Support CUDA Graph for MoE models

[buptzyb]

Description

Different from non-MoE models like llama2, MoE models have dynamic-shaped activations in FFN layers, so one cudagraph can only capture a part of one transformer layer, instead of covering the whole layer. We call this a "breaking-layer" cudagraph mode. This PR adds breaking-layer cudagraph supports for MoE models on the TE side, and fixes several related bugs in TE.

Fixes # (issue)

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:

• Fix wrong per_callable_module_params order bug in _make_graphed_callables when _order is given.
• Fix warmup argument mismatch bug in _make_graphed_callables when _order is given.
• Fix fp8 accuracy issue by adding fp8_group argument to make_graphed_callables() and modifing is_first_microbatch, skip_fp8_weight_update and fp8_meta code.
• Support MoE models cudagraph by filtering graphed TE modules during warmup.

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]

Technically this seems mostly reasonable, although I have questions and stylistic suggestions. Have you tested that it works with Mcore?

@ptrendx @ksivaman @sbhavani What is our priority for this feature? The custom Mcore logic in make_graphed_callables is already messy and fragile, and this PR does exacerbate those problems.

[timmoon10]

This change seems correct to me, but it's odd if the Mcore integration was working before. @ksivaman Have we run this with Mcore, or did we run with num_microbatches=1?

This changes the interpretation of per_callable_module_params from (num_chunks, layers_per_chunk, num_microbatches) to (num_chunks, num_microbatches, layers_per_chunk). This matches the interpretation of per_callable_* lists when capturing graphs:

TransformerEngine/transformer_engine/pytorch/graph.py

Lines 237 to 239 in 3b89c36

	per_callable_fwd_idx = (m_chunk * num_microbatches * num_layers) + (
	fwd_idx[m_chunk] * num_layers + l_no
	)

[timmoon10]

/te-ci pytorch

[yaox12]

/te-ci pytorch

[buptzyb]

Have you tested that it works with Mcore?

Yes, we also made some changes in Mcore, together with TE changes in this PR, to enable MoE cudagraph. You can refer to issue 193 in our Megatron-LM repo.

[yaox12]

/te-ci pytorch

[yaox12]

/te-ci pytorch

[yaox12]

/te-ci pytorch

[buptzyb]

Hi @timmoon10 , do you have more suggestions on this PR?

[timmoon10]

/te-ci pytorch L1

[timmoon10]

LGTM

[yaox12]

Merge this PR since pipeline 20710146 passed and Tim approved.