[Bugfix] Reject CUTLASS block-scaled FP8 when N is not a multiple of 128#48587
[Bugfix] Reject CUTLASS block-scaled FP8 when N is not a multiple of 128#48587sw072 wants to merge 1 commit into
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Online block-wise FP8 (--quantization fp8_per_block) selected CutlassFp8BlockScaledMMKernel for layers whose output dim N is not a multiple of the weight block size (128). The CUTLASS c3x block-scaled FP8 GEMM (sm90/sm100/sm120) tiles weight scales at a fixed (128,128) granularity and its dispatch only special-cases small/unaligned M (swap_ab); it has no kernel for a partial N tile, so gemm_op.can_implement() rejects the problem and vLLM aborts at runtime with "cutlass_gemm_caller ... Invalid status". Since CUTLASS_BLOCK_FP8_SUPPORTED is True and can_implement() did not check the shape, auto-selection picked CUTLASS and crashed instead of falling back. Add the N/K block-alignment check to can_implement() so selection falls back to a supporting kernel (e.g. Triton). Selection is per-layer, so aligned layers keep the CUTLASS path. Repro: Qwen3.5 GDN linear_attn.in_proj_a/b weights are [16, 1024] (N=16) on sm_120. Signed-off-by: shiwei <shiwei072@gmail.com>
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Purpose
--quantization fp8_per_blockcrashes at startup for models that have a linearlayer whose output dim N is not a multiple of the weight block size (128).
CutlassFp8BlockScaledMMKernelgets selected by kernel auto-selection, but theCUTLASS c3x block-scaled FP8 GEMM (sm90/sm100/sm120) tiles the weight scale
factors with a fixed (128,128) granularity along (N,K) and its dispatch only
special-cases small/unaligned M (swap_ab). There is no kernel for a partial N
tile, so
gemm_op.can_implement()rejects the problem at runtime:CUTLASS_BLOCK_FP8_SUPPORTEDis True andcan_implement()did not validate theshape, so selection picked CUTLASS and aborted instead of falling back.
Fix
Add an N (and K) block-alignment check to
CutlassFp8BlockScaledMMKernel.can_implement(). When N (or K) is not a multipleof the weight block size, report the kernel as unable to implement, so
per-layer selection falls back to a kernel that supports the shape (e.g.
TritonFp8BlockScaledMMKernel). Aligned layers keep the fast CUTLASS path.Repro
Model:
Qwen/Qwen3.5-0.8B(GDNlanguage_model.layers.*.linear_attn.in_proj_a/bweights are
[16, 1024], i.e. N=16), GPU: RTX PRO 5000 (Blackwell, sm_120),vLLM
0.23.1rc1.dev1060+g9e57de719.Kernel-level (before fix):
End-to-end:
vllm serve Qwen/Qwen3.5-0.8B --quantization fp8_per_block→ EngineCore fails to start (Invalid status). Workaround was--kernel-config '{"linear_backend":"triton"}'(forces ALL linear layers to Triton).autobackend; logs show bothCutlassFp8BlockScaledMMKernelandTritonFp8BlockScaledMMKernelselected (per-layer).Test Plan
--quantization fp8_per_blockon sm_120 and confirm startup + correct generation.