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…ht-only linear layers Wire FlashInfer's mm_bf16_fp4 (flashinfer-ai/flashinfer#3597, in FlashInfer v0.6.14, vLLM's current pin) into the NVFP4 linear kernel registry as FlashInferW4A16NvFp4LinearKernel. Like Marlin, the weight is repacked once at load time via prepare_bf16_fp4_weights; at run time the kernel dequantizes the FP4 weight and runs the matrix multiply in bf16, so activations are never quantized. The kernel is the default on SM121 (DGX Spark), where it is tuned, and opt-in via --linear-backend flashinfer_cutedsl on SM100/SM110/SM120. Marlin remains the default elsewhere and for fp16-activation models. The ModelOpt W4A16 method routes through init_nvfp4_linear_kernel(use_a16=True) so both it and the compressed-tensors W4A16 scheme share the selection. The GEMM is exposed as torch custom op vllm::flashinfer_mm_bf16_fp4 so it works under torch.compile and CUDA graph capture, and the existing FlashInfer autotune warmup tunes it during engine startup. AI-assisted (Claude Code). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Comment-only change. AI-assisted (Claude Code). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
…16_kernels Upstream added HummingNvFp4LinearKernel as a W4A16-capable kernel after this PR was written; fold it into _select_nvfp4_a16_kernel so an explicit --linear-backend=humming still serves W4A16 layers, and add the FlashInfer kernel to a16_kernels so the force-kernel guard accepts it. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Purpose
FlashInfer 0.6.14 added
mm_bf16_fp4(flashinfer-ai/flashinfer#3597), a GEMM that keeps activations in bf16 and dequantizes the FP4 weight inside the kernel, tuned for DGX Spark (SM121); vLLM's pin (0.6.17 on main) has included it for several releases. This PR wires it into vLLM's NVFP4 weight-only (W4A16) linear path, which today always runs on Marlin.Changes:
FlashInferW4A16NvFp4LinearKernel, following Marlin's two-stage pattern: repack the checkpoint weight once at load time (prepare_bf16_fp4_weights), then onemm_bf16_fp4call per forward.torch.compileand CUDA graph capture, and the existing FlashInfer autotune warmup tunes it during engine startup.--linear-backend flashinfer_cutedslopts in to the FlashInfer kernel.VLLM_BATCH_INVARIANTis set, and for fp16 models, because the FlashInfer kernel only accepts bf16 activations.--linear-backend marlinand--linear-backend hummingkeep selecting their kernels for W4A16 layers; a--linear-backendvalue with no weight-only kernel (for examplecutlass, which is W4A4-only) now fails with a clear error instead of crashing at load.W4A16_NVFP4and compressed-tensors NVFP4A16 both go through this shared selection. Availability is detected from the installed FlashInfer, so no requirements change.flashinfer-ai/flashinfer#4038 improves this kernel's decode performance on more GPUs behind the same API and needs no vLLM-side change.
Test Plan
tests/kernels/quantization/test_flashinfer_w4a16_nvfp4.py: kernel output vs a dequantized-weight reference across shapes (including N and K that are not multiples of the kernel's tiles), bias, 3D input, autotune on and off; kernel selection tests covering the auto, explicit-backend, batch-invariant, and fp16-model cases.tests/quantization/test_modelopt.pydispatch tests andtests/quantization/test_compressed_tensors.py::test_compressed_tensors_nvfp4.nm-testing/TinyLlama-1.1B-Chat-v1.0-NVFP4A16against a Marlin reference run, on RTX 5080 (SM120, opt-in backend) and DGX Spark (SM121, defaultautobackend), in eager mode and with torch.compile and CUDA graphs.Test Result
autodefault selects the FlashInfer kernel.🤖 Generated with Claude Code