TensorRT-LLMs/cpp/micro_benchmarks
Daniel Stokes 5773cfdcf2
feat: Add support for per expert activation scaling factors (#5013)
Signed-off-by: Daniel Stokes <40156487+djns99@users.noreply.github.com>
2025-06-28 09:10:35 +12:00
..
CMakeLists.txt opensource: Opensource MOE MXFP8-MXFP4 implementation (#5222) 2025-06-26 12:18:19 +08:00
gen-moe-benchmark-file.py feat: Add Mixture of Experts FP8xMXFP4 support (#4750) 2025-06-09 13:25:04 +08:00
mixtureOfExpertsBackendBenchmarkFixture.h feat: Add support for per expert activation scaling factors (#5013) 2025-06-28 09:10:35 +12:00
mixtureOfExpertsBackendBenchmarkLauncher.cu feat: Add Mixture of Experts FP8xMXFP4 support (#4750) 2025-06-09 13:25:04 +08:00
README.md Update TensorRT-LLM (#1891) 2024-07-04 14:37:19 +08:00

Micro Benchmarks

This folder contains benchmarks for specific components in TRT-LLM, using google-benchmark

Building

To build add the --micro_benchmark flag to build_wheel.py or pass -DBUILD_MICRO_BENCHMARKS=ON to cmake

Benchmark Documentations

Mixture Of Experts Backend Benchmark

Target mixtureOfExpertsBackendBenchmark

This benchmark covers the backend used by the MixtureOfExperts plugin. It allows you to benchmark different MOE configurations without building a TRT engine.

Usage:

./mixtureOfExpertsBackendBenchmark

# or

./mixtureOfExpertsBackendBenchmark --input_file <JSON benchmark definition>

For more information see:

./mixtureOfExpertsBackendBenchmark --help

The gen-moe-workload-file.py is a helper script that can generate workload files for MOE benchmarks. This is useful for sharing or comparing configurations, such as when generating a reproduction case for a performance bug