[Benchmark] Refactor benchmark script for fp8 & int8 #19627
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Signed-off-by: yewentao256 <zhyanwentao@126.com>
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yewentao256
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Summary of Changes
Hello @yewentao256, 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 refactors the benchmark scripts for FP8 and INT8 GEMM operations. The primary goal is to improve code organization and maintainability by centralizing configuration options and abstracting the setup logic for different quantization and execution modes into dedicated helper functions. This makes the benchmark code cleaner and easier to extend or modify in the future.
Highlights
- Refactor Benchmark Logic: Introduced
PROVIDER_CFGSdictionaries in bothbench_fp8_gemm.pyandbench_int8_gemm.pyto centralize and configure different quantization and GEMM execution strategies (tensor/channel weight, token/tensor activation, with/without activation quant). - Abstracted Runner Creation: Added
build_fp8_runnerandbuild_int8_runnerhelper functions to encapsulate the logic for setting up the specific benchmark run based on the chosen configuration, significantly simplifying the mainbenchmarkfunction. - Simplified Benchmark Function: Replaced large conditional blocks in the
benchmarkfunctions with calls to the newbuild_*_runnerhelpers, making the core benchmarking logic cleaner and easier to read. - Dynamic Provider List: Updated the
triton.testing.Benchmarkdefinition in both scripts to dynamically use the enabled providers from thePROVIDER_CFGSdictionary, making it easier to control which configurations are benchmarked.
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Code Review
The pull request refactors the benchmark scripts for fp8 and int8 quantization, significantly improving their structure and maintainability. Key changes include centralizing provider configurations into a PROVIDER_CFGS dictionary and using builder functions (build_fp8_runner, build_int8_runner) to dynamically create the benchmark execution logic. This replaces large if/elif blocks, making the code cleaner and easier to extend.
My review focuses on:
- Maintainability: Suggesting minor refactorings to reduce code duplication within the new builder functions.
- Consistency: Pointing out an inconsistency in SPDX headers between the two modified files and in how enabled providers are filtered.
- Usability: Highlighting the removal of helpful command-line argument descriptions.
- Code Hygiene: Noting an unused parameter in the builder functions.
Overall, the refactoring is a positive step. The suggestions aim to further polish the changes.
Signed-off-by: yewentao256 <zhyanwentao@126.com>
| type=str, | ||
| default=["meta-llama/Llama-3.1-8B-Instruct"], | ||
| choices=[*WEIGHT_SHAPES.keys()], | ||
| help="List of models to benchmark", |
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why remove the help here?
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I think the name "models" already show the meaning and we can save some space for "help", but certainly I can add it back if you wish.
2 participants
Purpose
Fixes #19364
Test
python bench_fp8_gemm.py BF16 vs FP8 GEMMs: batch_size torch-bf16 fp8-tensor-w-tensor-a fp8-channel-w-token-a fp8-tensor-w-tensor-a-noquant fp8-channel-w-token-a-noquant 0 1.0 2.560670 4.320622 4.042926 5.431362 5.383086 1 16.0 39.288055 70.105174 66.377700 88.413925 89.528846 2 64.0 160.525909 304.590458 286.944317 396.810053 405.164967 3 128.0 308.701123 539.554228 510.844428 703.054311 702.508732 4 256.0 541.459009 772.972745 742.389709 942.623998 952.026312 5 512.0 646.868024 866.601296 810.128518 1010.673914 1007.868419 6 1024.0 651.920065 1078.126444 1021.105427 1248.947844 1242.909159 7 2048.0 706.403803 1118.760714 1057.808026 1269.786307 1270.055588 8 4096.0 730.363859 1098.713779 1051.117136 1259.165539 1249.199156 9 8192.0 695.302733 1155.644699 1106.260386 1271.727617 1311.712990 10 16384.0 722.155455 1183.444196 1121.000376 1273.485919 1289.554055 ... Benchmark finished!