TensorRT-LLMs/tests/microbenchmarks
Yukun He d272f1a9bc
[TRTLLM-8821][feat] Apply AutoTuner to AllReduce Op for strategy tuning. (#8531)
Signed-off-by: Yukun He <23156053+hyukn@users.noreply.github.com>
2026-01-05 15:44:37 +08:00
..
all_reduce.py [TRTLLM-8821][feat] Apply AutoTuner to AllReduce Op for strategy tuning. (#8531) 2026-01-05 15:44:37 +08:00
build_time_benchmark.py [None][fix] Migrate to new cuda binding package name (#6700) 2025-08-07 16:29:55 -04:00
build_time_dashboard.py Update (#2978) 2025-03-23 16:39:35 +08:00
README.md Update (#2978) 2025-03-23 16:39:35 +08:00

!!! WARNING: This is not intended for external users to benchmark the performance numbers of the TRT-LLM product. !!! This folder contains the benchmark script used internally to assistant TRT-LLM development.

build_time_benchmark

# example 1: offline benmark for all the built-in models, see --help for all the options
python ./build_time_benchmark.py --model ALL

# By default, the benmark don't load the weights to save benchmark time, load the weights to test the TRT-LLM load and convert time
# WARNING: this can takes very long time if the model is large, or if you use a online HF model id since it can download the weights
python ./build_time_benchmark.py --model ALL --load

# example 2: benchmark a HF model model w/o downloading the model locally in advance
python ./build_time_benchmark.py --model "TinyLlama/TinyLlama_v1.1" # no weights loading
python ./build_time_benchmark.py --model "openai-community/gpt2" --load # with weights loading

# example 3: benchmark a local download HF model
python  ./build_time_benchmark.py --model /home/scratch.trt_llm_data/llm-models/falcon-rw-1b/

# example 4: benchmark one model with managed weights option, with verbose option
python ./build_time_benchmark.py --model llama2-70b.TP4 --managed_weights -v

# example 5: see help
python ./build_time_benchmark.py --help