llama.cpp/examples/server/bench/README.md

Server benchmark tools

Benchmark is using k6.

Install k6 and sse extension

SSE is not supported by default in k6, you have to build k6 with the xk6-sse extension.

Example:

go install go.k6.io/xk6/cmd/xk6@latest
xk6 build master \
--with github.com/phymbert/xk6-sse

Download a dataset

This dataset was originally proposed in vLLM benchmarks.

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

Download a model

Example for PHI-2

../../../scripts/hf.sh --repo ggml-org/models --file phi-2/ggml-model-q4_0.gguf

Start the server

The server must answer OAI Chat completion requests on http://localhost:8080/v1 or according to the environment variable SERVER_BENCH_URL.

Example:

server --host localhost --port 8080 \
  --model ggml-model-q4_0.gguf \
  --cont-batching \
  --metrics \
  --parallel 8 \
  --batch-size 512 \
  --ctx-size 4096 \
  -ngl 33

Run the benchmark

For 500 chat completions request with 8 concurrent users during maximum 10 minutes, run:

./k6 run script.js --duration 10m --iterations 500 --vus 8

The benchmark values can be overridden with:

• SERVER_BENCH_URL server url prefix for chat completions, default http://localhost:8080/v1
• SERVER_BENCH_N_PROMPTS total prompts to randomly select in the benchmark, default 480
• SERVER_BENCH_MODEL_ALIAS model alias to pass in the completion request, default my-model
• SERVER_BENCH_MAX_TOKENS max tokens to predict, default: 512
• SERVER_BENCH_DATASET path to the benchmark dataset file
• SERVER_BENCH_MAX_PROMPT_TOKENS maximum prompt tokens to filter out in the dataset: default 1024
• SERVER_BENCH_MAX_CONTEXT maximum context size of the completions request to filter out in the dataset: prompt + predicted tokens, default 2048

Note: the local tokenizer is just a string space split, real number of tokens will differ.

Or with k6 options:

SERVER_BENCH_N_PROMPTS=500 k6 run script.js --duration 10m --iterations 500 --vus 8

To debug http request use --http-debug="full".

Metrics

Following metrics are available computed from the OAI chat completions response usage:

• llamacpp_tokens_second Trend of usage.total_tokens / request duration
• llamacpp_prompt_tokens Trend of usage.prompt_tokens
• llamacpp_prompt_tokens_total_counter Counter of usage.prompt_tokens
• llamacpp_completion_tokens Trend of usage.completion_tokens
• llamacpp_completion_tokens_total_counter Counter of usage.completion_tokens
• llamacpp_completions_truncated_rate Rate of completions truncated, i.e. if finish_reason === 'length'
• llamacpp_completions_stop_rate Rate of completions stopped by the model, i.e. if finish_reason === 'stop'

The script will fail if too many completions are truncated, see llamacpp_completions_truncated_rate.

K6 metrics might be compared against server metrics, with:

curl http://localhost:8080/metrics

Using the CI python script

The bench.py script does several steps:

• start the server
• define good variable for k6
• run k6 script
• extract metrics from prometheus

It aims to be used in the CI, but you can run it manually:

LLAMA_SERVER_BIN_PATH=../../../cmake-build-release/bin/llama-server python bench.py \
              --runner-label local \
              --name local \
              --branch `git rev-parse --abbrev-ref HEAD` \
              --commit `git rev-parse HEAD` \
              --scenario script.js \
              --duration 5m \
              --hf-repo ggml-org/models	 \
              --hf-file phi-2/ggml-model-q4_0.gguf \
              --model-path-prefix models \
              --parallel 4 \
              -ngl 33 \
              --batch-size 2048 \
              --ubatch-size	256 \
              --ctx-size 4096 \
              --n-prompts 200 \
              --max-prompt-tokens 256 \
              --max-tokens 256