74 lines
2.3 KiB
Markdown
74 lines
2.3 KiB
Markdown
# Model Saving Fix
|
|
|
|
## Issue
|
|
|
|
During training sessions, PyTorch model saving operations sometimes fail with errors like:
|
|
|
|
```
|
|
RuntimeError: [enforce fail at inline_container.cc:626] . unexpected pos 18278784 vs 18278680
|
|
```
|
|
|
|
or
|
|
|
|
```
|
|
RuntimeError: [enforce fail at inline_container.cc:820] . PytorchStreamWriter failed writing file data/75: file write failed
|
|
```
|
|
|
|
These errors occur in the PyTorch serialization mechanism when saving models using `torch.save()`.
|
|
|
|
## Solution
|
|
|
|
We've implemented a robust model saving approach that uses multiple fallback methods if the primary save operation fails:
|
|
|
|
1. **Attempt 1**: Save to a backup file first, then copy to the target path.
|
|
2. **Attempt 2**: Use an older pickle protocol (pickle protocol 2) which can be more compatible.
|
|
3. **Attempt 3**: Save without the optimizer state, which can reduce file size and avoid serialization issues.
|
|
4. **Attempt 4**: Use TorchScript's `torch.jit.save()` instead of `torch.save()`, which uses a different serialization mechanism.
|
|
|
|
## Implementation
|
|
|
|
The solution is implemented in two parts:
|
|
|
|
1. A `robust_save` function that tries multiple saving approaches with fallbacks.
|
|
2. A monkey patch that replaces the Agent's `save` method with our robust version.
|
|
|
|
### Example Usage
|
|
|
|
```python
|
|
# Import the robust_save function
|
|
from live_training import robust_save
|
|
|
|
# Save a model with fallbacks
|
|
success = robust_save(agent, "models/my_model.pt")
|
|
if success:
|
|
print("Model saved successfully!")
|
|
else:
|
|
print("All save attempts failed")
|
|
```
|
|
|
|
## Testing
|
|
|
|
We've created a test script `test_save.py` that demonstrates the robust saving approach and verifies that it works correctly.
|
|
|
|
To run the test:
|
|
|
|
```bash
|
|
python test_save.py
|
|
```
|
|
|
|
This script creates a simple model, attempts to save it using both the standard and robust methods, and reports on the results.
|
|
|
|
## Future Improvements
|
|
|
|
Possible future improvements to the model saving mechanism:
|
|
|
|
1. Additional fallback methods like serializing individual neural network layers.
|
|
2. Automatic retry mechanism with exponential backoff.
|
|
3. Asynchronous saving to avoid blocking the training loop.
|
|
4. Checksumming saved models to verify integrity.
|
|
|
|
## Related Issues
|
|
|
|
For more information on similar issues with PyTorch model saving, see:
|
|
- https://github.com/pytorch/pytorch/issues/27736
|
|
- https://github.com/pytorch/pytorch/issues/24045 |