[PyTorch] Store module extra state in tensor

[timmoon10]

Description

TE modules implement get_extra_state/set_extra_state in order to include FP8 state in checkpoints. We currently pickle the FP8 state and store in a io.BtyesIO object, but this is problematic because PyTorch makes no guarantees if the extra state is not a torch.Tensor. This has resulted in problems with ONNX export and Hugging Face Transformers.

#363 changed from storing the extra state in a torch.Tensor to io.BytesIO in order to reduce the overhead from GPU-CPU memory transfers. This PR restores the original torch.Tensor format, but performs the memory transfers asynchronously to reduce overhead. It's similar to the approach used in the operation-based API (#1063). It should be backward compatible and I've been able to load existing checkpoints. The attention docs mention extra state, but I don't think this PR affects it.

Fixes #1317.

Type of change

• Documentation change (change only to the documentation, either a fix or a new content)
• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• Infra/Build change
• Code refractor

Changes

• Store module extra state in tensor instead of io.BytesIO

Checklist:

• I have read and followed the contributing guidelines
• The functionality is complete
• I have commented my code, particularly in hard-to-understand areas
• I have made corresponding changes to the documentation
• My changes generate no new warnings
• I have added tests that prove my fix is effective or that my feature works
• New and existing unit tests pass locally with my changes

[timmoon10]

/te-ci pytorch L0 L1