TensorRT-LLMs/tests/microbenchmarks
Iman Tabrizian 43bd861ce1
Update allreduce benchmark for torch (#6271)
Signed-off-by: Iman Tabrizian <10105175+tabrizian@users.noreply.github.com>
2025-08-05 23:25:23 -07:00
..
all_reduce.py Update allreduce benchmark for torch (#6271) 2025-08-05 23:25:23 -07:00
build_time_benchmark.py [nvbug/5387226] chore: add propogation for trust_remote_code to AutoConfig (#6001) 2025-07-16 16:05:38 +08:00
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