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

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:

Attempt 1: Save to a backup file first, then copy to the target path.
Attempt 2: Use an older pickle protocol (pickle protocol 2) which can be more compatible.
Attempt 3: Save without the optimizer state, which can reduce file size and avoid serialization issues.
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:

A robust_save function that tries multiple saving approaches with fallbacks.
A monkey patch that replaces the Agent's save method with our robust version.

Example Usage

# 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:

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:

Additional fallback methods like serializing individual neural network layers.
Automatic retry mechanism with exponential backoff.
Asynchronous saving to avoid blocking the training loop.
Checksumming saved models to verify integrity.

For more information on similar issues with PyTorch model saving, see:

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