# 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`
- **Domain:** pytorch
- **Category:** runtime_error
- **Verification:** ai_generated
- **Fix Rate:** 78%

## 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.

## Version Compatibility

| Version | Status | Introduced | Deprecated |
|---------|--------|------------|------------|
| 1.11 | active | — | — |
| 1.12 | active | — | — |
| 1.13 | active | — | — |
| 2.0 | active | — | — |
| 2.1 | active | — | — |
| 2.2 | active | — | — |
| 2.3 | active | — | — |

## Workarounds

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.** (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.
   ```
2. **If using SyncBatchNorm, ensure it is applied consistently across all ranks before DDP wrapping.** (80% success)
   ```
   If using SyncBatchNorm, ensure it is applied consistently across all ranks before DDP wrapping.
   ```

## Dead Ends

- **** — This doesn't fix the underlying parameter mismatch; it just changes the bucket size limit. (60% fail)
- **** — This can mask the issue but may lead to incorrect gradients if parameters are truly mismatched. (50% fail)