[Bugfix] Register reducer even if transformers_modules not available #19510
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Summary of Changes
Hello @eicherseiji, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request fixes a critical pickling error that prevented the deployment of certain models requiring trust_remote_code in multiprocessing environments. I've updated the serialization logic to use cloudpickle more robustly for VllmConfig objects, ensuring they can be passed between processes regardless of whether the dynamically loaded transformers_modules package is directly importable in the target process.
Highlights
- Bugfix: Pickling Error: I've addressed a
_pickle.PicklingErrorthat occurred when deploying models requiringtrust_remote_code(like DeepSeek-V2-Lite) in environments using multiprocessing (like Ray Serve/vLLM workers). The error happened because custom configuration classes loaded viatrust_remote_codeintotransformers_moduleswere not available in spawned processes, preventing standard pickling. - Serialization Logic Update: I've modified the
_register_config_reducerfunction invllm/transformers_utils/config.pyto ensure thatVllmConfigobjects, which may contain references to these custom classes, are always serialized usingcloudpickle's by-value mechanism when passed between processes viamultiprocessing.reducer. This works even if the custom class definition isn't directly importable in the target process. - Conditional Cloudpickle Registration: The registration of the
transformers_modulespackage itself withcloudpickle.register_pickle_by_valueis now conditional ontransformers_modulesbeing importable in the current process. This prevents errors in environments where this dynamic module isn't present, while still allowingVllmConfigobjects to be serialized correctly.
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Code Review
This pull request addresses a _pickle.PicklingError that occurred when transformers_modules could not be imported, which prevented the registration of a custom multiprocessing reducer for VllmConfig. The fix correctly ensures that the reducer for VllmConfig (using cloudpickle) is registered regardless of transformers_modules availability, as long as cloudpickle itself is available. The registration specific to transformers_modules remains conditional on its availability. This change enhances the robustness of pickling VllmConfig objects in multiprocessing scenarios. The provided reproducer and logs clearly illustrate the issue, and the fix appears to directly address it. The code changes are clean and logical.
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kouroshHakha
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kouroshHakha
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Actually could we devise a minimal ci test for this?
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Signed-off-by: Seiji Eicher <seiji@anyscale.com>
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…llm-project#19510) Signed-off-by: Seiji Eicher <seiji@anyscale.com>
…llm-project#19510) Signed-off-by: Seiji Eicher <seiji@anyscale.com>
…llm-project#19510) Signed-off-by: Seiji Eicher <seiji@anyscale.com> Signed-off-by: Jinzhen Lin <linjinzhen@hotmail.com>
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Essential Elements of an Effective PR Description Checklist
supported_models.mdandexamplesfor a new model.Purpose
Fix crash due to pickle serialization error, regression from #18640.
Reproducer:
Logs:
Test Plan
Test Result
The exception is not seen when running the reproducer script.
(Optional) Documentation Update