[Bugfix] Fix Qwen2.5-VL quantized model weights loading #23512
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This PR makes sure quantized model weights are loaded correctly. Currently, loading `RedHatAI/Qwen2.5-VL-7B-Instruct-FP8-Dynamic` will crash an A100s: ``` [core.py:708] File "/opt/venv/lib/python3.12/site-packages/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py", line 661, in process_weights_after_loading [core.py:708] layer.scheme.process_weights_after_loading(layer) [core.py:708] File "/opt/venv/lib/python3.12/site-packages/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a16_fp8.py", line 59, in process_weights_after_loading [core.py:708] prepare_fp8_layer_for_marlin(layer) [core.py:708] File "/opt/venv/lib/python3.12/site-packages/vllm/model_executor/layers/quantization/utils/marlin_utils_fp8.py", line 107, in prepare_fp8_layer_for_marlin [core.py:708] marlin_qweight = ops.gptq_marlin_repack(b_q_weight=qweight, [core.py:708] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [core.py:708] File "/opt/venv/lib/python3.12/site-packages/vllm/_custom_ops.py", line 938, in gptq_marlin_repack [core.py:708] return torch.ops._C.gptq_marlin_repack(b_q_weight, perm, size_k, size_n, [core.py:708] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [core.py:708] File "/opt/venv/lib/python3.12/site-packages/torch/_ops.py", line 1243, in __call__ [core.py:708] return self._op(*args, **kwargs) [core.py:708] ^^^^^^^^^^^^^^^^^^^^^^^^^ [core.py:708] RuntimeError: size_n = 6840 is not divisible by tile_n_size = 64 ``` The issue is introduced in vllm-project#22066 which changed the model implementation to use MergedColumnParallelLinear layer and pack 'gate_proj' and 'up_proj' params. Signed-off-by: Zifei Tong <[email protected]>
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This pull request correctly addresses a crash that occurs when loading quantized Qwen2.5-VL models. The issue stems from a missing packed_modules_mapping for the gate_up_proj layer, which was introduced when the model was updated to use a MergedColumnParallelLinear layer. By adding the necessary mapping, this change ensures that the quantization logic can correctly handle the packed weights, resolving the shape mismatch error during the gptq_marlin_repack operation. The fix is targeted, necessary, and follows the established pattern in vLLM for supporting packed layers in quantized models. The change is approved.
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Signed-off-by: Zifei Tong <[email protected]>
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This PR makes sure quantized model weights are loaded correctly. Currently, loading
RedHatAI/Qwen2.5-VL-7B-Instruct-FP8-Dynamicwill crash on A100s:The issue is introduced in #22066 which changed the model implementation to use MergedColumnParallelLinear layer and pack 'gate_proj' and 'up_proj' params.
Purpose
Test Plan
Test Result
(Optional) Documentation Update
Essential Elements of an Effective PR Description Checklist
supported_models.mdandexamplesfor a new model.