[draft] NVFP4 block-16 scale support for SM90 mixed-input grouped GEMM (CUTLASS 4.4.2)#1
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[draft] NVFP4 block-16 scale support for SM90 mixed-input grouped GEMM (CUTLASS 4.4.2)#1changjonathanc wants to merge 2 commits into
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Adds optional NVFP4 (e2m1 + e4m3-scale) block-16 scaling to the SM90 mixed-input collectives for weight-scaled W4A8 grouped GEMM (scale block 16 < GMMA K tile). Gated by UseNvfp4Block16Scales/Broadcast (false for all existing instantiations, so existing kernels are byte-for-byte unaffected). Base: CUTLASS v4.4.2 + TensorMapStorage workaround. 4.4.2 is required: its mixed-input GEMM is ~5x faster than 4.3.5, making W4A8 ~2x faster than Marlin at large batch.
Fold the duplicated 'chunk_size != 0' guard and the nested predicate into a single named condition; extract tile_k. No behavior change. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Summary
Adds optional NVFP4 (e2m1 data + e4m3 scale) block-16 scaling to the SM90 warp-specialized mixed-input grouped GEMM, so a weight-scaled W4A8 grouped GEMM works when the scale block size (16) is smaller than the GMMA K tile. All new behaviour is gated by
UseNvfp4Block16Scales/UseNvfp4Block16ScaleBroadcast, which are false for every pre-existing instantiation — so existing kernels are byte-for-byte unaffected.Targets the CUTLASS 4.4.2 base branch (
poolside-cutlass-v4.4.2).Why 4.4.2 (not 4.3.5)
Measured on H200 with a mixed-input W4A8 MoE grouped GEMM (capture-replay, isolated, locked-clock): 4.4.2's mixed-input grouped GEMM is ~5.3x faster than 4.3.5's. On 4.4.2 the W4A8 path is ~2x faster than a Marlin W4A16 baseline at large batch; on 4.3.5 it is ~2.6x slower — i.e. building W4A8 against 4.3.5 is a net regression. So the fork base must be 4.4.2.
Contents
poolside-cutlass-v4.4.2: NVIDIA CUTLASS v4.4.2 + an existing shared-memory workaround for an unnecessaryTensorMapStorageallocation (cherry-picked).UseNvfp4Block16Scales/UseNvfp4Block16ScaleBroadcast, false for every existing instantiation, so existing kernels are byte-for-byte unaffected.sm90_mma_array_tma_gmma_rs_warpspecialized_mixed_input.hpp:UseNvfp4Block16Scales/ScaleAtomM/ScaleAtomK; multi-columnSmemLayoutAtomScale; relaxedstatic_assert;grouped-GEMM init clamp; explicit
StrideScale; relaxedcan_implementchunk-size check.mixed_input_utils.hpp:UseNvfp4Block16ScaleBroadcast+get_mma_smem_layout_scale()broadcast view; refresh scales every k-block when multi-column.