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benchmark-kernel

by flashinfer-ai

Guide for benchmarking FlashInfer kernels with CUPTI timing

Installation

Pick a client and clone the repository into its skills directory.

Installation

Quick info

Author
flashinfer-ai
Category
Backend
GitHub repo

About this skill

Guide for benchmarking FlashInfer kernels with CUPTI timing

How to use

  1. Zainstaluj CUPTI dla najdokładniejszych pomiarów: uruchom pip install -U cupti-python. Wymaga CUDA 13 lub nowszej. Jeśli CUPTI nie jest dostępny, skill automatycznie przejdzie na CUDA Events i wyświetli ostrzeżenie.
  2. Wybierz rutynę testową z dostępnych opcji: Attention (BatchDecodeWithPagedKVCacheWrapper, BatchPrefillWithPagedKVCacheWrapper, BatchPrefillWithRaggedKVCacheWrapper, BatchMLAPagedAttentionWrapper), GEMM (bmm_fp8, gemm_fp8_nt_groupwise, group_gemm_fp8_nt_groupwise, mm_fp4) lub MOE (trtllm_fp4_block_scale_moe, trtllm_fp8_block_scale_moe, trtllm_fp8_per_tensor_scale_moe, cutlass_fused_moe).
  3. Uruchom benchmark dla wybranej rutyny za pomocą skryptu flashinfer_benchmark.py z parametrami określającymi typ testu i konfigurację.
  4. Skill zmierzy czysty czas wykonania jądra GPU bez narzutu komunikacji host-device (CUPTI) lub z minimalnym narzutem (CUDA Events).
  5. Wyniki będą zapisane w formacie CSV do analizy i porównania wydajności między różnymi backendami.

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