[Common][PyTorch] EP dispatch with unfused MXFP8 quantization

[phu0ngng]

Description

This PR adds MXFP8 support to the dispatch op of the NCCL EP path. The dispatch op is used in two places, and MXFP8 applies to both:

• Dispatch forward bfloat16 tokens are quantized to MXFP8 internally and dispatched to the target experts; recv is returned as a per-expert GroupedTensor.
• Combine backward the result-grad is scattered back to expert positions through the same (reverse) dispatch op, quantized to MXFP8, returning the expert-output grad as a per-expert GroupedTensor.

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

PyTorch frontend (transformer_engine/pytorch/ep.py, distributed.py, csrc/extensions/ep.cpp)**

• The dispatch op quantizes bfloat16 tokens to MXFP8 internally when the buffer's dispatch_quant_recipe is set (MXFP8BlockScaling only for now); dispatch-forward recv is returned as a per-expert GroupedTensor. A pre-quantized input is rejected.
• Combine backward reuses the dispatch op to scatter the result-grad: it quantizes the grad to MXFP8 and returns the expert-output grad as a per-expert GroupedTensor. Combine forward is unchanged (high-precision).
• Recv data and block scales share a single caller-supplied (optionally symm-mem-backed) buffer, sliced into data-then-scale regions; the same convention is used for the combine backward grad buffer.

Common backend (common/ep/ep_backend.cpp, include/.../ep.h, comm_window.h)**

• Backend and public headers extended to carry block-scale buffers/windows through the dispatch primitive.

NCCL EP submodule**

• Bumped 3rdparty/nccl-extensions to the revision providing block-scaled dispatch.

Tests (tests/cpp_distributed/test_ep.cu, tests/pytorch/distributed/run_ep.py, run_test_ep.sh)**

• Added C++ distributed coverage for the MXFP8 dispatch path.
• Added PyTorch MXFP8 test passes for dispatch forward (normal, zero-copy, eager IO modes) and combine backward, gated behind a dedicated NVTE_EP_MXFP8_PASS run since the grouped path pins the per-expert alignment process-wide.

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

Adds unfused MXFP8 quantization to NCCL expert-parallel dispatch and combine backward.

• Routes MXFP8 payloads and block scales through the common backend and PyTorch bindings.
• Returns per-expert grouped MXFP8 tensors and supports caller-provided or symmetric-memory-backed buffers.
• Adds MXFP8 distributed coverage and explicit symmetric-memory pool teardown.

Confidence Score: 5/5

The PR appears safe to merge.

No blocking failure remains.

Important Files Changed

Filename	Overview
transformer_engine/pytorch/ep.py	Adds recipe-controlled MXFP8 quantization, grouped dispatch outputs, and quantized combine-backward gradients.
transformer_engine/pytorch/csrc/extensions/ep.cpp	Extends EP bindings to validate and transport compact MXFP8 scale-inverse tensors.
transformer_engine/common/ep/ep_backend.cpp	Adds block-scale NCCL descriptors and selects scale-forwarding dispatch.
transformer_engine/pytorch/distributed.py	Adds explicit teardown for the process-wide symmetric-memory pool.
tests/pytorch/distributed/run_ep.py	Adds dedicated MXFP8 dispatch and combine-backward distributed coverage.

Flowchart

%%{init: {'theme': 'neutral'}}%%
flowchart LR
  BF16["BF16 tokens"] --> Quant["MXFP8 quantization"]
  Quant --> Data["E4M3 data"]
  Quant --> Scales["E8M0 scales"]
  Data --> Dispatch["NCCL EP dispatch"]
  Scales --> Dispatch
  Dispatch --> Grouped["Per-expert GroupedTensor"]
  Grad["Combine result gradient"] --> BwdQuant["MXFP8 quantization"]
  BwdQuant --> Reverse["Reverse dispatch"]
  Reverse --> ExpertGrad["Per-expert grouped gradient"]

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_{Reviews (2): Last reviewed commit: "restore gitignore" | Re-trigger Greptile}

[phu0ngng]

Let me separate the recipe for dispatch fwd and combine bwd, in case users want to use different quantization for fwd and bwd in the future.