[PyTorch] Userbuffers support in operation-based API

[timmoon10]

Description

This PR adds basic support in the linear operation for using Userbuffers to overlap tensor-parallel communication with GEMMs. This is implemented as fused operations:

model = te.ops.Sequential(
    te.ops.BasicLinear(...),
    te.ops.Bias(...),
    te.ops.ReduceScatter(...),
)  # Fused into UserbuffersForwardLinear

I've tried to avoid touching the core UB infrastructure in transformer_engine/pytorch/module/base.py, so I've kept the messy API and hackily worked around some bugs. This feature should be considered experimental and unstable.

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)
• [x] 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

• Add fused operation for linear forward with Userbuffers
• Add fused operation for linear backward with Userbuffers

Checklist:

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

[timmoon10]

/te-ci pytorch

[timmoon10]

/te-ci pytorch

[timmoon10]

/te-ci pytorch

[timmoon10]

/te-ci pytorch

[timmoon10]

/te-ci L1

[timmoon10]

/te-ci pytorch jax paddle L1

[timmoon10]

/te-ci pytorch L1