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Summary of Changes

Hello @Jintao-Huang, 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 improves the reliability of model weight saving in a distributed training context. By introducing a distributed barrier after the training loop, it ensures that all participating processes synchronize their state before the saving operation completes. This prevents potential inconsistencies or incomplete saves that could arise in a multi-node training setup, thereby enhancing the overall robustness and data integrity of the training process.

Highlights

  • Distributed Synchronization: The torch.distributed module has been imported to enable distributed synchronization capabilities within the training script.
  • Robust Weight Saving: A dist.barrier() call has been added immediately after the self.trainer.train() method to ensure that all distributed processes synchronize before proceeding, guaranteeing that model weights are completely and consistently saved across all nodes.
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Code Review

This pull request adds a dist.barrier() to synchronize processes after training in a distributed Megatron setup. This is a good practice to ensure all ranks have completed saving their model weights before the program proceeds or exits. However, I've identified a potential race condition with the current placement of the barrier that could lead to incomplete execution for certain ranks. My review includes a comment explaining this issue and suggesting a more robust placement for your consideration.


try:
self.trainer.train(train_dataset, val_dataset, data_collator)
dist.barrier() # Ensure all weights are saved completely
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high

While adding a barrier here correctly synchronizes processes after training and ensures weights are saved, its placement inside the try block could lead to a race condition. After passing the barrier, processes that are not the last_rank will execute the empty finally block and may exit, while the last_rank process is still running the visualization logic. If a job scheduler sees that most processes have terminated, it might kill the entire job, interrupting the visualization task on the last rank.

A more robust approach is to place the dist.barrier() after the finally block. This ensures all processes, including the last rank, complete their final tasks before synchronization and exit. As the finally block is not part of this diff, I cannot provide a direct code suggestion, but I recommend moving the barrier to improve the robustness of the shutdown sequence.

@Jintao-Huang Jintao-Huang merged commit 6283ea5 into modelscope:main Nov 18, 2025
1 of 2 checks passed
vx120 pushed a commit to vx120/ms-swift that referenced this pull request Nov 19, 2025
Jintao-Huang added a commit that referenced this pull request Nov 20, 2025
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2 participants