 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_27, dtype), kwargs = {}) %to_76 : [num_users=2] = call_method[target=to](args = (%view_27, torch.float32), kwargs = {}) %pow_38 : [num_users=1] = call_method[target=pow](args = (%to_76, 2), kwargs = {}) %mean_37 : [num_users=1] = call_method[target=mean](args = (%pow_38, -1), kwargs = {keepdim: True}) %add_55 : [num_users=1] = call_function[target=operator.add](args = (%mean_37, 1e-06), kwargs = {}) %rsqrt_37 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_55,), kwargs = {}) %mul_83 : [num_users=1] = call_function[target=operator.mul](args = (%to_76, %rsqrt_37), kwargs = {}) %language_model_model_layers_9_self_attn_q_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.9.self_attn.q_norm.weight] %to_77 : [num_users=1] = call_method[target=to](args = (%mul_83, %getattr_354), kwargs = {}) %mul_84 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_9_self_attn_q_norm_weight, %to_77), kwargs = {}) %transpose_36 : [num_users=1] = call_method[target=transpose](args = (%mul_84, 1, 2), kwargs = {}) %language_model_model_layers_9_self_attn_k_proj : [num_users=1] = call_module[target=language_model.model.layers.9.self_attn.k_proj](args = (%mul_82,), kwargs = {}) %view_28 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_9_self_attn_k_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_361 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_28, dtype), kwargs = {}) %to_78 : [num_users=2] = call_method[target=to](args = (%view_28, torch.float32), kwargs = {}) %pow_39 : [num_users=1] = call_method[target=pow](args = (%to_78, 2), kwargs = {}) %mean_38 : [num_users=1] = call_method[target=mean](args = (%pow_39, -1), kwargs = {keepdim: True}) %add_56 : [num_users=1] = call_function[target=operator.add](args = (%mean_38, 1e-06), kwargs = {}) %rsqrt_38 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_56,), kwargs = {}) %mul_85 : [num_users=1] = call_function[target=operator.mul](args = (%to_78, %rsqrt_38), kwargs = {}) %language_model_model_layers_9_self_attn_k_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.9.self_attn.k_norm.weight] %to_79 : [num_users=1] = call_method[target=to](args = (%mul_85, %getattr_361), kwargs = {}) %mul_86 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_9_self_attn_k_norm_weight, %to_79), kwargs = {}) %transpose_37 : [num_users=1] = call_method[target=transpose](args = (%mul_86, 1, 2), kwargs = {}) %language_model_model_layers_9_self_attn_v_proj : [num_users=1] = call_module[target=language_model.model.layers.9.self_attn.v_proj](args = (%mul_82,), kwargs = {}) %view_29 : [num_users=1] = call_method[target=view](args = (%language_model_model_layers_9_self_attn_v_proj, (1, 29, -1, 128)), kwargs = {}) %transpose_38 : [num_users=1] = call_method[target=transpose](args = (%view_29, 1, 2), kwargs = {}) %apply_rotary_pos_emb_9 : [num_users=2] = call_function[target=transformers.models.qwen3.modeling_qwen3.apply_rotary_pos_emb](args = (%transpose_36, %transpose_37, %to, %to_1), kwargs = {}) %getitem_56 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_9, 0), kwargs = {}) %getitem_57 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_9, 1), kwargs = {}) %update_9 : [num_users=2] = call_method[target=update](args = (%past_key_values, %getitem_57, %transpose_38, 9, {sin: %to_1, cos: %to, cache_position: %cache_position}), kwargs = {}) %getitem_58 : [num_users=1] = call_function[target=operator.getitem](args = (%update_9, 0), kwargs = {}) %getitem_59 : [num_users=1] = call_function[target=operator.getitem](args = (%update_9, 1), kwargs = {}) %getitem_60 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_58, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_18 : [num_users=1] = call_method[target=expand](args = (%getitem_60, 1, 8, 2, 29, 128), kwargs = {}) %reshape_29 : [num_users=1] = call_method[target=reshape](args = (%expand_18, 1, 16, 29, 128), kwargs = {}) %getitem_61 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_59, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_19 : [num_users=1] = call_method[target=expand](args = (%getitem_61, 1, 8, 2, 29, 128), kwargs = {}) %reshape_30 : [num_users=1] = call_method[target=reshape](args = (%expand_19, 1, 16, 29, 128), kwargs = {}) %scaled_dot_product_attention_9 : [num_users=1] = call_function[target=torch._C._nn.scaled_dot_product_attention](args = (%getitem_56, %reshape_29, %reshape_30), kwargs = {attn_mask: None, dropout_p: 0.0, scale: 0.08838834764831845, is_causal: True}) %transpose_39 : [num_users=1] = call_method[target=transpose](args = (%scaled_dot_product_attention_9, 1, 2), kwargs = {}) %contiguous_18 : [num_users=1] = call_method[target=contiguous](args = (%transpose_39,), kwargs = {}) %reshape_31 : [num_users=1] = call_method[target=reshape](args = (%contiguous_18, 1, 29, -1), kwargs = {}) %contiguous_19 : [num_users=1] = call_method[target=contiguous](args = (%reshape_31,), kwargs = {}) %language_model_model_layers_9_self_attn_o_proj : [num_users=1] = call_module[target=language_model.model.layers.9.self_attn.o_proj](args = (%contiguous_19,), kwargs = {}) %add_57 : [num_users=3] = call_function[target=operator.add](args = (%add_53, %language_model_model_layers_9_self_attn_o_proj), kwargs = {}) %getattr_380 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_57, dtype), kwargs = {}) %to_80 : [num_users=2] = call_method[target=to](args = (%add_57, torch.float32), kwargs = {}) %pow_40 : [num_users=1] = call_method[target=pow](args = (%to_80, 2), kwargs = {}) %mean_39 : [num_users=1] = call_method[target=mean](args = (%pow_40, -1), kwargs = {keepdim: True}) %add_58 : [num_users=1] = call_function[target=operator.add](args = (%mean_39, 1e-06), kwargs = {}) %rsqrt_39 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_58,), kwargs = {}) %mul_87 : [num_users=1] = call_function[target=operator.mul](args = (%to_80, %rsqrt_39), kwargs = {}) %language_model_model_layers_9_post_attention_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.9.post_attention_layernorm.weight] %to_81 : [num_users=1] = call_method[target=to](args = (%mul_87, %getattr_380), kwargs = {}) %mul_88 : [num_users=2] = call_function[target=operator.mul](args = (%language_model_model_layers_9_post_attention_layernorm_weight, %to_81), kwargs = {}) %language_model_model_layers_9_mlp_gate_proj : [num_users=1] = call_module[target=language_model.model.layers.9.mlp.gate_proj](args = (%mul_88,), kwargs = {}) %silu_9 : [num_users=1] = call_function[target=torch.nn.functional.silu](args = (%language_model_model_layers_9_mlp_gate_proj,), kwargs = {inplace: False}) %language_model_model_layers_9_mlp_up_proj : [num_users=1] = call_module[target=language_model.model.layers.9.mlp.up_proj](args = (%mul_88,), kwargs = {}) %mul_89 : [num_users=1] = call_function[target=operator.mul](args = (%silu_9, %language_model_model_layers_9_mlp_up_proj), kwargs = {}) %language_model_model_layers_9_mlp_down_proj : [num_users=1] = call_module[target=language_model.model.layers.9.mlp.down_proj](args = (%mul_89,), kwargs = {}) %add_59 : [num_users=3] = call_function[target=operator.add](args = (%add_57, %language_model_model_layers_9_mlp_down_proj), kwargs = {}) %getattr_385 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_59, dtype), kwargs = {}) %to_82 : [num_users=2] = call_method[target=to](args = (%add_59, torch.float32), kwargs = {}) %pow_41 : [num_users=1] = call_method[target=pow](args = (%to_82, 2), kwargs = {}) %mean_40 : [num_users=1] = call_method[target=mean](args = (%pow_41, -1), kwargs = {keepdim: True}) %add_60 : [num_users=1] = call_function[target=operator.add](args = (%mean_40, 1e-06), kwargs = {}) %rsqrt_40 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_60,), kwargs = {}) %mul_90 : [num_users=1] = call_function[target=operator.mul](args = (%to_82, %rsqrt_40), kwargs = {}) %language_model_model_layers_10_input_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.10.input_layernorm.weight] %to_83 : [num_users=1] = call_method[target=to](args = (%mul_90, %getattr_385), kwargs = {}) %mul_91 : [num_users=3] = call_function[target=operator.mul](args = (%language_model_model_layers_10_input_layernorm_weight, %to_83), kwargs = {}) %language_model_model_layers_10_self_attn_q_proj : [num_users=1] = call_module[target=language_model.model.layers.10.self_attn.q_proj](args = (%mul_91,), kwargs = {}) %view_30 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_10_self_attn_q_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_392 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_30, dtype), kwargs = {}) %to_84 : [num_users=2] = call_method[target=to](args = (%view_30, torch.float32), kwargs = {}) %pow_42 : [num_users=1] = call_method[target=pow](args = (%to_84, 2), kwargs = {}) %mean_41 : [num_users=1] = call_method[target=mean](args = (%pow_42, -1), kwargs = {keepdim: True}) %add_61 : [num_users=1] = call_function[target=operator.add](args = (%mean_41, 1e-06), kwargs = {}) %rsqrt_41 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_61,), kwargs = {}) %mul_92 : [num_users=1] = call_function[target=operator.mul](args = (%to_84, %rsqrt_41), kwargs = {}) %language_model_model_layers_10_self_attn_q_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.10.self_attn.q_norm.weight] %to_85 : [num_users=1] = call_method[target=to](args = (%mul_92, %getattr_392), kwargs = {}) %mul_93 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_10_self_attn_q_norm_weight, %to_85), kwargs = {}) %transpose_40 : [num_users=1] = call_method[target=transpose](args = (%mul_93, 1, 2), kwargs = {}) %language_model_model_layers_10_self_attn_k_proj : [num_users=1] = call_module[target=language_model.model.layers.10.self_attn.k_proj](args = (%mul_91,), kwargs = {}) %view_31 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_10_self_attn_k_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_399 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_31, dtype), kwargs = {}) %to_86 : [num_users=2] = call_method[target=to](args = (%view_31, torch.float32), kwargs = {}) %pow_43 : [num_users=1] = call_method[target=pow](args = (%to_86, 2), kwargs = {}) %mean_42 : [num_users=1] = call_method[target=mean](args = (%pow_43, -1), kwargs = {keepdim: True}) %add_62 : [num_users=1] = call_function[target=operator.add](args = (%mean_42, 1e-06), kwargs = {}) %rsqrt_42 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_62,), kwargs = {}) %mul_94 : [num_users=1] = call_function[target=operator.mul](args = (%to_86, %rsqrt_42), kwargs = {}) %language_model_model_layers_10_self_attn_k_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.10.self_attn.k_norm.weight] %to_87 : [num_users=1] = call_method[target=to](args = (%mul_94, %getattr_399), kwargs = {}) %mul_95 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_10_self_attn_k_norm_weight, %to_87), kwargs = {}) %transpose_41 : [num_users=1] = call_method[target=transpose](args = (%mul_95, 1, 2), kwargs = {}) %language_model_model_layers_10_self_attn_v_proj : [num_users=1] = call_module[target=language_model.model.layers.10.self_attn.v_proj](args = (%mul_91,), kwargs = {}) %view_32 : [num_users=1] = call_method[target=view](args = (%language_model_model_layers_10_self_attn_v_proj, (1, 29, -1, 128)), kwargs = {}) %transpose_42 : [num_users=1] = call_method[target=transpose](args = (%view_32, 1, 2), kwargs = {}) %apply_rotary_pos_emb_10 : [num_users=2] = call_function[target=transformers.models.qwen3.modeling_qwen3.apply_rotary_pos_emb](args = (%transpose_40, %transpose_41, %to, %to_1), kwargs = {}) %getitem_62 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_10, 0), kwargs = {}) %getitem_63 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_10, 1), kwargs = {}) %update_10 : [num_users=2] = call_method[target=update](args = (%past_key_values, %getitem_63, %transpose_42, 10, {sin: %to_1, cos: %to, cache_position: %cache_position}), kwargs = {}) %getitem_64 : [num_users=1] = call_function[target=operator.getitem](args = (%update_10, 0), kwargs = {}) %getitem_65 : [num_users=1] = call_function[target=operator.getitem](args = (%update_10, 1), kwargs = {}) %getitem_66 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_64, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_20 : [num_users=1] = call_method[target=expand](args = (%getitem_66, 1, 8, 2, 29, 128), kwargs = {}) %reshape_32 : [num_users=1] = call_method[target=reshape](args = (%expand_20, 1, 16, 29, 128), kwargs = {}) %getitem_67 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_65, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_21 : [num_users=1] = call_method[target=expand](args = (%getitem_67, 1, 8, 2, 29, 128), kwargs = {}) %reshape_33 : [num_users=1] = call_method[target=reshape](args = (%expand_21, 1, 16, 29, 128), kwargs = {}) %scaled_dot_product_attention_10 : [num_users=1] = call_function[target=torch._C._nn.scaled_dot_product_attention](args = (%getitem_62, %reshape_32, %reshape_33), kwargs = {attn_mask: None, dropout_p: 0.0, scale: 0.08838834764831845, is_causal: True}) %transpose_43 : [num_users=1] = call_method[target=transpose](args = (%scaled_dot_product_attention_10, 1, 2), kwargs = {}) %contiguous_20 : [num_users=1] = call_method[target=contiguous](args = (%transpose_43,), kwargs = {}) %reshape_34 : [num_users=1] = call_method[target=reshape](args = (%contiguous_20, 1, 29, -1), kwargs = {}) %contiguous_21 : [num_users=1] = call_method[target=contiguous](args = (%reshape_34,), kwargs = {}) %language_model_model_layers_10_self_attn_o_proj : [num_users=1] = call_module[target=language_model.model.layers.10.self_attn.o_proj](args = (%contiguous_21,), kwargs = {}) %add_63 : [num_users=3] = call_function[target=operator.add](args = (%add_59, %language_model_model_layers_10_self_attn_o_proj), kwargs = {}) %getattr_418 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_63, dtype), kwargs = {}) %to_88 : [num_users=2] = call_method[target=to](args = (%add_63, torch.float32), kwargs = {}) %pow_44 : [num_users=1] = call_method[target=pow](args = (%to_88, 2), kwargs = {}) %mean_43 : [num_users=1] = call_method[target=mean](args = (%pow_44, -1), kwargs = {keepdim: True}) %add_64 : [num_users=1] = call_function[target=operator.add](args = (%mean_43, 1e-06), kwargs = {}) %rsqrt_43 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_64,), kwargs = {}) %mul_96 : [num_users=1] = call_function[target=operator.mul](args = (%to_88, %rsqrt_43), kwargs = {}) %language_model_model_layers_10_post_attention_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.10.post_attention_layernorm.weight] %to_89 : [num_users=1] = call_method[target=to](args = (%mul_96, %getattr_418), kwargs = {}) %mul_97 : [num_users=2] = call_function[target=operator.mul](args = (%language_model_model_layers_10_post_attention_layernorm_weight, %to_89), kwargs = {}) %language_model_model_layers_10_mlp_gate_proj : [num_users=1] = call_module[target=language_model.model.layers.10.mlp.gate_proj](args = (%mul_97,), kwargs = {}) %silu_10 : [num_users=1] = call_function[target=torch.nn.functional.silu](args = (%language_model_model_layers_10_mlp_gate_proj,), kwargs = {inplace: False}) %language_model_model_layers_10_mlp_up_proj : [num_users=1] = call_module[target=language_model.model.layers.10.mlp.up_proj](args = (%mul_97,), kwargs = {}) %mul_98 : [num_users=1] = call_function[target=operator.mul](args = (%silu_10, %language_model_model_layers_10_mlp_up_proj), kwargs = {}) %language_model_model_layers_10_mlp_down_proj : [num_users=1] = call_module[target=language_model.model.layers.10.mlp.down_proj](args = (%mul_98,), kwargs = {}) %add_65 : [num_users=3] = call_function[target=operator.add](args = (%add_63, %language_model_model_layers_10_mlp_down_proj), kwargs = {}) %getattr_423 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_65, dtype), kwargs = {}) %to_90 : [num_users=2] = call_method[target=to](args = (%add_65, torch.float32), kwargs = {}) %pow_45 : [num_users=1] = call_method[target=pow](args = (%to_90, 2), kwargs = {}) %mean_44 : [num_users=1] = call_method[target=mean](args = (%pow_45, -1), kwargs = {keepdim: True}) %add_66 : [num_users=1] = call_function[target=operator.add](args = (%mean_44, 1e-06), kwargs = {}) %rsqrt_44 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_66,), kwargs = {}) %mul_99 : [num_users=1] = call_function[target=operator.mul](args = (%to_90, %rsqrt_44), kwargs = {}) %language_model_model_layers_11_input_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.11.input_layernorm.weight] %to_91 : [num_users=1] = call_method[target=to](args = (%mul_99, %getattr_423), kwargs = {}) %mul_100 : [num_users=3] = call_function[target=operator.mul](args = (%language_model_model_layers_11_input_layernorm_weight, %to_91), kwargs = {}) %language_model_model_layers_11_self_attn_q_proj : [num_users=1] = call_module[target=language_model.model.layers.11.self_attn.q_proj](args = (%mul_100,), kwargs = {}) %view_33 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_11_self_attn_q_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_430 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_33, dtype), kwargs = {}) %to_92 : [num_users=2] = call_method[target=to](args = (%view_33, torch.float32), kwargs = {}) %pow_46 : [num_users=1] = call_method[target=pow](args = (%to_92, 2), kwargs = {}) %mean_45 : [num_users=1] = call_method[target=mean](args = (%pow_46, -1), kwargs = {keepdim: True}) %add_67 : [num_users=1] = call_function[target=operator.add](args = (%mean_45, 1e-06), kwargs = {}) %rsqrt_45 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_67,), kwargs = {}) %mul_101 : [num_users=1] = call_function[target=operator.mul](args = (%to_92, %rsqrt_45), kwargs = {}) %language_model_model_layers_11_self_attn_q_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.11.self_attn.q_norm.weight] %to_93 : [num_users=1] = call_method[target=to](args = (%mul_101, %getattr_430), kwargs = {}) %mul_102 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_11_self_attn_q_norm_weight, %to_93), kwargs = {}) %transpose_44 : [num_users=1] = call_method[target=transpose](args = (%mul_102, 1, 2), kwargs = {}) %language_model_model_layers_11_self_attn_k_proj : [num_users=1] = call_module[target=language_model.model.layers.11.self_attn.k_proj](args = (%mul_100,), kwargs = {}) %view_34 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_11_self_attn_k_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_437 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_34, dtype), kwargs = {}) %to_94 : [num_users=2] = call_method[target=to](args = (%view_34, torch.float32), kwargs = {}) %pow_47 : [num_users=1] = call_method[target=pow](args = (%to_94, 2), kwargs = {}) %mean_46 : [num_users=1] = call_method[target=mean](args = (%pow_47, -1), kwargs = {keepdim: True}) %add_68 : [num_users=1] = call_function[target=operator.add](args = (%mean_46, 1e-06), kwargs = {}) %rsqrt_46 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_68,), kwargs = {}) %mul_103 : [num_users=1] = call_function[target=operator.mul](args = (%to_94, %rsqrt_46), kwargs = {}) %language_model_model_layers_11_self_attn_k_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.11.self_attn.k_norm.weight] %to_95 : [num_users=1] = call_method[target=to](args = (%mul_103, %getattr_437), kwargs = {}) %mul_104 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_11_self_attn_k_norm_weight, %to_95), kwargs = {}) %transpose_45 : [num_users=1] = call_method[target=transpose](args = (%mul_104, 1, 2), kwargs = {}) %language_model_model_layers_11_self_attn_v_proj : [num_users=1] = call_module[target=language_model.model.layers.11.self_attn.v_proj](args = (%mul_100,), kwargs = {}) %view_35 : [num_users=1] = call_method[target=view](args = (%language_model_model_layers_11_self_attn_v_proj, (1, 29, -1, 128)), kwargs = {}) %transpose_46 : [num_users=1] = call_method[target=transpose](args = (%view_35, 1, 2), kwargs = {}) %apply_rotary_pos_emb_11 : [num_users=2] = call_function[target=transformers.models.qwen3.modeling_qwen3.apply_rotary_pos_emb](args = (%transpose_44, %transpose_45, %to, %to_1), kwargs = {}) %getitem_68 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_11, 0), kwargs = {}) %getitem_69 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_11, 1), kwargs = {}) %update_11 : [num_users=2] = call_method[target=update](args = (%past_key_values, %getitem_69, %transpose_46, 11, {sin: %to_1, cos: %to, cache_position: %cache_position}), kwargs = {}) %getitem_70 : [num_users=1] = call_function[target=operator.getitem](args = (%update_11, 0), kwargs = {}) %getitem_71 : [num_users=1] = call_function[target=operator.getitem](args = (%update_11, 1), kwargs = {}) %getitem_72 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_70, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_22 : [num_users=1] = call_method[target=expand](args = (%getitem_72, 1, 8, 2, 29, 128), kwargs = {}) %reshape_35 : [num_users=1] = call_method[target=reshape](args = (%expand_22, 1, 16, 29, 128), kwargs = {}) %getitem_73 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_71, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_23 : [num_users=1] = call_method[target=expand](args = (%getitem_73, 1, 8, 2, 29, 128), kwargs = {}) %reshape_36 : [num_users=1] = call_method[target=reshape](args = (%expand_23, 1, 16, 29, 128), kwargs = {}) %scaled_dot_product_attention_11 : [num_users=1] = call_function[target=torch._C._nn.scaled_dot_product_attention](args = (%getitem_68, %reshape_35, %reshape_36), kwargs = {attn_mask: None, dropout_p: 0.0, scale: 0.08838834764831845, is_causal: True}) %transpose_47 : [num_users=1] = call_method[target=transpose](args = (%scaled_dot_product_attention_11, 1, 2), kwargs = {}) %contiguous_22 : [num_users=1] = call_method[target=contiguous](args = (%transpose_47,), kwargs = {}) %reshape_37 : [num_users=1] = call_method[target=reshape](args = (%contiguous_22, 1, 29, -1), kwargs = {}) %contiguous_23 : [num_users=1] = call_method[target=contiguous](args = (%reshape_37,), kwargs = {}) %language_model_model_layers_11_self_attn_o_proj : [num_users=1] = call_module[target=language_model.model.layers.11.self_attn.o_proj](args = (%contiguous_23,), kwargs = {}) %add_69 : [num_users=3] = call_function[target=operator.add](args = (%add_65, %language_model_model_layers_11_self_attn_o_proj), kwargs = {}) %getattr_456 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_69, dtype), kwargs = {}) %to_96 : [num_users=2] = call_method[target=to](args = (%add_69, torch.float32), kwargs = {}) %pow_48 : [num_users=1] = call_method[target=pow](args = (%to_96, 2), kwargs = {}) %mean_47 : [num_users=1] = call_method[target=mean](args = (%pow_48, -1), kwargs = {keepdim: True}) %add_70 : [num_users=1] = call_function[target=operator.add](args = (%mean_47, 1e-06), kwargs = {}) %rsqrt_47 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_70,), kwargs = {}) %mul_105 : [num_users=1] = call_function[target=operator.mul](args = (%to_96, %rsqrt_47), kwargs = {}) %language_model_model_layers_11_post_attention_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.11.post_attention_layernorm.weight] %to_97 : [num_users=1] = call_method[target=to](args = (%mul_105, %getattr_456), kwargs = {}) %mul_106 : [num_users=2] = call_function[target=operator.mul](args = (%language_model_model_layers_11_post_attention_layernorm_weight, %to_97), kwargs = {}) %language_model_model_layers_11_mlp_gate_proj : [num_users=1] = call_module[target=language_model.model.layers.11.mlp.gate_proj](args = (%mul_106,), kwargs = {}) %silu_11 : [num_users=1] = call_function[target=torch.nn.functional.silu](args = (%language_model_model_layers_11_mlp_gate_proj,), kwargs = {inplace: False}) %language_model_model_layers_11_mlp_up_proj : [num_users=1] = call_module[target=language_model.model.layers.11.mlp.up_proj](args = (%mul_106,), kwargs = {}) %mul_107 : [num_users=1] = call_function[target=operator.mul](args = (%silu_11, %language_model_model_layers_11_mlp_up_proj), kwargs = {}) %language_model_model_layers_11_mlp_down_proj : [num_users=1] = call_module[target=language_model.model.layers.11.mlp.down_proj](args = (%mul_107,), kwargs = {}) %add_71 : [num_users=3] = call_function[target=operator.add](args = (%add_69, %language_model_model_layers_11_mlp_down_proj), kwargs = {}) %getattr_461 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_71, dtype), kwargs = {}) %to_98 : [num_users=2] = call_method[target=to](args = (%add_71, torch.float32), kwargs = {}) %pow_49 : [num_users=1] = call_method[target=pow](args = (%to_98, 2), kwargs = {}) %mean_48 : [num_users=1] = call_method[target=mean](args = (%pow_49, -1), kwargs = {keepdim: True}) %add_72 : [num_users=1] = call_function[target=operator.add](args = (%mean_48, 1e-06), kwargs = {}) %rsqrt_48 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_72,), kwargs = {}) %mul_108 : [num_users=1] = call_function[target=operator.mul](args = (%to_98, %rsqrt_48), kwargs = {}) %language_model_model_layers_12_input_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.12.input_layernorm.weight] %to_99 : [num_users=1] = call_method[target=to](args = (%mul_108, %getattr_461), kwargs = {}) %mul_109 : [num_users=3] = call_function[target=operator.mul](args = (%language_model_model_layers_12_input_layernorm_weight, %to_99), kwargs = {}) %language_model_model_layers_12_self_attn_q_proj : [num_users=1] = call_module[target=language_model.model.layers.12.self_attn.q_proj](args = (%mul_109,), kwargs = {}) %view_36 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_12_self_attn_q_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_468 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_36, dtype), kwargs = {}) %to_100 : [num_users=2] = call_method[target=to](args = (%view_36, torch.float32), kwargs = {}) %pow_50 : [num_users=1] = call_method[target=pow](args = (%to_100, 2), kwargs = {}) %mean_49 : [num_users=1] = call_method[target=mean](args = (%pow_50, -1), kwargs = {keepdim: True}) %add_73 : [num_users=1] = call_function[target=operator.add](args = (%mean_49, 1e-06), kwargs = {}) %rsqrt_49 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_73,), kwargs = {}) %mul_110 : [num_users=1] = call_function[target=operator.mul](args = (%to_100, %rsqrt_49), kwargs = {}) %language_model_model_layers_12_self_attn_q_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.12.self_attn.q_norm.weight] %to_101 : [num_users=1] = call_method[target=to](args = (%mul_110, %getattr_468), kwargs = {}) %mul_111 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_12_self_attn_q_norm_weight, %to_101), kwargs = {}) %transpose_48 : [num_users=1] = call_method[target=transpose](args = (%mul_111, 1, 2), kwargs = {}) %language_model_model_layers_12_self_attn_k_proj : [num_users=1] = call_module[target=language_model.model.layers.12.self_attn.k_proj](args = (%mul_109,), kwargs = {}) %view_37 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_12_self_attn_k_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_475 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_37, dtype), kwargs = {}) %to_102 : [num_users=2] = call_method[target=to](args = (%view_37, torch.float32), kwargs = {}) %pow_51 : [num_users=1] = call_method[target=pow](args = (%to_102, 2), kwargs = {}) %mean_50 : [num_users=1] = call_method[target=mean](args = (%pow_51, -1), kwargs = {keepdim: True}) %add_74 : [num_users=1] = call_function[target=operator.add](args = (%mean_50, 1e-06), kwargs = {}) %rsqrt_50 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_74,), kwargs = {}) %mul_112 : [num_users=1] = call_function[target=operator.mul](args = (%to_102, %rsqrt_50), kwargs = {}) %language_model_model_layers_12_self_attn_k_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.12.self_attn.k_norm.weight] %to_103 : [num_users=1] = call_method[target=to](args = (%mul_112, %getattr_475), kwargs = {}) %mul_113 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_12_self_attn_k_norm_weight, %to_103), kwargs = {}) %transpose_49 : [num_users=1] = call_method[target=transpose](args = (%mul_113, 1, 2), kwargs = {}) %language_model_model_layers_12_self_attn_v_proj : [num_users=1] = call_module[target=language_model.model.layers.12.self_attn.v_proj](args = (%mul_109,), kwargs = {}) %view_38 : [num_users=1] = call_method[target=view](args = (%language_model_model_layers_12_self_attn_v_proj, (1, 29, -1, 128)), kwargs = {}) %transpose_50 : [num_users=1] = call_method[target=transpose](args = (%view_38, 1, 2), kwargs = {}) %apply_rotary_pos_emb_12 : [num_users=2] = call_function[target=transformers.models.qwen3.modeling_qwen3.apply_rotary_pos_emb](args = (%transpose_48, %transpose_49, %to, %to_1), kwargs = {}) %getitem_74 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_12, 0), kwargs = {}) %getitem_75 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_12, 1), kwargs = {}) %update_12 : [num_users=2] = call_method[target=update](args = (%past_key_values, %getitem_75, %transpose_50, 12, {sin: %to_1, cos: %to, cache_position: %cache_position}), kwargs = {}) %getitem_76 : [num_users=1] = call_function[target=operator.getitem](args = (%update_12, 0), kwargs = {}) %getitem_77 : [num_users=1] = call_function[target=operator.getitem](args = (%update_12, 1), kwargs = {}) %getitem_78 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_76, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_24 : [num_users=1] = call_method[target=expand](args = (%getitem_78, 1, 8, 2, 29, 128), kwargs = {}) %reshape_38 : [num_users=1] = call_method[target=reshape](args = (%expand_24, 1, 16, 29, 128), kwargs = {}) %getitem_79 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_77, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_25 : [num_users=1] = call_method[target=expand](args = (%getitem_79, 1, 8, 2, 29, 128), kwargs = {}) %reshape_39 : [num_users=1] = call_method[target=reshape](args = (%expand_25, 1, 16, 29, 128), kwargs = {}) %scaled_dot_product_attention_12 : [num_users=1] = call_function[target=torch._C._nn.scaled_dot_product_attention](args = (%getitem_74, %reshape_38, %reshape_39), kwargs = {attn_mask: None, dropout_p: 0.0, scale: 0.08838834764831845, is_causal: True}) %transpose_51 : [num_users=1] = call_method[target=transpose](args = (%scaled_dot_product_attention_12, 1, 2), kwargs = {}) %contiguous_24 : [num_users=1] = call_method[target=contiguous](args = (%transpose_51,), kwargs = {}) %reshape_40 : [num_users=1] = call_method[target=reshape](args = (%contiguous_24, 1, 29, -1), kwargs = {}) %contiguous_25 : [num_users=1] = call_method[target=contiguous](args = (%reshape_40,), kwargs = {}) %language_model_model_layers_12_self_attn_o_proj : [num_users=1] = call_module[target=language_model.model.layers.12.self_attn.o_proj](args = (%contiguous_25,), kwargs = {}) %add_75 : [num_users=3] = call_function[target=operator.add](args = (%add_71, %language_model_model_layers_12_self_attn_o_proj), kwargs = {}) %getattr_494 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_75, dtype), kwargs = {}) %to_104 : [num_users=2] = call_method[target=to](args = (%add_75, torch.float32), kwargs = {}) %pow_52 : [num_users=1] = call_method[target=pow](args = (%to_104, 2), kwargs = {}) %mean_51 : [num_users=1] = call_method[target=mean](args = (%pow_52, -1), kwargs = {keepdim: True}) %add_76 : [num_users=1] = call_function[target=operator.add](args = (%mean_51, 1e-06), kwargs = {}) %rsqrt_51 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_76,), kwargs = {}) %mul_114 : [num_users=1] = call_function[target=operator.mul](args = (%to_104, %rsqrt_51), kwargs = {}) %language_model_model_layers_12_post_attention_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.12.post_attention_layernorm.weight] %to_105 : [num_users=1] = call_method[target=to](args = (%mul_114, %getattr_494), kwargs = {}) %mul_115 : [num_users=2] = call_function[target=operator.mul](args = (%language_model_model_layers_12_post_attention_layernorm_weight, %to_105), kwargs = {}) %language_model_model_layers_12_mlp_gate_proj : [num_users=1] = call_module[target=language_model.model.layers.12.mlp.gate_proj](args = (%mul_115,), kwargs = {}) %silu_12 : [num_users=1] = call_function[target=torch.nn.functional.silu](args = (%language_model_model_layers_12_mlp_gate_proj,), kwargs = {inplace: False}) %language_model_model_layers_12_mlp_up_proj : [num_users=1] = call_module[target=language_model.model.layers.12.mlp.up_proj](args = (%mul_115,), kwargs = {}) %mul_116 : [num_users=1] = call_function[target=operator.mul](args = (%silu_12, %language_model_model_layers_12_mlp_up_proj), kwargs = {}) %language_model_model_layers_12_mlp_down_proj : [num_users=1] = call_module[target=language_model.model.layers.12.mlp.down_proj](args = (%mul_116,), kwargs = {}) %add_77 : [num_users=3] = call_function[target=operator.add](args = (%add_75, %language_model_model_layers_12_mlp_down_proj), kwargs = {}) %getattr_499 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_77, dtype), kwargs = {}) %to_106 : [num_users=2] = call_method[target=to](args = (%add_77, torch.float32), kwargs = {}) %pow_53 : [num_users=1] = call_method[target=pow](args = (%to_106, 2), kwargs = {}) %mean_52 : [num_users=1] = call_method[target=mean](args = (%pow_53, -1), kwargs = {keepdim: True}) %add_78 : [num_users=1] = call_function[target=operator.add](args = (%mean_52, 1e-06), kwargs = {}) %rsqrt_52 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_78,), kwargs = {}) %mul_117 : [num_users=1] = call_function[target=operator.mul](args = (%to_106, %rsqrt_52), kwargs = {}) %language_model_model_layers_13_input_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.13.input_layernorm.weight] %to_107 : [num_users=1] = call_method[target=to](args = (%mul_117, %getattr_499), kwargs = {}) %mul_118 : [num_users=3] = call_function[target=operator.mul](args = (%language_model_model_layers_13_input_layernorm_weight, %to_107), kwargs = {}) %language_model_model_layers_13_self_attn_q_proj : [num_users=1] = call_module[target=language_model.model.layers.13.self_attn.q_proj](args = (%mul_118,), kwargs = {}) %view_39 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_13_self_attn_q_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_506 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_39, dtype), kwargs = {}) %to_108 : [num_users=2] = call_method[target=to](args = (%view_39, torch.float32), kwargs = {}) %pow_54 : [num_users=1] = call_method[target=pow](args = (%to_108, 2), kwargs = {}) %mean_53 : [num_users=1] = call_method[target=mean](args = (%pow_54, -1), kwargs = {keepdim: True}) %add_79 : [num_users=1] = call_function[target=operator.add](args = (%mean_53, 1e-06), kwargs = {}) %rsqrt_53 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_79,), kwargs = {}) %mul_119 : [num_users=1] = call_function[target=operator.mul](args = (%to_108, %rsqrt_53), kwargs = {}) %language_model_model_layers_13_self_attn_q_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.13.self_attn.q_norm.weight] %to_109 : [num_users=1] = call_method[target=to](args = (%mul_119, %getattr_506), kwargs = {}) %mul_120 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_13_self_attn_q_norm_weight, %to_109), kwargs = {}) %transpose_52 : [num_users=1] = call_method[target=transpose](args = (%mul_120, 1, 2), kwargs = {}) %language_model_model_layers_13_self_attn_k_proj : [num_users=1] = call_module[target=language_model.model.layers.13.self_attn.k_proj](args = (%mul_118,), kwargs = {}) %view_40 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_13_self_attn_k_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_513 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_40, dtype), kwargs = {}) %to_110 : [num_users=2] = call_method[target=to](args = (%view_40, torch.float32), kwargs = {}) %pow_55 : [num_users=1] = call_method[target=pow](args = (%to_110, 2), kwargs = {}) %mean_54 : [num_users=1] = call_method[target=mean](args = (%pow_55, -1), kwargs = {keepdim: True}) %add_80 : [num_users=1] = call_function[target=operator.add](args = (%mean_54, 1e-06), kwargs = {}) %rsqrt_54 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_80,), kwargs = {}) %mul_121 : [num_users=1] = call_function[target=operator.mul](args = (%to_110, %rsqrt_54), kwargs = {}) %language_model_model_layers_13_self_attn_k_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.13.self_attn.k_norm.weight] %to_111 : [num_users=1] = call_method[target=to](args = (%mul_121, %getattr_513), kwargs = {}) %mul_122 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_13_self_attn_k_norm_weight, %to_111), kwargs = {}) %transpose_53 : [num_users=1] = call_method[target=transpose](args = (%mul_122, 1, 2), kwargs = {}) %language_model_model_layers_13_self_attn_v_proj : [num_users=1] = call_module[target=language_model.model.layers.13.self_attn.v_proj](args = (%mul_118,), kwargs = {}) %view_41 : [num_users=1] = call_method[target=view](args = (%language_model_model_layers_13_self_attn_v_proj, (1, 29, -1, 128)), kwargs = {}) %transpose_54 : [num_users=1] = call_method[target=transpose](args = (%view_41, 1, 2), kwargs = {}) %apply_rotary_pos_emb_13 : [num_users=2] = call_function[target=transformers.models.qwen3.modeling_qwen3.apply_rotary_pos_emb](args = (%transpose_52, %transpose_53, %to, %to_1), kwargs = {}) %getitem_80 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_13, 0), kwargs = {}) %getitem_81 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_13, 1), kwargs = {}) %update_13 : [num_users=2] = call_method[target=update](args = (%past_key_values, %getitem_81, %transpose_54, 13, {sin: %to_1, cos: %to, cache_position: %cache_position}), kwargs = {}) %getitem_82 : [num_users=1] = call_function[target=operator.getitem](args = (%update_13, 0), kwargs = {}) %getitem_83 : [num_users=1] = call_function[target=operator.getitem](args = (%update_13, 1), kwargs = {}) %getitem_84 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_82, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_26 : [num_users=1] = call_method[target=expand](args = (%getitem_84, 1, 8, 2, 29, 128), kwargs = {}) %reshape_41 : [num_users=1] = call_method[target=reshape](args = (%expand_26, 1, 16, 29, 128), kwargs = {}) %getitem_85 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_83, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_27 : [num_users=1] = call_method[target=expand](args = (%getitem_85, 1, 8, 2, 29, 128), kwargs = {}) %reshape_42 : [num_users=1] = call_method[target=reshape](args = (%expand_27, 1, 16, 29, 128), kwargs = {}) %scaled_dot_product_attention_13 : [num_users=1] = call_function[target=torch._C._nn.scaled_dot_product_attention](args = (%getitem_80, %reshape_41, %reshape_42), kwargs = {attn_mask: None, dropout_p: 0.0, scale: 0.08838834764831845, is_causal: True}) %transpose_55 : [num_users=1] = call_method[target=transpose](args = (%scaled_dot_product_attention_13, 1, 2), kwargs = {}) %contiguous_26 : [num_users=1] = call_method[target=contiguous](args = (%transpose_55,), kwargs = {}) %reshape_43 : [num_users=1] = call_method[target=reshape](args = (%contiguous_26, 1, 29, -1), kwargs = {}) %contiguous_27 : [num_users=1] = call_method[target=contiguous](args = (%reshape_43,), kwargs = {}) %language_model_model_layers_13_self_attn_o_proj : [num_users=1] = call_module[target=language_model.model.layers.13.self_attn.o_proj](args = (%contiguous_27,), kwargs = {}) %add_81 : [num_users=3] = call_function[target=operator.add](args = (%add_77, %language_model_model_layers_13_self_attn_o_proj), kwargs = {}) %getattr_532 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_81, dtype), kwargs = {}) %to_112 : [num_users=2] = call_method[target=to](args = (%add_81, torch.float32), kwargs = {}) %pow_56 : [num_users=1] = call_method[target=pow](args = (%to_112, 2), kwargs = {}) %mean_55 : [num_users=1] = call_method[target=mean](args = (%pow_56, -1), kwargs = {keepdim: True}) %add_82 : [num_users=1] = call_function[target=operator.add](args = (%mean_55, 1e-06), kwargs = {}) %rsqrt_55 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_82,), kwargs = {}) %mul_123 : [num_users=1] = call_function[target=operator.mul](args = (%to_112, %rsqrt_55), kwargs = {}) %language_model_model_layers_13_post_attention_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.13.post_attention_layernorm.weight] %to_113 : [num_users=1] = call_method[target=to](args = (%mul_123, %getattr_532), kwargs = {}) %mul_124 : [num_users=2] = call_function[target=operator.mul](args = (%language_model_model_layers_13_post_attention_layernorm_weight, %to_113), kwargs = {}) %language_model_model_layers_13_mlp_gate_proj : [num_users=1] = call_module[target=language_model.model.layers.13.mlp.gate_proj](args = (%mul_124,), kwargs = {}) %silu_13 : [num_users=1] = call_function[target=torch.nn.functional.silu](args = (%language_model_model_layers_13_mlp_gate_proj,), kwargs = {inplace: False}) %language_model_model_layers_13_mlp_up_proj : [num_users=1] = call_module[target=language_model.model.layers.13.mlp.up_proj](args = (%mul_124,), kwargs = {}) %mul_125 : [num_users=1] = call_function[target=operator.mul](args = (%silu_13, %language_model_model_layers_13_mlp_up_proj), kwargs = {}) %language_model_model_layers_13_mlp_down_proj : [num_users=1] = call_module[target=language_model.model.layers.13.mlp.down_proj](args = (%mul_125,), kwargs = {}) %add_83 : [num_users=3] = call_function[target=operator.add](args = (%add_81, %language_model_model_layers_13_mlp_down_proj), kwargs = {}) %getattr_537 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_83, dtype), kwargs = {}) %to_114 : [num_users=2] = call_method[target=to](args = (%add_83, torch.float32), kwargs = {}) %pow_57 : [num_users=1] = call_method[target=pow](args = (%to_114, 2), kwargs = {}) %mean_56 : [num_users=1] = call_method[target=mean](args = (%pow_57, -1), kwargs = {keepdim: True}) %add_84 : [num_users=1] = call_function[target=operator.add](args = (%mean_56, 1e-06), kwargs = {}) %rsqrt_56 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_84,), kwargs = {}) %mul_126 : [num_users=1] = call_function[target=operator.mul](args = (%to_114, %rsqrt_56), kwargs = {}) %language_model_model_layers_14_input_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.14.input_layernorm.weight] %to_115 : [num_users=1] = call_method[target=to](args = (%mul_126, %getattr_537), kwargs = {}) %mul_127 : [num_users=3] = call_function[target=operator.mul](args = (%language_model_model_layers_14_input_layernorm_weight, %to_115), kwargs = {}) %language_model_model_layers_14_self_attn_q_proj : [num_users=1] = call_module[target=language_model.model.layers.14.self_attn.q_proj](args = (%mul_127,), kwargs = {}) %view_42 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_14_self_attn_q_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_544 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_42, dtype), kwargs = {}) %to_116 : [num_users=2] = call_method[target=to](args = (%view_42, torch.float32), kwargs = {}) %pow_58 : [num_users=1] = call_method[target=pow](args = (%to_116, 2), kwargs = {}) %mean_57 : [num_users=1] = call_method[target=mean](args = (%pow_58, -1), kwargs = {keepdim: True}) %add_85 : [num_users=1] = call_function[target=operator.add](args = (%mean_57, 1e-06), kwargs = {}) %rsqrt_57 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_85,), kwargs = {}) %mul_128 : [num_users=1] = call_function[target=operator.mul](args = (%to_116, %rsqrt_57), kwargs = {}) %language_model_model_layers_14_self_attn_q_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.14.self_attn.q_norm.weight] %to_117 : [num_users=1] = call_method[target=to](args = (%mul_128, %getattr_544), kwargs = {}) %mul_129 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_14_self_attn_q_norm_weight, %to_117), kwargs = {}) %transpose_56 : [num_users=1] = call_method[target=transpose](args = (%mul_129, 1, 2), kwargs = {}) %language_model_model_layers_14_self_attn_k_proj : [num_users=1] = call_module[target=language_model.model.layers.14.self_attn.k_proj](args = (%mul_127,), kwargs = {}) %view_43 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_14_self_attn_k_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_551 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_43, dtype), kwargs = {}) %to_118 : [num_users=2] = call_method[target=to](args = (%view_43, torch.float32), kwargs = {}) %pow_59 : [num_users=1] = call_method[target=pow](args = (%to_118, 2), kwargs = {}) %mean_58 : [num_users=1] = call_method[target=mean](args = (%pow_59, -1), kwargs = {keepdim: True}) %add_86 : [num_users=1] = call_function[target=operator.add](args = (%mean_58, 1e-06), kwargs = {}) %rsqrt_58 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_86,), kwargs = {}) %mul_130 : [num_users=1] = call_function[target=operator.mul](args = (%to_118, %rsqrt_58), kwargs = {}) %language_model_model_layers_14_self_attn_k_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.14.self_attn.k_norm.weight] %to_119 : [num_users=1] = call_method[target=to](args = (%mul_130, %getattr_551), kwargs = {}) %mul_131 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_14_self_attn_k_norm_weight, %to_119), kwargs = {}) %transpose_57 : [num_users=1] = call_method[target=transpose](args = (%mul_131, 1, 2), kwargs = {}) %language_model_model_layers_14_self_attn_v_proj : [num_users=1] = call_module[target=language_model.model.layers.14.self_attn.v_proj](args = (%mul_127,), kwargs = {}) %view_44 : [num_users=1] = call_method[target=view](args = (%language_model_model_layers_14_self_attn_v_proj, (1, 29, -1, 128)), kwargs = {}) %transpose_58 : [num_users=1] = call_method[target=transpose](args = (%view_44, 1, 2), kwargs = {}) %apply_rotary_pos_emb_14 : [num_users=2] = call_function[target=transformers.models.qwen3.modeling_qwen3.apply_rotary_pos_emb](args = (%transpose_56, %transpose_57, %to, %to_1), kwargs = {}) %getitem_86 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_14, 0), kwargs = {}) %getitem_87 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_14, 1), kwargs = {}) %update_14 : [num_users=2] = call_method[target=update](args = (%past_key_values, %getitem_87, %transpose_58, 14, {sin: %to_1, cos: %to, cache_position: %cache_position}), kwargs = {}) %getitem_88 : [num_users=1] = call_function[target=operator.getitem](args = (%update_14, 0), kwargs = {}) %getitem_89 : [num_users=1] = call_function[target=operator.getitem](args = (%update_14, 1), kwargs = {}) %getitem_90 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_88, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_28 : [num_users=1] = call_method[target=expand](args = (%getitem_90, 1, 8, 2, 29, 128), kwargs = {}) %reshape_44 : [num_users=1] = call_method[target=reshape](args = (%expand_28, 1, 16, 29, 128), kwargs = {}) %getitem_91 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_89, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_29 : [num_users=1] = call_method[target=expand](args = (%getitem_91, 1, 8, 2, 29, 128), kwargs = {}) %reshape_45 : [num_users=1] = call_method[target=reshape](args = (%expand_29, 1, 16, 29, 128), kwargs = {}) %scaled_dot_product_attention_14 : [num_users=1] = call_function[target=torch._C._nn.scaled_dot_product_attention](args = (%getitem_86, %reshape_44, %reshape_45), kwargs = {attn_mask: None, dropout_p: 0.0, scale: 0.08838834764831845, is_causal: True}) %transpose_59 : [num_users=1] = call_method[target=transpose](args = (%scaled_dot_product_attention_14, 1, 2), kwargs = {}) %contiguous_28 : [num_users=1] = call_method[target=contiguous](args = (%transpose_59,), kwargs = {}) %reshape_46 : [num_users=1] = call_method[target=reshape](args = (%contiguous_28, 1, 29, -1), kwargs = {}) %contiguous_29 : [num_users=1] = call_method[target=contiguous](args = (%reshape_46,), kwargs = {}) %language_model_model_layers_14_self_attn_o_proj : [num_users=1] = call_module[target=language_model.model.layers.14.self_attn.o_proj](args = (%contiguous_29,), kwargs = {}) %add_87 : [num_users=3] = call_function[target=operator.add](args = (%add_83, %language_model_model_layers_14_self_attn_o_proj), kwargs = {}) %getattr_570 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_87, dtype), kwargs = {}) %to_120 : [num_users=2] = call_method[target=to](args = (%add_87, torch.float32), kwargs = {}) %pow_60 : [num_users=1] = call_method[target=pow](args = (%to_120, 2), kwargs = {}) %mean_59 : [num_users=1] = call_method[target=mean](args = (%pow_60, -1), kwargs = {keepdim: True}) %add_88 : [num_users=1] = call_function[target=operator.add](args = (%mean_59, 1e-06), kwargs = {}) %rsqrt_59 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_88,), kwargs = {}) %mul_132 : [num_users=1] = call_function[target=operator.mul](args = (%to_120, %rsqrt_59), kwargs = {}) %language_model_model_layers_14_post_attention_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.14.post_attention_layernorm.weight] %to_121 : [num_users=1] = call_method[target=to](args = (%mul_132, %getattr_570), kwargs = {}) %mul_133 : [num_users=2] = call_function[target=operator.mul](args = (%language_model_model_layers_14_post_attention_layernorm_weight, %to_121), kwargs = {}) %language_model_model_layers_14_mlp_gate_proj : [num_users=1] = call_module[target=language_model.model.layers.14.mlp.gate_proj](args = (%mul_133,), kwargs = {}) %silu_14 : [num_users=1] = call_function[target=torch.nn.functional.silu](args = (%language_model_model_layers_14_mlp_gate_proj,), kwargs = {inplace: False}) %language_model_model_layers_14_mlp_up_proj : [num_users=1] = call_module[target=language_model.model.layers.14.mlp.up_proj](args = (%mul_133,), kwargs = {}) %mul_134 : [num_users=1] = call_function[target=operator.mul](args = (%silu_14, %language_model_model_layers_14_mlp_up_proj), kwargs = {}) %language_model_model_layers_14_mlp_down_proj : [num_users=1] = call_module[target=language_model.model.layers.14.mlp.down_proj](args = (%mul_134,), kwargs = {}) %add_89 : [num_users=3] = call_function[target=operator.add](args = (%add_87, %language_model_model_layers_14_mlp_down_proj), kwargs = {}) %getattr_575 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_89, dtype), kwargs = {}) %to_122 : [num_users=2] = call_method[target=to](args = (%add_89, torch.float32), kwargs = {}) %pow_61 : [num_users=1] = call_method[target=pow](args = (%to_122, 2), kwargs = {}) %mean_60 : [num_users=1] = call_method[target=mean](args = (%pow_61, -1), kwargs = {keepdim: True}) %add_90 : [num_users=1] = call_function[target=operator.add](args = (%mean_60, 1e-06), kwargs = {}) %rsqrt_60 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_90,), kwargs = {}) %mul_135 : [num_users=1] = call_function[target=operator.mul](args = (%to_122, %rsqrt_60), kwargs = {}) %language_model_model_layers_15_input_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.15.input_layernorm.weight] %to_123 : [num_users=1] = call_method[target=to](args = (%mul_135, %getattr_575), kwargs = {}) %mul_136 : [num_users=3] = call_function[target=operator.mul](args = (%language_model_model_layers_15_input_layernorm_weight, %to_123), kwargs = {}) %language_model_model_layers_15_self_attn_q_proj : [num_users=1] = call_module[target=language_model.model.layers.15.self_attn.q_proj](args = (%mul_136,), kwargs = {}) %view_45 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_15_self_attn_q_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_582 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_45, dtype), kwargs = {}) %to_124 : [num_users=2] = call_method[target=to](args = (%view_45, torch.float32), kwargs = {}) %pow_62 : [num_users=1] = call_method[target=pow](args = (%to_124, 2), kwargs = {}) %mean_61 : [num_users=1] = call_method[target=mean](args = (%pow_62, -1), kwargs = {keepdim: True}) %add_91 : [num_users=1] = call_function[target=operator.add](args = (%mean_61, 1e-06), kwargs = {}) %rsqrt_61 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_91,), kwargs = {}) %mul_137 : [num_users=1] = call_function[target=operator.mul](args = (%to_124, %rsqrt_61), kwargs = {}) %language_model_model_layers_15_self_attn_q_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.15.self_attn.q_norm.weight] %to_125 : [num_users=1] = call_method[target=to](args = (%mul_137, %getattr_582), kwargs = {}) %mul_138 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_15_self_attn_q_norm_weight, %to_125), kwargs = {}) %transpose_60 : [num_users=1] = call_method[target=transpose](args = (%mul_138, 1, 2), kwargs = {}) %language_model_model_layers_15_self_attn_k_proj : [num_users=1] = call_module[target=language_model.model.layers.15.self_attn.k_proj](args = (%mul_136,), kwargs = {}) %view_46 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_15_self_attn_k_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_589 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_46, dtype), kwargs = {}) %to_126 : [num_users=2] = call_method[target=to](args = (%view_46, torch.float32), kwargs = {}) %pow_63 : [num_users=1] = call_method[target=pow](args = (%to_126, 2), kwargs = {}) %mean_62 : [num_users=1] = call_method[target=mean](args = (%pow_63, -1), kwargs = {keepdim: True}) %add_92 : [num_users=1] = call_function[target=operator.add](args = (%mean_62, 1e-06), kwargs = {}) %rsqrt_62 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_92,), kwargs = {}) %mul_139 : [num_users=1] = call_function[target=operator.mul](args = (%to_126, %rsqrt_62), kwargs = {}) %language_model_model_layers_15_self_attn_k_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.15.self_attn.k_norm.weight] %to_127 : [num_users=1] = call_method[target=to](args = (%mul_139, %getattr_589), kwargs = {}) %mul_140 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_15_self_attn_k_norm_weight, %to_127), kwargs = {}) %transpose_61 : [num_users=1] = call_method[target=transpose](args = (%mul_140, 1, 2), kwargs = {}) %language_model_model_layers_15_self_attn_v_proj : [num_users=1] = call_module[target=language_model.model.layers.15.self_attn.v_proj](args = (%mul_136,), kwargs = {}) %view_47 : [num_users=1] = call_method[target=view](args = (%language_model_model_layers_15_self_attn_v_proj, (1, 29, -1, 128)), kwargs = {}) %transpose_62 : [num_users=1] = call_method[target=transpose](args = (%view_47, 1, 2), kwargs = {}) %apply_rotary_pos_emb_15 : [num_users=2] = call_function[target=transformers.models.qwen3.modeling_qwen3.apply_rotary_pos_emb](args = (%transpose_60, %transpose_61, %to, %to_1), kwargs = {}) %getitem_92 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_15, 0), kwargs = {}) %getitem_93 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_15, 1), kwargs = {}) %update_15 : [num_users=2] = call_method[target=update](args = (%past_key_values, %getitem_93, %transpose_62, 15, {sin: %to_1, cos: %to, cache_position: %cache_position}), kwargs = {}) %getitem_94 : [num_users=1] = call_function[target=operator.getitem](args = (%update_15, 0), kwargs = {}) %getitem_95 : [num_users=1] = call_function[target=operator.getitem](args = (%update_15, 1), kwargs = {}) %getitem_96 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_94, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_30 : [num_users=1] = call_method[target=expand](args = (%getitem_96, 1, 8, 2, 29, 128), kwargs = {}) %reshape_47 : [num_users=1] = call_method[target=reshape](args = (%expand_30, 1, 16, 29, 128), kwargs = {}) %getitem_97 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_95, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_31 : [num_users=1] = call_method[target=expand](args = (%getitem_97, 1, 8, 2, 29, 128), kwargs = {}) %reshape_48 : [num_users=1] = call_method[target=reshape](args = (%expand_31, 1, 16, 29, 128), kwargs = {}) %scaled_dot_product_attention_15 : [num_users=1] = call_function[target=torch._C._nn.scaled_dot_product_attention](args = (%getitem_92, %reshape_47, %reshape_48), kwargs = {attn_mask: None, dropout_p: 0.0, scale: 0.08838834764831845, is_causal: True}) %transpose_63 : [num_users=1] = call_method[target=transpose](args = (%scaled_dot_product_attention_15, 1, 2), kwargs = {}) %contiguous_30 : [num_users=1] = call_method[target=contiguous](args = (%transpose_63,), kwargs = {}) %reshape_49 : [num_users=1] = call_method[target=reshape](args = (%contiguous_30, 1, 29, -1), kwargs = {}) %contiguous_31 : [num_users=1] = call_method[target=contiguous](args = (%reshape_49,), kwargs = {}) %language_model_model_layers_15_self_attn_o_proj : [num_users=1] = call_module[target=language_model.model.layers.15.self_attn.o_proj](args = (%contiguous_31,), kwargs = {}) %add_93 : [num_users=3] = call_function[target=operator.add](args = (%add_89, %language_model_model_layers_15_self_attn_o_proj), kwargs = {}) %getattr_608 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_93, dtype), kwargs = {}) %to_128 : [num_users=2] = call_method[target=to](args = (%add_93, torch.float32), kwargs = {}) %pow_64 : [num_users=1] = call_method[target=pow](args = (%to_128, 2), kwargs = {}) %mean_63 : [num_users=1] = call_method[target=mean](args = (%pow_64, -1), kwargs = {keepdim: True}) %add_94 : [num_users=1] = call_function[target=operator.add](args = (%mean_63, 1e-06), kwargs = {}) %rsqrt_63 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_94,), kwargs = {}) %mul_141 : [num_users=1] = call_function[target=operator.mul](args = (%to_128, %rsqrt_63), kwargs = {}) %language_model_model_layers_15_post_attention_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.15.post_attention_layernorm.weight] %to_129 : [num_users=1] = call_method[target=to](args = (%mul_141, %getattr_608), kwargs = {}) %mul_142 : [num_users=2] = call_function[target=operator.mul](args = (%language_model_model_layers_15_post_attention_layernorm_weight, %to_129), kwargs = {}) %language_model_model_layers_15_mlp_gate_proj : [num_users=1] = call_module[target=language_model.model.layers.15.mlp.gate_proj](args = (%mul_142,), kwargs = {}) %silu_15 : [num_users=1] = call_function[target=torch.nn.functional.silu](args = (%language_model_model_layers_15_mlp_gate_proj,), kwargs = {inplace: False}) %language_model_model_layers_15_mlp_up_proj : [num_users=1] = call_module[target=language_model.model.layers.15.mlp.up_proj](args = (%mul_142,), kwargs = {}) %mul_143 : [num_users=1] = call_function[target=operator.mul](args = (%silu_15, %language_model_model_layers_15_mlp_up_proj), kwargs = {}) %language_model_model_layers_15_mlp_down_proj : [num_users=1] = call_module[target=language_model.model.layers.15.mlp.down_proj](args = (%mul_143,), kwargs = {}) %add_95 : [num_users=3] = call_function[target=operator.add](args = (%add_93, %language_model_model_layers_15_mlp_down_proj), kwargs = {}) %getattr_613 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_95, dtype), kwargs = {}) %to_130 : [num_users=2] = call_method[target=to](args = (%add_95, torch.float32), kwargs = {}) %pow_65 : [num_users=1] = call_method[target=pow](args = (%to_130, 2), kwargs = {}) %mean_64 : [num_users=1] = call_method[target=mean](args = (%pow_65, -1), kwargs = {keepdim: True}) %add_96 : [num_users=1] = call_function[target=operator.add](args = (%mean_64, 1e-06), kwargs = {}) %rsqrt_64 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_96,), kwargs = {}) %mul_144 : [num_users=1] = call_function[target=operator.mul](args = (%to_130, %rsqrt_64), kwargs = {}) %language_model_model_layers_16_input_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.16.input_layernorm.weight] %to_131 : [num_users=1] = call_method[target=to](args = (%mul_144, %getattr_613), kwargs = {}) %mul_145 : [num_users=3] = call_function[target=operator.mul](args = (%language_model_model_layers_16_input_layernorm_weight, %to_131), kwargs = {}) %language_model_model_layers_16_self_attn_q_proj : [num_users=1] = call_module[target=language_model.model.layers.16.self_attn.q_proj](args = (%mul_145,), kwargs = {}) %view_48 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_16_self_attn_q_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_620 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_48, dtype), kwargs = {}) %to_132 : [num_users=2] = call_method[target=to](args = (%view_48, torch.float32), kwargs = {}) %pow_66 : [num_users=1] = call_method[target=pow](args = (%to_132, 2), kwargs = {}) %mean_65 : [num_users=1] = call_method[target=mean](args = (%pow_66, -1), kwargs = {keepdim: True}) %add_97 : [num_users=1] = call_function[target=operator.add](args = (%mean_65, 1e-06), kwargs = {}) %rsqrt_65 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_97,), kwargs = {}) %mul_146 : [num_users=1] = call_function[target=operator.mul](args = (%to_132, %rsqrt_65), kwargs = {}) %language_model_model_layers_16_self_attn_q_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.16.self_attn.q_norm.weight] %to_133 : [num_users=1] = call_method[target=to](args = (%mul_146, %getattr_620), kwargs = {}) %mul_147 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_16_self_attn_q_norm_weight, %to_133), kwargs = {}) %transpose_64 : [num_users=1] = call_method[target=transpose](args = (%mul_147, 1, 2), kwargs = {}) %language_model_model_layers_16_self_attn_k_proj : [num_users=1] = call_module[target=language_model.model.layers.16.self_attn.k_proj](args = (%mul_145,), kwargs = {}) %view_49 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_16_self_attn_k_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_627 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_49, dtype), kwargs = {}) %to_134 : [num_users=2] = call_method[target=to](args = (%view_49, torch.float32), kwargs = {}) %pow_67 : [num_users=1] = call_method[target=pow](args = (%to_134, 2), kwargs = {}) %mean_66 : [num_users=1] = call_method[target=mean](args = (%pow_67, -1), kwargs = {keepdim: True}) %add_98 : [num_users=1] = call_function[target=operator.add](args = (%mean_66, 1e-06), kwargs = {}) %rsqrt_66 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_98,), kwargs = {}) %mul_148 : [num_users=1] = call_function[target=operator.mul](args = (%to_134, %rsqrt_66), kwargs = {}) %language_model_model_layers_16_self_attn_k_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.16.self_attn.k_norm.weight] %to_135 : [num_users=1] = call_method[target=to](args = (%mul_148, %getattr_627), kwargs = {}) %mul_149 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_16_self_attn_k_norm_weight, %to_135), kwargs = {}) %transpose_65 : [num_users=1] = call_method[target=transpose](args = (%mul_149, 1, 2), kwargs = {}) %language_model_model_layers_16_self_attn_v_proj : [num_users=1] = call_module[target=language_model.model.layers.16.self_attn.v_proj](args = (%mul_145,), kwargs = {}) %view_50 : [num_users=1] = call_method[target=view](args = (%language_model_model_layers_16_self_attn_v_proj, (1, 29, -1, 128)), kwargs = {}) %transpose_66 : [num_users=1] = call_method[target=transpose](args = (%view_50, 1, 2), kwargs = {}) %apply_rotary_pos_emb_16 : [num_users=2] = call_function[target=transformers.models.qwen3.modeling_qwen3.apply_rotary_pos_emb](args = (%transpose_64, %transpose_65, %to, %to_1), kwargs = {}) %getitem_98 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_16, 0), kwargs = {}) %getitem_99 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_16, 1), kwargs = {}) %update_16 : [num_users=2] = call_method[target=update](args = (%past_key_values, %getitem_99, %transpose_66, 16, {sin: %to_1, cos: %to, cache_position: %cache_position}), kwargs = {}) %getitem_100 : [num_users=1] = call_function[target=operator.getitem](args = (%update_16, 0), kwargs = {}) %getitem_101 : [num_users=1] = call_function[target=operator.getitem](args = (%update_16, 1), kwargs = {}) %getitem_102 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_100, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_32 : [num_users=1] = call_method[target=expand](args = (%getitem_102, 1, 8, 2, 29, 128), kwargs = {}) %reshape_50 : [num_users=1] = call_method[target=reshape](args = (%expand_32, 1, 16, 29, 128), kwargs = {}) %getitem_103 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_101, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_33 : [num_users=1] = call_method[target=expand](args = (%getitem_103, 1, 8, 2, 29, 128), kwargs = {}) %reshape_51 : [num_users=1] = call_method[target=reshape](args = (%expand_33, 1, 16, 29, 128), kwargs = {}) %scaled_dot_product_attention_16 : [num_users=1] = call_function[target=torch._C._nn.scaled_dot_product_attention](args = (%getitem_98, %reshape_50, %reshape_51), kwargs = {attn_mask: None, dropout_p: 0.0, scale: 0.08838834764831845, is_causal: True}) %transpose_67 : [num_users=1] = call_method[target=transpose](args = (%scaled_dot_product_attention_16, 1, 2), kwargs = {}) %contiguous_32 : [num_users=1] = call_method[target=contiguous](args = (%transpose_67,), kwargs = {}) %reshape_52 : [num_users=1] = call_method[target=reshape](args = (%contiguous_32, 1, 29, -1), kwargs = {}) %contiguous_33 : [num_users=1] = call_method[target=contiguous](args = (%reshape_52,), kwargs = {}) %language_model_model_layers_16_self_attn_o_proj : [num_users=1] = call_module[target=language_model.model.layers.16.self_attn.o_proj](args = (%contiguous_33,), kwargs = {}) %add_99 : [num_users=3] = call_function[target=operator.add](args = (%add_95, %language_model_model_layers_16_self_attn_o_proj), kwargs = {}) %getattr_646 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_99, dtype), kwargs = {}) %to_136 : [num_users=2] = call_method[target=to](args = (%add_99, torch.float32), kwargs = {}) %pow_68 : [num_users=1] = call_method[target=pow](args = (%to_136, 2), kwargs = {}) %mean_67 : [num_users=1] = call_method[target=mean](args = (%pow_68, -1), kwargs = {keepdim: True}) %add_100 : [num_users=1] = call_function[target=operator.add](args = (%mean_67, 1e-06), kwargs = {}) %rsqrt_67 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_100,), kwargs = {}) %mul_150 : [num_users=1] = call_function[target=operator.mul](args = (%to_136, %rsqrt_67), kwargs = {}) %language_model_model_layers_16_post_attention_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.16.post_attention_layernorm.weight] %to_137 : [num_users=1] = call_method[target=to](args = (%mul_150, %getattr_646), kwargs = {}) %mul_151 : [num_users=2] = call_function[target=operator.mul](args = (%language_model_model_layers_16_post_attention_layernorm_weight, %to_137), kwargs = {}) %language_model_model_layers_16_mlp_gate_proj : [num_users=1] = call_module[target=language_model.model.layers.16.mlp.gate_proj](args = (%mul_151,), kwargs = {}) %silu_16 : [num_users=1] = call_function[target=torch.nn.functional.silu](args = (%language_model_model_layers_16_mlp_gate_proj,), kwargs = {inplace: False}) %language_model_model_layers_16_mlp_up_proj : [num_users=1] = call_module[target=language_model.model.layers.16.mlp.up_proj](args = (%mul_151,), kwargs = {}) %mul_152 : [num_users=1] = call_function[target=operator.mul](args = (%silu_16, %language_model_model_layers_16_mlp_up_proj), kwargs = {}) %language_model_model_layers_16_mlp_down_proj : [num_users=1] = call_module[target=language_model.model.layers.16.mlp.down_proj](args = (%mul_152,), kwargs = {}) %add_101 : [num_users=3] = call_function[target=operator.add](args = (%add_99, %language_model_model_layers_16_mlp_down_proj), kwargs = {}) %getattr_651 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_101, dtype), kwargs = {}) %to_138 : [num_users=2] = call_method[target=to](args = (%add_101, torch.float32), kwargs = {}) %pow_69 : [num_users=1] = call_method[target=pow](args = (%to_138, 2), kwargs = {}) %mean_68 : [num_users=1] = call_method[target=mean](args = (%pow_69, -1), kwargs = {keepdim: True}) %add_102 : [num_users=1] = call_function[target=operator.add](args = (%mean_68, 1e-06), kwargs = {}) %rsqrt_68 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_102,), kwargs = {}) %mul_153 : [num_users=1] = call_function[target=operator.mul](args = (%to_138, %rsqrt_68), kwargs = {}) %language_model_model_layers_17_input_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.17.input_layernorm.weight] %to_139 : [num_users=1] = call_method[target=to](args = (%mul_153, %getattr_651), kwargs = {}) %mul_154 : [num_users=3] = call_function[target=operator.mul](args = (%language_model_model_layers_17_input_layernorm_weight, %to_139), kwargs = {}) %language_model_model_layers_17_self_attn_q_proj : [num_users=1] = call_module[target=language_model.model.layers.17.self_attn.q_proj](args = (%mul_154,), kwargs = {}) %view_51 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_17_self_attn_q_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_658 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_51, dtype), kwargs = {}) %to_140 : [num_users=2] = call_method[target=to](args = (%view_51, torch.float32), kwargs = {}) %pow_70 : [num_users=1] = call_method[target=pow](args = (%to_140, 2), kwargs = {}) %mean_69 : [num_users=1] = call_method[target=mean](args = (%pow_70, -1), kwargs = {keepdim: True}) %add_103 : [num_users=1] = call_function[target=operator.add](args = (%mean_69, 1e-06), kwargs = {}) %rsqrt_69 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_103,), kwargs = {}) %mul_155 : [num_users=1] = call_function[target=operator.mul](args = (%to_140, %rsqrt_69), kwargs = {}) %language_model_model_layers_17_self_attn_q_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.17.self_attn.q_norm.weight] %to_141 : [num_users=1] = call_method[target=to](args = (%mul_155, %getattr_658), kwargs = {}) %mul_156 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_17_self_attn_q_norm_weight, %to_141), kwargs = {}) %transpose_68 : [num_users=1] = call_method[target=transpose](args = (%mul_156, 1, 2), kwargs = {}) %language_model_model_layers_17_self_attn_k_proj : [num_users=1] = call_module[target=language_model.model.layers.17.self_attn.k_proj](args = (%mul_154,), kwargs = {}) %view_52 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_17_self_attn_k_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_665 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_52, dtype), kwargs = {}) %to_142 : [num_users=2] = call_method[target=to](args = (%view_52, torch.float32), kwargs = {}) %pow_71 : [num_users=1] = call_method[target=pow](args = (%to_142, 2), kwargs = {}) %mean_70 : [num_users=1] = call_method[target=mean](args = (%pow_71, -1), kwargs = {keepdim: True}) %add_104 : [num_users=1] = call_function[target=operator.add](args = (%mean_70, 1e-06), kwargs = {}) %rsqrt_70 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_104,), kwargs = {}) %mul_157 : [num_users=1] = call_function[target=operator.mul](args = (%to_142, %rsqrt_70), kwargs = {}) %language_model_model_layers_17_self_attn_k_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.17.self_attn.k_norm.weight] %to_143 : [num_users=1] = call_method[target=to](args = (%mul_157, %getattr_665), kwargs = {}) %mul_158 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_17_self_attn_k_norm_weight, %to_143), kwargs = {}) %transpose_69 : [num_users=1] = call_method[target=transpose](args = (%mul_158, 1, 2), kwargs = {}) %language_model_model_layers_17_self_attn_v_proj : [num_users=1] = call_module[target=language_model.model.layers.17.self_attn.v_proj](args = (%mul_154,), kwargs = {}) %view_53 : [num_users=1] = call_method[target=view](args = (%language_model_model_layers_17_self_attn_v_proj, (1, 29, -1, 128)), kwargs = {}) %transpose_70 : [num_users=1] = call_method[target=transpose](args = (%view_53, 1, 2), kwargs = {}) %apply_rotary_pos_emb_17 : [num_users=2] = call_function[target=transformers.models.qwen3.modeling_qwen3.apply_rotary_pos_emb](args = (%transpose_68, %transpose_69, %to, %to_1), kwargs = {}) %getitem_104 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_17, 0), kwargs = {}) %getitem_105 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_17, 1), kwargs = {}) %update_17 : [num_users=2] = call_method[target=update](args = (%past_key_values, %getitem_105, %transpose_70, 17, {sin: %to_1, cos: %to, cache_position: %cache_position}), kwargs = {}) %getitem_106 : [num_users=1] = call_function[target=operator.getitem](args = (%update_17, 0), kwargs = {}) %getitem_107 : [num_users=1] = call_function[target=operator.getitem](args = (%update_17, 1), kwargs = {}) %getitem_108 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_106, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_34 : [num_users=1] = call_method[target=expand](args = (%getitem_108, 1, 8, 2, 29, 128), kwargs = {}) %reshape_53 : [num_users=1] = call_method[target=reshape](args = (%expand_34, 1, 16, 29, 128), kwargs = {}) %getitem_109 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_107, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_35 : [num_users=1] = call_method[target=expand](args = (%getitem_109, 1, 8, 2, 29, 128), kwargs = {}) %reshape_54 : [num_users=1] = call_method[target=reshape](args = (%expand_35, 1, 16, 29, 128), kwargs = {}) %scaled_dot_product_attention_17 : [num_users=1] = call_function[target=torch._C._nn.scaled_dot_product_attention](args = (%getitem_104, %reshape_53, %reshape_54), kwargs = {attn_mask: None, dropout_p: 0.0, scale: 0.08838834764831845, is_causal: True}) %transpose_71 : [num_users=1] = call_method[target=transpose](args = (%scaled_dot_product_attention_17, 1, 2), kwargs = {}) %contiguous_34 : [num_users=1] = call_method[target=contiguous](args = (%transpose_71,), kwargs = {}) %reshape_55 : [num_users=1] = call_method[target=reshape](args = (%contiguous_34, 1, 29, -1), kwargs = {}) %contiguous_35 : [num_users=1] = call_method[target=contiguous](args = (%reshape_55,), kwargs = {}) %language_model_model_layers_17_self_attn_o_proj : [num_users=1] = call_module[target=language_model.model.layers.17.self_attn.o_proj](args = (%contiguous_35,), kwargs = {}) %add_105 : [num_users=3] = call_function[target=operator.add](args = (%add_101, %language_model_model_layers_17_self_attn_o_proj), kwargs = {}) %getattr_684 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_105, dtype), kwargs = {}) %to_144 : [num_users=2] = call_method[target=to](args = (%add_105, torch.float32), kwargs = {}) %pow_72 : [num_users=1] = call_method[target=pow](args = (%to_144, 2), kwargs = {}) %mean_71 : [num_users=1] = call_method[target=mean](args = (%pow_72, -1), kwargs = {keepdim: True}) %add_106 : [num_users=1] = call_function[target=operator.add](args = (%mean_71, 1e-06), kwargs = {}) %rsqrt_71 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_106,), kwargs = {}) %mul_159 : [num_users=1] = call_function[target=operator.mul](args = (%to_144, %rsqrt_71), kwargs = {}) %language_model_model_layers_17_post_attention_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.17.post_attention_layernorm.weight] %to_145 : [num_users=1] = call_method[target=to](args = (%mul_159, %getattr_684), kwargs = {}) %mul_160 : [num_users=2] = call_function[target=operator.mul](args = (%language_model_model_layers_17_post_attention_layernorm_weight, %to_145), kwargs = {}) %language_model_model_layers_17_mlp_gate_proj : [num_users=1] = call_module[target=language_model.model.layers.17.mlp.gate_proj](args = (%mul_160,), kwargs = {}) %silu_17 : [num_users=1] = call_function[target=torch.nn.functional.silu](args = (%language_model_model_layers_17_mlp_gate_proj,), kwargs = {inplace: False}) %language_model_model_layers_17_mlp_up_proj : [num_users=1] = call_module[target=language_model.model.layers.17.mlp.up_proj](args = (%mul_160,), kwargs = {}) %mul_161 : [num_users=1] = call_function[target=operator.mul](args = (%silu_17, %language_model_model_layers_17_mlp_up_proj), kwargs = {}) %language_model_model_layers_17_mlp_down_proj : [num_users=1] = call_module[target=language_model.model.layers.17.mlp.down_proj](args = (%mul_161,), kwargs = {}) %add_107 : [num_users=3] = call_function[target=operator.add](args = (%add_105, %language_model_model_layers_17_mlp_down_proj), kwargs = {}) %getattr_689 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_107, dtype), kwargs = {}) %to_146 : [num_users=2] = call_method[target=to](args = (%add_107, torch.float32), kwargs = {}) %pow_73 : [num_users=1] = call_method[target=pow](args = (%to_146, 2), kwargs = {}) %mean_72 : [num_users=1] = call_method[target=mean](args = (%pow_73, -1), kwargs = {keepdim: True}) %add_108 : [num_users=1] = call_function[target=operator.add](args = (%mean_72, 1e-06), kwargs = {}) %rsqrt_72 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_108,), kwargs = {}) %mul_162 : [num_users=1] = call_function[target=operator.mul](args = (%to_146, %rsqrt_72), kwargs = {}) %language_model_model_layers_18_input_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.18.input_layernorm.weight] %to_147 : [num_users=1] = call_method[target=to](args = (%mul_162, %getattr_689), kwargs = {}) %mul_163 : [num_users=3] = call_function[target=operator.mul](args = (%language_model_model_layers_18_input_layernorm_weight, %to_147), kwargs = {}) %language_model_model_layers_18_self_attn_q_proj : [num_users=1] = call_module[target=language_model.model.layers.18.self_attn.q_proj](args = (%mul_163,), kwargs = {}) %view_54 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_18_self_attn_q_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_696 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_54, dtype), kwargs = {}) %to_148 : [num_users=2] = call_method[target=to](args = (%view_54, torch.float32), kwargs = {}) %pow_74 : [num_users=1] = call_method[target=pow](args = (%to_148, 2), kwargs = {}) %mean_73 : [num_users=1] = call_method[target=mean](args = (%pow_74, -1), kwargs = {keepdim: True}) %add_109 : [num_users=1] = call_function[target=operator.add](args = (%mean_73, 1e-06), kwargs = {}) %rsqrt_73 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_109,), kwargs = {}) %mul_164 : [num_users=1] = call_function[target=operator.mul](args = (%to_148, %rsqrt_73), kwargs = {}) %language_model_model_layers_18_self_attn_q_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.18.self_attn.q_norm.weight] %to_149 : [num_users=1] = call_method[target=to](args = (%mul_164, %getattr_696), kwargs = {}) %mul_165 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_18_self_attn_q_norm_weight, %to_149), kwargs = {}) %transpose_72 : [num_users=1] = call_method[target=transpose](args = (%mul_165, 1, 2), kwargs = {}) %language_model_model_layers_18_self_attn_k_proj : [num_users=1] = call_module[target=language_model.model.layers.18.self_attn.k_proj](args = (%mul_163,), kwargs = {}) %view_55 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_18_self_attn_k_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_703 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_55, dtype), kwargs = {}) %to_150 : [num_users=2] = call_method[target=to](args = (%view_55, torch.float32), kwargs = {}) %pow_75 : [num_users=1] = call_method[target=pow](args = (%to_150, 2), kwargs = {}) %mean_74 : [num_users=1] = call_method[target=mean](args = (%pow_75, -1), kwargs = {keepdim: True}) %add_110 : [num_users=1] = call_function[target=operator.add](args = (%mean_74, 1e-06), kwargs = {}) %rsqrt_74 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_110,), kwargs = {}) %mul_166 : [num_users=1] = call_function[target=operator.mul](args = (%to_150, %rsqrt_74), kwargs = {}) %language_model_model_layers_18_self_attn_k_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.18.self_attn.k_norm.weight] %to_151 : [num_users=1] = call_method[target=to](args = (%mul_166, %getattr_703), kwargs = {}) %mul_167 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_18_self_attn_k_norm_weight, %to_151), kwargs = {}) %transpose_73 : [num_users=1] = call_method[target=transpose](args = (%mul_167, 1, 2), kwargs = {}) %language_model_model_layers_18_self_attn_v_proj : [num_users=1] = call_module[target=language_model.model.layers.18.self_attn.v_proj](args = (%mul_163,), kwargs = {}) %view_56 : [num_users=1] = call_method[target=view](args = (%language_model_model_layers_18_self_attn_v_proj, (1, 29, -1, 128)), kwargs = {}) %transpose_74 : [num_users=1] = call_method[target=transpose](args = (%view_56, 1, 2), kwargs = {}) %apply_rotary_pos_emb_18 : [num_users=2] = call_function[target=transformers.models.qwen3.modeling_qwen3.apply_rotary_pos_emb](args = (%transpose_72, %transpose_73, %to, %to_1), kwargs = {}) %getitem_110 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_18, 0), kwargs = {}) %getitem_111 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_18, 1), kwargs = {}) %update_18 : [num_users=2] = call_method[target=update](args = (%past_key_values, %getitem_111, %transpose_74, 18, {sin: %to_1, cos: %to, cache_position: %cache_position}), kwargs = {}) %getitem_112 : [num_users=1] = call_function[target=operator.getitem](args = (%update_18, 0), kwargs = {}) %getitem_113 : [num_users=1] = call_function[target=operator.getitem](args = (%update_18, 1), kwargs = {}) %getitem_114 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_112, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_36 : [num_users=1] = call_method[target=expand](args = (%getitem_114, 1, 8, 2, 29, 128), kwargs = {}) %reshape_56 : [num_users=1] = call_method[target=reshape](args = (%expand_36, 1, 16, 29, 128), kwargs = {}) %getitem_115 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_113, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_37 : [num_users=1] = call_method[target=expand](args = (%getitem_115, 1, 8, 2, 29, 128), kwargs = {}) %reshape_57 : [num_users=1] = call_method[target=reshape](args = (%expand_37, 1, 16, 29, 128), kwargs = {}) %scaled_dot_product_attention_18 : [num_users=1] = call_function[target=torch._C._nn.scaled_dot_product_attention](args = (%getitem_110, %reshape_56, %reshape_57), kwargs = {attn_mask: None, dropout_p: 0.0, scale: 0.08838834764831845, is_causal: True}) %transpose_75 : [num_users=1] = call_method[target=transpose](args = (%scaled_dot_product_attention_18, 1, 2), kwargs = {}) %contiguous_36 : [num_users=1] = call_method[target=contiguous](args = (%transpose_75,), kwargs = {}) %reshape_58 : [num_users=1] = call_method[target=reshape](args = (%contiguous_36, 1, 29, -1), kwargs = {}) %contiguous_37 : [num_users=1] = call_method[target=contiguous](args = (%reshape_58,), kwargs = {}) %language_model_model_layers_18_self_attn_o_proj : [num_users=1] = call_module[target=language_model.model.layers.18.self_attn.o_proj](args = (%contiguous_37,), kwargs = {}) %add_111 : [num_users=3] = call_function[target=operator.add](args = (%add_107, %language_model_model_layers_18_self_attn_o_proj), kwargs = {}) %getattr_722 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_111, dtype), kwargs = {}) %to_152 : [num_users=2] = call_method[target=to](args = (%add_111, torch.float32), kwargs = {}) %pow_76 : [num_users=1] = call_method[target=pow](args = (%to_152, 2), kwargs = {}) %mean_75 : [num_users=1] = call_method[target=mean](args = (%pow_76, -1), kwargs = {keepdim: True}) %add_112 : [num_users=1] = call_function[target=operator.add](args = (%mean_75, 1e-06), kwargs = {}) %rsqrt_75 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_112,), kwargs = {}) %mul_168 : [num_users=1] = call_function[target=operator.mul](args = (%to_152, %rsqrt_75), kwargs = {}) %language_model_model_layers_18_post_attention_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.18.post_attention_layernorm.weight] %to_153 : [num_users=1] = call_method[target=to](args = (%mul_168, %getattr_722), kwargs = {}) %mul_169 : [num_users=2] = call_function[target=operator.mul](args = (%language_model_model_layers_18_post_attention_layernorm_weight, %to_153), kwargs = {}) %language_model_model_layers_18_mlp_gate_proj : [num_users=1] = call_module[target=language_model.model.layers.18.mlp.gate_proj](args = (%mul_169,), kwargs = {}) %silu_18 : [num_users=1] = call_function[target=torch.nn.functional.silu](args = (%language_model_model_layers_18_mlp_gate_proj,), kwargs = {inplace: False}) %language_model_model_layers_18_mlp_up_proj : [num_users=1] = call_module[target=language_model.model.layers.18.mlp.up_proj](args = (%mul_169,), kwargs = {}) %mul_170 : [num_users=1] = call_function[target=operator.mul](args = (%silu_18, %language_model_model_layers_18_mlp_up_proj), kwargs = {}) %language_model_model_layers_18_mlp_down_proj : [num_users=1] = call_module[target=language_model.model.layers.18.mlp.down_proj](args = (%mul_170,), kwargs = {}) %add_113 : [num_users=3] = call_function[target=operator.add](args = (%add_111, %language_model_model_layers_18_mlp_down_proj), kwargs = {}) %getattr_727 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_113, dtype), kwargs = {}) %to_154 : [num_users=2] = call_method[target=to](args = (%add_113, torch.float32), kwargs = {}) %pow_77 : [num_users=1] = call_method[target=pow](args = (%to_154, 2), kwargs = {}) %mean_76 : [num_users=1] = call_method[target=mean](args = (%pow_77, -1), kwargs = {keepdim: True}) %add_114 : [num_users=1] = call_function[target=operator.add](args = (%mean_76, 1e-06), kwargs = {}) %rsqrt_76 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_114,), kwargs = {}) %mul_171 : [num_users=1] = call_function[target=operator.mul](args = (%to_154, %rsqrt_76), kwargs = {}) %language_model_model_layers_19_input_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.19.input_layernorm.weight] %to_155 : [num_users=1] = call_method[target=to](args = (%mul_171, %getattr_727), kwargs = {}) %mul_172 : [num_users=3] = call_function[target=operator.mul](args = (%language_model_model_layers_19_input_layernorm_weight, %to_155), kwargs = {}) %language_model_model_layers_19_self_attn_q_proj : [num_users=1] = call_module[target=language_model.model.layers.19.self_attn.q_proj](args = (%mul_172,), kwargs = {}) %view_57 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_19_self_attn_q_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_734 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_57, dtype), kwargs = {}) %to_156 : [num_users=2] = call_method[target=to](args = (%view_57, torch.float32), kwargs = {}) %pow_78 : [num_users=1] = call_method[target=pow](args = (%to_156, 2), kwargs = {}) %mean_77 : [num_users=1] = call_method[target=mean](args = (%pow_78, -1), kwargs = {keepdim: True}) %add_115 : [num_users=1] = call_function[target=operator.add](args = (%mean_77, 1e-06), kwargs = {}) %rsqrt_77 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_115,), kwargs = {}) %mul_173 : [num_users=1] = call_function[target=operator.mul](args = (%to_156, %rsqrt_77), kwargs = {}) %language_model_model_layers_19_self_attn_q_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.19.self_attn.q_norm.weight] %to_157 : [num_users=1] = call_method[target=to](args = (%mul_173, %getattr_734), kwargs = {}) %mul_174 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_19_self_attn_q_norm_weight, %to_157), kwargs = {}) %transpose_76 : [num_users=1] = call_method[target=transpose](args = (%mul_174, 1, 2), kwargs = {}) %language_model_model_layers_19_self_attn_k_proj : [num_users=1] = call_module[target=language_model.model.layers.19.self_attn.k_proj](args = (%mul_172,), kwargs = {}) %view_58 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_19_self_attn_k_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_741 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_58, dtype), kwargs = {}) %to_158 : [num_users=2] = call_method[target=to](args = (%view_58, torch.float32), kwargs = {}) %pow_79 : [num_users=1] = call_method[target=pow](args = (%to_158, 2), kwargs = {}) %mean_78 : [num_users=1] = call_method[target=mean](args = (%pow_79, -1), kwargs = {keepdim: True}) %add_116 : [num_users=1] = call_function[target=operator.add](args = (%mean_78, 1e-06), kwargs = {}) %rsqrt_78 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_116,), kwargs = {}) %mul_175 : [num_users=1] = call_function[target=operator.mul](args = (%to_158, %rsqrt_78), kwargs = {}) %language_model_model_layers_19_self_attn_k_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.19.self_attn.k_norm.weight] %to_159 : [num_users=1] = call_method[target=to](args = (%mul_175, %getattr_741), kwargs = {}) %mul_176 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_19_self_attn_k_norm_weight, %to_159), kwargs = {}) %transpose_77 : [num_users=1] = call_method[target=transpose](args = (%mul_176, 1, 2), kwargs = {}) %language_model_model_layers_19_self_attn_v_proj : [num_users=1] = call_module[target=language_model.model.layers.19.self_attn.v_proj](args = (%mul_172,), kwargs = {}) %view_59 : [num_users=1] = call_method[target=view](args = (%language_model_model_layers_19_self_attn_v_proj, (1, 29, -1, 128)), kwargs = {}) %transpose_78 : [num_users=1] = call_method[target=transpose](args = (%view_59, 1, 2), kwargs = {}) %apply_rotary_pos_emb_19 : [num_users=2] = call_function[target=transformers.models.qwen3.modeling_qwen3.apply_rotary_pos_emb](args = (%transpose_76, %transpose_77, %to, %to_1), kwargs = {}) %getitem_116 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_19, 0), kwargs = {}) %getitem_117 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_19, 1), kwargs = {}) %update_19 : [num_users=2] = call_method[target=update](args = (%past_key_values, %getitem_117, %transpose_78, 19, {sin: %to_1, cos: %to, cache_position: %cache_position}), kwargs = {}) %getitem_118 : [num_users=1] = call_function[target=operator.getitem](args = (%update_19, 0), kwargs = {}) %getitem_119 : [num_users=1] = call_function[target=operator.getitem](args = (%update_19, 1), kwargs = {}) %getitem_120 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_118, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_38 : [num_users=1] = call_method[target=expand](args = (%getitem_120, 1, 8, 2, 29, 128), kwargs = {}) %reshape_59 : [num_users=1] = call_method[target=reshape](args = (%expand_38, 1, 16, 29, 128), kwargs = {}) %getitem_121 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_119, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_39 : [num_users=1] = call_method[target=expand](args = (%getitem_121, 1, 8, 2, 29, 128), kwargs = {}) %reshape_60 : [num_users=1] = call_method[target=reshape](args = (%expand_39, 1, 16, 29, 128), kwargs = {}) %scaled_dot_product_attention_19 : [num_users=1] = call_function[target=torch._C._nn.scaled_dot_product_attention](args = (%getitem_116, %reshape_59, %reshape_60), kwargs = {attn_mask: None, dropout_p: 0.0, scale: 0.08838834764831845, is_causal: True}) %transpose_79 : [num_users=1] = call_method[target=transpose](args = (%scaled_dot_product_attention_19, 1, 2), kwargs = {}) %contiguous_38 : [num_users=1] = call_method[target=contiguous](args = (%transpose_79,), kwargs = {}) %reshape_61 : [num_users=1] = call_method[target=reshape](args = (%contiguous_38, 1, 29, -1), kwargs = {}) %contiguous_39 : [num_users=1] = call_method[target=contiguous](args = (%reshape_61,), kwargs = {}) %language_model_model_layers_19_self_attn_o_proj : [num_users=1] = call_module[target=language_model.model.layers.19.self_attn.o_proj](args = (%contiguous_39,), kwargs = {}) %add_117 : [num_users=3] = call_function[target=operator.add](args = (%add_113, %language_model_model_layers_19_self_attn_o_proj), kwargs = {}) %getattr_760 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_117, dtype), kwargs = {}) %to_160 : [num_users=2] = call_method[target=to](args = (%add_117, torch.float32), kwargs = {}) %pow_80 : [num_users=1] = call_method[target=pow](args = (%to_160, 2), kwargs = {}) %mean_79 : [num_users=1] = call_method[target=mean](args = (%pow_80, -1), kwargs = {keepdim: True}) %add_118 : [num_users=1] = call_function[target=operator.add](args = (%mean_79, 1e-06), kwargs = {}) %rsqrt_79 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_118,), kwargs = {}) %mul_177 : [num_users=1] = call_function[target=operator.mul](args = (%to_160, %rsqrt_79), kwargs = {}) %language_model_model_layers_19_post_attention_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.19.post_attention_layernorm.weight] %to_161 : [num_users=1] = call_method[target=to](args = (%mul_177, %getattr_760), kwargs = {}) %mul_178 : [num_users=2] = call_function[target=operator.mul](args = (%language_model_model_layers_19_post_attention_layernorm_weight, %to_161), kwargs = {}) %language_model_model_layers_19_mlp_gate_proj : [num_users=1] = call_module[target=language_model.model.layers.19.mlp.gate_proj](args = (%mul_178,), kwargs = {}) %silu_19 : [num_users=1] = call_function[target=torch.nn.functional.silu](args = (%language_model_model_layers_19_mlp_gate_proj,), kwargs = {inplace: False}) %language_model_model_layers_19_mlp_up_proj : [num_users=1] = call_module[target=language_model.model.layers.19.mlp.up_proj](args = (%mul_178,), kwargs = {}) %mul_179 : [num_users=1] = call_function[target=operator.mul](args = (%silu_19, %language_model_model_layers_19_mlp_up_proj), kwargs = {}) %language_model_model_layers_19_mlp_down_proj : [num_users=1] = call_module[target=language_model.model.layers.19.mlp.down_proj](args = (%mul_179,), kwargs = {}) %add_119 : [num_users=3] = call_function[target=operator.add](args = (%add_117, %language_model_model_layers_19_mlp_down_proj), kwargs = {}) %getattr_765 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_119, dtype), kwargs = {}) %to_162 : [num_users=2] = call_method[target=to](args = (%add_119, torch.float32), kwargs = {}) %pow_81 : [num_users=1] = call_method[target=pow](args = (%to_162, 2), kwargs = {}) %mean_80 : [num_users=1] = call_method[target=mean](args = (%pow_81, -1), kwargs = {keepdim: True}) %add_120 : [num_users=1] = call_function[target=operator.add](args = (%mean_80, 1e-06), kwargs = {}) %rsqrt_80 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_120,), kwargs = {}) %mul_180 : [num_users=1] = call_function[target=operator.mul](args = (%to_162, %rsqrt_80), kwargs = {}) %language_model_model_layers_20_input_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.20.input_layernorm.weight] %to_163 : [num_users=1] = call_method[target=to](args = (%mul_180, %getattr_765), kwargs = {}) %mul_181 : [num_users=3] = call_function[target=operator.mul](args = (%language_model_model_layers_20_input_layernorm_weight, %to_163), kwargs = {}) %language_model_model_layers_20_self_attn_q_proj : [num_users=1] = call_module[target=language_model.model.layers.20.self_attn.q_proj](args = (%mul_181,), kwargs = {}) %view_60 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_20_self_attn_q_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_772 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_60, dtype), kwargs = {}) %to_164 : [num_users=2] = call_method[target=to](args = (%view_60, torch.float32), kwargs = {}) %pow_82 : [num_users=1] = call_method[target=pow](args = (%to_164, 2), kwargs = {}) %mean_81 : [num_users=1] = call_method[target=mean](args = (%pow_82, -1), kwargs = {keepdim: True}) %add_121 : [num_users=1] = call_function[target=operator.add](args = (%mean_81, 1e-06), kwargs = {}) %rsqrt_81 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_121,), kwargs = {}) %mul_182 : [num_users=1] = call_function[target=operator.mul](args = (%to_164, %rsqrt_81), kwargs = {}) %language_model_model_layers_20_self_attn_q_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.20.self_attn.q_norm.weight] %to_165 : [num_users=1] = call_method[target=to](args = (%mul_182, %getattr_772), kwargs = {}) %mul_183 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_20_self_attn_q_norm_weight, %to_165), kwargs = {}) %transpose_80 : [num_users=1] = call_method[target=transpose](args = (%mul_183, 1, 2), kwargs = {}) %language_model_model_layers_20_self_attn_k_proj : [num_users=1] = call_module[target=language_model.model.layers.20.self_attn.k_proj](args = (%mul_181,), kwargs = {}) %view_61 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_20_self_attn_k_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_779 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_61, dtype), kwargs = {}) %to_166 : [num_users=2] = call_method[target=to](args = (%view_61, torch.float32), kwargs = {}) %pow_83 : [num_users=1] = call_method[target=pow](args = (%to_166, 2), kwargs = {}) %mean_82 : [num_users=1] = call_method[target=mean](args = (%pow_83, -1), kwargs = {keepdim: True}) %add_122 : [num_users=1] = call_function[target=operator.add](args = (%mean_82, 1e-06), kwargs = {}) %rsqrt_82 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_122,), kwargs = {}) %mul_184 : [num_users=1] = call_function[target=operator.mul](args = (%to_166, %rsqrt_82), kwargs = {}) %language_model_model_layers_20_self_attn_k_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.20.self_attn.k_norm.weight] %to_167 : [num_users=1] = call_method[target=to](args = (%mul_184, %getattr_779), kwargs = {}) %mul_185 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_20_self_attn_k_norm_weight, %to_167), kwargs = {}) %transpose_81 : [num_users=1] = call_method[target=transpose](args = (%mul_185, 1, 2), kwargs = {}) %language_model_model_layers_20_self_attn_v_proj : [num_users=1] = call_module[target=language_model.model.layers.20.self_attn.v_proj](args = (%mul_181,), kwargs = {}) %view_62 : [num_users=1] = call_method[target=view](args = (%language_model_model_layers_20_self_attn_v_proj, (1, 29, -1, 128)), kwargs = {}) %transpose_82 : [num_users=1] = call_method[target=transpose](args = (%view_62, 1, 2), kwargs = {}) %apply_rotary_pos_emb_20 : [num_users=2] = call_function[target=transformers.models.qwen3.modeling_qwen3.apply_rotary_pos_emb](args = (%transpose_80, %transpose_81, %to, %to_1), kwargs = {}) %getitem_122 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_20, 0), kwargs = {}) %getitem_123 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_20, 1), kwargs = {}) %update_20 : [num_users=2] = call_method[target=update](args = (%past_key_values, %getitem_123, %transpose_82, 20, {sin: %to_1, cos: %to, cache_position: %cache_position}), kwargs = {}) %getitem_124 : [num_users=1] = call_function[target=operator.getitem](args = (%update_20, 0), kwargs = {}) %getitem_125 : [num_users=1] = call_function[target=operator.getitem](args = (%update_20, 1), kwargs = {}) %getitem_126 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_124, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_40 : [num_users=1] = call_method[target=expand](args = (%getitem_126, 1, 8, 2, 29, 128), kwargs = {}) %reshape_62 : [num_users=1] = call_method[target=reshape](args = (%expand_40, 1, 16, 29, 128), kwargs = {}) %getitem_127 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_125, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_41 : [num_users=1] = call_method[target=expand](args = (%getitem_127, 1, 8, 2, 29, 128), kwargs = {}) %reshape_63 : [num_users=1] = call_method[target=reshape](args = (%expand_41, 1, 16, 29, 128), kwargs = {}) %scaled_dot_product_attention_20 : [num_users=1] = call_function[target=torch._C._nn.scaled_dot_product_attention](args = (%getitem_122, %reshape_62, %reshape_63), kwargs = {attn_mask: None, dropout_p: 0.0, scale: 0.08838834764831845, is_causal: True}) %transpose_83 : [num_users=1] = call_method[target=transpose](args = (%scaled_dot_product_attention_20, 1, 2), kwargs = {}) %contiguous_40 : [num_users=1] = call_method[target=contiguous](args = (%transpose_83,), kwargs = {}) %reshape_64 : [num_users=1] = call_method[target=reshape](args = (%contiguous_40, 1, 29, -1), kwargs = {}) %contiguous_41 : [num_users=1] = call_method[target=contiguous](args = (%reshape_64,), kwargs = {}) %language_model_model_layers_20_self_attn_o_proj : [num_users=1] = call_module[target=language_model.model.layers.20.self_attn.o_proj](args = (%contiguous_41,), kwargs = {}) %add_123 : [num_users=3] = call_function[target=operator.add](args = (%add_119, %language_model_model_layers_20_self_attn_o_proj), kwargs = {}) %getattr_798 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_123, dtype), kwargs = {}) %to_168 : [num_users=2] = call_method[target=to](args = (%add_123, torch.float32), kwargs = {}) %pow_84 : [num_users=1] = call_method[target=pow](args = (%to_168, 2), kwargs = {}) %mean_83 : [num_users=1] = call_method[target=mean](args = (%pow_84, -1), kwargs = {keepdim: True}) %add_124 : [num_users=1] = call_function[target=operator.add](args = (%mean_83, 1e-06), kwargs = {}) %rsqrt_83 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_124,), kwargs = {}) %mul_186 : [num_users=1] = call_function[target=operator.mul](args = (%to_168, %rsqrt_83), kwargs = {}) %language_model_model_layers_20_post_attention_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.20.post_attention_layernorm.weight] %to_169 : [num_users=1] = call_method[target=to](args = (%mul_186, %getattr_798), kwargs = {}) %mul_187 : [num_users=2] = call_function[target=operator.mul](args = (%language_model_model_layers_20_post_attention_layernorm_weight, %to_169), kwargs = {}) %language_model_model_layers_20_mlp_gate_proj : [num_users=1] = call_module[target=language_model.model.layers.20.mlp.gate_proj](args = (%mul_187,), kwargs = {}) %silu_20 : [num_users=1] = call_function[target=torch.nn.functional.silu](args = (%language_model_model_layers_20_mlp_gate_proj,), kwargs = {inplace: False}) %language_model_model_layers_20_mlp_up_proj : [num_users=1] = call_module[target=language_model.model.layers.20.mlp.up_proj](args = (%mul_187,), kwargs = {}) %mul_188 : [num_users=1] = call_function[target=operator.mul](args = (%silu_20, %language_model_model_layers_20_mlp_up_proj), kwargs = {}) %language_model_model_layers_20_mlp_down_proj : [num_users=1] = call_module[target=language_model.model.layers.20.mlp.down_proj](args = (%mul_188,), kwargs = {}) %add_125 : [num_users=3] = call_function[target=operator.add](args = (%add_123, %language_model_model_layers_20_mlp_down_proj), kwargs = {}) %getattr_803 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_125, dtype), kwargs = {}) %to_170 : [num_users=2] = call_method[target=to](args = (%add_125, torch.float32), kwargs = {}) %pow_85 : [num_users=1] = call_method[target=pow](args = (%to_170, 2), kwargs = {}) %mean_84 : [num_users=1] = call_method[target=mean](args = (%pow_85, -1), kwargs = {keepdim: True}) %add_126 : [num_users=1] = call_function[target=operator.add](args = (%mean_84, 1e-06), kwargs = {}) %rsqrt_84 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_126,), kwargs = {}) %mul_189 : [num_users=1] = call_function[target=operator.mul](args = (%to_170, %rsqrt_84), kwargs = {}) %language_model_model_layers_21_input_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.21.input_layernorm.weight] %to_171 : [num_users=1] = call_method[target=to](args = (%mul_189, %getattr_803), kwargs = {}) %mul_190 : [num_users=3] = call_function[target=operator.mul](args = (%language_model_model_layers_21_input_layernorm_weight, %to_171), kwargs = {}) %language_model_model_layers_21_self_attn_q_proj : [num_users=1] = call_module[target=language_model.model.layers.21.self_attn.q_proj](args = (%mul_190,), kwargs = {}) %view_63 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_21_self_attn_q_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_810 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_63, dtype), kwargs = {}) %to_172 : [num_users=2] = call_method[target=to](args = (%view_63, torch.float32), kwargs = {}) %pow_86 : [num_users=1] = call_method[target=pow](args = (%to_172, 2), kwargs = {}) %mean_85 : [num_users=1] = call_method[target=mean](args = (%pow_86, -1), kwargs = {keepdim: True}) %add_127 : [num_users=1] = call_function[target=operator.add](args = (%mean_85, 1e-06), kwargs = {}) %rsqrt_85 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_127,), kwargs = {}) %mul_191 : [num_users=1] = call_function[target=operator.mul](args = (%to_172, %rsqrt_85), kwargs = {}) %language_model_model_layers_21_self_attn_q_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.21.self_attn.q_norm.weight] %to_173 : [num_users=1] = call_method[target=to](args = (%mul_191, %getattr_810), kwargs = {}) %mul_192 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_21_self_attn_q_norm_weight, %to_173), kwargs = {}) %transpose_84 : [num_users=1] = call_method[target=transpose](args = (%mul_192, 1, 2), kwargs = {}) %language_model_model_layers_21_self_attn_k_proj : [num_users=1] = call_module[target=language_model.model.layers.21.self_attn.k_proj](args = (%mul_190,), kwargs = {}) %view_64 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_21_self_attn_k_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_817 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_64, dtype), kwargs = {}) %to_174 : [num_users=2] = call_method[target=to](args = (%view_64, torch.float32), kwargs = {}) %pow_87 : [num_users=1] = call_method[target=pow](args = (%to_174, 2), kwargs = {}) %mean_86 : [num_users=1] = call_method[target=mean](args = (%pow_87, -1), kwargs = {keepdim: True}) %add_128 : [num_users=1] = call_function[target=operator.add](args = (%mean_86, 1e-06), kwargs = {}) %rsqrt_86 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_128,), kwargs = {}) %mul_193 : [num_users=1] = call_function[target=operator.mul](args = (%to_174, %rsqrt_86), kwargs = {}) %language_model_model_layers_21_self_attn_k_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.21.self_attn.k_norm.weight] %to_175 : [num_users=1] = call_method[target=to](args = (%mul_193, %getattr_817), kwargs = {}) %mul_194 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_21_self_attn_k_norm_weight, %to_175), kwargs = {}) %transpose_85 : [num_users=1] = call_method[target=transpose](args = (%mul_194, 1, 2), kwargs = {}) %language_model_model_layers_21_self_attn_v_proj : [num_users=1] = call_module[target=language_model.model.layers.21.self_attn.v_proj](args = (%mul_190,), kwargs = {}) %view_65 : [num_users=1] = call_method[target=view](args = (%language_model_model_layers_21_self_attn_v_proj, (1, 29, -1, 128)), kwargs = {}) %transpose_86 : [num_users=1] = call_method[target=transpose](args = (%view_65, 1, 2), kwargs = {}) %apply_rotary_pos_emb_21 : [num_users=2] = call_function[target=transformers.models.qwen3.modeling_qwen3.apply_rotary_pos_emb](args = (%transpose_84, %transpose_85, %to, %to_1), kwargs = {}) %getitem_128 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_21, 0), kwargs = {}) %getitem_129 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_21, 1), kwargs = {}) %update_21 : [num_users=2] = call_method[target=update](args = (%past_key_values, %getitem_129, %transpose_86, 21, {sin: %to_1, cos: %to, cache_position: %cache_position}), kwargs = {}) %getitem_130 : [num_users=1] = call_function[target=operator.getitem](args = (%update_21, 0), kwargs = {}) %getitem_131 : [num_users=1] = call_function[target=operator.getitem](args = (%update_21, 1), kwargs = {}) %getitem_132 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_130, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_42 : [num_users=1] = call_method[target=expand](args = (%getitem_132, 1, 8, 2, 29, 128), kwargs = {}) %reshape_65 : [num_users=1] = call_method[target=reshape](args = (%expand_42, 1, 16, 29, 128), kwargs = {}) %getitem_133 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_131, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_43 : [num_users=1] = call_method[target=expand](args = (%getitem_133, 1, 8, 2, 29, 128), kwargs = {}) %reshape_66 : [num_users=1] = call_method[target=reshape](args = (%expand_43, 1, 16, 29, 128), kwargs = {}) %scaled_dot_product_attention_21 : [num_users=1] = call_function[target=torch._C._nn.scaled_dot_product_attention](args = (%getitem_128, %reshape_65, %reshape_66), kwargs = {attn_mask: None, dropout_p: 0.0, scale: 0.08838834764831845, is_causal: True}) %transpose_87 : [num_users=1] = call_method[target=transpose](args = (%scaled_dot_product_attention_21, 1, 2), kwargs = {}) %contiguous_42 : [num_users=1] = call_method[target=contiguous](args = (%transpose_87,), kwargs = {}) %reshape_67 : [num_users=1] = call_method[target=reshape](args = (%contiguous_42, 1, 29, -1), kwargs = {}) %contiguous_43 : [num_users=1] = call_method[target=contiguous](args = (%reshape_67,), kwargs = {}) %language_model_model_layers_21_self_attn_o_proj : [num_users=1] = call_module[target=language_model.model.layers.21.self_attn.o_proj](args = (%contiguous_43,), kwargs = {}) %add_129 : [num_users=3] = call_function[target=operator.add](args = (%add_125, %language_model_model_layers_21_self_attn_o_proj), kwargs = {}) %getattr_836 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_129, dtype), kwargs = {}) %to_176 : [num_users=2] = call_method[target=to](args = (%add_129, torch.float32), kwargs = {}) %pow_88 : [num_users=1] = call_method[target=pow](args = (%to_176, 2), kwargs = {}) %mean_87 : [num_users=1] = call_method[target=mean](args = (%pow_88, -1), kwargs = {keepdim: True}) %add_130 : [num_users=1] = call_function[target=operator.add](args = (%mean_87, 1e-06), kwargs = {}) %rsqrt_87 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_130,), kwargs = {}) %mul_195 : [num_users=1] = call_function[target=operator.mul](args = (%to_176, %rsqrt_87), kwargs = {}) %language_model_model_layers_21_post_attention_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.21.post_attention_layernorm.weight] %to_177 : [num_users=1] = call_method[target=to](args = (%mul_195, %getattr_836), kwargs = {}) %mul_196 : [num_users=2] = call_function[target=operator.mul](args = (%language_model_model_layers_21_post_attention_layernorm_weight, %to_177), kwargs = {}) %language_model_model_layers_21_mlp_gate_proj : [num_users=1] = call_module[target=language_model.model.layers.21.mlp.gate_proj](args = (%mul_196,), kwargs = {}) %silu_21 : [num_users=1] = call_function[target=torch.nn.functional.silu](args = (%language_model_model_layers_21_mlp_gate_proj,), kwargs = {inplace: False}) %language_model_model_layers_21_mlp_up_proj : [num_users=1] = call_module[target=language_model.model.layers.21.mlp.up_proj](args = (%mul_196,), kwargs = {}) %mul_197 : [num_users=1] = call_function[target=operator.mul](args = (%silu_21, %language_model_model_layers_21_mlp_up_proj), kwargs = {}) %language_model_model_layers_21_mlp_down_proj : [num_users=1] = call_module[target=language_model.model.layers.21.mlp.down_proj](args = (%mul_197,), kwargs = {}) %add_131 : [num_users=3] = call_function[target=operator.add](args = (%add_129, %language_model_model_layers_21_mlp_down_proj), kwargs = {}) %getattr_841 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_131, dtype), kwargs = {}) %to_178 : [num_users=2] = call_method[target=to](args = (%add_131, torch.float32), kwargs = {}) %pow_89 : [num_users=1] = call_method[target=pow](args = (%to_178, 2), kwargs = {}) %mean_88 : [num_users=1] = call_method[target=mean](args = (%pow_89, -1), kwargs = {keepdim: True}) %add_132 : [num_users=1] = call_function[target=operator.add](args = (%mean_88, 1e-06), kwargs = {}) %rsqrt_88 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_132,), kwargs = {}) %mul_198 : [num_users=1] = call_function[target=operator.mul](args = (%to_178, %rsqrt_88), kwargs = {}) %language_model_model_layers_22_input_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.22.input_layernorm.weight] %to_179 : [num_users=1] = call_method[target=to](args = (%mul_198, %getattr_841), kwargs = {}) %mul_199 : [num_users=3] = call_function[target=operator.mul](args = (%language_model_model_layers_22_input_layernorm_weight, %to_179), kwargs = {}) %language_model_model_layers_22_self_attn_q_proj : [num_users=1] = call_module[target=language_model.model.layers.22.self_attn.q_proj](args = (%mul_199,), kwargs = {}) %view_66 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_22_self_attn_q_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_848 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_66, dtype), kwargs = {}) %to_180 : [num_users=2] = call_method[target=to](args = (%view_66, torch.float32), kwargs = {}) %pow_90 : [num_users=1] = call_method[target=pow](args = (%to_180, 2), kwargs = {}) %mean_89 : [num_users=1] = call_method[target=mean](args = (%pow_90, -1), kwargs = {keepdim: True}) %add_133 : [num_users=1] = call_function[target=operator.add](args = (%mean_89, 1e-06), kwargs = {}) %rsqrt_89 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_133,), kwargs = {}) %mul_200 : [num_users=1] = call_function[target=operator.mul](args = (%to_180, %rsqrt_89), kwargs = {}) %language_model_model_layers_22_self_attn_q_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.22.self_attn.q_norm.weight] %to_181 : [num_users=1] = call_method[target=to](args = (%mul_200, %getattr_848), kwargs = {}) %mul_201 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_22_self_attn_q_norm_weight, %to_181), kwargs = {}) %transpose_88 : [num_users=1] = call_method[target=transpose](args = (%mul_201, 1, 2), kwargs = {}) %language_model_model_layers_22_self_attn_k_proj : [num_users=1] = call_module[target=language_model.model.layers.22.self_attn.k_proj](args = (%mul_199,), kwargs = {}) %view_67 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_22_self_attn_k_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_855 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_67, dtype), kwargs = {}) %to_182 : [num_users=2] = call_method[target=to](args = (%view_67, torch.float32), kwargs = {}) %pow_91 : [num_users=1] = call_method[target=pow](args = (%to_182, 2), kwargs = {}) %mean_90 : [num_users=1] = call_method[target=mean](args = (%pow_91, -1), kwargs = {keepdim: True}) %add_134 : [num_users=1] = call_function[target=operator.add](args = (%mean_90, 1e-06), kwargs = {}) %rsqrt_90 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_134,), kwargs = {}) %mul_202 : [num_users=1] = call_function[target=operator.mul](args = (%to_182, %rsqrt_90), kwargs = {}) %language_model_model_layers_22_self_attn_k_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.22.self_attn.k_norm.weight] %to_183 : [num_users=1] = call_method[target=to](args = (%mul_202, %getattr_855), kwargs = {}) %mul_203 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_22_self_attn_k_norm_weight, %to_183), kwargs = {}) %transpose_89 : [num_users=1] = call_method[target=transpose](args = (%mul_203, 1, 2), kwargs = {}) %language_model_model_layers_22_self_attn_v_proj : [num_users=1] = call_module[target=language_model.model.layers.22.self_attn.v_proj](args = (%mul_199,), kwargs = {}) %view_68 : [num_users=1] = call_method[target=view](args = (%language_model_model_layers_22_self_attn_v_proj, (1, 29, -1, 128)), kwargs = {}) %transpose_90 : [num_users=1] = call_method[target=transpose](args = (%view_68, 1, 2), kwargs = {}) %apply_rotary_pos_emb_22 : [num_users=2] = call_function[target=transformers.models.qwen3.modeling_qwen3.apply_rotary_pos_emb](args = (%transpose_88, %transpose_89, %to, %to_1), kwargs = {}) %getitem_134 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_22, 0), kwargs = {}) %getitem_135 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_22, 1), kwargs = {}) %update_22 : [num_users=2] = call_method[target=update](args = (%past_key_values, %getitem_135, %transpose_90, 22, {sin: %to_1, cos: %to, cache_position: %cache_position}), kwargs = {}) %getitem_136 : [num_users=1] = call_function[target=operator.getitem](args = (%update_22, 0), kwargs = {}) %getitem_137 : [num_users=1] = call_function[target=operator.getitem](args = (%update_22, 1), kwargs = {}) %getitem_138 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_136, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_44 : [num_users=1] = call_method[target=expand](args = (%getitem_138, 1, 8, 2, 29, 128), kwargs = {}) %reshape_68 : [num_users=1] = call_method[target=reshape](args = (%expand_44, 1, 16, 29, 128), kwargs = {}) %getitem_139 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_137, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_45 : [num_users=1] = call_method[target=expand](args = (%getitem_139, 1, 8, 2, 29, 128), kwargs = {}) %reshape_69 : [num_users=1] = call_method[target=reshape](args = (%expand_45, 1, 16, 29, 128), kwargs = {}) %scaled_dot_product_attention_22 : [num_users=1] = call_function[target=torch._C._nn.scaled_dot_product_attention](args = (%getitem_134, %reshape_68, %reshape_69), kwargs = {attn_mask: None, dropout_p: 0.0, scale: 0.08838834764831845, is_causal: True}) %transpose_91 : [num_users=1] = call_method[target=transpose](args = (%scaled_dot_product_attention_22, 1, 2), kwargs = {}) %contiguous_44 : [num_users=1] = call_method[target=contiguous](args = (%transpose_91,), kwargs = {}) %reshape_70 : [num_users=1] = call_method[target=reshape](args = (%contiguous_44, 1, 29, -1), kwargs = {}) %contiguous_45 : [num_users=1] = call_method[target=contiguous](args = (%reshape_70,), kwargs = {}) %language_model_model_layers_22_self_attn_o_proj : [num_users=1] = call_module[target=language_model.model.layers.22.self_attn.o_proj](args = (%contiguous_45,), kwargs = {}) %add_135 : [num_users=3] = call_function[target=operator.add](args = (%add_131, %language_model_model_layers_22_self_attn_o_proj), kwargs = {}) %getattr_874 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_135, dtype), kwargs = {}) %to_184 : [num_users=2] = call_method[target=to](args = (%add_135, torch.float32), kwargs = {}) %pow_92 : [num_users=1] = call_method[target=pow](args = (%to_184, 2), kwargs = {}) %mean_91 : [num_users=1] = call_method[target=mean](args = (%pow_92, -1), kwargs = {keepdim: True}) %add_136 : [num_users=1] = call_function[target=operator.add](args = (%mean_91, 1e-06), kwargs = {}) %rsqrt_91 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_136,), kwargs = {}) %mul_204 : [num_users=1] = call_function[target=operator.mul](args = (%to_184, %rsqrt_91), kwargs = {}) %language_model_model_layers_22_post_attention_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.22.post_attention_layernorm.weight] %to_185 : [num_users=1] = call_method[target=to](args = (%mul_204, %getattr_874), kwargs = {}) %mul_205 : [num_users=2] = call_function[target=operator.mul](args = (%language_model_model_layers_22_post_attention_layernorm_weight, %to_185), kwargs = {}) %language_model_model_layers_22_mlp_gate_proj : [num_users=1] = call_module[target=language_model.model.layers.22.mlp.gate_proj](args = (%mul_205,), kwargs = {}) %silu_22 : [num_users=1] = call_function[target=torch.nn.functional.silu](args = (%language_model_model_layers_22_mlp_gate_proj,), kwargs = {inplace: False}) %language_model_model_layers_22_mlp_up_proj : [num_users=1] = call_module[target=language_model.model.layers.22.mlp.up_proj](args = (%mul_205,), kwargs = {}) %mul_206 : [num_users=1] = call_function[target=operator.mul](args = (%silu_22, %language_model_model_layers_22_mlp_up_proj), kwargs = {}) %language_model_model_layers_22_mlp_down_proj : [num_users=1] = call_module[target=language_model.model.layers.22.mlp.down_proj](args = (%mul_206,), kwargs = {}) %add_137 : [num_users=3] = call_function[target=operator.add](args = (%add_135, %language_model_model_layers_22_mlp_down_proj), kwargs = {}) %getattr_879 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_137, dtype), kwargs = {}) %to_186 : [num_users=2] = call_method[target=to](args = (%add_137, torch.float32), kwargs = {}) %pow_93 : [num_users=1] = call_method[target=pow](args = (%to_186, 2), kwargs = {}) %mean_92 : [num_users=1] = call_method[target=mean](args = (%pow_93, -1), kwargs = {keepdim: True}) %add_138 : [num_users=1] = call_function[target=operator.add](args = (%mean_92, 1e-06), kwargs = {}) %rsqrt_92 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_138,), kwargs = {}) %mul_207 : [num_users=1] = call_function[target=operator.mul](args = (%to_186, %rsqrt_92), kwargs = {}) %language_model_model_layers_23_input_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.23.input_layernorm.weight] %to_187 : [num_users=1] = call_method[target=to](args = (%mul_207, %getattr_879), kwargs = {}) %mul_208 : [num_users=3] = call_function[target=operator.mul](args = (%language_model_model_layers_23_input_layernorm_weight, %to_187), kwargs = {}) %language_model_model_layers_23_self_attn_q_proj : [num_users=1] = call_module[target=language_model.model.layers.23.self_attn.q_proj](args = (%mul_208,), kwargs = {}) %view_69 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_23_self_attn_q_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_886 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_69, dtype), kwargs = {}) %to_188 : [num_users=2] = call_method[target=to](args = (%view_69, torch.float32), kwargs = {}) %pow_94 : [num_users=1] = call_method[target=pow](args = (%to_188, 2), kwargs = {}) %mean_93 : [num_users=1] = call_method[target=mean](args = (%pow_94, -1), kwargs = {keepdim: True}) %add_139 : [num_users=1] = call_function[target=operator.add](args = (%mean_93, 1e-06), kwargs = {}) %rsqrt_93 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_139,), kwargs = {}) %mul_209 : [num_users=1] = call_function[target=operator.mul](args = (%to_188, %rsqrt_93), kwargs = {}) %language_model_model_layers_23_self_attn_q_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.23.self_attn.q_norm.weight] %to_189 : [num_users=1] = call_method[target=to](args = (%mul_209, %getattr_886), kwargs = {}) %mul_210 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_23_self_attn_q_norm_weight, %to_189), kwargs = {}) %transpose_92 : [num_users=1] = call_method[target=transpose](args = (%mul_210, 1, 2), kwargs = {}) %language_model_model_layers_23_self_attn_k_proj : [num_users=1] = call_module[target=language_model.model.layers.23.self_attn.k_proj](args = (%mul_208,), kwargs = {}) %view_70 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_23_self_attn_k_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_893 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_70, dtype), kwargs = {}) %to_190 : [num_users=2] = call_method[target=to](args = (%view_70, torch.float32), kwargs = {}) %pow_95 : [num_users=1] = call_method[target=pow](args = (%to_190, 2), kwargs = {}) %mean_94 : [num_users=1] = call_method[target=mean](args = (%pow_95, -1), kwargs = {keepdim: True}) %add_140 : [num_users=1] = call_function[target=operator.add](args = (%mean_94, 1e-06), kwargs = {}) %rsqrt_94 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_140,), kwargs = {}) %mul_211 : [num_users=1] = call_function[target=operator.mul](args = (%to_190, %rsqrt_94), kwargs = {}) %language_model_model_layers_23_self_attn_k_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.23.self_attn.k_norm.weight] %to_191 : [num_users=1] = call_method[target=to](args = (%mul_211, %getattr_893), kwargs = {}) %mul_212 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_23_self_attn_k_norm_weight, %to_191), kwargs = {}) %transpose_93 : [num_users=1] = call_method[target=transpose](args = (%mul_212, 1, 2), kwargs = {}) %language_model_model_layers_23_self_attn_v_proj : [num_users=1] = call_module[target=language_model.model.layers.23.self_attn.v_proj](args = (%mul_208,), kwargs = {}) %view_71 : [num_users=1] = call_method[target=view](args = (%language_model_model_layers_23_self_attn_v_proj, (1, 29, -1, 128)), kwargs = {}) %transpose_94 : [num_users=1] = call_method[target=transpose](args = (%view_71, 1, 2), kwargs = {}) %apply_rotary_pos_emb_23 : [num_users=2] = call_function[target=transformers.models.qwen3.modeling_qwen3.apply_rotary_pos_emb](args = (%transpose_92, %transpose_93, %to, %to_1), kwargs = {}) %getitem_140 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_23, 0), kwargs = {}) %getitem_141 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_23, 1), kwargs = {}) %update_23 : [num_users=2] = call_method[target=update](args = (%past_key_values, %getitem_141, %transpose_94, 23, {sin: %to_1, cos: %to, cache_position: %cache_position}), kwargs = {}) %getitem_142 : [num_users=1] = call_function[target=operator.getitem](args = (%update_23, 0), kwargs = {}) %getitem_143 : [num_users=1] = call_function[target=operator.getitem](args = (%update_23, 1), kwargs = {}) %getitem_144 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_142, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_46 : [num_users=1] = call_method[target=expand](args = (%getitem_144, 1, 8, 2, 29, 128), kwargs = {}) %reshape_71 : [num_users=1] = call_method[target=reshape](args = (%expand_46, 1, 16, 29, 128), kwargs = {}) %getitem_145 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_143, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_47 : [num_users=1] = call_method[target=expand](args = (%getitem_145, 1, 8, 2, 29, 128), kwargs = {}) %reshape_72 : [num_users=1] = call_method[target=reshape](args = (%expand_47, 1, 16, 29, 128), kwargs = {}) %scaled_dot_product_attention_23 : [num_users=1] = call_function[target=torch._C._nn.scaled_dot_product_attention](args = (%getitem_140, %reshape_71, %reshape_72), kwargs = {attn_mask: None, dropout_p: 0.0, scale: 0.08838834764831845, is_causal: True}) %transpose_95 : [num_users=1] = call_method[target=transpose](args = (%scaled_dot_product_attention_23, 1, 2), kwargs = {}) %contiguous_46 : [num_users=1] = call_method[target=contiguous](args = (%transpose_95,), kwargs = {}) %reshape_73 : [num_users=1] = call_method[target=reshape](args = (%contiguous_46, 1, 29, -1), kwargs = {}) %contiguous_47 : [num_users=1] = call_method[target=contiguous](args = (%reshape_73,), kwargs = {}) %language_model_model_layers_23_self_attn_o_proj : [num_users=1] = call_module[target=language_model.model.layers.23.self_attn.o_proj](args = (%contiguous_47,), kwargs = {}) %add_141 : [num_users=3] = call_function[target=operator.add](args = (%add_137, %language_model_model_layers_23_self_attn_o_proj), kwargs = {}) %getattr_912 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_141, dtype), kwargs = {}) %to_192 : [num_users=2] = call_method[target=to](args = (%add_141, torch.float32), kwargs = {}) %pow_96 : [num_users=1] = call_method[target=pow](args = (%to_192, 2), kwargs = {}) %mean_95 : [num_users=1] = call_method[target=mean](args = (%pow_96, -1), kwargs = {keepdim: True}) %add_142 : [num_users=1] = call_function[target=operator.add](args = (%mean_95, 1e-06), kwargs = {}) %rsqrt_95 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_142,), kwargs = {}) %mul_213 : [num_users=1] = call_function[target=operator.mul](args = (%to_192, %rsqrt_95), kwargs = {}) %language_model_model_layers_23_post_attention_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.23.post_attention_layernorm.weight] %to_193 : [num_users=1] = call_method[target=to](args = (%mul_213, %getattr_912), kwargs = {}) %mul_214 : [num_users=2] = call_function[target=operator.mul](args = (%language_model_model_layers_23_post_attention_layernorm_weight, %to_193), kwargs = {}) %language_model_model_layers_23_mlp_gate_proj : [num_users=1] = call_module[target=language_model.model.layers.23.mlp.gate_proj](args = (%mul_214,), kwargs = {}) %silu_23 : [num_users=1] = call_function[target=torch.nn.functional.silu](args = (%language_model_model_layers_23_mlp_gate_proj,), kwargs = {inplace: False}) %language_model_model_layers_23_mlp_up_proj : [num_users=1] = call_module[target=language_model.model.layers.23.mlp.up_proj](args = (%mul_214,), kwargs = {}) %mul_215 : [num_users=1] = call_function[target=operator.mul](args = (%silu_23, %language_model_model_layers_23_mlp_up_proj), kwargs = {}) %language_model_model_layers_23_mlp_down_proj : [num_users=1] = call_module[target=language_model.model.layers.23.mlp.down_proj](args = (%mul_215,), kwargs = {}) %add_143 : [num_users=3] = call_function[target=operator.add](args = (%add_141, %language_model_model_layers_23_mlp_down_proj), kwargs = {}) %getattr_917 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_143, dtype), kwargs = {}) %to_194 : [num_users=2] = call_method[target=to](args = (%add_143, torch.float32), kwargs = {}) %pow_97 : [num_users=1] = call_method[target=pow](args = (%to_194, 2), kwargs = {}) %mean_96 : [num_users=1] = call_method[target=mean](args = (%pow_97, -1), kwargs = {keepdim: True}) %add_144 : [num_users=1] = call_function[target=operator.add](args = (%mean_96, 1e-06), kwargs = {}) %rsqrt_96 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_144,), kwargs = {}) %mul_216 : [num_users=1] = call_function[target=operator.mul](args = (%to_194, %rsqrt_96), kwargs = {}) %language_model_model_layers_24_input_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.24.input_layernorm.weight] %to_195 : [num_users=1] = call_method[target=to](args = (%mul_216, %getattr_917), kwargs = {}) %mul_217 : [num_users=3] = call_function[target=operator.mul](args = (%language_model_model_layers_24_input_layernorm_weight, %to_195), kwargs = {}) %language_model_model_layers_24_self_attn_q_proj : [num_users=1] = call_module[target=language_model.model.layers.24.self_attn.q_proj](args = (%mul_217,), kwargs = {}) %view_72 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_24_self_attn_q_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_924 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_72, dtype), kwargs = {}) %to_196 : [num_users=2] = call_method[target=to](args = (%view_72, torch.float32), kwargs = {}) %pow_98 : [num_users=1] = call_method[target=pow](args = (%to_196, 2), kwargs = {}) %mean_97 : [num_users=1] = call_method[target=mean](args = (%pow_98, -1), kwargs = {keepdim: True}) %add_145 : [num_users=1] = call_function[target=operator.add](args = (%mean_97, 1e-06), kwargs = {}) %rsqrt_97 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_145,), kwargs = {}) %mul_218 : [num_users=1] = call_function[target=operator.mul](args = (%to_196, %rsqrt_97), kwargs = {}) %language_model_model_layers_24_self_attn_q_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.24.self_attn.q_norm.weight] %to_197 : [num_users=1] = call_method[target=to](args = (%mul_218, %getattr_924), kwargs = {}) %mul_219 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_24_self_attn_q_norm_weight, %to_197), kwargs = {}) %transpose_96 : [num_users=1] = call_method[target=transpose](args = (%mul_219, 1, 2), kwargs = {}) %language_model_model_layers_24_self_attn_k_proj : [num_users=1] = call_module[target=language_model.model.layers.24.self_attn.k_proj](args = (%mul_217,), kwargs = {}) %view_73 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_24_self_attn_k_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_931 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_73, dtype), kwargs = {}) %to_198 : [num_users=2] = call_method[target=to](args = (%view_73, torch.float32), kwargs = {}) %pow_99 : [num_users=1] = call_method[target=pow](args = (%to_198, 2), kwargs = {}) %mean_98 : [num_users=1] = call_method[target=mean](args = (%pow_99, -1), kwargs = {keepdim: True}) %add_146 : [num_users=1] = call_function[target=operator.add](args = (%mean_98, 1e-06), kwargs = {}) %rsqrt_98 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_146,), kwargs = {}) %mul_220 : [num_users=1] = call_function[target=operator.mul](args = (%to_198, %rsqrt_98), kwargs = {}) %language_model_model_layers_24_self_attn_k_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.24.self_attn.k_norm.weight] %to_199 : [num_users=1] = call_method[target=to](args = (%mul_220, %getattr_931), kwargs = {}) %mul_221 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_24_self_attn_k_norm_weight, %to_199), kwargs = {}) %transpose_97 : [num_users=1] = call_method[target=transpose](args = (%mul_221, 1, 2), kwargs = {}) %language_model_model_layers_24_self_attn_v_proj : [num_users=1] = call_module[target=language_model.model.layers.24.self_attn.v_proj](args = (%mul_217,), kwargs = {}) %view_74 : [num_users=1] = call_method[target=view](args = (%language_model_model_layers_24_self_attn_v_proj, (1, 29, -1, 128)), kwargs = {}) %transpose_98 : [num_users=1] = call_method[target=transpose](args = (%view_74, 1, 2), kwargs = {}) %apply_rotary_pos_emb_24 : [num_users=2] = call_function[target=transformers.models.qwen3.modeling_qwen3.apply_rotary_pos_emb](args = (%transpose_96, %transpose_97, %to, %to_1), kwargs = {}) %getitem_146 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_24, 0), kwargs = {}) %getitem_147 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_24, 1), kwargs = {}) %update_24 : [num_users=2] = call_method[target=update](args = (%past_key_values, %getitem_147, %transpose_98, 24, {sin: %to_1, cos: %to, cache_position: %cache_position}), kwargs = {}) %getitem_148 : [num_users=1] = call_function[target=operator.getitem](args = (%update_24, 0), kwargs = {}) %getitem_149 : [num_users=1] = call_function[target=operator.getitem](args = (%update_24, 1), kwargs = {}) %getitem_150 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_148, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_48 : [num_users=1] = call_method[target=expand](args = (%getitem_150, 1, 8, 2, 29, 128), kwargs = {}) %reshape_74 : [num_users=1] = call_method[target=reshape](args = (%expand_48, 1, 16, 29, 128), kwargs = {}) %getitem_151 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_149, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_49 : [num_users=1] = call_method[target=expand](args = (%getitem_151, 1, 8, 2, 29, 128), kwargs = {}) %reshape_75 : [num_users=1] = call_method[target=reshape](args = (%expand_49, 1, 16, 29, 128), kwargs = {}) %scaled_dot_product_attention_24 : [num_users=1] = call_function[target=torch._C._nn.scaled_dot_product_attention](args = (%getitem_146, %reshape_74, %reshape_75), kwargs = {attn_mask: None, dropout_p: 0.0, scale: 0.08838834764831845, is_causal: True}) %transpose_99 : [num_users=1] = call_method[target=transpose](args = (%scaled_dot_product_attention_24, 1, 2), kwargs = {}) %contiguous_48 : [num_users=1] = call_method[target=contiguous](args = (%transpose_99,), kwargs = {}) %reshape_76 : [num_users=1] = call_method[target=reshape](args = (%contiguous_48, 1, 29, -1), kwargs = {}) %contiguous_49 : [num_users=1] = call_method[target=contiguous](args = (%reshape_76,), kwargs = {}) %language_model_model_layers_24_self_attn_o_proj : [num_users=1] = call_module[target=language_model.model.layers.24.self_attn.o_proj](args = (%contiguous_49,), kwargs = {}) %add_147 : [num_users=3] = call_function[target=operator.add](args = (%add_143, %language_model_model_layers_24_self_attn_o_proj), kwargs = {}) %getattr_950 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_147, dtype), kwargs = {}) %to_200 : [num_users=2] = call_method[target=to](args = (%add_147, torch.float32), kwargs = {}) %pow_100 : [num_users=1] = call_method[target=pow](args = (%to_200, 2), kwargs = {}) %mean_99 : [num_users=1] = call_method[target=mean](args = (%pow_100, -1), kwargs = {keepdim: True}) %add_148 : [num_users=1] = call_function[target=operator.add](args = (%mean_99, 1e-06), kwargs = {}) %rsqrt_99 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_148,), kwargs = {}) %mul_222 : [num_users=1] = call_function[target=operator.mul](args = (%to_200, %rsqrt_99), kwargs = {}) %language_model_model_layers_24_post_attention_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.24.post_attention_layernorm.weight] %to_201 : [num_users=1] = call_method[target=to](args = (%mul_222, %getattr_950), kwargs = {}) %mul_223 : [num_users=2] = call_function[target=operator.mul](args = (%language_model_model_layers_24_post_attention_layernorm_weight, %to_201), kwargs = {}) %language_model_model_layers_24_mlp_gate_proj : [num_users=1] = call_module[target=language_model.model.layers.24.mlp.gate_proj](args = (%mul_223,), kwargs = {}) %silu_24 : [num_users=1] = call_function[target=torch.nn.functional.silu](args = (%language_model_model_layers_24_mlp_gate_proj,), kwargs = {inplace: False}) %language_model_model_layers_24_mlp_up_proj : [num_users=1] = call_module[target=language_model.model.layers.24.mlp.up_proj](args = (%mul_223,), kwargs = {}) %mul_224 : [num_users=1] = call_function[target=operator.mul](args = (%silu_24, %language_model_model_layers_24_mlp_up_proj), kwargs = {}) %language_model_model_layers_24_mlp_down_proj : [num_users=1] = call_module[target=language_model.model.layers.24.mlp.down_proj](args = (%mul_224,), kwargs = {}) %add_149 : [num_users=3] = call_function[target=operator.add](args = (%add_147, %language_model_model_layers_24_mlp_down_proj), kwargs = {}) %getattr_955 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_149, dtype), kwargs = {}) %to_202 : [num_users=2] = call_method[target=to](args = (%add_149, torch.float32), kwargs = {}) %pow_101 : [num_users=1] = call_method[target=pow](args = (%to_202, 2), kwargs = {}) %mean_100 : [num_users=1] = call_method[target=mean](args = (%pow_101, -1), kwargs = {keepdim: True}) %add_150 : [num_users=1] = call_function[target=operator.add](args = (%mean_100, 1e-06), kwargs = {}) %rsqrt_100 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_150,), kwargs = {}) %mul_225 : [num_users=1] = call_function[target=operator.mul](args = (%to_202, %rsqrt_100), kwargs = {}) %language_model_model_layers_25_input_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.25.input_layernorm.weight] %to_203 : [num_users=1] = call_method[target=to](args = (%mul_225, %getattr_955), kwargs = {}) %mul_226 : [num_users=3] = call_function[target=operator.mul](args = (%language_model_model_layers_25_input_layernorm_weight, %to_203), kwargs = {}) %language_model_model_layers_25_self_attn_q_proj : [num_users=1] = call_module[target=language_model.model.layers.25.self_attn.q_proj](args = (%mul_226,), kwargs = {}) %view_75 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_25_self_attn_q_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_962 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_75, dtype), kwargs = {}) %to_204 : [num_users=2] = call_method[target=to](args = (%view_75, torch.float32), kwargs = {}) %pow_102 : [num_users=1] = call_method[target=pow](args = (%to_204, 2), kwargs = {}) %mean_101 : [num_users=1] = call_method[target=mean](args = (%pow_102, -1), kwargs = {keepdim: True}) %add_151 : [num_users=1] = call_function[target=operator.add](args = (%mean_101, 1e-06), kwargs = {}) %rsqrt_101 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_151,), kwargs = {}) %mul_227 : [num_users=1] = call_function[target=operator.mul](args = (%to_204, %rsqrt_101), kwargs = {}) %language_model_model_layers_25_self_attn_q_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.25.self_attn.q_norm.weight] %to_205 : [num_users=1] = call_method[target=to](args = (%mul_227, %getattr_962), kwargs = {}) %mul_228 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_25_self_attn_q_norm_weight, %to_205), kwargs = {}) %transpose_100 : [num_users=1] = call_method[target=transpose](args = (%mul_228, 1, 2), kwargs = {}) %language_model_model_layers_25_self_attn_k_proj : [num_users=1] = call_module[target=language_model.model.layers.25.self_attn.k_proj](args = (%mul_226,), kwargs = {}) %view_76 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_25_self_attn_k_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_969 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_76, dtype), kwargs = {}) %to_206 : [num_users=2] = call_method[target=to](args = (%view_76, torch.float32), kwargs = {}) %pow_103 : [num_users=1] = call_method[target=pow](args = (%to_206, 2), kwargs = {}) %mean_102 : [num_users=1] = call_method[target=mean](args = (%pow_103, -1), kwargs = {keepdim: True}) %add_152 : [num_users=1] = call_function[target=operator.add](args = (%mean_102, 1e-06), kwargs = {}) %rsqrt_102 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_152,), kwargs = {}) %mul_229 : [num_users=1] = call_function[target=operator.mul](args = (%to_206, %rsqrt_102), kwargs = {}) %language_model_model_layers_25_self_attn_k_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.25.self_attn.k_norm.weight] %to_207 : [num_users=1] = call_method[target=to](args = (%mul_229, %getattr_969), kwargs = {}) %mul_230 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_25_self_attn_k_norm_weight, %to_207), kwargs = {}) %transpose_101 : [num_users=1] = call_method[target=transpose](args = (%mul_230, 1, 2), kwargs = {}) %language_model_model_layers_25_self_attn_v_proj : [num_users=1] = call_module[target=language_model.model.layers.25.self_attn.v_proj](args = (%mul_226,), kwargs = {}) %view_77 : [num_users=1] = call_method[target=view](args = (%language_model_model_layers_25_self_attn_v_proj, (1, 29, -1, 128)), kwargs = {}) %transpose_102 : [num_users=1] = call_method[target=transpose](args = (%view_77, 1, 2), kwargs = {}) %apply_rotary_pos_emb_25 : [num_users=2] = call_function[target=transformers.models.qwen3.modeling_qwen3.apply_rotary_pos_emb](args = (%transpose_100, %transpose_101, %to, %to_1), kwargs = {}) %getitem_152 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_25, 0), kwargs = {}) %getitem_153 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_25, 1), kwargs = {}) %update_25 : [num_users=2] = call_method[target=update](args = (%past_key_values, %getitem_153, %transpose_102, 25, {sin: %to_1, cos: %to, cache_position: %cache_position}), kwargs = {}) %getitem_154 : [num_users=1] = call_function[target=operator.getitem](args = (%update_25, 0), kwargs = {}) %getitem_155 : [num_users=1] = call_function[target=operator.getitem](args = (%update_25, 1), kwargs = {}) %getitem_156 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_154, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_50 : [num_users=1] = call_method[target=expand](args = (%getitem_156, 1, 8, 2, 29, 128), kwargs = {}) %reshape_77 : [num_users=1] = call_method[target=reshape](args = (%expand_50, 1, 16, 29, 128), kwargs = {}) %getitem_157 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_155, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_51 : [num_users=1] = call_method[target=expand](args = (%getitem_157, 1, 8, 2, 29, 128), kwargs = {}) %reshape_78 : [num_users=1] = call_method[target=reshape](args = (%expand_51, 1, 16, 29, 128), kwargs = {}) %scaled_dot_product_attention_25 : [num_users=1] = call_function[target=torch._C._nn.scaled_dot_product_attention](args = (%getitem_152, %reshape_77, %reshape_78), kwargs = {attn_mask: None, dropout_p: 0.0, scale: 0.08838834764831845, is_causal: True}) %transpose_103 : [num_users=1] = call_method[target=transpose](args = (%scaled_dot_product_attention_25, 1, 2), kwargs = {}) %contiguous_50 : [num_users=1] = call_method[target=contiguous](args = (%transpose_103,), kwargs = {}) %reshape_79 : [num_users=1] = call_method[target=reshape](args = (%contiguous_50, 1, 29, -1), kwargs = {}) %contiguous_51 : [num_users=1] = call_method[target=contiguous](args = (%reshape_79,), kwargs = {}) %language_model_model_layers_25_self_attn_o_proj : [num_users=1] = call_module[target=language_model.model.layers.25.self_attn.o_proj](args = (%contiguous_51,), kwargs = {}) %add_153 : [num_users=3] = call_function[target=operator.add](args = (%add_149, %language_model_model_layers_25_self_attn_o_proj), kwargs = {}) %getattr_988 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_153, dtype), kwargs = {}) %to_208 : [num_users=2] = call_method[target=to](args = (%add_153, torch.float32), kwargs = {}) %pow_104 : [num_users=1] = call_method[target=pow](args = (%to_208, 2), kwargs = {}) %mean_103 : [num_users=1] = call_method[target=mean](args = (%pow_104, -1), kwargs = {keepdim: True}) %add_154 : [num_users=1] = call_function[target=operator.add](args = (%mean_103, 1e-06), kwargs = {}) %rsqrt_103 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_154,), kwargs = {}) %mul_231 : [num_users=1] = call_function[target=operator.mul](args = (%to_208, %rsqrt_103), kwargs = {}) %language_model_model_layers_25_post_attention_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.25.post_attention_layernorm.weight] %to_209 : [num_users=1] = call_method[target=to](args = (%mul_231, %getattr_988), kwargs = {}) %mul_232 : [num_users=2] = call_function[target=operator.mul](args = (%language_model_model_layers_25_post_attention_layernorm_weight, %to_209), kwargs = {}) %language_model_model_layers_25_mlp_gate_proj : [num_users=1] = call_module[target=language_model.model.layers.25.mlp.gate_proj](args = (%mul_232,), kwargs = {}) %silu_25 : [num_users=1] = call_function[target=torch.nn.functional.silu](args = (%language_model_model_layers_25_mlp_gate_proj,), kwargs = {inplace: False}) %language_model_model_layers_25_mlp_up_proj : [num_users=1] = call_module[target=language_model.model.layers.25.mlp.up_proj](args = (%mul_232,), kwargs = {}) %mul_233 : [num_users=1] = call_function[target=operator.mul](args = (%silu_25, %language_model_model_layers_25_mlp_up_proj), kwargs = {}) %language_model_model_layers_25_mlp_down_proj : [num_users=1] = call_module[target=language_model.model.layers.25.mlp.down_proj](args = (%mul_233,), kwargs = {}) %add_155 : [num_users=3] = call_function[target=operator.add](args = (%add_153, %language_model_model_layers_25_mlp_down_proj), kwargs = {}) %getattr_993 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_155, dtype), kwargs = {}) %to_210 : [num_users=2] = call_method[target=to](args = (%add_155, torch.float32), kwargs = {}) %pow_105 : [num_users=1] = call_method[target=pow](args = (%to_210, 2), kwargs = {}) %mean_104 : [num_users=1] = call_method[target=mean](args = (%pow_105, -1), kwargs = {keepdim: True}) %add_156 : [num_users=1] = call_function[target=operator.add](args = (%mean_104, 1e-06), kwargs = {}) %rsqrt_104 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_156,), kwargs = {}) %mul_234 : [num_users=1] = call_function[target=operator.mul](args = (%to_210, %rsqrt_104), kwargs = {}) %language_model_model_layers_26_input_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.26.input_layernorm.weight] %to_211 : [num_users=1] = call_method[target=to](args = (%mul_234, %getattr_993), kwargs = {}) %mul_235 : [num_users=3] = call_function[target=operator.mul](args = (%language_model_model_layers_26_input_layernorm_weight, %to_211), kwargs = {}) %language_model_model_layers_26_self_attn_q_proj : [num_users=1] = call_module[target=language_model.model.layers.26.self_attn.q_proj](args = (%mul_235,), kwargs = {}) %view_78 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_26_self_attn_q_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_1000 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_78, dtype), kwargs = {}) %to_212 : [num_users=2] = call_method[target=to](args = (%view_78, torch.float32), kwargs = {}) %pow_106 : [num_users=1] = call_method[target=pow](args = (%to_212, 2), kwargs = {}) %mean_105 : [num_users=1] = call_method[target=mean](args = (%pow_106, -1), kwargs = {keepdim: True}) %add_157 : [num_users=1] = call_function[target=operator.add](args = (%mean_105, 1e-06), kwargs = {}) %rsqrt_105 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_157,), kwargs = {}) %mul_236 : [num_users=1] = call_function[target=operator.mul](args = (%to_212, %rsqrt_105), kwargs = {}) %language_model_model_layers_26_self_attn_q_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.26.self_attn.q_norm.weight] %to_213 : [num_users=1] = call_method[target=to](args = (%mul_236, %getattr_1000), kwargs = {}) %mul_237 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_26_self_attn_q_norm_weight, %to_213), kwargs = {}) %transpose_104 : [num_users=1] = call_method[target=transpose](args = (%mul_237, 1, 2), kwargs = {}) %language_model_model_layers_26_self_attn_k_proj : [num_users=1] = call_module[target=language_model.model.layers.26.self_attn.k_proj](args = (%mul_235,), kwargs = {}) %view_79 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_26_self_attn_k_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_1007 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_79, dtype), kwargs = {}) %to_214 : [num_users=2] = call_method[target=to](args = (%view_79, torch.float32), kwargs = {}) %pow_107 : [num_users=1] = call_method[target=pow](args = (%to_214, 2), kwargs = {}) %mean_106 : [num_users=1] = call_method[target=mean](args = (%pow_107, -1), kwargs = {keepdim: True}) %add_158 : [num_users=1] = call_function[target=operator.add](args = (%mean_106, 1e-06), kwargs = {}) %rsqrt_106 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_158,), kwargs = {}) %mul_238 : [num_users=1] = call_function[target=operator.mul](args = (%to_214, %rsqrt_106), kwargs = {}) %language_model_model_layers_26_self_attn_k_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.26.self_attn.k_norm.weight] %to_215 : [num_users=1] = call_method[target=to](args = (%mul_238, %getattr_1007), kwargs = {}) %mul_239 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_26_self_attn_k_norm_weight, %to_215), kwargs = {}) %transpose_105 : [num_users=1] = call_method[target=transpose](args = (%mul_239, 1, 2), kwargs = {}) %language_model_model_layers_26_self_attn_v_proj : [num_users=1] = call_module[target=language_model.model.layers.26.self_attn.v_proj](args = (%mul_235,), kwargs = {}) %view_80 : [num_users=1] = call_method[target=view](args = (%language_model_model_layers_26_self_attn_v_proj, (1, 29, -1, 128)), kwargs = {}) %transpose_106 : [num_users=1] = call_method[target=transpose](args = (%view_80, 1, 2), kwargs = {}) %apply_rotary_pos_emb_26 : [num_users=2] = call_function[target=transformers.models.qwen3.modeling_qwen3.apply_rotary_pos_emb](args = (%transpose_104, %transpose_105, %to, %to_1), kwargs = {}) %getitem_158 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_26, 0), kwargs = {}) %getitem_159 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_26, 1), kwargs = {}) %update_26 : [num_users=2] = call_method[target=update](args = (%past_key_values, %getitem_159, %transpose_106, 26, {sin: %to_1, cos: %to, cache_position: %cache_position}), kwargs = {}) %getitem_160 : [num_users=1] = call_function[target=operator.getitem](args = (%update_26, 0), kwargs = {}) %getitem_161 : [num_users=1] = call_function[target=operator.getitem](args = (%update_26, 1), kwargs = {}) %getitem_162 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_160, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_52 : [num_users=1] = call_method[target=expand](args = (%getitem_162, 1, 8, 2, 29, 128), kwargs = {}) %reshape_80 : [num_users=1] = call_method[target=reshape](args = (%expand_52, 1, 16, 29, 128), kwargs = {}) %getitem_163 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_161, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_53 : [num_users=1] = call_method[target=expand](args = (%getitem_163, 1, 8, 2, 29, 128), kwargs = {}) %reshape_81 : [num_users=1] = call_method[target=reshape](args = (%expand_53, 1, 16, 29, 128), kwargs = {}) %scaled_dot_product_attention_26 : [num_users=1] = call_function[target=torch._C._nn.scaled_dot_product_attention](args = (%getitem_158, %reshape_80, %reshape_81), kwargs = {attn_mask: None, dropout_p: 0.0, scale: 0.08838834764831845, is_causal: True}) %transpose_107 : [num_users=1] = call_method[target=transpose](args = (%scaled_dot_product_attention_26, 1, 2), kwargs = {}) %contiguous_52 : [num_users=1] = call_method[target=contiguous](args = (%transpose_107,), kwargs = {}) %reshape_82 : [num_users=1] = call_method[target=reshape](args = (%contiguous_52, 1, 29, -1), kwargs = {}) %contiguous_53 : [num_users=1] = call_method[target=contiguous](args = (%reshape_82,), kwargs = {}) %language_model_model_layers_26_self_attn_o_proj : [num_users=1] = call_module[target=language_model.model.layers.26.self_attn.o_proj](args = (%contiguous_53,), kwargs = {}) %add_159 : [num_users=3] = call_function[target=operator.add](args = (%add_155, %language_model_model_layers_26_self_attn_o_proj), kwargs = {}) %getattr_1026 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_159, dtype), kwargs = {}) %to_216 : [num_users=2] = call_method[target=to](args = (%add_159, torch.float32), kwargs = {}) %pow_108 : [num_users=1] = call_method[target=pow](args = (%to_216, 2), kwargs = {}) %mean_107 : [num_users=1] = call_method[target=mean](args = (%pow_108, -1), kwargs = {keepdim: True}) %add_160 : [num_users=1] = call_function[target=operator.add](args = (%mean_107, 1e-06), kwargs = {}) %rsqrt_107 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_160,), kwargs = {}) %mul_240 : [num_users=1] = call_function[target=operator.mul](args = (%to_216, %rsqrt_107), kwargs = {}) %language_model_model_layers_26_post_attention_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.26.post_attention_layernorm.weight] %to_217 : [num_users=1] = call_method[target=to](args = (%mul_240, %getattr_1026), kwargs = {}) %mul_241 : [num_users=2] = call_function[target=operator.mul](args = (%language_model_model_layers_26_post_attention_layernorm_weight, %to_217), kwargs = {}) %language_model_model_layers_26_mlp_gate_proj : [num_users=1] = call_module[target=language_model.model.layers.26.mlp.gate_proj](args = (%mul_241,), kwargs = {}) %silu_26 : [num_users=1] = call_function[target=torch.nn.functional.silu](args = (%language_model_model_layers_26_mlp_gate_proj,), kwargs = {inplace: False}) %language_model_model_layers_26_mlp_up_proj : [num_users=1] = call_module[target=language_model.model.layers.26.mlp.up_proj](args = (%mul_241,), kwargs = {}) %mul_242 : [num_users=1] = call_function[target=operator.mul](args = (%silu_26, %language_model_model_layers_26_mlp_up_proj), kwargs = {}) %language_model_model_layers_26_mlp_down_proj : [num_users=1] = call_module[target=language_model.model.layers.26.mlp.down_proj](args = (%mul_242,), kwargs = {}) %add_161 : [num_users=3] = call_function[target=operator.add](args = (%add_159, %language_model_model_layers_26_mlp_down_proj), kwargs = {}) %getattr_1031 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_161, dtype), kwargs = {}) %to_218 : [num_users=2] = call_method[target=to](args = (%add_161, torch.float32), kwargs = {}) %pow_109 : [num_users=1] = call_method[target=pow](args = (%to_218, 2), kwargs = {}) %mean_108 : [num_users=1] = call_method[target=mean](args = (%pow_109, -1), kwargs = {keepdim: True}) %add_162 : [num_users=1] = call_function[target=operator.add](args = (%mean_108, 1e-06), kwargs = {}) %rsqrt_108 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_162,), kwargs = {}) %mul_243 : [num_users=1] = call_function[target=operator.mul](args = (%to_218, %rsqrt_108), kwargs = {}) %language_model_model_layers_27_input_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.27.input_layernorm.weight] %to_219 : [num_users=1] = call_method[target=to](args = (%mul_243, %getattr_1031), kwargs = {}) %mul_244 : [num_users=3] = call_function[target=operator.mul](args = (%language_model_model_layers_27_input_layernorm_weight, %to_219), kwargs = {}) %language_model_model_layers_27_self_attn_q_proj : [num_users=1] = call_module[target=language_model.model.layers.27.self_attn.q_proj](args = (%mul_244,), kwargs = {}) %view_81 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_27_self_attn_q_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_1038 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_81, dtype), kwargs = {}) %to_220 : [num_users=2] = call_method[target=to](args = (%view_81, torch.float32), kwargs = {}) %pow_110 : [num_users=1] = call_method[target=pow](args = (%to_220, 2), kwargs = {}) %mean_109 : [num_users=1] = call_method[target=mean](args = (%pow_110, -1), kwargs = {keepdim: True}) %add_163 : [num_users=1] = call_function[target=operator.add](args = (%mean_109, 1e-06), kwargs = {}) %rsqrt_109 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_163,), kwargs = {}) %mul_245 : [num_users=1] = call_function[target=operator.mul](args = (%to_220, %rsqrt_109), kwargs = {}) %language_model_model_layers_27_self_attn_q_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.27.self_attn.q_norm.weight] %to_221 : [num_users=1] = call_method[target=to](args = (%mul_245, %getattr_1038), kwargs = {}) %mul_246 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_27_self_attn_q_norm_weight, %to_221), kwargs = {}) %transpose_108 : [num_users=1] = call_method[target=transpose](args = (%mul_246, 1, 2), kwargs = {}) %language_model_model_layers_27_self_attn_k_proj : [num_users=1] = call_module[target=language_model.model.layers.27.self_attn.k_proj](args = (%mul_244,), kwargs = {}) %view_82 : [num_users=2] = call_method[target=view](args = (%language_model_model_layers_27_self_attn_k_proj, (1, 29, -1, 128)), kwargs = {}) %getattr_1045 : [num_users=1] = call_function[target=builtins.getattr](args = (%view_82, dtype), kwargs = {}) %to_222 : [num_users=2] = call_method[target=to](args = (%view_82, torch.float32), kwargs = {}) %pow_111 : [num_users=1] = call_method[target=pow](args = (%to_222, 2), kwargs = {}) %mean_110 : [num_users=1] = call_method[target=mean](args = (%pow_111, -1), kwargs = {keepdim: True}) %add_164 : [num_users=1] = call_function[target=operator.add](args = (%mean_110, 1e-06), kwargs = {}) %rsqrt_110 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_164,), kwargs = {}) %mul_247 : [num_users=1] = call_function[target=operator.mul](args = (%to_222, %rsqrt_110), kwargs = {}) %language_model_model_layers_27_self_attn_k_norm_weight : [num_users=1] = get_attr[target=language_model.model.layers.27.self_attn.k_norm.weight] %to_223 : [num_users=1] = call_method[target=to](args = (%mul_247, %getattr_1045), kwargs = {}) %mul_248 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_layers_27_self_attn_k_norm_weight, %to_223), kwargs = {}) %transpose_109 : [num_users=1] = call_method[target=transpose](args = (%mul_248, 1, 2), kwargs = {}) %language_model_model_layers_27_self_attn_v_proj : [num_users=1] = call_module[target=language_model.model.layers.27.self_attn.v_proj](args = (%mul_244,), kwargs = {}) %view_83 : [num_users=1] = call_method[target=view](args = (%language_model_model_layers_27_self_attn_v_proj, (1, 29, -1, 128)), kwargs = {}) %transpose_110 : [num_users=1] = call_method[target=transpose](args = (%view_83, 1, 2), kwargs = {}) %apply_rotary_pos_emb_27 : [num_users=2] = call_function[target=transformers.models.qwen3.modeling_qwen3.apply_rotary_pos_emb](args = (%transpose_108, %transpose_109, %to, %to_1), kwargs = {}) %getitem_164 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_27, 0), kwargs = {}) %getitem_165 : [num_users=1] = call_function[target=operator.getitem](args = (%apply_rotary_pos_emb_27, 1), kwargs = {}) %update_27 : [num_users=2] = call_method[target=update](args = (%past_key_values, %getitem_165, %transpose_110, 27, {sin: %to_1, cos: %to, cache_position: %cache_position}), kwargs = {}) %getitem_166 : [num_users=1] = call_function[target=operator.getitem](args = (%update_27, 0), kwargs = {}) %getitem_167 : [num_users=1] = call_function[target=operator.getitem](args = (%update_27, 1), kwargs = {}) %getitem_168 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_166, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_54 : [num_users=1] = call_method[target=expand](args = (%getitem_168, 1, 8, 2, 29, 128), kwargs = {}) %reshape_83 : [num_users=1] = call_method[target=reshape](args = (%expand_54, 1, 16, 29, 128), kwargs = {}) %getitem_169 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_167, (slice(None, None, None), slice(None, None, None), None, slice(None, None, None), slice(None, None, None))), kwargs = {}) %expand_55 : [num_users=1] = call_method[target=expand](args = (%getitem_169, 1, 8, 2, 29, 128), kwargs = {}) %reshape_84 : [num_users=1] = call_method[target=reshape](args = (%expand_55, 1, 16, 29, 128), kwargs = {}) %scaled_dot_product_attention_27 : [num_users=1] = call_function[target=torch._C._nn.scaled_dot_product_attention](args = (%getitem_164, %reshape_83, %reshape_84), kwargs = {attn_mask: None, dropout_p: 0.0, scale: 0.08838834764831845, is_causal: True}) %transpose_111 : [num_users=1] = call_method[target=transpose](args = (%scaled_dot_product_attention_27, 1, 2), kwargs = {}) %contiguous_54 : [num_users=1] = call_method[target=contiguous](args = (%transpose_111,), kwargs = {}) %reshape_85 : [num_users=1] = call_method[target=reshape](args = (%contiguous_54, 1, 29, -1), kwargs = {}) %contiguous_55 : [num_users=1] = call_method[target=contiguous](args = (%reshape_85,), kwargs = {}) %language_model_model_layers_27_self_attn_o_proj : [num_users=1] = call_module[target=language_model.model.layers.27.self_attn.o_proj](args = (%contiguous_55,), kwargs = {}) %add_165 : [num_users=3] = call_function[target=operator.add](args = (%add_161, %language_model_model_layers_27_self_attn_o_proj), kwargs = {}) %getattr_1064 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_165, dtype), kwargs = {}) %to_224 : [num_users=2] = call_method[target=to](args = (%add_165, torch.float32), kwargs = {}) %pow_112 : [num_users=1] = call_method[target=pow](args = (%to_224, 2), kwargs = {}) %mean_111 : [num_users=1] = call_method[target=mean](args = (%pow_112, -1), kwargs = {keepdim: True}) %add_166 : [num_users=1] = call_function[target=operator.add](args = (%mean_111, 1e-06), kwargs = {}) %rsqrt_111 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_166,), kwargs = {}) %mul_249 : [num_users=1] = call_function[target=operator.mul](args = (%to_224, %rsqrt_111), kwargs = {}) %language_model_model_layers_27_post_attention_layernorm_weight : [num_users=1] = get_attr[target=language_model.model.layers.27.post_attention_layernorm.weight] %to_225 : [num_users=1] = call_method[target=to](args = (%mul_249, %getattr_1064), kwargs = {}) %mul_250 : [num_users=2] = call_function[target=operator.mul](args = (%language_model_model_layers_27_post_attention_layernorm_weight, %to_225), kwargs = {}) %language_model_model_layers_27_mlp_gate_proj : [num_users=1] = call_module[target=language_model.model.layers.27.mlp.gate_proj](args = (%mul_250,), kwargs = {}) %silu_27 : [num_users=1] = call_function[target=torch.nn.functional.silu](args = (%language_model_model_layers_27_mlp_gate_proj,), kwargs = {inplace: False}) %language_model_model_layers_27_mlp_up_proj : [num_users=1] = call_module[target=language_model.model.layers.27.mlp.up_proj](args = (%mul_250,), kwargs = {}) %mul_251 : [num_users=1] = call_function[target=operator.mul](args = (%silu_27, %language_model_model_layers_27_mlp_up_proj), kwargs = {}) %language_model_model_layers_27_mlp_down_proj : [num_users=1] = call_module[target=language_model.model.layers.27.mlp.down_proj](args = (%mul_251,), kwargs = {}) %add_167 : [num_users=2] = call_function[target=operator.add](args = (%add_165, %language_model_model_layers_27_mlp_down_proj), kwargs = {}) %getattr_1069 : [num_users=1] = call_function[target=builtins.getattr](args = (%add_167, dtype), kwargs = {}) %to_226 : [num_users=2] = call_method[target=to](args = (%add_167, torch.float32), kwargs = {}) %pow_113 : [num_users=1] = call_method[target=pow](args = (%to_226, 2), kwargs = {}) %mean_112 : [num_users=1] = call_method[target=mean](args = (%pow_113, -1), kwargs = {keepdim: True}) %add_168 : [num_users=1] = call_function[target=operator.add](args = (%mean_112, 1e-06), kwargs = {}) %rsqrt_112 : [num_users=1] = call_function[target=torch.rsqrt](args = (%add_168,), kwargs = {}) %mul_252 : [num_users=1] = call_function[target=operator.mul](args = (%to_226, %rsqrt_112), kwargs = {}) %language_model_model_norm_weight : [num_users=1] = get_attr[target=language_model.model.norm.weight] %to_227 : [num_users=1] = call_method[target=to](args = (%mul_252, %getattr_1069), kwargs = {}) %mul_253 : [num_users=1] = call_function[target=operator.mul](args = (%language_model_model_norm_weight, %to_227), kwargs = {}) %getitem_170 : [num_users=1] = call_function[target=operator.getitem](args = (%mul_253, (slice(None, None, None), slice(-1, None, None), slice(None, None, None))), kwargs = {}) %language_model_lm_head : [num_users=1] = call_module[target=language_model.lm_head](args = (%getitem_170,), kwargs = {}) return [language_model_lm_head, past_key_values] =============================================================== Processing nodes: 0%| | 0/2098 [00:00} [Placeholder] node=cache_position, type=typing.Optional[torch.LongTensor], {'shape': (dyn(29),), 'is_attn_mask': False, 'attn_mask_type': None, 'is_kv_cache': False, 'name': 'cache_position'} hf_patched function at _call_function: transformers.models.qwen3.modeling_qwen3.apply_rotary_pos_emb [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... Processing nodes: 100%|██████████| 2098/2098 [00:01<00:00, 1235.29node/s] [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model....[2026-09-17 01:32:56] qbcompiler.model_dict.parser.parser : [INFO] [parser] Load and set dformat model done. [2026-09-17 01:32:56] qbcompiler.model_dict.parser.parser : [INFO] [parser] =================================================================== Summary by Operator Type[Model Loaded] =================================================================== OpType | Count | Unsupported | Supported(%) ------------------------------+-------+-------------+-------------- Adding | 56| 56| 0.00 AddingConstant | 113| 113| 0.00 Cast | 228| 228| 0.00 Convolution | 197| 197| 0.00 Embedding | 1| 1| 0.00 Expand | 56| 56| 0.00 Gather | 2| 2| 0.00 HFCache | 1| 1| 0.00 HFCacheUpdate | 28| 28| 0.00 HFPatchedFunction | 28| 28| 0.00 Identity | 112| 112| 0.00 Input | 2| 2| 0.00 InputConstant | 115| 115| 0.00 MatMul | 56| 56| 0.00 Multiply | 254| 254| 0.00 MultiplyConstant | 28| 28| 0.00 Output | 1| 1| 0.00 Pow | 113| 113| 0.00 ReduceMean | 113| 113| 0.00 Reshape | 564| 564| 0.00 Rsqrt | 113| 113| 0.00 Slice | 1| 1| 0.00 Softmax | 28| 28| 0.00 StatefulAttentionMaskWrapper | 28| 28| 0.00 Swish | 28| 28| 0.00 Transpose | 140| 140| 0.00 Unsqueeze | 56| 56| 0.00 ------------------------------+-------+-------------+-------------- All | 2462| 2462| 0.00 =================================================================== [2026-09-17 01:32:56] qbcompiler.model_dict.parser.parser : [INFO] [parser] Number of Subgraphs: 1 graph order: [0] stateful_lstm_subgraphs: {} backend: torch model inputs: [] model outputs: [] [2026-09-17 01:32:56] qbcompiler.model_dict.parser.parser : [INFO] outputs: [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model...[2026-09-17 01:32:57] qbcompiler.model_dict.parser.parser : [INFO] sparse_moe_subgraph: not found [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model......[2026-09-17 01:36:14] qbcompiler.model_dict.parser.parser : [INFO] [parser] =================================================================== Summary by Operator Type[HL compilation done. Device=Aries_rb] =================================================================== OpType | Count | Unsupported | Supported(%) ------------------------------+-------+-------------+-------------- Adding | 56| 0| 100.00 Convolution | 197| 0| 100.00 Embedding | 1| 1| 0.00 HeaderView | 84| 0| 100.00 Input | 2| 1| 50.00 InputConstant | 2| 0| 100.00 MatMul | 56| 0| 100.00 Multiply | 28| 0| 100.00 MultiplyConstant | 28| 0| 100.00 Output | 1| 0| 100.00 Reshape | 1| 1| 0.00 RmsNormalization | 113| 0| 100.00 Slice | 2| 0| 100.00 Softmax | 28| 0| 100.00 StatefulAttentionMaskWrapper | 28| 0| 100.00 StatefulKVCacheWrapper | 56| 0| 100.00 StatefulRoPEWrapper | 56| 0| 100.00 Swish | 28| 0| 100.00 Transpose | 28| 0| 100.00 ------------------------------+-------+-------------+-------------- All | 795| 3| 99.62 =================================================================== [2026-09-17 01:36:14] qbcompiler.model_dict.parser.parser : [INFO] [parser] Number of Subgraphs: 2 graph order: [0, 1] stateful_lstm_subgraphs: {} backend: torch model inputs: ['name: input_ids, src_shape: (1,dyn(29)), dataformat: UNKNOWN, origin_name: input_ids'] model outputs: ['name: language_model_lm_head, src_shape: (1,1,1,1024), dataformat: NHWC, origin_name: language_model_lm_head'] [2026-09-17 01:36:14] qbcompiler.model_dict.parser.parser : [INFO] outputs: [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model...[2026-09-17 01:36:20] qbcompiler.model_dict.parser.parser : [INFO] model model's number of operations 25.0884 GOPS, 12.5442 MACs [2026-09-17 01:36:20] qbcompiler.model_dict.parser.parser : [INFO] ================================================================================ [2026-09-17 01:36:20] qbcompiler.model_dict.parser.parser : [INFO] Supported: 25.0884 GOPS, 12.5442 MACs [2026-09-17 01:36:20] qbcompiler.model_dict.parser.parser : [INFO] Unsupported: 0.0000 GOPS, 0.0000 MACs [2026-09-17 01:36:20] qbcompiler.model_dict.parser.parser : [INFO] ================================================================================ [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model...... [⢿] Parsing deep learning model... [⡿] Parsing deep learning model.... [⣟] Parsing deep learning model..... [⣯] Parsing deep learning model...... [⣷] Parsing deep learning model... [⣾] Parsing deep learning model.... [⣽] Parsing deep learning model..... [⣻] Parsing deep learning model......[2026-09-17 01:36:43] qbcompiler.compiler.compiler : [INFO] write intermediate file: /tmp/qbcompiler/frontend/8bed7d10-74a1-4300-a6b8-8e90584b8db3/model.mblt done. [⢿] Parsing deep learning model... ✔ Parsing deep learning model... Done! [2026-09-17 01:36:43.806] Setting Quantizer Config (json)... [2026-09-17 01:36:43.806] Setting Compile Config (typed)... [2026-09-17 01:36:43.806] Performing Quantization (FB)... [2026-09-17 01:37:02.199] complete high-level model optimizing [2026-09-17 01:37:02.496] Found 216 calibration files in directory: /home/jovyan/Uday_DB_Ins_PoC_Prep/qwen3-autorag-compile/qwen3_autorag_compile_package/mblt-rag/calib_data/qwen3-embedding-0.6b-autorag [2026-09-17 01:37:02.498] Quantizer::Utils::UtilsQuantize getCalTensorImpl | Unsupported file type: [2026-09-17 01:37:32.395] Quantizer::Utils::UtilsQuantize getCalTensorImpl | Unsupported file type: .json [2026-09-17 01:37:32.395] Quantizer::Utils::UtilsQuantize getCalTensorImpl | Unsupported file type: .json [2026-09-17 01:37:32.395] Quantizer::Utils::UtilsQuantize getCalTensorImpl | Loaded 213 calibration tensors. (3 failed) >> Initializing quantization schemes  +=====================+ | ENABLED SCHEMES |  +=====================+ | SpinR1 | | SpinR2 | | OptimizeFeedForward |  +=====================+ 3 scheme(s) enabled SpinR1: Will apply to 57 blocks SpinR2: Will apply to 28 attention blocks  SpinR1  [Starting...] 0% SpinR1  [Attenti...] │░░░░░░░░░░░░░░░░░░░░░░░░░│  1% │ 1/57 │ 0:00<0:00 SpinR1  [ConvBlo...] │░░░░░░░░░░░░░░░░░░░░░░░░░│  3% │ 2/57 │ 0:00<0:19 SpinR1  [Attenti...] │█░░░░░░░░░░░░░░░░░░░░░░░░│  5% │ 3/57 │ 0:01<0:30 SpinR1  [ConvBlo...] │█░░░░░░░░░░░░░░░░░░░░░░░░│  7% │ 4/57 │ 0:02<0:30 SpinR1  [Attenti...] │██░░░░░░░░░░░░░░░░░░░░░░░│  8% │ 5/57 │ 0:02<0:29 SpinR1  [ConvBlo...] │██░░░░░░░░░░░░░░░░░░░░░░░│  10% │ 6/57 │ 0:03<0:27 SpinR1  [Attenti...] │███░░░░░░░░░░░░░░░░░░░░░░│  12% │ 7/57 │ 0:03<0:27 SpinR1  [ConvBlo...] │███░░░░░░░░░░░░░░░░░░░░░░│  14% │ 8/57 │ 0:04<0:25 SpinR1  [Attenti...] │███░░░░░░░░░░░░░░░░░░░░░░│  15% │ 9/57 │ 0:05<0:28 SpinR1  [ConvBlo...] │████░░░░░░░░░░░░░░░░░░░░░│  17% │ 10/57 │ 0:06<0:29 SpinR1  [Attenti...] │████░░░░░░░░░░░░░░░░░░░░░│  19% │ 11/57 │ 0:07<0:30 SpinR1  [ConvBlo...] │█████░░░░░░░░░░░░░░░░░░░░│  21% │ 12/57 │ 0:08<0:30 SpinR1  [Attenti...] │█████░░░░░░░░░░░░░░░░░░░░│  22% │ 13/57 │ 0:08<0:29 SpinR1  [ConvBlo...] │██████░░░░░░░░░░░░░░░░░░░│  24% │ 14/57 │ 0:09<0:28 SpinR1  [Attenti...] │██████░░░░░░░░░░░░░░░░░░░│  26% │ 15/57 │ 0:10<0:28 SpinR1  [ConvBlo...] │███████░░░░░░░░░░░░░░░░░░│  28% │ 16/57 │ 0:10<0:27 SpinR1  [Attenti...] │███████░░░░░░░░░░░░░░░░░░│  29% │ 17/57 │ 0:11<0:26 SpinR1  [ConvBlo...] │███████░░░░░░░░░░░░░░░░░░│  31% │ 18/57 │ 0:11<0:25 SpinR1  [Attenti...] │████████░░░░░░░░░░░░░░░░░│  33% │ 19/57 │ 0:12<0:25 SpinR1  [ConvBlo...] │████████░░░░░░░░░░░░░░░░░│  35% │ 20/57 │ 0:13<0:24 SpinR1  [Attenti...] │█████████░░░░░░░░░░░░░░░░│  36% │ 21/57 │ 0:14<0:24 SpinR1  [ConvBlo...] │█████████░░░░░░░░░░░░░░░░│  38% │ 22/57 │ 0:14<0:23 SpinR1  [Attenti...] │██████████░░░░░░░░░░░░░░░│  40% │ 23/57 │ 0:15<0:23 SpinR1  [ConvBlo...] │██████████░░░░░░░░░░░░░░░│  42% │ 24/57 │ 0:16<0:22 SpinR1  [Attenti...] │██████████░░░░░░░░░░░░░░░│  43% │ 25/57 │ 0:16<0:21 SpinR1  [ConvBlo...] │███████████░░░░░░░░░░░░░░│  45% │ 26/57 │ 0:17<0:21 SpinR1  [Attenti...] │███████████░░░░░░░░░░░░░░│  47% │ 27/57 │ 0:18<0:20 SpinR1  [ConvBlo...] │████████████░░░░░░░░░░░░░│  49% │ 28/57 │ 0:18<0:19 SpinR1  [Attenti...] │████████████░░░░░░░░░░░░░│  50% │ 29/57 │ 0:19<0:19 SpinR1  [ConvBlo...] │█████████████░░░░░░░░░░░░│  52% │ 30/57 │ 0:20<0:18 SpinR1  [Attenti...] │█████████████░░░░░░░░░░░░│  54% │ 31/57 │ 0:21<0:17 SpinR1  [ConvBlo...] │██████████████░░░░░░░░░░░│  56% │ 32/57 │ 0:22<0:17 SpinR1  [Attenti...] │██████████████░░░░░░░░░░░│  57% │ 33/57 │ 0:22<0:16 SpinR1  [ConvBlo...] │██████████████░░░░░░░░░░░│  59% │ 34/57 │ 0:23<0:15 SpinR1  [Attenti...] │███████████████░░░░░░░░░░│  61% │ 35/57 │ 0:24<0:15 SpinR1  [ConvBlo...] │███████████████░░░░░░░░░░│  63% │ 36/57 │ 0:24<0:14 SpinR1  [Attenti...] │████████████████░░░░░░░░░│  64% │ 37/57 │ 0:25<0:13 SpinR1  [ConvBlo...] │████████████████░░░░░░░░░│  66% │ 38/57 │ 0:26<0:13 SpinR1  [Attenti...] │█████████████████░░░░░░░░│  68% │ 39/57 │ 0:26<0:12 SpinR1  [ConvBlo...] │█████████████████░░░░░░░░│  70% │ 40/57 │ 0:27<0:11 SpinR1  [Attenti...] │█████████████████░░░░░░░░│  71% │ 41/57 │ 0:28<0:11 SpinR1  [ConvBlo...] │██████████████████░░░░░░░│  73% │ 42/57 │ 0:29<0:10 SpinR1  [Attenti...] │██████████████████░░░░░░░│  75% │ 43/57 │ 0:29<0:09 SpinR1  [ConvBlo...] │███████████████████░░░░░░│  77% │ 44/57 │ 0:30<0:09 SpinR1  [Attenti...] │███████████████████░░░░░░│  78% │ 45/57 │ 0:31<0:08 SpinR1  [ConvBlo...] │████████████████████░░░░░│  80% │ 46/57 │ 0:32<0:07 SpinR1  [Attenti...] │████████████████████░░░░░│  82% │ 47/57 │ 0:32<0:06 SpinR1  [ConvBlo...] │█████████████████████░░░░│  84% │ 48/57 │ 0:33<0:06 SpinR1  [Attenti...] │█████████████████████░░░░│  85% │ 49/57 │ 0:34<0:05 SpinR1  [ConvBlo...] │█████████████████████░░░░│  87% │ 50/57 │ 0:34<0:04 SpinR1  [Attenti...] │██████████████████████░░░│  89% │ 51/57 │ 0:35<0:04 SpinR1  [ConvBlo...] │██████████████████████░░░│  91% │ 52/57 │ 0:36<0:03 SpinR1  [Attenti...] │███████████████████████░░│  92% │ 53/57 │ 0:37<0:02 SpinR1  [ConvBlo...] │███████████████████████░░│  94% │ 54/57 │ 0:38<0:02 SpinR1  [Attenti...] │████████████████████████░│  96% │ 55/57 │ 0:39<0:01 SpinR1  [ConvBlo...] │████████████████████████░│  98% │ 56/57 │ 0:39<0:00 SpinR1  [ConvBlo...] │█████████████████████████│ 100% │ 57/57 │ 0:40<0:00 SpinR1  [Done ] │█████████████████████████│ 100% │ 57/57 │ 0:40 ✓  SpinR2  [Starting...] 0% SpinR2  [languag...] │░░░░░░░░░░░░░░░░░░░░░░░░░│  3% │ 1/28 │ 0:00<0:00 SpinR2  [languag...] │█░░░░░░░░░░░░░░░░░░░░░░░░│  7% │ 2/28 │ 0:00<0:05 SpinR2  [languag...] │██░░░░░░░░░░░░░░░░░░░░░░░│  10% │ 3/28 │ 0:00<0:08 SpinR2  [languag...] │███░░░░░░░░░░░░░░░░░░░░░░│  14% │ 4/28 │ 0:01<0:07 SpinR2  [languag...] │████░░░░░░░░░░░░░░░░░░░░░│  17% │ 5/28 │ 0:01<0:06 SpinR2  [languag...] │█████░░░░░░░░░░░░░░░░░░░░│  21% │ 6/28 │ 0:01<0:05 SpinR2  [languag...] │██████░░░░░░░░░░░░░░░░░░░│  25% │ 7/28 │ 0:01<0:05 SpinR2  [languag...] │███████░░░░░░░░░░░░░░░░░░│  28% │ 8/28 │ 0:02<0:05 SpinR2  [languag...] │████████░░░░░░░░░░░░░░░░░│  32% │ 9/28 │ 0:02<0:05 SpinR2  [languag...] │████████░░░░░░░░░░░░░░░░░│  35% │ 10/28 │ 0:02<0:05 SpinR2  [languag...] │█████████░░░░░░░░░░░░░░░░│  39% │ 11/28 │ 0:03<0:04 SpinR2  [languag...] │██████████░░░░░░░░░░░░░░░│  42% │ 12/28 │ 0:03<0:04 SpinR2  [languag...] │███████████░░░░░░░░░░░░░░│  46% │ 13/28 │ 0:03<0:04 SpinR2  [languag...] │████████████░░░░░░░░░░░░░│  50% │ 14/28 │ 0:04<0:04 SpinR2  [languag...] │█████████████░░░░░░░░░░░░│  53% │ 15/28 │ 0:04<0:03 SpinR2  [languag...] │██████████████░░░░░░░░░░░│  57% │ 16/28 │ 0:04<0:03 SpinR2  [languag...] │███████████████░░░░░░░░░░│  60% │ 17/28 │ 0:04<0:03 SpinR2  [languag...] │████████████████░░░░░░░░░│  64% │ 18/28 │ 0:05<0:02 SpinR2  [languag...] │████████████████░░░░░░░░░│  67% │ 19/28 │ 0:05<0:02 SpinR2  [languag...] │█████████████████░░░░░░░░│  71% │ 20/28 │ 0:05<0:02 SpinR2  [languag...] │██████████████████░░░░░░░│  75% │ 21/28 │ 0:05<0:01 SpinR2  [languag...] │███████████████████░░░░░░│  78% │ 22/28 │ 0:06<0:01 SpinR2  [languag...] │████████████████████░░░░░│  82% │ 23/28 │ 0:06<0:01 SpinR2  [languag...] │█████████████████████░░░░│  85% │ 24/28 │ 0:06<0:01 SpinR2  [languag...] │██████████████████████░░░│  89% │ 25/28 │ 0:07<0:00 SpinR2  [languag...] │███████████████████████░░│  92% │ 26/28 │ 0:07<0:00 SpinR2  [languag...] │████████████████████████░│  96% │ 27/28 │ 0:08<0:00 SpinR2  [languag...] │█████████████████████████│ 100% │ 28/28 │ 0:08<0:00 SpinR2  [Done ] │█████████████████████████│ 100% │ 28/28 │ 0:08 ✓ OptimizeFeedForward: Will apply to 28 FFN modules  Running Calibration (batch=1) [Starting...] 0% Running Calibration (batch=1) │█░░░░░░░░░░░░░░░░░░░░░░░░│  6% │ 1/16 │ 0:00<0:00 Running Calibration (batch=1) │███░░░░░░░░░░░░░░░░░░░░░░│  12% │ 2/16 │ 0:03<0:21 Running Calibration (batch=1) │████░░░░░░░░░░░░░░░░░░░░░│  18% │ 3/16 │ 0:07<0:31 Running Calibration (batch=1) │██████░░░░░░░░░░░░░░░░░░░│  25% │ 4/16 │ 0:08<0:24 Running Calibration (batch=1) │███████░░░░░░░░░░░░░░░░░░│  31% │ 5/16 │ 0:12<0:26 Running Calibration (batch=1) │█████████░░░░░░░░░░░░░░░░│  37% │ 6/16 │ 0:15<0:25 Running Calibration (batch=1) │██████████░░░░░░░░░░░░░░░│  43% │ 7/16 │ 0:21<0:27 Running Calibration (batch=1) │████████████░░░░░░░░░░░░░│  50% │ 8/16 │ 0:22<0:22 Running Calibration (batch=1) │██████████████░░░░░░░░░░░│  56% │ 9/16 │ 0:27<0:21 Running Calibration (batch=1) │███████████████░░░░░░░░░░│  62% │ 10/16 │ 0:29<0:17 Running Calibration (batch=1) │█████████████████░░░░░░░░│  68% │ 11/16 │ 0:31<0:14 Running Calibration (batch=1) │██████████████████░░░░░░░│  75% │ 12/16 │ 0:39<0:13 Running Calibration (batch=1) │████████████████████░░░░░│  81% │ 13/16 │ 0:43<0:10 Running Calibration (batch=1) │█████████████████████░░░░│  87% │ 14/16 │ 0:48<0:06 Running Calibration (batch=1) │███████████████████████░░│  93% │ 15/16 │ 0:52<0:03 Running Calibration (batch=1) │█████████████████████████│ 100% │ 16/16 │ 0:57<0:00 Running Calibration (batch=1) [Done ] │█████████████████████████│ 100% │ 16/16 │ 1:02 ✓  OptimizeFFN  [Starting...] 0% OptimizeFFN  [languag...] │░░░░░░░░░░░░░░░░░░░░░░░░░│  3% │ 1/28 │ 0:00<0:00 OptimizeFFN  [languag...] │█░░░░░░░░░░░░░░░░░░░░░░░░│  7% │ 2/28 │ 0:00<0:00 OptimizeFFN  [languag...] │██░░░░░░░░░░░░░░░░░░░░░░░│  10% │ 3/28 │ 0:00<0:00 OptimizeFFN  [languag...] │███░░░░░░░░░░░░░░░░░░░░░░│  14% │ 4/28 │ 0:00<0:00 OptimizeFFN  [languag...] │████░░░░░░░░░░░░░░░░░░░░░│  17% │ 5/28 │ 0:00<0:00 OptimizeFFN  [languag...] │█████░░░░░░░░░░░░░░░░░░░░│  21% │ 6/28 │ 0:00<0:00 OptimizeFFN  [languag...] │██████░░░░░░░░░░░░░░░░░░░│  25% │ 7/28 │ 0:00<0:00 OptimizeFFN  [languag...] │███████░░░░░░░░░░░░░░░░░░│  28% │ 8/28 │ 0:00<0:00 OptimizeFFN  [languag...] │████████░░░░░░░░░░░░░░░░░│  32% │ 9/28 │ 0:00<0:00 OptimizeFFN  [languag...] │████████░░░░░░░░░░░░░░░░░│  35% │ 10/28 │ 0:00<0:00 OptimizeFFN  [languag...] │█████████░░░░░░░░░░░░░░░░│  39% │ 11/28 │ 0:00<0:00 OptimizeFFN  [languag...] │██████████░░░░░░░░░░░░░░░│  42% │ 12/28 │ 0:00<0:00 OptimizeFFN  [languag...] │███████████░░░░░░░░░░░░░░│  46% │ 13/28 │ 0:00<0:00 OptimizeFFN  [languag...] │████████████░░░░░░░░░░░░░│  50% │ 14/28 │ 0:00<0:00 OptimizeFFN  [languag...] │█████████████░░░░░░░░░░░░│  53% │ 15/28 │ 0:00<0:00 OptimizeFFN  [languag...] │██████████████░░░░░░░░░░░│  57% │ 16/28 │ 0:00<0:00 OptimizeFFN  [languag...] │███████████████░░░░░░░░░░│  60% │ 17/28 │ 0:00<0:00 OptimizeFFN  [languag...] │████████████████░░░░░░░░░│  64% │ 18/28 │ 0:00<0:00 OptimizeFFN  [languag...] │████████████████░░░░░░░░░│  67% │ 19/28 │ 0:00<0:00 OptimizeFFN  [languag...] │█████████████████░░░░░░░░│  71% │ 20/28 │ 0:00<0:00 OptimizeFFN  [languag...] │██████████████████░░░░░░░│  75% │ 21/28 │ 0:00<0:00 OptimizeFFN  [languag...] │███████████████████░░░░░░│  78% │ 22/28 │ 0:00<0:00 OptimizeFFN  [languag...] │████████████████████░░░░░│  82% │ 23/28 │ 0:00<0:00 OptimizeFFN  [languag...] │█████████████████████░░░░│  85% │ 24/28 │ 0:00<0:00 OptimizeFFN  [languag...] │██████████████████████░░░│  89% │ 25/28 │ 0:00<0:00 OptimizeFFN  [languag...] │███████████████████████░░│  92% │ 26/28 │ 0:00<0:00 OptimizeFFN  [languag...] │████████████████████████░│  96% │ 27/28 │ 0:00<0:00 OptimizeFFN  [mul_29/...] │█████████████████████████│ 100% │ 28/28 │ 0:00<0:00 OptimizeFFN  [Done ] │█████████████████████████│ 100% │ 28/28 │ 0:00 ✓  Running Calibration (batch=1) [Starting...] 0% Running Calibration (batch=1) │█░░░░░░░░░░░░░░░░░░░░░░░░│  6% │ 1/16 │ 0:00<0:00 Running Calibration (batch=1) │███░░░░░░░░░░░░░░░░░░░░░░│  12% │ 2/16 │ 0:12<1:24 Running Calibration (batch=1) │████░░░░░░░░░░░░░░░░░░░░░│  18% │ 3/16 │ 0:39<2:49 Running Calibration (batch=1) │██████░░░░░░░░░░░░░░░░░░░│  25% │ 4/16 │ 0:52<2:36 Running Calibration (batch=1) │███████░░░░░░░░░░░░░░░░░░│  31% │ 5/16 │ 1:10<2:35 Running Calibration (batch=1) │█████████░░░░░░░░░░░░░░░░│  37% │ 6/16 │ 1:27<2:26 Running Calibration (batch=1) │██████████░░░░░░░░░░░░░░░│  43% │ 7/16 │ 1:48<2:19 Running Calibration (batch=1) │████████████░░░░░░░░░░░░░│  50% │ 8/16 │ 1:55<1:55 Running Calibration (batch=1) │██████████████░░░░░░░░░░░│  56% │ 9/16 │ 2:06<1:38 Running Calibration (batch=1) │███████████████░░░░░░░░░░│  62% │ 10/16 │ 2:13<1:20 Running Calibration (batch=1) │█████████████████░░░░░░░░│  68% │ 11/16 │ 2:20<1:03 Running Calibration (batch=1) │██████████████████░░░░░░░│  75% │ 12/16 │ 2:36<0:52 Running Calibration (batch=1) │████████████████████░░░░░│  81% │ 13/16 │ 2:50<0:39 Running Calibration (batch=1) │█████████████████████░░░░│  87% │ 14/16 │ 3:04<0:26 Running Calibration (batch=1) │███████████████████████░░│  93% │ 15/16 │ 3:16<0:13 Running Calibration (batch=1) │█████████████████████████│ 100% │ 16/16 │ 3:28<0:00 Running Calibration (batch=1) [Done ] │█████████████████████████│ 100% │ 16/16 │ 3:42 ✓  Running Calibration (batch=1) [Starting...] 0% Running Calibration (batch=1) │░░░░░░░░░░░░░░░░░░░░░░░░░│  0% │ 1/213 │ 0:00<0:00 Running Calibration (batch=1) │░░░░░░░░░░░░░░░░░░░░░░░░░│  0% │ 2/213 │ 0:02<5:16 Running Calibration (batch=1) │░░░░░░░░░░░░░░░░░░░░░░░░░│  1% │ 3/213 │ 0:09<10:57 Running Calibration (batch=1) │░░░░░░░░░░░░░░░░░░░░░░░░░│  1% │ 4/213 │ 0:10<9:29 Running Calibration (batch=1) │░░░░░░░░░░░░░░░░░░░░░░░░░│  2% │ 5/213 │ 0:15<10:36 Running Calibration (batch=1) │░░░░░░░░░░░░░░░░░░░░░░░░░│  2% │ 6/213 │ 0:19<10:59 Running Calibration (batch=1) │░░░░░░░░░░░░░░░░░░░░░░░░░│  3% │ 7/213 │ 0:24<12:12 Running Calibration (batch=1) │░░░░░░░░░░░░░░░░░░░░░░░░░│  3% │ 8/213 │ 0:26<11:16 Running Calibration (batch=1) │█░░░░░░░░░░░░░░░░░░░░░░░░│  4% │ 9/213 │ 0:27<10:23 Running Calibration (batch=1) │█░░░░░░░░░░░░░░░░░░░░░░░░│  4% │ 10/213 │ 0:31<10:41 Running Calibration (batch=1) │█░░░░░░░░░░░░░░░░░░░░░░░░│  5% │ 11/213 │ 0:33<10:24 Running Calibration (batch=1) │█░░░░░░░░░░░░░░░░░░░░░░░░│  5% │ 12/213 │ 0:36<10:08 Running Calibration (batch=1) │█░░░░░░░░░░░░░░░░░░░░░░░░│  6% │ 13/213 │ 0:43<11:09 Running Calibration (batch=1) │█░░░░░░░░░░░░░░░░░░░░░░░░│  6% │ 14/213 │ 0:47<11:16 Running Calibration (batch=1) │█░░░░░░░░░░░░░░░░░░░░░░░░│  7% │ 15/213 │ 0:51<11:20 Running Calibration (batch=1) │█░░░░░░░░░░░░░░░░░░░░░░░░│  7% │ 16/213 │ 0:56<11:38 Running Calibration (batch=1) │█░░░░░░░░░░░░░░░░░░░░░░░░│  7% │ 17/213 │ 1:00<11:43 Running Calibration (batch=1) │██░░░░░░░░░░░░░░░░░░░░░░░│  8% │ 18/213 │ 1:04<11:43 Running Calibration (batch=1) │██░░░░░░░░░░░░░░░░░░░░░░░│  8% │ 19/213 │ 1:08<11:41 Running Calibration (batch=1) │██░░░░░░░░░░░░░░░░░░░░░░░│  9% │ 20/213 │ 1:13<11:52 Running Calibration (batch=1) │██░░░░░░░░░░░░░░░░░░░░░░░│  9% │ 21/213 │ 1:18<12:02 Running Calibration (batch=1) │██░░░░░░░░░░░░░░░░░░░░░░░│  10% │ 22/213 │ 1:23<12:03 Running Calibration (batch=1) │██░░░░░░░░░░░░░░░░░░░░░░░│  10% │ 23/213 │ 1:26<11:53 Running Calibration (batch=1) │██░░░░░░░░░░░░░░░░░░░░░░░│  11% │ 24/213 │ 1:30<11:51 Running Calibration (batch=1) │██░░░░░░░░░░░░░░░░░░░░░░░│  11% │ 25/213 │ 1:34<11:48 Running Calibration (batch=1) │███░░░░░░░░░░░░░░░░░░░░░░│  12% │ 26/213 │ 1:38<11:46 Running Calibration (batch=1) │███░░░░░░░░░░░░░░░░░░░░░░│  12% │ 27/213 │ 1:39<11:27 Running Calibration (batch=1) │███░░░░░░░░░░░░░░░░░░░░░░│  13% │ 28/213 │ 1:44<11:33 Running Calibration (batch=1) │███░░░░░░░░░░░░░░░░░░░░░░│  13% │ 29/213 │ 1:50<11:41 Running Calibration (batch=1) │███░░░░░░░░░░░░░░░░░░░░░░│  14% │ 30/213 │ 1:55<11:42 Running Calibration (batch=1) │███░░░░░░░░░░░░░░░░░░░░░░│  14% │ 31/213 │ 1:59<11:40 Running Calibration (batch=1) │███░░░░░░░░░░░░░░░░░░░░░░│  15% │ 32/213 │ 2:03<11:39 Running Calibration (batch=1) │███░░░░░░░░░░░░░░░░░░░░░░│  15% │ 33/213 │ 2:07<11:37 Running Calibration (batch=1) │███░░░░░░░░░░░░░░░░░░░░░░│  15% │ 34/213 │ 2:09<11:21 Running Calibration (batch=1) │████░░░░░░░░░░░░░░░░░░░░░│  16% │ 35/213 │ 2:12<11:13 Running Calibration (batch=1) │████░░░░░░░░░░░░░░░░░░░░░│  16% │ 36/213 │ 2:18<11:19 Running Calibration (batch=1) │████░░░░░░░░░░░░░░░░░░░░░│  17% │ 37/213 │ 2:21<11:11 Running Calibration (batch=1) │████░░░░░░░░░░░░░░░░░░░░░│  17% │ 38/213 │ 2:25<11:08 Running Calibration (batch=1) │████░░░░░░░░░░░░░░░░░░░░░│  18% │ 39/213 │ 2:28<11:03 Running Calibration (batch=1) │████░░░░░░░░░░░░░░░░░░░░░│  18% │ 40/213 │ 2:32<11:00 Running Calibration (batch=1) │████░░░░░░░░░░░░░░░░░░░░░│  19% │ 41/213 │ 2:38<11:05 Running Calibration (batch=1) │████░░░░░░░░░░░░░░░░░░░░░│  19% │ 42/213 │ 2:42<11:01 Running Calibration (batch=1) │█████░░░░░░░░░░░░░░░░░░░░│  20% │ 43/213 │ 2:46<10:57 Running Calibration (batch=1) │█████░░░░░░░░░░░░░░░░░░░░│  20% │ 44/213 │ 2:54<11:11 Running Calibration (batch=1) │█████░░░░░░░░░░░░░░░░░░░░│  21% │ 45/213 │ 3:00<11:12 Running Calibration (batch=1) │█████░░░░░░░░░░░░░░░░░░░░│  21% │ 46/213 │ 3:05<11:13 Running Calibration (batch=1) │█████░░░░░░░░░░░░░░░░░░░░│  22% │ 47/213 │ 3:10<11:13 Running Calibration (batch=1) │█████░░░░░░░░░░░░░░░░░░░░│  22% │ 48/213 │ 3:14<11:09 Running Calibration (batch=1) │█████░░░░░░░░░░░░░░░░░░░░│  23% │ 49/213 │ 3:18<11:05 Running Calibration (batch=1) │█████░░░░░░░░░░░░░░░░░░░░│  23% │ 50/213 │ 3:20<10:53 Running Calibration (batch=1) │█████░░░░░░░░░░░░░░░░░░░░│  23% │ 51/213 │ 3:22<10:44 Running Calibration (batch=1) │██████░░░░░░░░░░░░░░░░░░░│  24% │ 52/213 │ 3:24<10:33 Running Calibration (batch=1) │██████░░░░░░░░░░░░░░░░░░░│  24% │ 53/213 │ 3:28<10:30 Running Calibration (batch=1) │██████░░░░░░░░░░░░░░░░░░░│  25% │ 54/213 │ 3:32<10:26 Running Calibration (batch=1) │██████░░░░░░░░░░░░░░░░░░░│  25% │ 55/213 │ 3:37<10:25 Running Calibration (batch=1) │██████░░░░░░░░░░░░░░░░░░░│  26% │ 56/213 │ 3:43<10:27 Running Calibration (batch=1) │██████░░░░░░░░░░░░░░░░░░░│  26% │ 57/213 │ 3:47<10:23 Running Calibration (batch=1) │██████░░░░░░░░░░░░░░░░░░░│  27% │ 58/213 │ 3:52<10:21 Running Calibration (batch=1) │██████░░░░░░░░░░░░░░░░░░░│  27% │ 59/213 │ 3:55<10:14 Running Calibration (batch=1) │███████░░░░░░░░░░░░░░░░░░│  28% │ 60/213 │ 4:00<10:13 Running Calibration (batch=1) │███████░░░░░░░░░░░░░░░░░░│  28% │ 61/213 │ 4:05<10:12 Running Calibration (batch=1) │███████░░░░░░░░░░░░░░░░░░│  29% │ 62/213 │ 4:07<10:03 Running Calibration (batch=1) │███████░░░░░░░░░░░░░░░░░░│  29% │ 63/213 │ 4:13<10:04 Running Calibration (batch=1) │███████░░░░░░░░░░░░░░░░░░│  30% │ 64/213 │ 4:18<10:01 Running Calibration (batch=1) │███████░░░░░░░░░░░░░░░░░░│  30% │ 65/213 │ 4:23<9:59 Running Calibration (batch=1) │███████░░░░░░░░░░░░░░░░░░│  30% │ 66/213 │ 4:28<9:58 Running Calibration (batch=1) │███████░░░░░░░░░░░░░░░░░░│  31% │ 67/213 │ 4:32<9:53 Running Calibration (batch=1) │███████░░░░░░░░░░░░░░░░░░│  31% │ 68/213 │ 4:38<9:53 Running Calibration (batch=1) │████████░░░░░░░░░░░░░░░░░│  32% │ 69/213 │ 4:44<9:54 Running Calibration (batch=1) │████████░░░░░░░░░░░░░░░░░│  32% │ 70/213 │ 4:48<9:49 Running Calibration (batch=1) │████████░░░░░░░░░░░░░░░░░│  33% │ 71/213 │ 4:52<9:45 Running Calibration (batch=1) │████████░░░░░░░░░░░░░░░░░│  33% │ 72/213 │ 4:57<9:41 Running Calibration (batch=1) │████████░░░░░░░░░░░░░░░░░│  34% │ 73/213 │ 5:02<9:39 Running Calibration (batch=1) │████████░░░░░░░░░░░░░░░░░│  34% │ 74/213 │ 5:05<9:32 Running Calibration (batch=1) │████████░░░░░░░░░░░░░░░░░│  35% │ 75/213 │ 5:10<9:31 Running Calibration (batch=1) │████████░░░░░░░░░░░░░░░░░│  35% │ 76/213 │ 5:13<9:24 Running Calibration (batch=1) │█████████░░░░░░░░░░░░░░░░│  36% │ 77/213 │ 5:15<9:17 Running Calibration (batch=1) │█████████░░░░░░░░░░░░░░░░│  36% │ 78/213 │ 5:17<9:09 Running Calibration (batch=1) │█████████░░░░░░░░░░░░░░░░│  37% │ 79/213 │ 5:20<9:02 Running Calibration (batch=1) │█████████░░░░░░░░░░░░░░░░│  37% │ 80/213 │ 5:26<9:02 Running Calibration (batch=1) │█████████░░░░░░░░░░░░░░░░│  38% │ 81/213 │ 5:30<8:58 Running Calibration (batch=1) │█████████░░░░░░░░░░░░░░░░│  38% │ 82/213 │ 5:40<9:03 Running Calibration (batch=1) │█████████░░░░░░░░░░░░░░░░│  38% │ 83/213 │ 5:43<8:57 Running Calibration (batch=1) │█████████░░░░░░░░░░░░░░░░│  39% │ 84/213 │ 5:46<8:51 Running Calibration (batch=1) │█████████░░░░░░░░░░░░░░░░│  39% │ 85/213 │ 5:49<8:46 Running Calibration (batch=1) │██████████░░░░░░░░░░░░░░░│  40% │ 86/213 │ 5:54<8:42 Running Calibration (batch=1) │██████████░░░░░░░░░░░░░░░│  40% │ 87/213 │ 5:57<8:38 Running Calibration (batch=1) │██████████░░░░░░░░░░░░░░░│  41% │ 88/213 │ 6:01<8:33 Running Calibration (batch=1) │██████████░░░░░░░░░░░░░░░│  41% │ 89/213 │ 6:03<8:27 Running Calibration (batch=1) │██████████░░░░░░░░░░░░░░░│  42% │ 90/213 │ 6:07<8:22 Running Calibration (batch=1) │██████████░░░░░░░░░░░░░░░│  42% │ 91/213 │ 6:10<8:16 Running Calibration (batch=1) │██████████░░░░░░░░░░░░░░░│  43% │ 92/213 │ 6:16<8:14 Running Calibration (batch=1) │██████████░░░░░░░░░░░░░░░│  43% │ 93/213 │ 6:25<8:17 Running Calibration (batch=1) │███████████░░░░░░░░░░░░░░│  44% │ 94/213 │ 6:28<8:12 Running Calibration (batch=1) │███████████░░░░░░░░░░░░░░│  44% │ 95/213 │ 6:32<8:07 Running Calibration (batch=1) │███████████░░░░░░░░░░░░░░│  45% │ 96/213 │ 6:35<8:02 Running Calibration (batch=1) │███████████░░░░░░░░░░░░░░│  45% │ 97/213 │ 6:39<7:57 Running Calibration (batch=1) │███████████░░░░░░░░░░░░░░│  46% │ 98/213 │ 6:44<7:54 Running Calibration (batch=1) │███████████░░░░░░░░░░░░░░│  46% │ 99/213 │ 6:48<7:50 Running Calibration (batch=1) │███████████░░░░░░░░░░░░░░│  46% │ 100/213 │ 6:52<7:45 Running Calibration (batch=1) │███████████░░░░░░░░░░░░░░│  47% │ 101/213 │ 6:56<7:41 Running Calibration (batch=1) │███████████░░░░░░░░░░░░░░│  47% │ 102/213 │ 7:00<7:37 Running Calibration (batch=1) │████████████░░░░░░░░░░░░░│  48% │ 103/213 │ 7:05<7:34 Running Calibration (batch=1) │████████████░░░░░░░░░░░░░│  48% │ 104/213 │ 7:09<7:29 Running Calibration (batch=1) │████████████░░░░░░░░░░░░░│  49% │ 105/213 │ 7:13<7:26 Running Calibration (batch=1) │████████████░░░░░░░░░░░░░│  49% │ 106/213 │ 7:17<7:21 Running Calibration (batch=1) │████████████░░░░░░░░░░░░░│  50% │ 107/213 │ 7:23<7:18 Running Calibration (batch=1) │████████████░░░░░░░░░░░░░│  50% │ 108/213 │ 7:29<7:17 Running Calibration (batch=1) │████████████░░░░░░░░░░░░░│  51% │ 109/213 │ 7:36<7:15 Running Calibration (batch=1) │████████████░░░░░░░░░░░░░│  51% │ 110/213 │ 7:44<7:15 Running Calibration (batch=1) │█████████████░░░░░░░░░░░░│  52% │ 111/213 │ 7:48<7:10 Running Calibration (batch=1) │█████████████░░░░░░░░░░░░│  52% │ 112/213 │ 7:59<7:12 Running Calibration (batch=1) │█████████████░░░░░░░░░░░░│  53% │ 113/213 │ 8:08<7:12 Running Calibration (batch=1) │█████████████░░░░░░░░░░░░│  53% │ 114/213 │ 8:17<7:11 Running Calibration (batch=1) │█████████████░░░░░░░░░░░░│  53% │ 115/213 │ 8:23<7:09 Running Calibration (batch=1) │█████████████░░░░░░░░░░░░│  54% │ 116/213 │ 8:28<7:05 Running Calibration (batch=1) │█████████████░░░░░░░░░░░░│  54% │ 117/213 │ 8:33<7:01 Running Calibration (batch=1) │█████████████░░░░░░░░░░░░│  55% │ 118/213 │ 8:38<6:57 Running Calibration (batch=1) │█████████████░░░░░░░░░░░░│  55% │ 119/213 │ 8:43<6:53 Running Calibration (batch=1) │██████████████░░░░░░░░░░░│  56% │ 120/213 │ 8:52<6:52 Running Calibration (batch=1) │██████████████░░░░░░░░░░░│  56% │ 121/213 │ 9:01<6:51 Running Calibration (batch=1) │██████████████░░░░░░░░░░░│  57% │ 122/213 │ 9:09<6:49 Running Calibration (batch=1) │██████████████░░░░░░░░░░░│  57% │ 123/213 │ 9:20<6:49 Running Calibration (batch=1) │██████████████░░░░░░░░░░░│  58% │ 124/213 │ 9:26<6:46 Running Calibration (batch=1) │██████████████░░░░░░░░░░░│  58% │ 125/213 │ 9:29<6:41 Running Calibration (batch=1) │██████████████░░░░░░░░░░░│  59% │ 126/213 │ 9:34<6:36 Running Calibration (batch=1) │██████████████░░░░░░░░░░░│  59% │ 127/213 │ 9:38<6:31 Running Calibration (batch=1) │███████████████░░░░░░░░░░│  60% │ 128/213 │ 9:44<6:28 Running Calibration (batch=1) │███████████████░░░░░░░░░░│  60% │ 129/213 │ 9:52<6:25 Running Calibration (batch=1) │███████████████░░░░░░░░░░│  61% │ 130/213 │ 9:58<6:22 Running Calibration (batch=1) │███████████████░░░░░░░░░░│  61% │ 131/213 │ 10:04<6:18 Running Calibration (batch=1) │███████████████░░░░░░░░░░│  61% │ 132/213 │ 10:08<6:13 Running Calibration (batch=1) │███████████████░░░░░░░░░░│  62% │ 133/213 │ 10:12<6:08 Running Calibration (batch=1) │███████████████░░░░░░░░░░│  62% │ 134/213 │ 10:19<6:05 Running Calibration (batch=1) │███████████████░░░░░░░░░░│  63% │ 135/213 │ 10:20<5:58 Running Calibration (batch=1) │███████████████░░░░░░░░░░│  63% │ 136/213 │ 10:24<5:53 Running Calibration (batch=1) │████████████████░░░░░░░░░│  64% │ 137/213 │ 10:27<5:48 Running Calibration (batch=1) │████████████████░░░░░░░░░│  64% │ 138/213 │ 10:32<5:43 Running Calibration (batch=1) │████████████████░░░░░░░░░│  65% │ 139/213 │ 10:37<5:39 Running Calibration (batch=1) │████████████████░░░░░░░░░│  65% │ 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│█████████████████░░░░░░░░│  71% │ 152/213 │ 11:24<4:34 Running Calibration (batch=1) │█████████████████░░░░░░░░│  71% │ 153/213 │ 11:28<4:29 Running Calibration (batch=1) │██████████████████░░░░░░░│  72% │ 154/213 │ 11:31<4:24 Running Calibration (batch=1) │██████████████████░░░░░░░│  72% │ 155/213 │ 11:35<4:20 Running Calibration (batch=1) │██████████████████░░░░░░░│  73% │ 156/213 │ 11:38<4:15 Running Calibration (batch=1) │██████████████████░░░░░░░│  73% │ 157/213 │ 11:41<4:10 Running Calibration (batch=1) │██████████████████░░░░░░░│  74% │ 158/213 │ 11:47<4:06 Running Calibration (batch=1) │██████████████████░░░░░░░│  74% │ 159/213 │ 11:51<4:01 Running Calibration (batch=1) │██████████████████░░░░░░░│  75% │ 160/213 │ 11:55<3:56 Running Calibration (batch=1) │██████████████████░░░░░░░│  75% │ 161/213 │ 11:58<3:52 Running Calibration (batch=1) │███████████████████░░░░░░│  76% │ 162/213 │ 12:02<3:47 Running Calibration (batch=1) │███████████████████░░░░░░│  76% │ 163/213 │ 12:09<3:43 Running Calibration (batch=1) │███████████████████░░░░░░│  76% │ 164/213 │ 12:13<3:39 Running Calibration (batch=1) │███████████████████░░░░░░│  77% │ 165/213 │ 12:18<3:34 Running Calibration (batch=1) │███████████████████░░░░░░│  77% │ 166/213 │ 12:24<3:30 Running Calibration (batch=1) │███████████████████░░░░░░│  78% │ 167/213 │ 12:31<3:26 Running Calibration (batch=1) │███████████████████░░░░░░│  78% │ 168/213 │ 12:34<3:22 Running Calibration (batch=1) │███████████████████░░░░░░│  79% │ 169/213 │ 12:39<3:17 Running Calibration (batch=1) │███████████████████░░░░░░│  79% │ 170/213 │ 12:43<3:13 Running Calibration (batch=1) │████████████████████░░░░░│  80% │ 171/213 │ 12:46<3:08 Running Calibration (batch=1) │████████████████████░░░░░│  80% │ 172/213 │ 12:50<3:03 Running Calibration (batch=1) │████████████████████░░░░░│  81% │ 173/213 │ 12:56<2:59 Running Calibration (batch=1) │████████████████████░░░░░│  81% │ 174/213 │ 13:01<2:55 Running Calibration (batch=1) │████████████████████░░░░░│  82% │ 175/213 │ 13:05<2:50 Running Calibration (batch=1) │████████████████████░░░░░│  82% │ 176/213 │ 13:12<2:46 Running Calibration (batch=1) │████████████████████░░░░░│  83% │ 177/213 │ 13:18<2:42 Running Calibration (batch=1) │████████████████████░░░░░│  83% │ 178/213 │ 13:22<2:37 Running Calibration (batch=1) │█████████████████████░░░░│  84% │ 179/213 │ 13:24<2:32 Running Calibration (batch=1) │█████████████████████░░░░│  84% │ 180/213 │ 13:26<2:27 Running Calibration (batch=1) │█████████████████████░░░░│  84% │ 181/213 │ 13:28<2:22 Running Calibration (batch=1) │█████████████████████░░░░│  85% │ 182/213 │ 13:30<2:18 Running Calibration (batch=1) │█████████████████████░░░░│  85% │ 183/213 │ 13:36<2:13 Running Calibration (batch=1) │█████████████████████░░░░│  86% │ 184/213 │ 13:39<2:09 Running Calibration (batch=1) │█████████████████████░░░░│  86% │ 185/213 │ 13:44<2:04 Running Calibration (batch=1) │█████████████████████░░░░│  87% │ 186/213 │ 13:49<2:00 Running Calibration (batch=1) │█████████████████████░░░░│  87% │ 187/213 │ 13:51<1:55 Running Calibration (batch=1) │██████████████████████░░░│  88% │ 188/213 │ 13:55<1:51 Running Calibration (batch=1) │██████████████████████░░░│  88% │ 189/213 │ 13:58<1:46 Running Calibration (batch=1) │██████████████████████░░░│  89% │ 190/213 │ 14:03<1:42 Running Calibration (batch=1) │██████████████████████░░░│  89% │ 191/213 │ 14:08<1:37 Running Calibration (batch=1) │██████████████████████░░░│  90% │ 192/213 │ 14:11<1:33 Running Calibration (batch=1) │██████████████████████░░░│  90% │ 193/213 │ 14:19<1:29 Running Calibration (batch=1) │██████████████████████░░░│  91% │ 194/213 │ 14:23<1:24 Running Calibration (batch=1) │██████████████████████░░░│  91% │ 195/213 │ 14:26<1:19 Running Calibration (batch=1) │███████████████████████░░│  92% │ 196/213 │ 14:32<1:15 Running Calibration (batch=1) │███████████████████████░░│  92% │ 197/213 │ 14:37<1:11 Running Calibration (batch=1) │███████████████████████░░│  92% │ 198/213 │ 14:41<1:06 Running Calibration (batch=1) │███████████████████████░░│  93% │ 199/213 │ 14:46<1:02 Running Calibration (batch=1) │███████████████████████░░│  93% │ 200/213 │ 14:48<0:57 Running Calibration (batch=1) │███████████████████████░░│  94% │ 201/213 │ 14:52<0:53 Running Calibration (batch=1) │███████████████████████░░│  94% │ 202/213 │ 14:56<0:48 Running Calibration (batch=1) │███████████████████████░░│  95% │ 203/213 │ 14:59<0:44 Running Calibration (batch=1) │███████████████████████░░│  95% │ 204/213 │ 15:03<0:39 Running Calibration (batch=1) │████████████████████████░│  96% │ 205/213 │ 15:07<0:35 Running Calibration (batch=1) │████████████████████████░│  96% │ 206/213 │ 15:18<0:31 Running Calibration (batch=1) │████████████████████████░│  97% │ 207/213 │ 15:22<0:26 Running Calibration (batch=1) │████████████████████████░│  97% │ 208/213 │ 15:27<0:22 Running Calibration (batch=1) │████████████████████████░│  98% │ 209/213 │ 15:29<0:17 Running Calibration (batch=1) │████████████████████████░│  98% │ 210/213 │ 15:34<0:13 Running Calibration (batch=1) │████████████████████████░│  99% │ 211/213 │ 15:39<0:08 Running Calibration (batch=1) │████████████████████████░│  99% │ 212/213 │ 15:41<0:04 Running Calibration (batch=1) │█████████████████████████│ 100% │ 213/213 │ 15:45<0:00 Running Calibration (batch=1) [Done ] │█████████████████████████│ 100% │ 213/213 │ 15:47 ✓ >> Weight Quantization (201 layers)  Weight Quant │░░░░░░░░░░░░░░░░░░░░│ 0% 1/201 0:00<0:00 [bridg...] Weight Quant │░░░░░░░░░░░░░░░░░░░░│ 0% 2/201 0:00<0:00 [input...] Weight Quant │░░░░░░░░░░░░░░░░░░░░│ 0% 3/201 0:00<0:00 [cos/r...] Weight Quant │░░░░░░░░░░░░░░░░░░░░│ 1% 4/201 14:47<16:16:14 [/attn...] Weight Quant │░░░░░░░░░░░░░░░░░░░░│ 1% 5/201 26:02<21:22:13 [/attn...] Weight Quant │░░░░░░░░░░░░░░░░░░░░│ 2% 6/201 26:02<17:00:34 [_add ] Weight Quant │░░░░░░░░░░░░░░░░░░░░│ 2% 7/201 26:03<14:06:37 [add/r...] Weight Quant │░░░░░░░░░░░░░░░░░░░░│ 3% 8/201 47:40<22:01:13 [langu...] Weight Quant │░░░░░░░░░░░░░░░░░░░░│ 3% 9/201 47:40<19:10:07 [_add_0 ] Weight Quant │░░░░░░░░░░░░░░░░░░░░│ 4% 10/201 47:40<16:57:10 [add_0...] Weight Quant │░░░░░░░░░░░░░░░░░░░░│ 4% 11/201 1:01:47<19:40:13 [/attn...] Weight Quant │█░░░░░░░░░░░░░░░░░░░│ 5% 12/201 1:13:28<21:09:11 [/attn...] Weight Quant │█░░░░░░░░░░░░░░░░░░░│ 5% 13/201 1:13:28<19:17:18 [_add_1 ] Weight Quant │█░░░░░░░░░░░░░░░░░░░│ 6% 14/201 1:13:29<17:42:46 [add_1...] Weight Quant │█░░░░░░░░░░░░░░░░░░░│ 6% 15/201 1:34:38<21:04:08 [langu...] Weight Quant │█░░░░░░░░░░░░░░░░░░░│ 7% 16/201 1:34:38<19:33:33 [_add_2 ] Weight Quant │█░░░░░░░░░░░░░░░░░░░│ 7% 17/201 1:34:38<18:14:19 [add_2...] Weight Quant │█░░░░░░░░░░░░░░░░░░░│ 8% 18/201 1:48:31<19:34:36 [/attn...] Weight Quant │█░░░░░░░░░░░░░░░░░░░│ 8% 19/201 2:00:23<20:24:00 [/attn...] Weight Quant │█░░░░░░░░░░░░░░░░░░░│ 9% 20/201 2:00:23<19:13:15 [_add_3 ] Weight Quant │█░░░░░░░░░░░░░░░░░░░│ 9% 21/201 2:00:24<18:09:39 [add_3...] Weight Quant │██░░░░░░░░░░░░░░░░░░│ 10% 22/201 2:21:46<20:15:16 [langu...] Weight Quant │██░░░░░░░░░░░░░░░░░░│ 10% 23/201 2:21:46<19:13:35 [_add_4 ] Weight Quant │██░░░░░░░░░░░░░░░░░░│ 11% 24/201 2:21:47<18:17:20 [add_4...] Weight Quant │██░░░░░░░░░░░░░░░░░░│ 11% 25/201 2:35:15<19:05:04 [/attn...] Weight Quant │██░░░░░░░░░░░░░░░░░░│ 12% 26/201 2:46:35<19:32:48 [/attn...] Weight Quant │██░░░░░░░░░░░░░░░░░░│ 12% 27/201 2:46:35<18:41:18 [_add_5 ] Weight Quant │██░░░░░░░░░░░░░░░░░░│ 13% 28/201 2:46:35<17:53:38 [add_5...] Weight Quant │██░░░░░░░░░░░░░░░░░░│ 13% 29/201 3:12:53<19:51:49 [langu...] Weight Quant │██░░░░░░░░░░░░░░░░░░│ 14% 30/201 3:12:53<19:04:05 [_add_6 ] Weight Quant │██░░░░░░░░░░░░░░░░░░│ 14% 31/201 3:12:56<18:19:44 [add_6...] Weight Quant │███░░░░░░░░░░░░░░░░░│ 15% 32/201 3:28:02<19:00:52 [/attn...] Weight Quant │███░░░░░░░░░░░░░░░░░│ 15% 33/201 3:40:31<19:24:38 [/attn...] Weight Quant │███░░░░░░░░░░░░░░░░░│ 16% 34/201 3:40:31<18:42:39 [_add_7 ] Weight Quant │███░░░░░░░░░░░░░░░░░│ 16% 35/201 3:40:31<18:03:11 [add_7...] Weight Quant │███░░░░░░░░░░░░░░░░░│ 17% 36/201 4:02:23<19:09:35 [langu...] Weight Quant │███░░░░░░░░░░░░░░░░░│ 17% 37/201 4:02:23<18:30:55 [_add_8 ] Weight Quant │███░░░░░░░░░░░░░░░░░│ 18% 38/201 4:02:23<17:54:22 [add_8...] Weight Quant │███░░░░░░░░░░░░░░░░░│ 18% 39/201 4:15:56<18:17:52 [/attn...] Weight Quant │███░░░░░░░░░░░░░░░░░│ 19% 40/201 4:27:48<18:32:27 [/attn...] Weight Quant │███░░░░░░░░░░░░░░░░░│ 19% 41/201 4:27:48<17:57:56 [_add_9 ] Weight Quant │████░░░░░░░░░░░░░░░░│ 20% 42/201 4:27:50<17:25:12 [add_9...] Weight Quant │████░░░░░░░░░░░░░░░░│ 20% 43/201 4:49:21<18:15:26 [langu...] Weight Quant │████░░░░░░░░░░░░░░░░│ 21% 44/201 4:49:21<17:43:14 [_add_10 ] Weight Quant │████░░░░░░░░░░░░░░░░│ 21% 45/201 4:49:22<17:12:32 [add_1...] Weight Quant │████░░░░░░░░░░░░░░░░│ 22% 46/201 5:02:57<17:30:16 [/attn...] Weight Quant │████░░░░░░░░░░░░░░░░│ 22% 47/201 5:13:34<17:36:37 [/attn...] Weight Quant │████░░░░░░░░░░░░░░░░│ 23% 48/201 5:13:34<17:07:28 [_add_11 ] Weight Quant │████░░░░░░░░░░░░░░░░│ 23% 49/201 5:13:35<16:39:33 [add_1...] Weight Quant │████░░░░░░░░░░░░░░░░│ 24% 50/201 5:38:30<17:30:03 [langu...] Weight Quant │████░░░░░░░░░░░░░░░░│ 24% 51/201 5:38:30<17:02:17 [_add_12 ] Weight Quant │█████░░░░░░░░░░░░░░░│ 25% 52/201 5:38:30<16:35:37 [add_1...] Weight Quant │█████░░░░░░░░░░░░░░░│ 25% 53/201 5:52:32<16:50:09 [/attn...] Weight Quant │█████░░░░░░░░░░░░░░░│ 26% 54/201 6:01:25<16:49:17 [/attn...] Weight Quant │█████░░░░░░░░░░░░░░░│ 26% 55/201 6:01:26<16:23:54 [_add_13 ] Weight Quant │█████░░░░░░░░░░░░░░░│ 27% 56/201 6:01:26<15:59:28 [add_1...] Weight Quant │█████░░░░░░░░░░░░░░░│ 27% 57/201 6:30:00<16:49:50 [langu...] Weight Quant │█████░░░░░░░░░░░░░░░│ 28% 58/201 6:30:00<16:25:17 [_add_14 ] Weight Quant │█████░░░░░░░░░░░░░░░│ 28% 59/201 6:30:00<16:01:35 [add_1...] Weight Quant │█████░░░░░░░░░░░░░░░│ 29% 60/201 6:43:50<16:11:56 [/attn...] Weight Quant │█████░░░░░░░░░░░░░░░│ 29% 61/201 6:52:48<16:10:05 [/attn...] Weight Quant │██████░░░░░░░░░░░░░░│ 30% 62/201 6:52:48<15:47:25 [_add_15 ] Weight Quant │██████░░░░░░░░░░░░░░│ 30% 63/201 6:52:49<15:25:30 [add_1...] Weight Quant │██████░░░░░░░░░░░░░░│ 31% 64/201 7:23:35<16:11:41 [langu...] Weight Quant │██████░░░░░░░░░░░░░░│ 31% 65/201 7:23:35<15:49:34 [_add_16 ] Weight Quant │██████░░░░░░░░░░░░░░│ 32% 66/201 7:23:36<15:28:09 [add_1...] Weight Quant │██████░░░░░░░░░░░░░░│ 32% 67/201 7:38:19<15:37:28 [/attn...] Weight Quant │██████░░░░░░░░░░░░░░│ 33% 68/201 7:48:32<15:37:04 [/attn...] Weight Quant │██████░░░░░░░░░░░░░░│ 33% 69/201 7:48:32<15:16:23 [_add_17 ] Weight Quant │██████░░░░░░░░░░░░░░│ 34% 70/201 7:48:32<14:56:21 [add_1...] Weight Quant │██████░░░░░░░░░░░░░░│ 34% 71/201 8:16:02<15:28:18 [langu...] Weight Quant │███████░░░░░░░░░░░░░│ 35% 72/201 8:16:02<15:08:15 [_add_18 ] Weight Quant │███████░░░░░░░░░░░░░│ 35% 73/201 8:16:03<14:48:45 [add_1...] Weight Quant │███████░░░░░░░░░░░░░│ 36% 74/201 8:29:46<14:53:51 [/attn...] Weight Quant │███████░░░░░░░░░░░░░│ 36% 75/201 8:39:07<14:50:55 [/attn...] Weight Quant │███████░░░░░░░░░░░░░│ 37% 76/201 8:39:07<14:32:07 [_add_19 ] Weight Quant │███████░░░░░░░░░░░░░│ 37% 77/201 8:39:08<14:13:51 [add_1...] Weight Quant │███████░░░░░░░░░░░░░│ 38% 78/201 9:07:12<14:41:13 [langu...] Weight Quant │███████░░░░░░░░░░░░░│ 38% 79/201 9:07:13<14:22:55 [_add_20 ] Weight Quant │███████░░░░░░░░░░░░░│ 39% 80/201 9:07:14<14:05:05 [add_2...] Weight Quant │███████░░░░░░░░░░░░░│ 39% 81/201 9:21:09<14:08:45 [/attn...] Weight Quant │████████░░░░░░░░░░░░│ 40% 82/201 9:29:47<14:04:08 [/attn...] Weight Quant │████████░░░░░░░░░░░░│ 40% 83/201 9:29:47<13:46:54 [_add_21 ] Weight Quant │████████░░░░░░░░░░░░│ 41% 84/201 9:29:48<13:30:05 [add_2...] Weight Quant │████████░░░░░░░░░░░░│ 41% 85/201 9:58:13<13:53:14 [langu...] Weight Quant │████████░░░░░░░░░░░░│ 42% 86/201 9:58:13<13:36:24 [_add_22 ] Weight Quant │████████░░░░░░░░░░░░│ 42% 87/201 9:58:14<13:19:58 [add_2...] Weight Quant │████████░░░░░░░░░░░░│ 43% 88/201 10:11:48<13:21:40 [/attn...] Weight Quant │████████░░░░░░░░░░░░│ 43% 89/201 10:21:07<13:17:34 [/attn...] Weight Quant │████████░░░░░░░░░░░░│ 44% 90/201 10:21:07<13:01:38 [_add_23 ] Weight Quant │████████░░░░░░░░░░░░│ 44% 91/201 10:21:07<12:46:03 [add_2...] Weight Quant │█████████░░░░░░░░░░░│ 45% 92/201 10:49:47<13:05:27 [langu...] Weight Quant │█████████░░░░░░░░░░░│ 45% 93/201 10:49:47<12:49:51 [_add_24 ] Weight Quant │█████████░░░░░░░░░░░│ 46% 94/201 10:49:47<12:34:36 [add_2...] Weight Quant │█████████░░░░░░░░░░░│ 46% 95/201 11:04:04<12:35:55 [/attn...] Weight Quant │█████████░░░░░░░░░░░│ 47% 96/201 11:13:10<12:31:07 [/attn...] Weight Quant │█████████░░░░░░░░░░░│ 47% 97/201 11:13:10<12:16:17 [_add_25 ] Weight Quant │█████████░░░░░░░░░░░│ 48% 98/201 11:13:12<12:01:47 [add_2...] Weight Quant │█████████░░░░░░░░░░░│ 48% 99/201 11:41:49<12:17:38 [langu...] Weight Quant │█████████░░░░░░░░░░░│ 49% 100/201 11:41:49<12:03:05 [_add_26 ] Weight Quant │█████████░░░░░░░░░░░│ 49% 101/201 11:41:50<11:48:51 [add_2...] Weight Quant │██████████░░░░░░░░░░│ 50% 102/201 11:55:18<11:48:13 [/attn...] Weight Quant │██████████░░░░░░░░░░│ 50% 103/201 12:04:35<11:43:16 [/attn...] Weight Quant │██████████░░░░░░░░░░│ 51% 104/201 12:04:35<11:29:25 [_add_27 ] Weight Quant │██████████░░░░░░░░░░│ 51% 105/201 12:04:36<11:15:49 [add_2...] Weight Quant │██████████░░░░░░░░░░│ 52% 106/201 12:31:44<11:27:17 [langu...] Weight Quant │██████████░░░░░░░░░░│ 52% 107/201 12:31:44<11:13:43 [_add_28 ] Weight Quant │██████████░░░░░░░░░░│ 53% 108/201 12:31:44<11:00:24 [add_2...] Weight Quant │██████████░░░░░░░░░░│ 53% 109/201 12:45:03<10:58:48 [/attn...] Weight Quant │██████████░░░░░░░░░░│ 54% 110/201 12:54:16<10:53:31 [/attn...] Weight Quant │██████████░░░░░░░░░░│ 54% 111/201 12:54:16<10:40:32 [_add_29 ] Weight Quant │███████████░░░░░░░░░│ 55% 112/201 12:54:17<10:27:48 [add_2...] Weight Quant │███████████░░░░░░░░░│ 55% 113/201 13:19:00<10:34:55 [langu...] Weight Quant │███████████░░░░░░░░░│ 56% 114/201 13:19:00<10:22:13 [_add_30 ] Weight Quant │███████████░░░░░░░░░│ 56% 115/201 13:19:00<10:09:46 [add_3...] Weight Quant │███████████░░░░░░░░░│ 57% 116/201 13:32:31<10:07:37 [/attn...] Weight Quant │███████████░░░░░░░░░│ 57% 117/201 13:41:54<10:02:15 [/attn...] Weight Quant │███████████░░░░░░░░░│ 58% 118/201 13:41:54<9:50:05 [_add_31 ] Weight Quant │███████████░░░░░░░░░│ 58% 119/201 13:41:55<9:38:07 [add_3...] Weight Quant │███████████░░░░░░░░░│ 59% 120/201 14:06:58<9:43:37 [langu...] Weight Quant │███████████░░░░░░░░░│ 59% 121/201 14:06:58<9:31:42 [_add_32 ] Weight Quant │████████████░░░░░░░░│ 60% 122/201 14:06:58<9:19:58 [add_3...] Weight Quant │████████████░░░░░░░░│ 60% 123/201 14:20:09<9:16:59 [/attn...] Weight Quant │████████████░░░░░░░░│ 61% 124/201 14:29:12<9:11:12 [/attn...] Weight Quant │████████████░░░░░░░░│ 61% 125/201 14:29:12<8:59:44 [_add_33 ] Weight Quant │████████████░░░░░░░░│ 62% 126/201 14:29:12<8:48:28 [add_3...] Weight Quant │████████████░░░░░░░░│ 62% 127/201 14:54:36<8:52:30 [langu...] Weight Quant │████████████░░░░░░░░│ 63% 128/201 14:54:36<8:41:16 [_add_34 ] Weight Quant │████████████░░░░░░░░│ 63% 129/201 14:54:37<8:30:12 [add_3...] Weight Quant │████████████░░░░░░░░│ 64% 130/201 15:08:00<8:26:47 [/attn...] Weight Quant │████████████░░░░░░░░│ 64% 131/201 15:16:52<8:20:45 [/attn...] Weight Quant │█████████████░░░░░░░│ 65% 132/201 15:16:52<8:09:55 [_add_35 ] Weight Quant │█████████████░░░░░░░│ 65% 133/201 15:16:52<7:59:16 [add_3...] Weight Quant │█████████████░░░░░░░│ 66% 134/201 15:41:36<8:01:25 [langu...] Weight Quant │█████████████░░░░░░░│ 66% 135/201 15:41:36<7:50:48 [_add_36 ] Weight Quant │█████████████░░░░░░░│ 67% 136/201 15:41:37<7:40:21 [add_3...] Weight Quant │█████████████░░░░░░░│ 67% 137/201 15:55:51<7:36:50 [/attn...] Weight Quant │█████████████░░░░░░░│ 68% 138/201 16:04:43<7:30:40 [/attn...] Weight Quant │█████████████░░░░░░░│ 68% 139/201 16:04:43<7:20:25 [_add_37 ] Weight Quant │█████████████░░░░░░░│ 69% 140/201 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[2026-09-17 23:07:01.878] ================ Input Information =============== [2026-09-17 23:07:01.878] Input Name = bridge0;input_ids/embedding/reshape Input Shape = (1 1 -1 1024) [2026-09-17 23:07:01.878] ================================================== [2026-09-17 23:07:01.878] ================ Output Information =============== [2026-09-17 23:07:01.878] Output Name = language_model_lm_head Output Shape = (1 1 1 1024) [2026-09-17 23:07:01.878] ================================================== [2026-09-17 23:07:02.249] prepareBackendInputFB: NormalDLCPUOffload mode [2026-09-17 23:07:02.249] KV cache is used in the model. Multi inference scheme is not supported. (use batch llm mode instead) [2026-09-17 23:07:02.249] Compiling the Quantized model (FB)... [2026-09-17 23:07:02.250] <<<<<<<<<<<<<<<<<<<<<<[Banckend Compiler] Runtime Config Initialized: 1>>>>>>>>>>>>>>>>>>>>>> [2026-09-17 23:07:04.442] HW: aries-rb, Build Mode: Single init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% 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99% opt_div [*******************************] 100% opt_div [*******************************] 100% Weight Binary Write [ ] 0% Weight Binary Write [ ] 0% Weight Binary Write [ ] 0% Weight Binary Write [ ] 0% Weight Binary Write [ ] 1% Weight Binary Write [ ] 1% Weight Binary Write [ ] 1% Weight Binary Write [ ] 1% Weight Binary Write [ ] 1% Weight Binary Write [ ] 2% Weight Binary Write [ ] 2% Weight Binary Write [ ] 2% Weight Binary Write [ ] 2% Weight Binary Write [ ] 2% Weight Binary Write [ ] 3% Weight Binary Write [ ] 3% Weight Binary Write [ ] 3% Weight Binary Write [ ] 3% Weight Binary Write [ ] 3% Weight Binary Write [ ] 4% Weight Binary Write [ ] 4% Weight Binary Write [ ] 4% Weight Binary Write [ ] 4% Weight Binary Write [ ] 4% Weight Binary Write [ ] 5% Weight Binary Write [ ] 5% Weight Binary Write [* ] 5% Weight Binary Write [* ] 5% Weight Binary Write [* ] 5% Weight Binary Write [* ] 6% Weight Binary Write [* ] 6% Weight Binary Write [* ] 6% Weight Binary Write [* ] 6% Weight Binary Write [* ] 6% Weight Binary Write [* ] 7% Weight Binary Write [* ] 7% Weight Binary Write [* ] 7% Weight Binary Write [* ] 7% Weight Binary Write [* ] 7% Weight Binary Write [* ] 8% Weight Binary Write [* ] 8% Weight Binary Write [* ] 8% Weight Binary Write [* ] 8% Weight Binary Write [* ] 8% Weight Binary Write [* ] 9% Weight Binary Write [* ] 9% Weight Binary Write [* ] 9% Weight Binary Write [* ] 9% Weight Binary Write [* ] 9% Weight Binary Write [* ] 10% Weight Binary Write [* ] 10% Weight Binary Write [* ] 10% Weight Binary Write [** ] 10% Weight Binary Write [** ] 10% Weight Binary Write [** ] 11% Weight Binary Write [** ] 11% Weight Binary Write [** ] 11% Weight Binary Write [** ] 11% Weight Binary Write [** ] 11% Weight Binary Write [** ] 12% Weight Binary Write [** ] 12% Weight Binary Write [** ] 12% Weight Binary Write [** ] 12% Weight Binary Write [** ] 12% Weight Binary Write [** ] 13% Weight Binary Write [** ] 13% Weight Binary Write [** ] 13% Weight Binary Write [** ] 13% Weight Binary Write [** ] 13% Weight Binary Write [** ] 14% Weight Binary Write [** ] 14% Weight Binary Write [** ] 14% Weight Binary Write [** ] 14% Weight Binary Write [** ] 14% Weight Binary Write [** ] 15% Weight Binary Write [** ] 15% Weight Binary Write [** ] 15% Weight Binary Write [** ] 15% Weight Binary Write [*** ] 15% Weight Binary Write [*** ] 16% Weight Binary Write [*** ] 16% Weight Binary Write [*** ] 16% Weight Binary Write [*** ] 16% Weight Binary Write [*** ] 16% Weight Binary Write [*** ] 17% Weight Binary Write [*** ] 17% Weight Binary Write [*** ] 17% Weight Binary Write [*** ] 17% Weight Binary Write [*** ] 17% Weight Binary Write [*** ] 18% Weight Binary Write [*** ] 18% Weight Binary Write [*** ] 18% Weight Binary Write [*** ] 18% Weight Binary Write [*** ] 18% Weight Binary Write [*** ] 19% Weight Binary Write [*** ] 19% Weight Binary Write [*** ] 19% Weight Binary Write [*** ] 19% Weight Binary Write [*** ] 19% Weight Binary Write [*** ] 20% 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Weight Binary Write [*********** ] 60% Weight Binary Write [*********** ] 60% Weight Binary Write [*********** ] 60% Weight Binary Write [*********** ] 60% Weight Binary Write [*********** ] 60% Weight Binary Write [*********** ] 61% Weight Binary Write [*********** ] 61% Weight Binary Write [*********** ] 61% Weight Binary Write [*********** ] 61% Weight Binary Write [*********** ] 61% Weight Binary Write [*********** ] 62% Weight Binary Write [*********** ] 62% Weight Binary Write [*********** ] 62% Weight Binary Write [*********** ] 62% Weight Binary Write [*********** ] 62% Weight Binary Write [*********** ] 63% Weight Binary Write [************ ] 63% Weight Binary Write [************ ] 63% Weight Binary Write [************ ] 63% Weight Binary Write [************ ] 63% Weight Binary Write [************ ] 64% Weight Binary Write [************ ] 64% Weight Binary Write [************ ] 64% Weight Binary Write [************ ] 64% Weight Binary Write [************ ] 64% Weight Binary 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Binary Write [************* ] 70% Weight Binary Write [************* ] 70% Weight Binary Write [************* ] 70% Weight Binary Write [************* ] 70% Weight Binary Write [************* ] 70% Weight Binary Write [************* ] 71% Weight Binary Write [************* ] 71% Weight Binary Write [************* ] 71% Weight Binary Write [************* ] 71% Weight Binary Write [************* ] 71% Weight Binary Write [************* ] 72% Weight Binary Write [************* ] 72% Weight Binary Write [************* ] 72% Weight Binary Write [************* ] 72% Weight Binary Write [************* ] 72% Weight Binary Write [************* ] 73% Weight Binary Write [************* ] 73% Weight Binary Write [************* ] 73% Weight Binary Write [************** ] 73% Weight Binary Write [************** ] 73% Weight Binary Write [************** ] 74% Weight Binary Write [************** ] 74% Weight Binary Write [************** ] 74% Weight Binary Write [************** ] 74% Weight Binary 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Weight Binary Write [*************** ] 79% Weight Binary Write [*************** ] 79% Weight Binary Write [*************** ] 80% Weight Binary Write [*************** ] 80% Weight Binary Write [*************** ] 80% Weight Binary Write [*************** ] 80% Weight Binary Write [*************** ] 80% Weight Binary Write [*************** ] 81% Weight Binary Write [*************** ] 81% Weight Binary Write [*************** ] 81% Weight Binary Write [*************** ] 81% Weight Binary Write [*************** ] 81% Weight Binary Write [*************** ] 82% Weight Binary Write [*************** ] 82% Weight Binary Write [*************** ] 82% Weight Binary Write [*************** ] 82% Weight Binary Write [*************** ] 82% Weight Binary Write [*************** ] 83% Weight Binary Write [*************** ] 83% Weight Binary Write [*************** ] 83% Weight Binary Write [*************** ] 83% Weight Binary Write [*************** ] 83% Weight Binary Write [*************** ] 84% Weight Binary Write [**************** ] 84% Weight Binary Write [**************** ] 84% Weight Binary Write [**************** ] 84% Weight Binary Write [**************** ] 84% Weight Binary Write [**************** ] 85% Weight Binary Write [**************** ] 85% Weight Binary Write [**************** ] 85% Weight Binary Write [**************** ] 85% Weight Binary Write [**************** ] 85% Weight Binary Write [**************** ] 86% Weight Binary Write [**************** ] 86% Weight Binary Write [**************** ] 86% Weight Binary Write [**************** ] 86% Weight Binary Write [**************** ] 86% Weight Binary Write [**************** ] 87% Weight Binary Write [**************** ] 87% Weight Binary Write [**************** ] 87% Weight Binary Write [**************** ] 87% Weight Binary Write [**************** ] 87% Weight Binary Write [**************** ] 88% Weight Binary Write [**************** ] 88% Weight Binary Write [**************** ] 88% Weight Binary Write [**************** ] 88% Weight Binary Write [**************** ] 88% Weight Binary Write [**************** ] 89% Weight Binary Write [**************** ] 89% Weight Binary Write [***************** ] 89% Weight Binary Write [***************** ] 89% Weight Binary Write [***************** ] 89% Weight Binary Write [***************** ] 90% Weight Binary Write [***************** ] 90% Weight Binary Write [***************** ] 90% Weight Binary Write [***************** ] 90% Weight Binary Write [***************** ] 90% Weight Binary Write [***************** ] 91% Weight Binary Write [***************** ] 91% Weight Binary Write [***************** ] 91% Weight Binary Write [***************** ] 91% Weight Binary Write [***************** ] 91% Weight Binary Write [***************** ] 92% Weight Binary Write [***************** ] 92% Weight Binary Write [***************** ] 92% Weight Binary Write [***************** ] 92% Weight Binary Write [***************** ] 92% Weight Binary Write [***************** ] 93% Weight Binary Write [***************** ] 93% Weight Binary Write [***************** ] 93% Weight Binary Write [***************** ] 93% Weight Binary Write [***************** ] 93% Weight Binary Write [***************** ] 94% Weight Binary Write [***************** ] 94% Weight Binary Write [***************** ] 94% Weight Binary Write [****************** ] 94% Weight Binary Write [****************** ] 94% Weight Binary Write [****************** ] 95% Weight Binary Write [****************** ] 95% Weight Binary Write [****************** ] 95% Weight Binary Write [****************** ] 95% Weight Binary Write [****************** ] 95% Weight Binary Write [****************** ] 96% Weight Binary Write [****************** ] 96% Weight Binary Write [****************** ] 96% Weight Binary Write [****************** ] 96% Weight Binary Write [****************** ] 96% Weight Binary Write [****************** ] 97% Weight Binary Write [****************** ] 97% Weight Binary Write [****************** ] 97% Weight Binary Write [****************** ] 97% Weight Binary Write [****************** ] 97% Weight Binary Write [****************** ] 98% Weight Binary Write [****************** ] 98% Weight Binary Write [****************** ] 98% Weight Binary Write [****************** ] 98% Weight Binary Write [****************** ] 98% Weight Binary Write [****************** ] 99% Weight Binary Write [****************** ] 99% Weight Binary Write [****************** ] 99% Weight Binary Write [****************** ] 99% Weight Binary Write [*******************] 100% Weight Binary Write [*******************] 100% [2026-09-17 23:12:17.177] Build Finished. [2026-09-17 23:12:17.599] <<<<<<<<<<<<<<<<<<<<<<[Banckend Compiler] Runtime Config Initialized: 2>>>>>>>>>>>>>>>>>>>>>> [2026-09-17 23:12:19.637] HW: aries-rb, Build Mode: Global init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% 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Binary Write [***************** ] 93% Weight Binary Write [***************** ] 93% Weight Binary Write [***************** ] 93% Weight Binary Write [***************** ] 93% Weight Binary Write [***************** ] 94% Weight Binary Write [***************** ] 94% Weight Binary Write [***************** ] 94% Weight Binary Write [****************** ] 94% Weight Binary Write [****************** ] 94% Weight Binary Write [****************** ] 95% Weight Binary Write [****************** ] 95% Weight Binary Write [****************** ] 95% Weight Binary Write [****************** ] 95% Weight Binary Write [****************** ] 95% Weight Binary Write [****************** ] 96% Weight Binary Write [****************** ] 96% Weight Binary Write [****************** ] 96% Weight Binary Write [****************** ] 96% Weight Binary Write [****************** ] 96% Weight Binary Write [****************** ] 97% Weight Binary Write [****************** ] 97% Weight Binary Write [****************** ] 97% Weight Binary Write [****************** ] 97% Weight Binary Write [****************** ] 97% Weight Binary Write [****************** ] 98% Weight Binary Write [****************** ] 98% Weight Binary Write [****************** ] 98% Weight Binary Write [****************** ] 98% Weight Binary Write [****************** ] 98% Weight Binary Write [****************** ] 99% Weight Binary Write [****************** ] 99% Weight Binary Write [****************** ] 99% Weight Binary Write [****************** ] 99% Weight Binary Write [*******************] 100% Weight Binary Write [*******************] 100% [2026-09-17 23:21:29.488] Build Finished. [2026-09-17 23:21:30.697] <<<<<<<<<<<<<<<<<<<<<<[Banckend Compiler] Runtime Config Initialized: 3>>>>>>>>>>>>>>>>>>>>>> [2026-09-17 23:21:32.754] HW: aries-rb, Build Mode: GlobalCluster init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 0% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 1% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 2% init_div [ ] 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99% opt_div [*******************************] 100% opt_div [*******************************] 100% Weight Binary Write [ ] 0% Weight Binary Write [ ] 0% Weight Binary Write [ ] 0% Weight Binary Write [ ] 0% Weight Binary Write [ ] 1% Weight Binary Write [ ] 1% Weight Binary Write [ ] 1% Weight Binary Write [ ] 1% Weight Binary Write [ ] 1% Weight Binary Write [ ] 2% Weight Binary Write [ ] 2% Weight Binary Write [ ] 2% Weight Binary Write [ ] 2% Weight Binary Write [ ] 2% Weight Binary Write [ ] 3% Weight Binary Write [ ] 3% Weight Binary Write [ ] 3% Weight Binary Write [ ] 3% Weight Binary Write [ ] 3% Weight Binary Write [ ] 4% Weight Binary Write [ ] 4% Weight Binary Write [ ] 4% Weight Binary Write [ ] 4% Weight Binary Write [ ] 4% Weight Binary Write [ ] 5% Weight Binary Write [ ] 5% Weight Binary Write [* ] 5% Weight Binary Write [* ] 5% Weight Binary Write [* ] 5% Weight Binary Write [* ] 6% Weight Binary Write [* ] 6% Weight Binary Write [* ] 6% Weight Binary Write [* ] 6% 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Binary Write [***************** ] 93% Weight Binary Write [***************** ] 93% Weight Binary Write [***************** ] 93% Weight Binary Write [***************** ] 93% Weight Binary Write [***************** ] 94% Weight Binary Write [***************** ] 94% Weight Binary Write [***************** ] 94% Weight Binary Write [****************** ] 94% Weight Binary Write [****************** ] 94% Weight Binary Write [****************** ] 95% Weight Binary Write [****************** ] 95% Weight Binary Write [****************** ] 95% Weight Binary Write [****************** ] 95% Weight Binary Write [****************** ] 95% Weight Binary Write [****************** ] 96% Weight Binary Write [****************** ] 96% Weight Binary Write [****************** ] 96% Weight Binary Write [****************** ] 96% Weight Binary Write [****************** ] 96% Weight Binary Write [****************** ] 97% Weight Binary Write [****************** ] 97% Weight Binary Write [****************** ] 97% Weight Binary Write [****************** ] 97% Weight Binary Write [****************** ] 97% Weight Binary Write [****************** ] 98% Weight Binary Write [****************** ] 98% Weight Binary Write [****************** ] 98% Weight Binary Write [****************** ] 98% Weight Binary Write [****************** ] 98% Weight Binary Write [****************** ] 99% Weight Binary Write [****************** ] 99% Weight Binary Write [****************** ] 99% Weight Binary Write [****************** ] 99% Weight Binary Write [*******************] 100% Weight Binary Write [*******************] 100% [2026-09-17 23:36:05.858] Build Finished. [2026-09-17 23:36:06.386] Elaborating MXQ... [2026-09-17 23:36:06.507] Exporting MXQ to /home/jovyan/Uday_DB_Ins_PoC_Prep/qwen3-autorag-compile/qwen3_autorag_compile_package/mblt-rag/mxq/aries-rb/embedder/qwen3-embedding-0.6b-autorag/Qwen3-Embedding-0.6B_w8v8_R1R2_autorag.mxq... [2026-09-17 23:36:06.855] Verifying exported MXQ... [2026-09-17 23:36:07.272] Compilation was successful. [compile] mxq in 79475s -> /home/jovyan/Uday_DB_Ins_PoC_Prep/qwen3-autorag-compile/qwen3_autorag_compile_package/mblt-rag/mxq/aries-rb/embedder/qwen3-embedding-0.6b-autorag/Qwen3-Embedding-0.6B_w8v8_R1R2_autorag.mxq [compile] embedding table rotated by spinWeight/model/R1/global_rotation.pth [compile] embedding table (151669, 1024) -> /home/jovyan/Uday_DB_Ins_PoC_Prep/qwen3-autorag-compile/qwen3_autorag_compile_package/mblt-rag/mxq/aries-rb/embedder/qwen3-embedding-0.6b-autorag/Qwen3-Embedding-0.6B_w8v8_R1R2_autorag.embed.pt (R1-rotated) [compile] wrote /home/jovyan/Uday_DB_Ins_PoC_Prep/qwen3-autorag-compile/qwen3_autorag_compile_package/mblt-rag/mxq/aries-rb/embedder/qwen3-embedding-0.6b-autorag/Qwen3-Embedding-0.6B_w8v8_R1R2_autorag.mxq.provenance.json [recipe] model.yaml -> /home/jovyan/Uday_DB_Ins_PoC_Prep/qwen3-autorag-compile/qwen3_autorag_compile_package/mblt-rag/mxq/aries-rb/embedder/qwen3-embedding-0.6b-autorag/model.yaml