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Summary of ChangesHello @addsubmuldiv, 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 primarily focuses on enhancing the stability and compatibility of the system, particularly for NPU (Neural Processing Unit) environments. It resolves critical issues related to distributed training process group initialization by preventing port collisions and improves vLLM integration on NPU by ensuring stable device identification and avoiding incompatible model patching. Additionally, it refines dataset loading to gracefully handle Highlights
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Code Review
This pull request introduces several fixes and improvements for running on NPU platforms. Key changes include handling DatasetDict returns for robustness, preventing HCCL port collisions in multi-job NPU environments, and providing a fallback for get_device_uuid on NPUs to ensure stable IPC communication. The changes are well-reasoned and improve platform compatibility. I've added a couple of minor suggestions to improve code clarity and remove leftover debugging code.
| self.device = torch.device(Torch.get_device()) | ||
| # fix: In some worker paths, omitting local_rank can pick the wrong device / trigger get_device arg issues. | ||
| # fix: Pass local_rank when available so each worker binds to the expected local device. | ||
| print(f"VLLM Worker local_rank: {getattr(self, 'local_rank', None)} <<<<<<<<<<<<< {Torch.get_device()}") |
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
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