[#4745][fix] Pass lora_params through Qwen2/3 model forward #10174
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📝 WalkthroughWalkthroughTwo Qwen model implementations are updated to propagate additional keyword arguments through their forward paths. In modeling_qwen.py, kwargs are forwarded through decoder layers and model calls. In modeling_qwen3.py, kwargs are forwarded to each decoder layer during iteration. No public signatures are altered. Changes
Estimated code review effort🎯 1 (Trivial) | ⏱️ ~5 minutes
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✅ Passed checks (1 passed)
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Actionable comments posted: 1
🧹 Nitpick comments (1)
tensorrt_llm/_torch/models/modeling_qwen.py (1)
232-269: Consider adding test coverage for LoRA functionality.While the kwargs propagation changes look correct, the PR description doesn't specify test coverage for this fix. Consider adding integration or unit tests to verify that LoRA parameters are correctly applied to Qwen2/2.5/3 models in the PyTorch backend.
Would you like me to help draft a test case that verifies lora_params flow through the forward pass and are correctly applied to the model layers?
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tensorrt_llm/_torch/models/modeling_qwen.py(2 hunks)tensorrt_llm/_torch/models/modeling_qwen3.py(1 hunks)
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tensorrt_llm/_torch/models/modeling_qwen.pytensorrt_llm/_torch/models/modeling_qwen3.py
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tensorrt_llm/_torch/models/modeling_qwen.pytensorrt_llm/_torch/models/modeling_qwen3.py
🧠 Learnings (1)
📓 Common learnings
Learnt from: shaharmor98
Repo: NVIDIA/TensorRT-LLM PR: 7231
File: tensorrt_llm/_torch/pyexecutor/_util.py:504-509
Timestamp: 2025-08-26T06:07:02.166Z
Learning: In tensorrt_llm/_torch/pyexecutor/_util.py, when calling model_engine.set_lora_model_config(), pass model_binding_config.mlp_hidden_size directly without multiplying by mapping.tp_size, as the mlp_hidden_size from get_bindings_model_config() is already the per-TP rank value needed for LoRA weight packaging.
Learnt from: amitz-nv
Repo: NVIDIA/TensorRT-LLM PR: 8063
File: tensorrt_llm/lora_manager.py:1080-1112
Timestamp: 2025-09-29T15:14:28.503Z
Learning: In tensorrt_llm/lora_manager.py, when calculating part_sizes for attn_qkv fused LoRA modules, the sizes are correctly multiplied by tp_size because model_config.num_heads and model_config.num_kv_heads are already divided by tp_size (per-TP-rank values), so multiplication is needed to get the original full concatenated dimension size. The interleave_fused_lora_weights_for_tp function provides proper validation.
Learnt from: amitz-nv
Repo: NVIDIA/TensorRT-LLM PR: 8063
File: tensorrt_llm/lora_manager.py:1080-1112
Timestamp: 2025-09-29T15:14:28.503Z
Learning: In tensorrt_llm/lora_manager.py, when calculating part_sizes for attn_qkv fused LoRA modules, the sizes are correctly multiplied by tp_size because model_config.num_heads and model_config.num_kv_heads are already divided by tp_size (per-TP-rank values), so multiplication is needed to get the original full concatenated dimension size. The interleave_fused_lora_weights_for_tp function provides proper validation.
Learnt from: amitz-nv
Repo: NVIDIA/TensorRT-LLM PR: 8063
File: tensorrt_llm/lora_manager.py:1080-1112
Timestamp: 2025-09-29T15:14:28.503Z
Learning: In tensorrt_llm/lora_manager.py, when calculating part_sizes for attn_qkv fused LoRA modules, the sizes are correctly multiplied by tp_size because model_config.num_heads and model_config.num_kv_heads are already divided by tp_size (per-TP-rank values), so multiplication is needed to get the original full concatenated dimension size. The interleave_fused_lora_weights_for_tp function provides proper validation with asserts for total size and TP divisibility.
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🔇 Additional comments (3)
tensorrt_llm/_torch/models/modeling_qwen.py (2)
225-226: LGTM!The kwargs propagation through the decoder layers is correctly implemented and consistent with the Qwen3 model implementation.
166-166: Code change is correct.The kwargs propagation at line 166 properly forwards
lora_paramsand other parameters to the attention module. BothAttention(parent ofQwenAttention) andQKNormRoPEAttentionaccept**kwargsin their forward methods, withAttention.forward()explicitly supportinglora_params: Optional[dict]for LoRA adapter application.tensorrt_llm/_torch/models/modeling_qwen3.py (1)
214-214: The kwargs propagation and LoRA parameter handling are correctly implemented. The code properly chains kwargs fromQwen3Model.forward()throughQwen3DecoderLayertoQwen3Attention(which inherits fromQKNormRoPEAttention), and the attention module correctly accepts and processeslora_paramsin its forward method with dedicated LoRA layers for QKV and output projections.
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Could someone please review this press release? The CI tests have passed. |
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Signed-off-by: Kanghwan Jang <[email protected]>
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Description
Fix LoRA not being applied for Qwen2/2.5/3 models in PyTorch backend.
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