Benchmark

We provide several profiling tools to benchmark our models.

profile with dataset

Download the dataset below or create your own dataset.

wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json

Profiling your model with profile_throughput.py

python profile_throughput.py \
 ShareGPT_V3_unfiltered_cleaned_split.json \
 /path/to/your/model \
 --concurrency 64

profile without dataset

profile_generation.py perform benchmark with dummy data.

pip install nvidia-ml-py

python profile_generation.py \
 /path/to/your/model \
 --concurrency 1 8 --prompt-tokens 1 512 --completion-tokens 2048 512

profile serving

Tools above profile models with Python API. profile_serving.py is used to do benchmark on serving.

wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json

python profile_serving.py \
    ${TritonServerAddress} \
    /path/to/tokenizer \ # ends with .model for most models. Otherwise, please pass model_path/triton_models/tokenizer.
    ShareGPT_V3_unfiltered_cleaned_split.json \
    --concurrency 64

profile restful api

profile_restful_api.py is used to do benchmark on api server.

wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json

python profile_restful_api.py \
    ${ServerAddress} \
    /path/to/tokenizer \ # ends with .model for most models. Otherwise, please pass model_path/triton_models/tokenizer.
    ShareGPT_V3_unfiltered_cleaned_split.json \
    --concurrency 64