pytorch runtime_error ai_generated partial

RuntimeError: BucketView has wrong size: expected X, got Y. This could be due to mismatched model parameters across ranks.

ID: pytorch/ddp-bucket-view-error

Also available as: JSON · Markdown · 中文
78%Fix Rate
83%Confidence
1Evidence
2023-11-20First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
1.11 active — — —
1.12 active — — —
1.13 active — — —
2.0 active — — —
2.1 active — — —
2.2 active — — —
2.3 active — — —

Root Cause

In Distributed Data Parallel (DDP), the model parameters have different sizes or structures across different ranks, often because of inconsistent model initialization or different batch normalization configurations.

generic

中文

在分布式数据并行 (DDP) 中，不同 rank 上的模型参数大小或结构不同，通常是因为模型初始化不一致或批归一化配置不同。

Official Documentation

https://pytorch.org/docs/stable/distributed.html#distributed-data-parallel

Workarounds

  1. 85% success Ensure all ranks have the same model architecture and initialization. Use a consistent seed and load the same pretrained weights on all ranks before wrapping with DDP. 
    Ensure all ranks have the same model architecture and initialization. Use a consistent seed and load the same pretrained weights on all ranks before wrapping with DDP.
  2. 80% success If using SyncBatchNorm, ensure it is applied consistently across all ranks before DDP wrapping. 
    If using SyncBatchNorm, ensure it is applied consistently across all ranks before DDP wrapping.

中文步骤

  1. Ensure all ranks have the same model architecture and initialization. Use a consistent seed and load the same pretrained weights on all ranks before wrapping with DDP.
  2. If using SyncBatchNorm, ensure it is applied consistently across all ranks before DDP wrapping.

Dead Ends

Common approaches that don't work:

  1. 60% fail

    This doesn't fix the underlying parameter mismatch; it just changes the bucket size limit.

  2. 50% fail

    This can mask the issue but may lead to incorrect gradients if parameters are truly mismatched.