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[Perf][Kernel][Quantization] Triton row kernel for per-token-group FP8 quant at large token counts - #60903
[Perf][Kernel][Quantization] Triton row kernel for per-token-group FP8 quant at large token counts#60903Dorfu wants to merge 1 commit into
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…8 quant at large token counts One Triton program per row: the full row is loaded in one coalesced read, all group scales computed in registers, the row stored in one write. Registered as torch.ops.vllm.per_token_group_fp8_quant_row with the same signature as _C.per_token_group_fp8_quant; the T>=2048 dispatch lives inside the opaque op so torch.compile never traces a data-dependent branch, and small token counts keep the CUDA kernel (faster there). Fusion-pattern QUANT_OPS tables are pointed at the new op on CUDA so RMSNorm+quant and MLA+quant fusion continue to match. Bitwise-identical to the _C kernel across all scale layouts (tests included; torch.library.opcheck passes). H200, group_size=128, bf16, pre-allocated outputs: 1.64x at 4096x4096, 1.83x at 17083x4096, 1.58x at 17083x2048; UE8M0, non-power-of-two hidden sizes, 3-D inputs, ROCm and XPU keep their existing paths unchanged. Assisted-by: Claude (AI assistant); all changes reviewed and validated by the submitter Signed-off-by: Daniel Shin <danlshin@amazon.com>
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Purpose
per_token_group_quant_fp8dispatches totorch.ops._C.per_token_group_fp8_quant, which assigns 16 threads per group. After #56478 and #55330 this kernel is fast at small token counts, but at prefill-sized inputs it remains well below bandwidth-bound for a pure streaming op that reads each element once and writes fp8 plus one scale per group.This PR adds a Triton kernel with one program per row: the full row is loaded in one coalesced read, reshaped in registers to
H/group_sizegroups, all group scales computed at once, and the row stored in one write. It is exposed astorch.ops.vllm.per_token_group_fp8_quant_rowwith the same signature as the_Cop. The token-count dispatch lives inside the opaque op, so torch.compile never traces a data-dependent branch:T >= 2048takes the row kernel, and smaller inputs keep the CUDA kernel, which wins there on launch cost. The_Ckernel itself is unchanged and still handles every case the row kernel does not: UE8M0 scales, non-power-of-two hidden size, H > 8192, 3-D inputs, and non-contiguous inputs. ROCm and XPU behavior is untouched.Because
per_token_group_quant_fp8now emits the new op on CUDA, the fusion-patternQUANT_OPStables incompilation/passes/fusion/matcher_utils.pyandrms_quant_fusion.pyare pointed at it there, so RMSNorm+quant and MLA+quant pattern matching continues to fire. This is the lesson of #49621, where a Triton wrapper over this op broke pattern matching by changing op identity; this PR keeps the matchers and the emitted op in lockstep.Correctness
Bitwise-identical to
_C.per_token_group_fp8_quant, bit for bit rather than approximately: fp32 absmax with the same eps floor, IEEE round-to-nearest division viatl.math.div_rnsince plain/may compile to approximate division, and round-to-nearest-satfinite fp8 conversion. The included tests assert zero differing bits on quantized values and scales across token counts 1..17083, H in {2048, 4096}, all three scale layouts: row-major, column-major, and TMA-aligned, fp16 and bf16 inputs, all-zero rows for the eps floor, and inf inputs for the satfinite clamp.torch.library.opcheckpasses for the registered op. The test_schema subcheck is skipped because torch's clone-and-compare harness callsallcloseon the mutated fp8 output andmul_cudahas no fp8 kernel.Microbenchmark
H200, CUDA 13, torch 2.13,
group_size=128, bf16 input, pre-allocated outputs, 300 timed iterations after warmup:_Cafter #55330/#56478_CEnd-to-end
On an FP8 block-quant MoE serving workload with prefill-heavy multimodal traffic, Qwen3-VL-235B-A22B-Instruct-FP8 at 2 replicas x TP=4 on 8x H200, a 4-run A/B measured a consistent ~0.5% throughput gain with all pairwise comparisons positive. This is modest, as expected for a kernel that is a small share of step time, and in family with the 0.85-1.7% reported in #55330. Outputs are bitwise-identical by construction.
Test Plan
tests/kernels/quantization/test_per_token_group_quant_row.py: bitwise equivalence across the full layout matrix, dispatch fallbacks, opcheck. Run on H200 against current main: 86 passed.per_token_group_quant_fp8wrapper, including UE8M0 fallback and fusion-matcher table resolutionAI Assisted: all changes reviewed and validated on hardware by the submitter.