fix update_weights_from_tensor#8150
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Summary of Changes
Hello @SuperCB, 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 introduces a critical fix to the update_weights_from_tensor functionality by applying a specific PyTorch patch. The monkey_patch_torch_reductions call ensures that tensor operations, particularly reductions, behave as expected during weight updates, which is vital for the stability and correctness of the model in a distributed environment.
Highlights
- Weight Update Robustness: A call to
monkey_patch_torch_reductions()has been added at the beginning of theupdate_weights_from_tensormethod withintp_worker.py. This change is intended to apply necessary PyTorch patches, likely to ensure correct behavior of tensor reduction operations during model weight updates, especially in a distributed or tensor-parallel context. - New Dependency Import: The
monkey_patch_torch_reductionsfunction is now imported fromsglang.srt.patch_torchto support the new patching logic in the weight update process.
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Code Review
This PR fixes a bug in update_weights_from_tensor by applying a monkey patch for correct tensor deserialization across processes. The change is correct and necessary. I've added one comment with a suggestion to improve the code structure by moving the patch call to the worker's initialization, which would make the change more robust and maintainable.
| return success, message | ||
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| def update_weights_from_tensor(self, recv_req: UpdateWeightsFromTensorReqInput): | ||
| monkey_patch_torch_reductions() |
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Applying monkey_patch_torch_reductions() here fixes deserialization issues. However, consider applying this patch once during TpModelWorker.__init__ instead, to ensure it's applied at worker initialization and avoid potential repeated calls. This centralizes process-level patches and clarifies the patch as a fundamental requirement for the worker.
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@SuperCB can you share more details in the PR Description? FYI @zhaochenyang20 |
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