[PyTorch] Relax dimension constraints for using fused grouped MLP

[ksivaman]

Description

Previously we required all weight dims to be modulo256, this PR changes it to modulo64, which is the actual constraint from the kernel.

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 refactoring

Changes

• Enable fused grouped MLP path for 64 modulo dims as well.
• Fix shapes when creating SF views to pass to cutedsl.

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

[greptile-apps]

Greptile Summary

This PR relaxes the fused grouped MLP dimension gate from % 256 to % 64 (the actual kernel constraint) and fixes the MXFP8 scale-factor view reshapes to use ceiling division ((dim + 127) // 128) instead of floor division — the latter would produce a zero-sized dimension for any weight size that is a multiple of 64 but not 128. A new assertion guards that the token-count dimension (in_shape[0]) remains a multiple of 128, which is a separate kernel requirement.

Confidence Score: 5/5

Safe to merge; all remaining findings are P2 style/coverage suggestions that do not affect correctness.

The core logic changes — gate relaxation from % 256 to % 64 and ceiling-division SF views — are correct and consistent across forward and backward paths. Only P2 issues remain: an assert that should be a RuntimeError, stale comments, and a test-coverage gap for non-128-multiple hidden sizes.

forward_grouped_mlp.py (assert → RuntimeError, stale comments); tests/pytorch/test_fusible_ops.py (consider adding hidden_size=64)

Important Files Changed

Filename	Overview
transformer_engine/pytorch/ops/_common.py	Relaxes dimension gating from % 256 to % 64 in both validate_grouped_mlp_dims and fuse_grouped_mlp_ops; logic is consistent across both functions.
transformer_engine/pytorch/ops/fused/forward_grouped_mlp.py	Adds token-count assertion (% 128) and fixes all SF-view reshapes from floor to ceiling division; assert should be a RuntimeError, and scale-shape comments are stale.
transformer_engine/pytorch/ops/fused/backward_grouped_mlp.py	Applies the same ceiling-division fix to fc2_dy, fc2_w, and fc1_w scale views; changes are mechanically symmetric with the forward pass.
tests/pytorch/test_fusible_ops.py	Replaces per-class setup_class hooks with a module-level autouse fixture, adds hidden_size parametrization (128, 256); both values are multiples of 128 so the ceiling-division fix is not directly exercised.
tests/pytorch/utils.py	Extends reset_rng_states to also save/restore Python random module state, making RNG reproducibility more complete.

Flowchart

%%{init: {'theme': 'neutral'}}%%
flowchart TD
    A["Input tensor\nin_shape = M x K"] --> B{"M % 128 == 0?"}
    B -- No --> ERR["RuntimeError\n(assert currently)"]
    B -- Yes --> C{"Weight dims\n% 64 == 0?\n(was % 256)"}
    C -- No --> SKIP["Skip fused path\n(fallback to unfused)"]
    C -- Yes --> D["Quantize inputs/weights\n(MXFP8 group quantize)"]
    D --> E["Build SF view\nceil(dim/128) blocks\n(was dim//128)"]
    E --> F["Forward GEMM + SwiGLU kernel"]
    F --> G["FC2 GEMM kernel"]
    G --> H["Output tensor"]
    H --> BWD["Backward pass\nSame ceiling-div SF views\nfor fc2_dy, fc1_w, fc2_w"]

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_{Reviews (4): Last reviewed commit: "reset rng as before, assert input dim" | Re-trigger Greptile}

[timmoon10]

LGTM

[ksivaman]

/te-ci pytorch L0

[ksivaman]

/te-ci pytorch L0

[ksivaman]

/te-ci pytorch L0