sglang.0.4.8.post1/sglang/benchmark/fbgemm

Benchmark FBGEMM Grouped GEMM

Benchmark FBGEMM Grouped GEMM in both Triton and CUDA version and SGLang Triton Grouped GEMM, it will be used to compare the bandwidth of different implementations.

Requirements

pip install fbgemm-gpu-genai

Usage

python3 benchmark/fbgemm/benchmark_fbgemm_grouped_gemm.py --model Qwen/Qwen2-57B-A14B-Instruct --tp-size 4 --use-fp8-w8a8

For example, in H200, the Qwen2-57B-A14B-Instruct TP4 fp8w8a8 grouped gemm bandwidth result is as follows:

grouped-gemm-performance:
   batch_size  FBGEMM Triton Grouped GEMM FP8  FBGEMM CUTLASS F8F8BF16 Rowwise  SGLang Grouped GEMM FP8
0       256.0                     3704.841339                      3042.626402              2254.725030
1       512.0                     3691.426346                      3029.065684              2269.504543
2      1024.0                     3653.938629                      2258.471467              2358.319020
3      2048.0                     3596.644313                      2271.611904              2476.895397
4      4096.0                     3468.496435                      2231.283986              2179.473910

The theoretical peak bandwidth of H200 is 4.8 TB/s. Taking batch_size 256 as an example, the bandwidth of FBGEMM Triton Grouped GEMM FP8 is 3704.841339 GB/s, the bandwidth of FBGEMM CUTLASS F8F8BF16 Rowwise is 3042.626402 GB/s, and the bandwidth of SGLang Grouped GEMM FP8 is 2254.725030 GB/s. Therefore, FBGEMM Triton Grouped GEMM FP8 achieves 77.9% of H200's theoretical peak bandwidth, FBGEMM CUTLASS F8F8BF16 Rowwise achieves 63.4% of H200's theoretical peak bandwidth, and SGLang Grouped GEMM FP8 achieves 46.9% of H200's theoretical peak bandwidth.