qwen2.5-omni: offload multimodal features to CPU after GPU processing - #41891
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Signed-off-by: Yipeng Hu <i26268@metax-tech.com>
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Code Review
This pull request modifies the Qwen2.5-Omni model by implementing device-aware offloading during processor calls and cleaning up unused parameters in audio and video processing methods. A review comment highlights a potential bug where the mm_kwargs dictionary is modified in-place, suggesting a refactor to use a copy instead to avoid side effects and reduce code duplication.
| if "device" not in mm_kwargs and torch.cuda.is_available(): | ||
| mm_kwargs["device"] = "cuda" | ||
| hf_inputs = ( | ||
| super() | ||
| ._call_hf_processor( | ||
| prompt=prompt, | ||
| mm_data=mm_data, | ||
| mm_kwargs=mm_kwargs, | ||
| tok_kwargs=tok_kwargs, | ||
| ) | ||
| .to("cpu") | ||
| ) | ||
| else: | ||
| hf_inputs = super()._call_hf_processor( | ||
| prompt=prompt, | ||
| mm_data=mm_data, | ||
| mm_kwargs=mm_kwargs, | ||
| tok_kwargs=tok_kwargs, | ||
| ) |
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Currently, we don't expect to call hf processor on GPU, see: #22070
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This pull request has merge conflicts that must be resolved before it can be |
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Purpose
Fix the issue of high GPU memory usage during multimodal processing in the Qwen2.5-Omni model.
In the
_call_hf_processormethod, when CUDA is available andmm_kwargsdoes not specify a device, after multimodal feature processing is completed on the GPU, the features are immediately offloaded to CPU memory via.to("cpu").Problems Solved
Multimodal data → GPU processing → Results stay in GPU memory → Memory always occupied
Reduce GPU memory usage, free up GPU memory resources, improve GPU utilization, and increase inference speed
Core Solution:
_call_hf_processor()Feature Offloading OptimizationCondition: GPU available + No device specified
Processing Flow: Call parent class
super()._call_hf_processor()to extract features, then immediately transfer features back to CPU via.to("cpu")Test Plan
Unit Test: Use Qwen2.5-Omni model for multimodal inference (image/audio/video) to verify functional correctness and performance
Test Results
Qwen2.5-Omni (Thinker) Performance Test Report
Test Subject: MetaX Plugin Modified vs vLLM Original
Test Environment: MetaX-C500 Hardware Environment
1. Pure Text Inference
Summary: In pure text scenarios, the plugin version achieved a comprehensive slight lead with faster response speed.
2. Image Understanding
Summary: With visual modality introduced, the plugin version's advantage expanded with significantly lower end-to-end latency.
3. Video Understanding
Summary: Performance improvement in video scenarios is extremely significant. Plugin throughput (TPS) doubled, latency halved.
4. Interleaved Audio-Video
Summary: In the most complex multimodal interleaved scenario, the plugin version maintains huge advantages with far higher processing efficiency than the original.
Summary Comparison Table
Files Involved
vllm/model_executor/models/qwen2_5_omni_thinker.py