QMoE CUDA EP — FP4/FP8/WFP4AFP8 Quantized Mixture-of-Experts + MoE GEMM Refactor by tianleiwu · Pull Request #28467 · microsoft/onnxruntime

Tianlei Wu (tianleiwu) · 2026-05-12T00:16:03Z

Description

Update QMoE contrib operator for the CUDA EP to supports quantized Mixture-of-Experts inference with INT4, INT8, FP4 (MXFP4 e2m1), FP8 (e4m3fn), and WFP4AFP8 (mixed FP4 weight × FP8 activation) quantization formats.

This also refactors the existing MoE GEMM infrastructure to support TMA warp-specialized grouped GEMM on Hopper (SM90), native MXFP4 on Blackwell (SM120), and block-scaled tensor ops on SM100+, with automatic fallback to dequantization on older architectures.

Note that this is modified from TensorRT-LLM MoE implementation. There is a section in moe_qmoe.md about the modifications.

Summary of Changes

New QMoE Operator

File	Change
`onnxruntime/core/graph/contrib_ops/contrib_defs.cc`	Register `QMoE` op schema (com.microsoft domain, opset 1)
`onnxruntime/contrib_ops/cuda/moe/moe_quantization.cc/h`	QMoE CUDA kernel implementation with dynamic runner selection
`onnxruntime/contrib_ops/cuda/moe/qmoe_kernels.cu/h`	Softmax top-k router, sparse mixer, zero-point pre-packing kernels
`onnxruntime/contrib_ops/cuda/moe/moe_base.h`	Shared MoE base class updates for quantization attributes
`docs/contrib_ops/cuda/moe_qmoe.md`	Comprehensive operator documentation (inputs, attributes, quantization formats)

MoE GEMM Refactor

File	Change
`onnxruntime/contrib_ops/cuda/llm/moe_gemm/moe_gemm_kernels.h`	Unified `CutlassMoeFCRunner` template with FP4/FP8/WFP4AFP8 specializations
`onnxruntime/contrib_ops/cuda/llm/moe_gemm/moe_gemm_template_dispatch.h`	Three-family dispatch: Ampere GemmGrouped, TMA warp-specialized, block-scaled tensor ops
`onnxruntime/contrib_ops/cuda/llm/moe_gemm/moe_gemm_profiler.cc/h`	MoE-specific GEMM tactic profiler for auto-tuning
`onnxruntime/contrib_ops/cuda/llm/moe_gemm/common.h`	Shared MoE GEMM types and config structs
`onnxruntime/contrib_ops/cuda/llm/moe_gemm/launchers/`	SM80/SM90/SM120 launcher instantiations (including generated .cu files)

CUTLASS Extensions

File	Change
`onnxruntime/contrib_ops/cuda/llm/cutlass_extensions/arch/`	Grid dependency control, TMA copy traits, multi-mem copy operations
`onnxruntime/contrib_ops/cuda/llm/cutlass_extensions/gemm/collective/`	Mixed-input and gated GEMM collective builders for SM90
`onnxruntime/contrib_ops/cuda/llm/cutlass_extensions/gemm/kernel/`	Fused MoE kernel traits/routines, MoE problem visitors, gated GEMM kernels
`onnxruntime/contrib_ops/cuda/llm/cutlass_extensions/epilogue/`	MoE finalize epilogue, per-row/per-col scale epilogues
`onnxruntime/contrib_ops/cuda/llm/cutlass_extensions/system_barrier.h`	System barrier for multi-CTA synchronization

Common CUDA Utilities

onnxruntime/contrib_ops/cuda/llm/common/cuda_fp8_utils.cu/h — FP8 conversion, quantization, dequantization kernels
onnxruntime/contrib_ops/cuda/llm/common/memory_utils.cu/h — Device memory transpose, permute, type conversion utilities
onnxruntime/contrib_ops/cuda/llm/common/cuda_type_utils.cuh — Unified type traits for half/bfloat16/float/fp8/fp4
onnxruntime/contrib_ops/cuda/llm/common/quantization.h — Quantization parameter structs and helpers
onnxruntime/contrib_ops/cuda/llm/common/reduce_kernel_utils.cuh — Warp/block reduction primitives
onnxruntime/contrib_ops/cuda/llm/kernels/quantization.cuh — FP4/FP8 quantization kernels
onnxruntime/contrib_ops/cuda/llm/kernels/pre_quant_scale_kernel.cu/h — Pre-quantization scaling kernel

GEMM Profiler Refactor

File	Change
`onnxruntime/contrib_ops/cuda/llm/gemm_profiler.cc/h`	Refactored GEMM profiler interface for tactic selection
`onnxruntime/contrib_ops/cuda/llm/cutlass_heuristic.cc/h`	Updated heuristics for new kernel families
`onnxruntime/contrib_ops/cuda/llm/cutlass_extensions/gemm_configs.h`	Extended GEMM config enums for TMA warp-specialized and gated configs

Build System

File	Change
`cmake/CMakeLists.txt`	Add `ENABLE_FP4`, `ENABLE_FP8`, `ENABLE_CUDA_FP4_QMOE`, `ORT_QUICK_BUILD`, `PLACEHOLDER_KERNELS` options
`cmake/external/cuda_configuration.cmake`	FP4/FP8 capability detection based on CUDA version and SM arch
`cmake/external/cutlass.cmake`	CUTLASS version bump
`cmake/onnxruntime_providers_cuda.cmake`	Add MoE GEMM source files and conditional FP4/FP8 kernel compilation
`cmake/onnxruntime_python.cmake`	Add `onnxruntime_pybind_quant.cc` for Python quantization bindings

Python Quantization Bindings

File	Change
`onnxruntime/python/onnxruntime_pybind_quant.cc`	C++ pybind module for MoE weight preprocessing (quantize, pack, preprocess)
`onnxruntime/python/tools/quantization/quant_utils.py`	FP4/FP8 quantization utilities
`setup.py`	Include new pybind module in package build

Tests

File	Change
`onnxruntime/test/python/transformers/test_qmoe_cuda.py`	INT4/INT8 QMoE tests (Phi3 topology, SwiGLU, blockwise, asymmetric)
`onnxruntime/test/python/transformers/test_qmoe_fp4_cuda.py`	MXFP4 QMoE tests
`onnxruntime/test/python/transformers/test_qmoe_fp8_cuda.py`	FP8 QMoE tests
`onnxruntime/test/python/transformers/test_qmoe_wfp4afp8_cuda.py`	WFP4AFP8 mixed-precision QMoE tests
`onnxruntime/test/python/transformers/test_moe_cuda.py`	Updated existing MoE tests for refactored infrastructure
`onnxruntime/test/contrib_ops/moe_test.cc`	C++ MoE unit tests updated

Existing MoE Refactor

onnxruntime/contrib_ops/cuda/moe/moe.cc/h — Refactored to share base with QMoE
onnxruntime/contrib_ops/cuda/moe/ft_moe/ → onnxruntime/contrib_ops/cuda/llm/moe_gemm/ — Relocated and rewritten MoE GEMM kernels
Removed old cuda/quantization/moe_quantization.cc/h in favor of new cuda/moe/moe_quantization.cc/h

Testing

INT4/INT8 QMoE: python -m pytest onnxruntime/test/python/transformers/test_qmoe_cuda.py -v (requires CUDA GPU, SM75+)
FP4 QMoE: python -m pytest onnxruntime/test/python/transformers/test_qmoe_fp4_cuda.py -v (requires SM120+ for native, falls back on older)
FP8 QMoE: python -m pytest onnxruntime/test/python/transformers/test_qmoe_fp8_cuda.py -v (requires SM90+ for native)
WFP4AFP8 QMoE: python -m pytest onnxruntime/test/python/transformers/test_qmoe_wfp4afp8_cuda.py -v (requires SM100+)
Existing MoE: python -m pytest onnxruntime/test/python/transformers/test_moe_cuda.py -v
C++ MoE tests: Build with CUDA EP enabled, run onnxruntime_test_all --gtest_filter=*MoE*
All tests compare QMoE output against PyTorch reference implementations with configurable tolerance

Motivation and Context

Modern LLMs increasingly use Mixture-of-Experts architectures (e.g., Mixtral, DeepSeek, Phi-3.5-MoE) for efficient scaling. These models benefit significantly from weight quantization to reduce memory bandwidth and enable larger models on fewer GPUs. This PR:

Adds native low-precision MoE support — FP4 and FP8 quantized weights avoid the dequantization overhead of INT4/INT8 on supported hardware (Hopper, Blackwell).
Introduces WFP4AFP8 — A novel mixed-precision mode where weights are MXFP4 and activations are dynamically quantized to FP8, enabling 2× weight compression with minimal accuracy loss on Blackwell GPUs.
Refactors MoE GEMM infrastructure — The previous FasterTransformer-derived MoE GEMM code is replaced with a modern CUTLASS 4.x-based dispatch system supporting three kernel families across SM75–SM120+.
Adds auto-tuning — The GEMM profiler enables runtime tactic selection for optimal performance across different expert sizes and batch configurations.

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…-fp4 A-bf16 kernel

…28583) ## Description Follow-up refinements to QMoE CUDA EP (#28467): rename build options for consistency, fix CodeQL warnings, refactor `PrePack` from nested lambdas into named helper methods, and replace CPU data-transformation loops with GPU kernels. ### Motivation and Context PR #28467 introduced the QMoE operator with a 373-line `PrePack` function containing 5 nested lambdas that performed weight/scale prepacking at model load time. Reviewer feedback requested: 1. Rename `onnxruntime_ENABLE_CUDA_*` cmake options to `onnxruntime_USE_*` for naming consistency. 2. Fix CodeQL empty-except warnings in test code. 3. Extract lambdas into named private methods for readability and testability. 4. Replace CPU loops (block-scale swizzle, FP4 col-to-row repack) with GPU kernels to avoid unnecessary CPU↔GPU round-trips during model loading. ## Key Changes | Commit | Scope | Description | |--------|-------|-------------| | `594642a` | Build system | Rename `ENABLE_CUDA_FP4_QMOE`→`USE_FP4_QMOE`, `ENABLE_CUDA_FP8_QMOE`→`USE_FP8_QMOE` in cmake, C++ defines, and 340+ generated .cu files | | `594642a` | Test | Fix CodeQL empty-except warning in `test_qmoe_cuda.py` | | `e8d364b` | QMoE operator | Extract 5 lambdas into private helper methods: `PrePackTransposeAndPack`, `PrePackCopyToGpu`, `PrePackSwizzleBlockScales`, `PrePackRepackFP4Weights`, `PrePackComputeBias` | | `e8d364b` | CUDA kernels | Add `QMoERepackFP4ColToRowKernel` — repacks column-major FP4 packed weights to row-major on GPU (replaces per-expert CPU loop) | | `e8d364b` | CUDA kernels | `PrePackSwizzleBlockScales` now calls existing `LaunchQMoEBlockScaleInterleave` GPU kernel (replaces CPU `SwizzleMXFPXBlockScalesToGpu` loop) | ## Impact - **No behavioral change** — all transformations produce identical output tensors. - **Model load only** — `PrePack` runs once during `InferenceSession::Initialize()`, not on the inference hot path. - **Performance**: Eliminates CPU→GPU→CPU→GPU round-trips for block-scale swizzling and FP4 weight repacking. Data stays on GPU throughout. ## Testing - Build verified with `onnxruntime_USE_FP4_QMOE=ON onnxruntime_USE_FP8_QMOE=ON` (CUDA 13.0, SM90). - All new symbols confirmed linked in `libonnxruntime_providers_cuda.so` via `nm`. - Existing `test_qmoe_fp4_cuda.py`, `test_qmoe_wfp4afp8_cuda.py`, `test_qmoe_cuda.py` cover the affected code paths.

Re-enables the fused `com.microsoft::MoE` op for Gemma 4 now that ORT main supports standard SwiGLU. Replaces the static-unroll fallback that was shipped in #324 as a workaround. ## Why now [microsoft/onnxruntime#28467](microsoft/onnxruntime#28467) (QMoE CUDA EP + MoE GEMM Refactor, merged on main) plumbs the existing schema attributes (`swiglu_fusion`, `activation_alpha`, `activation_beta`, `swiglu_limit`) all the way through to the kernel. Before that PR, the CUDA MoE kernel hardcoded the GPT-OSS values (`alpha=1.702, beta=1.0, limit=7.0`, interleaved) and silently produced wrong output for any model using standard SwiGLU. That was the root cause tracked in [microsoft/onnxruntime-genai#2062](microsoft/onnxruntime-genai#2062). ## Attribute set ``` activation_type = 'swiglu' activation_alpha = 1.0 # no GPT-OSS 1.702 multiplier activation_beta = 0.0 # no GPT-OSS '+1' bias on the up branch swiglu_limit = inf # no clipping swiglu_fusion = 1 # interleaved normalize_routing_weights = 1 k = top_k ``` ## Weight layout HuggingFace stores `experts.gate_up_proj` chunked as `[E, 2*inter, H]` (first `inter` rows are gate, next `inter` are up). The CPU MoE kernel still only accepts interleaved layout (`contrib_ops/cpu/moe/moe_cpu.cc:27`); the new CUDA kernel accepts either. We emit `swiglu_fusion=1` (interleaved) for maximum portability and reshape at graph-emit time via `Reshape → Transpose → Reshape` on the initializer — ORT folds the chain to a single static tensor at session load. ## Fallback `_dispatch_moe_fallback` (static per-expert unroll) is kept verbatim for EPs that don't advertise `supports_fused_moe`. The fused path is only taken when `ep_capabilities().supports_fused_moe` is true (default for the CUDA / DML / default EPs). ## Validation (H200, ORT `1.27.0.dev20260511001` which contains #28467) | Check | Result | |---|---| | 15 `gemma4` graph-construction tests | all pass | | fp16 build of `google/gemma-4-26b-a4b-it` | 30 MoE nodes emitted, all attributes correctly set | | `InferenceSession` load on CUDA EP | **12.3s** (vs 959s with the previous unrolled fallback — ~78× faster session creation) | | Prefill (B=1, S=4) | 0.65s, logits well-behaved (no NaN/Inf, top-k IDs in valid vocab range) | | lintrunner | clean | ## Compatibility - Requires ORT main (or any future release containing #28467). On older ORT releases (≤1.27 GA) the fused op will silently use the GPT-OSS hardcoded constants and produce wrong output. Users should keep the unrolled fallback build (pre-#324 reversion) until they upgrade ORT. Signed-off-by: Justin Chu <justinchu@microsoft.com> Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Adds a new ONNX graph pass that rewrites every com.microsoft::MoE node into a com.microsoft::QMoE node with the per-expert FC1/FC2 weight initializers quantized to symmetric int4 (default) or int8, plus corresponding fp16 scale initializers. Motivation: mobius (and similar exporters) emit the fused com.microsoft::MoE op with the per-expert weights as 3-D fp16/bf16/fp32 initializers. The existing weight-quantization passes (OnnxKQuantQuantization, OnnxBlockWiseRtnQuantization, OnnxBnb4Quantization) only target MatMul nodes, so for MoE models the per-expert weights (~80% of total parameters) stay at the model's compute dtype, leaving just ~6% size reduction after quantization. The QMoE op is the correct target for MoE weights and is supported by the CUDA + experimental CPU kernels in ORT main (PR microsoft/onnxruntime#28467). Implementation: - Walks the graph and finds every com.microsoft::MoE node whose fc1_experts_weights and fc2_experts_weights are 3-D static initializers. - For each expert, calls ORT's pybind quantize_matmul_{4,8}bits to produce per-expert int4/int8 weights + symmetric fp16 scales, then CUTLASS-prepacks them via pack_weights_for_cuda_mixed_gemm so the QMoE kernels can consume the bytes directly. - Stacks per-expert tensors along axis 0 and registers them as new initializers (uint8 weight + fp16 scale per expert). - Replaces the MoE node with a QMoE node carrying the original activation/routing attributes plus expert_weight_bits, optional block_size, and quant_type='int'. - Orphaned fp16 weight initializers are dropped. Supports per-row scales (block_size=0, default) and block-wise scales (block_size ≥ 16, must be power of two). Nodes can be selectively excluded via nodes_to_exclude. The pass requires a CUDA-enabled ONNX Runtime build because pack_weights_for_cuda_mixed_gemm is only exposed when ORT is compiled with USE_CUDA. A descriptive RuntimeError is raised at run time when the binding is unavailable. Limitations / out-of-scope: - fc3 inputs (3-fold MoE variants) are not supported and trigger a warning-skip per node. - Only symmetric int quantization (matching the kernel's preferred layout). FP4 / FP8 / WFP4AFP8 quant_types are left for a follow-up. - Calibration-aware quantization (GPTQ / AWQ) is out of scope; this pass is pure RTN. Tests: 5 unit tests covering (a) end-to-end MoE → QMoE conversion with int4 + per-row scales, (b) block-wise int4, (c) graceful skip when weights are not static initializers, (d) bits validation, and (e) block_size validation. The CUTLASS prepack helper is patched during tests so CI without onnxruntime-gpu can still exercise the graph transform. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> Signed-off-by: Justin Chu <11205048+justinchuby@users.noreply.github.com>

Sandro (SuperSandro2000) · 2026-07-26T19:56:55Z

There is still an include for moe_kernel.h which seems to fail after this PR https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/contrib_ops/cuda/collective/sharded_moe.h#L6

QMoE CUDA FP4/FP8/WFP4AFP8 + MoE Refactor

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refine

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Merge remote-tracking branch 'origin/main' into tlwu/20260511/qmoe_cuda

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fix build

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refine build

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share source filters between cuda and cuda plugin

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remove fc3_global_scale; use float8e8m0; wfp4afp8 blackwell support

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K=128 tile support, epilogue fusion, expanded tile configs for SM90 W…

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…-fp4 A-bf16 kernel

update op docs

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Tianlei Wu (tianleiwu) merged commit 548ab6e into main May 20, 2026
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QMoE CUDA: Rename build options, refactor PrePack, add GPU kernels #28583

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Justin Chu (justinchuby) mentioned this pull request Jun 1, 2026

Add OnnxMoEQuantization pass (com.microsoft::MoE → QMoE) microsoft/Olive#2491

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com.microsoft::QMoE should prepack int4/int8 weights in PrePack(), like MatMulNBits does #28748

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Fix: Add CMake package files to Windows artifact microsoft#28467

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onnxruntime: disable NCCL per default as it fails to compile since the last update NixOS/nixpkgs#546053

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fix: drop orphaned sharded_moe #31139

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onnxruntime: fix cuda build NixOS/nixpkgs#547077

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[CUDA] Port ShardedMoE to the new MoE GEMM backend #31191

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QMoE CUDA EP — FP4/FP8/WFP4AFP8 Quantized Mixture-of-Experts + MoE GEMM Refactor - #28467

QMoE CUDA EP — FP4/FP8/WFP4AFP8 Quantized Mixture-of-Experts + MoE GEMM Refactor#28467
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Description

Summary of Changes

New QMoE Operator

MoE GEMM Refactor

CUTLASS Extensions

Common CUDA Utilities

GEMM Profiler Refactor

Build System

Python Quantization Bindings

Tests

Existing MoE Refactor

Testing

Motivation and Context

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Tianlei Wu (tianleiwu) commented May 12, 2026 •

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