[PyTorch] Normalization ops

[timmoon10]

Description

This PR extends the operation-based API (see #707) with LayerNorm, RMSNorm, and FP8 cast operations.

Compare with the existing module-based API:

# Module-based APImodule1=te.LayerNormLinear(...)
# Operation-based APImodule2=te.ops.Sequential(
te.ops.LayerNorm(...),
te.ops.Linear(...),
)

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

Please list the changes introduced in this PR:

• LayerNorm operation
• FP8 cast operation
• RMSNorm operation

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

[timmoon10]

/te-ci pytorch

[timmoon10]

/te-ci pytorch

[timmoon10]

/te-ci pytorch

[timmoon10]

/te-ci pytorch

[timmoon10]

/te-ci pytorch

[timmoon10]

/te-ci pytorch

Edit: te-ci/docs failure disappears when job is rerun.

[timmoon10]

/te-ci pytorch

[timmoon10]

/te-ci pytorch

[timmoon10]

/te-ci pytorch

[timmoon10]

/te-ci pytorch

[timmoon10]

Merging with approval from @ptrendx and @ksivaman.

[binxuan]

Hi one question regarding this new layernorm implementation, my understanding is that this new implementation can support multi-dimension layernorm weight while previous implemenation can only support one-dimension weight.

So I have N different 1-D tensors and previously I have to initiate N different layernorm and apply them separately. But with this new implementation, then we can apply one 2-dimensional layernorm to N stacked different tensors with shape (N, dim)? is my understanding correct?

[timmoon10]

@binxuan This implementation matches torch.nn.LayerNorm:

x_2d=x.reshape(-1, prod(normalized_shape))
y_2d=layer_norm_2d(x_2d, weight.reshape(-1), bias.reshape(-1))
y=y_2d.reshape(x.size())