Text Generation Benchmarking tool Usage: text-generation-benchmark [OPTIONS] --tokenizer-name Options: -t, --tokenizer-name The name of the tokenizer (as in model_id on the huggingface hub, or local path) [env: TOKENIZER_NAME=] --revision The revision to use for the tokenizer if on the hub [env: REVISION=] [default: main] -b, --batch-size The various batch sizes to benchmark for, the idea is to get enough batching to start seeing increased latency, this usually means you're moving from memory bound (usual as BS=1) to compute bound, and this is a sweet spot for the maximum batch size for the model under test -s, --sequence-length This is the initial prompt sent to the text-generation-server length in token. Longer prompt will slow down the benchmark. Usually the latency grows somewhat linearly with this for the prefill step. Most importantly, the prefill step is usually not the one dominating your runtime, so it's ok to keep it short. [env: SEQUENCE_LENGTH=] [default: 10] -d, --decode-length This is how many tokens will be generated by the server and averaged out to give the `decode` latency. This is the *critical* number you want to optimize for LLM spend most of their time doing decoding. Decode latency is usually quite stable. [env: DECODE_LENGTH=] [default: 8] -r, --runs How many runs should we average from [env: RUNS=] [default: 10] -w, --warmups Number of warmup cycles [env: WARMUPS=] [default: 1] -m, --master-shard-uds-path The location of the grpc socket. This benchmark tool bypasses the router completely and directly talks to the gRPC processes [env: MASTER_SHARD_UDS_PATH=] [default: /tmp/text-generation-server-0] --temperature Generation parameter in case you want to specifically test/debug particular decoding strategies, for full doc refer to the `text-generation-server` [env: TEMPERATURE=] --top-k Generation parameter in case you want to specifically test/debug particular decoding strategies, for full doc refer to the `text-generation-server` [env: TOP_K=] --top-p Generation parameter in case you want to specifically test/debug particular decoding strategies, for full doc refer to the `text-generation-server` [env: TOP_P=] --typical-p Generation parameter in case you want to specifically test/debug particular decoding strategies, for full doc refer to the `text-generation-server` [env: TYPICAL_P=] --repetition-penalty Generation parameter in case you want to specifically test/debug particular decoding strategies, for full doc refer to the `text-generation-server` [env: REPETITION_PENALTY=] --frequency-penalty Generation parameter in case you want to specifically test/debug particular decoding strategies, for full doc refer to the `text-generation-server` [env: FREQUENCY_PENALTY=] --watermark Generation parameter in case you want to specifically test/debug particular decoding strategies, for full doc refer to the `text-generation-server` [env: WATERMARK=] --do-sample Generation parameter in case you want to specifically test/debug particular decoding strategies, for full doc refer to the `text-generation-server` [env: DO_SAMPLE=] --top-n-tokens Generation parameter in case you want to specifically test/debug particular decoding strategies, for full doc refer to the `text-generation-server` [env: TOP_N_TOKENS=] -h, --help Print help (see a summary with '-h') -V, --version Print version