[Perf] Further tunings for SM100 FP8 CUTLASS kernel #19566
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Summary of Changes
Hello @ilmarkov, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request implements additional performance tunings for the SM100 FP8 CUTLASS GEMM kernel. The changes involve adjusting kernel configurations based on the matrix M dimension (batch size) to improve TFLOP/s, particularly for larger batch sizes, as evidenced by the included benchmark data.
Highlights
- Performance Tuning: Further optimized FP8 GEMM kernels for SM100 (Blackwell) GPUs, building on previous work.
- Targeted Optimizations: Introduced specific CUTLASS configurations (TileShape, ClusterShape) tailored for different ranges of the M dimension (batch size), particularly focusing on M >= 128.
- Benchmark Results: Provided detailed benchmark results on B200 hardware using Llama-3.1-8B-Instruct, demonstrating speedups ranging from 1.13x to 1.4x for various matrix dimensions and batch sizes, especially in the M=128 and M=256 ranges.
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Code Review
This pull request introduces performance optimizations for SM100 FP8 CUTLASS kernels. It includes adjustments to tile and cluster shapes, and a new configuration for M values where 128 < next_pow_2(M) <= 256. The changes are well-structured and the dispatch logic has been updated correctly.
| template <typename, typename, typename> typename Epilogue> | ||
| struct sm100_fp8_config_default { | ||
| // M in (128, inf) | ||
| // M in (256, inf) |
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The comment indicates that this configuration applies to M in the range (256, inf). Double check that this is the intended range, and that the upper bound is indeed unbounded.
| template <typename InType, typename OutType, | ||
| template <typename, typename, typename> typename Epilogue> | ||
| struct sm100_fp8_config_M256 { | ||
| // M in (128, 256] |
There was a problem hiding this comment.
The comment indicates that this configuration applies to M in the range (128, 256]. Consider adding a unit test to verify that this config is used when M is 129, 192, and 256.
| out, a, b, std::forward<EpilogueArgs>(args)...); | ||
| } else { | ||
| // m in (128, inf) | ||
| // m in (256, inf) |
There was a problem hiding this comment.
The comment indicates that this configuration applies to M in the range (256, inf). Consider adding a unit test to verify that this config is used when M is 257, 512, and 1024.
4 participants
Additional performance optimizations after #18778
Tune CUTLASS configs for M >= 128.
For Llama 8B on B200, these tunings offer a GEMM improvement of:
Kernel benchmarks using #17126 on B200.
python benchmarks/kernels/bench_fp8_gemm.py --model meta-llama/Llama-3.1-8B-Instruct --tp-sizes 1meta-llama/Llama-3.1-8B-Instruct, N=6144 K=4096, BF16 vs FP8 GEMMs TFLOP/s
meta-llama/Llama-3.1-8B-Instruct, N=4096 K=4096, BF16 vs FP8 GEMMs TFLOP/s
meta-llama/Llama-3.1-8B-Instruct, N=28672 K=4096, BF16 vs FP8 GEMMs TFLOP/s
meta-llama/Llama-3.1-8B-Instruct, N=4096 K=14336, BF16 vs FP8 GEMMs TFLOP/s
Raw results: