feat(moe): add unified activation parity - #4613
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📝 WalkthroughWalkthroughThe MoE API now uses typed activation configurations with validation and public exports. Weight preparation, backend capability checks, runner execution, cache keys, benchmarks, documentation, and tests now propagate activation-specific behavior and parameters. ChangesTyped activation support
Estimated code review effort: 5 (Critical) | ~120 minutes Merge Risk: 🟡 Moderate · up to This PR changes the unified MoE activation API and backend plumbing, while the current head still contains concrete documentation and lint defects, including invalid examples, missing code-fence languages, stale backend guidance, and Ruff findings that can fail CI. These issues are localized and do not indicate runtime or security impact, but the PR is not merge-ready until they are fixed or explicitly accepted. Sequence Diagram(s)sequenceDiagram
participant Caller
participant MoEConfig
participant WeightPreparation
participant MoERunner
participant BackendKernel
Caller->>MoEConfig: select typed activation
MoEConfig->>WeightPreparation: pass activation and quantization variant
WeightPreparation->>MoERunner: provide shaped weights and activation metadata
MoERunner->>MoERunner: validate capability and cache key
MoERunner->>BackendKernel: launch with activation parameters
BackendKernel-->>Caller: return MoE output
Suggested reviewers: 🚥 Pre-merge checks | ✅ 5✅ Passed checks (5 passed)
Full details: Description checkExplanation The description explains the typed activation API, backend capabilities, preparation changes, testing, validation results, and reviewer notes. It includes the main template sections and completed checklists; the optional Related Issues section is not present. ✨ Finishing Touches 💡 1🛠️ Fix failing CI checks 💡
🧪 Generate unit tests (beta)
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/bot run tests/moe |
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In `@flashinfer/fused_moe/prepare.py`:
- Around line 539-553: Update the docstrings for the TRTLLM FP4, TRTLLM BF16,
CUTLASS BF16, and CuTe-DSL preparation helpers to document ReLU2 support:
ungated GEMM1 uses I rows with [E, I, H] weights and skips gated/SwiGLU row
reordering, while gated activations retain the existing 2*I contract and
reordering.
In `@tests/moe/test_unified_moe_cutlass.py`:
- Line 356: Update the pytest.raises match pattern in the relevant test to use a
raw string with the dot escaped, ensuring it matches the literal “torch.float32”
message and satisfies Ruff RUF043.
Apply the same fix in `@tests/moe/test_unified_moe.py` around lines 886 - 894:
Covered as the second localized test-file lint finding.
In `@tests/moe/test_unified_moe_fuzz.py`:
- Around line 1959-1973: Update the expected-backend validation around
expected_backend_available to ignore intentional exclusions: account for
_BACKEND_FILTER selections when determining whether the expected backend should
be asserted, and clear the flag when cfg.expected_backend appears in
quarantined_backends from LEDGER.skip_backend. Preserve the assertion for
available, non-excluded backends so deliberately filtered or quarantined cases
skip the “filtered before execution” and “did not execute” failures.
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📒 Files selected for processing (14)
benchmarks/routines/moe.pydocs/design_docs/flashinfer_moe_api.mdflashinfer/fused_moe/__init__.pyflashinfer/fused_moe/api.pyflashinfer/fused_moe/layer.pyflashinfer/fused_moe/prepare.pyflashinfer/fused_moe/runners.pytests/moe/test_b12x_fused_moe.pytests/moe/test_unified_moe.pytests/moe/test_unified_moe_b12x.pytests/moe/test_unified_moe_cutlass.pytests/moe/test_unified_moe_fp8.pytests/moe/test_unified_moe_fuzz.pytests/moe/test_unified_moe_mxfp4.py
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[FAILED] Pipeline #63682423 — 4/16 executed test jobs passed Compared with nightly #63648836. Unit Tests
✅ Pass · 🟡 Old failure · ❌ New failure · ⏱ Test timeout · Multi-GPU and Multi-Node Tests — 4/6 passed
Failure detailsPR-related regressions
Could not compare
Timeouts, infrastructure, or incomplete jobs
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[SUCCESS] Pipeline #64656587: 16/16 executed test jobs passed |
The flat-versus-unified assertion was circular. _CutlassRunnerBase.forward() calls cutlass_fused_moe with quant_scales=self._quant_scales(inputs), input_sf=self._input_sf(inputs), the self._use_* flags and **self._activation_params; the test helper rebuilt that same call with those same expressions read off the runner. A mis-ordered scale, a wrong input_sf, a dropped activation scalar or a wrong backend flag reached both sides identically, so the comparison stayed bit-exact and proved only that the kernel is deterministic when called twice with identical arguments. Rebuild the flat arguments from the prepared view's named keys instead. _independent_quant_scales spells out each backend's scale list and ordering from view["fc1_dequant_scale"] and friends, and _independent_activation_params lowers the typed activation to CUTLASS scalar tensors directly rather than calling runners._cutlass_activation_params, which is the lowering under test. Backend flags come from the config class. use_fused_finalize is written out as True rather than read from the runner, so a runner that stops using the fused finalize surfaces as a difference instead of being followed silently. Writing the contract out independently exposed one detail the old helper could not reach: the flat NVFP4 and MXFP4 ABI takes packed weights viewed as int64, and the binding rejects raw uint8. The runners did that view inside _pack_weight_inputs, so a test that reused their packing never saw it. Checked by injection. Swapping the NVFP4 gemm1 and gemm2 scale groups is caught by the binding's own shape check before either assertion runs, so the useful probe is a shape-preserving one: perturbing fc1_dequant_scale by 0.1% moves the output 0.025 in absolute terms, well inside the reference bound of 2e-1 and invisible to the old helper, and the rebuilt comparison fails on it. Drop the design doc's claim that Identity is not advertised by any unified runner. All nine CUTLASS runners declare it and the support matrix in the same file lists it, so the two contradicted each other. Verified on SM100 (B200, CUDA 13): seven unified MoE suites give 914 passed, 74 skipped; the CUTLASS suite alone gives 217 passed, 41 skipped. The skips are SM90-only paths whose comparisons need H100 CI to execute. AI-assisted (Claude Opus 5). Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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[FAILED] Pipeline #64695786 — 14/16 executed test jobs passed Compared with nightly #64639043. Unit Tests
✅ Pass · 🟡 Old failure · ❌ New failure · ⏱ Test timeout · Multi-GPU and Multi-Node Tests — 6/6 passed
Failure detailsPR-related regressions
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Two of the quant_scales orderings written for the independent flat comparison were wrong, and neither could fail on this machine: W4A8 and Humming are SM90-only and skip on SM100, so both would have reached H100 CI unchecked. W4A8 groups by parameter, not by gemm. The runner returns inputs[6:14], which against _required_weight_keys is fc1/fc2 expert scales, then fc1/fc2 act scales, then fc1/fc2 zeros, then fc1/fc2 alphas -- not every fc1 entry followed by every fc2 entry. Humming places the gemm2 activation scale third, between the two gemm groups, where the cutlass_fused_moe docstring lists it as "reserved scalar or per-local-expert gemm2 activation scale". It was written last instead. Add a CPU parametrized test over the eight backends with a key-to-slot mapping, so the arch-gated ones are covered anywhere. Sentinels are int32 to keep the .view(torch.int32) calls no-ops, and comparison is by value rather than identity so viewed entries participate. cutlass_fp8_per_tensor is excluded: it folds the activation scale into the gemm1 dequant and inserts a literal, so it has no ordering to pin. Reverting either fix fails the matching case. Verified on SM100 (B200, CUDA 13): the CUTLASS suite gives 225 passed, 41 skipped. AI-assisted (Claude Opus 5). Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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The ordering test only covered _independent_quant_scales, which is the test helper. A wrong order in the runner's own _quant_scales -- the production path -- would still pass, which is backwards: the helper exists to check the runner, not the other way round. Assert both against _EXPECTED_SCALE_ORDER. The runner side builds its input list from _required_weight_keys, relying on _pack_weight_inputs returning tensors in that same key order; every CUTLASS runner does. _quant_scales only indexes its argument and touches no instance state, so __new__ without __init__ is enough to call it. Reverting the Humming order in the runner now fails the matching case, which it did not before. AI-assisted (Claude Opus 5). Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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[CANCELED] Pipeline #64762419: canceled |
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[SUCCESS] Pipeline #64777999: 16/16 executed test jobs passed |
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#4805) <!-- .github/pull_request_template.md --> ## 📌 Description Keep the Unified MoE activation matrix synchronized with runner declarations and add contract fuzz coverage for the expanded CUTLASS and b12x backend set. - Add `scripts/generate_moe_activation_matrix.py` with `--check` and `--write`; a CPU test detects incomplete per-quant mappings and documentation drift. - Add 22 curated seeds for seven CUTLASS runners and two b12x runners without changing the historical random seed stream. - Fix issues exposed by the new coverage: - **NVFP4-native input snapping.** The shared FP8 range shim made NVFP4 inputs too small and produced all-zero kernel output; NVFP4 handlers now snap to an exactly representable NVFP4 grid. - **Independent MXFP8 weight quantization.** Production quantizes BF16 weights to MXFP8, so the oracle now independently quantizes/dequantizes the original BF16 `w1`/`w2` and can detect scale-packing or swizzle errors. - **Shared typed-activation math.** `_bf16_reference` and `_semantic_reference` now use `_apply_typed_activation()`, preventing formula drift such as the previously omitted `SiTU.clamp_limit`. - **Non-gated activation coverage.** New Identity/GELU/ReLU/SiLU seeds exercise `gemm1_rows == intermediate_size`, rather than the gated `2 * intermediate_size` geometry. - **b12x W4A16 reference weights.** Only weights are snapped to the NVFP4 grid before checkpoint-style preparation, making requantization effectively lossless while activations remain BF16. - **Toolchain preflight.** The fuzzer skips b12x without CUDA 13+/CuTeDSL and CUTLASS FP8-block without CUDA 12.8+ before `MoELayer` can hide the original rejection reason. - **Tactic coverage.** Contract-handler `get_valid_tactics()` errors now fail the test instead of silently removing tactic coverage. The generated matrix records activation classes; scalar and architecture-specific restrictions remain in each runner's `check_support()`. ## 🔍 Related Issues Follow-up to #4613 and the CUTLASS runner expansion in #4610. ## 🚀 Pull Request Checklist Thank you for contributing to FlashInfer! Before we review your pull request, please make sure the following items are complete. ### ✅ Pre-commit Checks - [x] I have installed `pre-commit` by running `pip install pre-commit` (or used your preferred method). - [x] I have installed the hooks with `pre-commit install`. - [x] I have run the hooks manually with `pre-commit run --all-files` and fixed any reported issues. ## 🧪 Tests - [x] Tests have been added or updated as needed. - [ ] All tests are passing (`unittest`, etc.). Local validation on SM100 / CUDA 13.1: - CPU-only: `3 passed` (activation matrix), `17 passed` (fuzzer metadata/preflight). - Contract seeds: `12 passed, 10 architecture skips`. - Generator `--check` and `--write` are clean. ## Reviewer Notes - Earlier CI passed SM90 and SM120/CUDA 13 contracts; current-head architecture CI is still required, and SM121 remains unverified. - b12x allocates output internally, so output poisoning does not apply; numerical, all-zero, and non-finite checks still run. - `b12x_w4a16` tolerance will be tightened after SM120/121 calibration. --------- Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
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📌 Description
This PR replaces the unreleased enum-only unified MoE activation configuration with typed, frozen activation values and closes same-backend activation gaps between the flat and unified MoE APIs.
Typed activation API
The old API:
is replaced by typed values:
ActivationConfigis now a non-instantiable frozen base class. Each activation is represented by a concrete immutable value such asSwiGLU(alpha, beta, limit),SiTU(...), orReLU2().ActivationTyperemains the low-level kernel ABI enum and is carried by each class through.type. Unified API users do not pass it directly. The default isSwiGLU().This couples activation identity with its valid parameter schema, prevents irrelevant or silently dropped fields, makes the complete semantics hashable for autotune/cache identity, and lets runners expose capabilities directly as
supported activation classes. Backend-specific enums, strings, and tensors are lowered through backend-specific preparation, with explicit rejection of semantics a backend cannot represent.
Backend activation capabilities
Each runner now declares its supported typed activation classes explicitly (readable without constructing anything), e.g.:
NOTE: CUTLASS SiTU uses the backend's own
situ_beta/situ_linear_betakeys rather than the TRTLLMgemm1_*spelling, and per-expert overrides must use that spelling too — a foreign key is rejected rather than ignored.SiTU()defaults to the canonical Kimi-K3 scales (4.0 / 25.0), which are the CUTLASSSituAdaptorcompile-time defaults, so CUTLASS materializes tensors only for a non-default value. The TRTLLM path cannot do the same: it reuses the SwiGLU alpha/beta channels, whose null default is 1.0 / 1.0 for SiTU, so its runners require the tensors even at the default.NOTE: Every CUTLASS runner shares one launcher path and one kernel dispatch table, so all nine quantizations advertise the same ten activations -- the full set the flat CUTLASS API accepts. Each is covered by a bit-exact comparison against
cutlass_fused_moelaunched from the same prepared view, so the unified layer is proven to add no numerical change rather than assumed to.Gelu,Relu,Silu, andIdentitywere reachable flat but had no typed value, which is the parity gap this closes;SwigluBiasgets no separate class because the binding already remapsSwigluplus scalars onto it.Preparation and parameter plumbing
2 * intermediate_sizeintermediate_sizegemm1_betaandgemm1_clamp_limit.🚀 Pull Request Checklist
✅ Pre-commit Checks
pre-commitby runningpip install pre-commit(or used your preferred method).pre-commit install.pre-commit run --all-filesand fixed any reported issues.🧪 Tests
unittest, etc.).Validated on SM100 (B200, CUDA 13), seven unified MoE suites:
tests/moe/test_unified_moe.py
tests/moe/test_unified_moe_fuzz.py
tests/moe/test_unified_moe_fp8.py
tests/moe/test_unified_moe_cutlass.py
tests/moe/test_unified_moe_mxfp4.py
tests/moe/test_unified_moe_b12x.py
tests/moe/test_unified_moe_mxint4.py
914 passed, 74 skipped.
The added skip is the CUTLASS W4A16 SiTU numerical case, which needs SM90 hardware to execute.
Reviewer Notes
ActivationConfig(type=...)and singleton spellings without a compatibility shim.