[Kernel] Support Nvfp4 Cutedsl Moe Swiglu-oai and Relu2(non-gated) Activation - #47106
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Enable FlashInfer CuteDSL NVFP4 MoE to use the activation-type API so SwiGLU-OAI variants and non-gated ReLU2 can share the same backend path. Signed-off-by: Tiekai Bi <tiekaib@nvidia.com>
Add focused coverage for FlashInfer CuteDSL NVFP4 SwiGLU-OAI layout handling and non-gated ReLU2 execution so activation-specific backend wiring is guarded. Signed-off-by: Tiekai Bi <tiekaib@nvidia.com>
Signed-off-by: Tiekai Bi <tiekaib@nvidia.com>
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flashinfer-ai/flashinfer#3737 was merged. We can test this with flashinfer main branch now. |
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This pull request has merge conflicts that must be resolved before it can be |
…meter Signed-off-by: Tiekai Bi <tiekaib@nvidia.com>
Done, resolve merge conflict |
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Thanks for doing this. Just tiny changes on how to handle default value!
| self.gemm1_alpha = ( | ||
| float(quant_config.gemm1_alpha) | ||
| if quant_config.gemm1_alpha is not None | ||
| else 1.702 | ||
| ) | ||
| self.gemm1_beta = ( | ||
| float(quant_config.gemm1_beta) | ||
| if quant_config.gemm1_beta is not None | ||
| else 1.0 | ||
| ) | ||
| self.gemm1_clamp_limit = ( | ||
| float(quant_config.gemm1_clamp_limit) | ||
| if quant_config.gemm1_clamp_limit is not None | ||
| else 7.0 | ||
| ) |
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I think we want to set these args at None and don't apply them if the parameter in quant_config is None, otherwise there will be other model use this file which has these value in default, causing accuracy bug.
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Good point! It won't affect accuracy unless the activation type is swiglu-oai for flashinfer, but it's indeed a little confusing. Modify done.
Signed-off-by: Tiekai Bi <tiekaib@nvidia.com>
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This pull request has merge conflicts that must be resolved before it can be |
Upstream vllm-project#49580 landed CuTeDSL NVFP4 MoE for ReLU2 and now passes activation_type unconditionally, so the version probe this branch added (has_flashinfer_cutedsl_moe_nvfp4_activation_type) is obsolete: an older FlashInfer already fails on upstream's own relu2 path. Drop the probe and keep only the SwiGLU-OAI work layered on upstream's implementation. - experts/flashinfer_cutedsl_moe.py: take upstream's unconditional activation_type, add SWIGLUOAI/SWIGLUOAI_UNINTERLEAVE to the supported activations, and forward only the SwiGLU params the model actually sets. The kernel defaults swiglu_{alpha,beta,limit} to floats (1.0/0.0/FLT_MAX), so None must not be passed through. - oracle/nvfp4.py: register FLASHINFER_CUTEDSL in NVFP4_BACKENDS_WITH_CLAMP unconditionally and list it in the clamp error message, which upstream had extended with cutlass/marlin/humming. - flashinfer_fp4_moe.py: keep the SWIGLUOAI interleaved-w13 handling as reorder_w13_to_w31_for_flashinfer_cutedsl() on top of upstream's is_gated guard. - flashinfer_utils.py: drop the duplicate SWIGLUOAI entries the auto-merge left in ACTIVATION_TO_FI_ACTIVATION; upstream already maps both. - tests: rebase onto upstream's test file, parametrized over SILU/RELU2_NO_MUL/SWIGLUOAI. Tested on B200 (SM100): test_flashinfer_cutedsl_nvfp4_moe.py 3 passed, test_flashinfer_cutedsl_layout.py 3 passed, test_cutedsl_moe.py 11 passed, test_nvfp4_moe.py 84 passed. Signed-off-by: Tiekai Bi <tiekaib@nvidia.com> Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
The test inherited atol=rtol=2e-1 from the upstream relu2 test, but the outputs here are O(1e-2) while NVFP4 noise is O(1e-3): for SILU and RELU2_NO_MUL the tolerance exceeded the reference tensor's full dynamic range, so a kernel returning zeros would have passed. Two of the four activations the backend advertises had no coverage at all. - Parametrize over (activation, alpha, beta, limit) and add cases for SWIGLUOAI_UNINTERLEAVE and SILU-with-clamp. The latter exercises the NVFP4_BACKENDS_WITH_CLAMP entry this branch adds, which was untested. - Drop swiglu_limit from 7.0 to 0.3: the GEMM1 output is O(0.5) at these tensor scales, so the OAI default never clamped anything. - Build the reference from vLLM's own SiluAndMul / SiluAndMulWithClamp / SwigluOAIAndMul ops instead of torch_moe, which cannot parametrize the activation (it calls the registered op with default constructor args). - Tighten to atol=3e-2 (worst measured error 1.8e-2) and add a cosine check, which catches structural errors an absolute tolerance cannot. Mutation-tested: removing **swiglu_kwargs fails silu-clamp, swigluoai and swigluoai_uninterleave while leaving silu and relu2_no_mul passing; disabling the SWIGLUOAI interleaved-w13 branch fails swigluoai. Full run: 100 passed across the four cutedsl/nvfp4 MoE suites on B200. Signed-off-by: Tiekai Bi <tiekaib@nvidia.com> Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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…tivation (vllm-project#47106) Signed-off-by: Tiekai Bi <tiekaib@nvidia.com> Co-authored-by: Claude Opus 5 <noreply@anthropic.com> Co-authored-by: Yongye Zhu <zyy1102000@gmail.com>
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Purpose
FlashInfer modified the CuteDSL NVFP4 MoE kernel to support the SwiGLU-OAI activation (flashinfer-ai/flashinfer#3737). The CuteDSL NVFP4 MoE kernel now also supports ReLU² non-gated activation. This PR achieves compatibility with both new activations by passing the activation type and related parameters into the CuteDSL MoE kernel.
Test Plan
Test input layout prepare:
.venv/bin/python -m pytest tests/kernels/moe/test_flashinfer_cutedsl_layout.py -vTest swiglu oai and relu2 accuracy:
.venv/bin/python -m pytest tests/kernels/moe/test_flashinfer_cutedsl_nvfp4_moe.py -vTest original model can still run successfully:
.venv/bin/python -m pytest tests/models/quantization/test_nvfp4.py -v -k "flashinfer_cutedsl"Test Result
Test input layout prepare:
.venv/bin/python -m pytest tests/kernels/moe/test_flashinfer_cutedsl_layout.py -vTest swiglu oai and relu2 accuracy:
.venv/bin/python -m pytest tests/kernels/moe/test_flashinfer_cutedsl_nvfp4_moe.py -vTest original model can still run successfully:
.venv/bin/python -m pytest tests/models/quantization/test_nvfp4.py -v -k "flashinfer_cutedsl"Essential Elements of an Effective PR Description Checklist
supported_models.mdandexamplesfor a new model.