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https://github.com/ggerganov/llama.cpp.git
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120 lines
4.2 KiB
Markdown
120 lines
4.2 KiB
Markdown
### Server benchmark tools
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Benchmark is using [k6](https://k6.io/).
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##### Install k6 and sse extension
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SSE is not supported by default in k6, you have to build k6 with the [xk6-sse](https://github.com/phymbert/xk6-sse) extension.
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Example:
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```shell
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go install go.k6.io/xk6/cmd/xk6@latest
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xk6 build master \
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--with github.com/phymbert/xk6-sse
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```
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#### Download a dataset
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This dataset was originally proposed in [vLLM benchmarks](https://github.com/vllm-project/vllm/blob/main/benchmarks/README.md).
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```shell
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wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
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```
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#### Download a model
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Example for PHI-2
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```shell
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../../../scripts/hf.sh --repo ggml-org/models --file phi-2/ggml-model-q4_0.gguf
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```
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#### Start the server
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The server must answer OAI Chat completion requests on `http://localhost:8080/v1` or according to the environment variable `SERVER_BENCH_URL`.
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Example:
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```shell
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server --host localhost --port 8080 \
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--model ggml-model-q4_0.gguf \
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--cont-batching \
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--metrics \
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--parallel 8 \
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--batch-size 512 \
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--ctx-size 4096 \
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-ngl 33
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```
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#### Run the benchmark
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For 500 chat completions request with 8 concurrent users during maximum 10 minutes, run:
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```shell
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./k6 run script.js --duration 10m --iterations 500 --vus 8
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```
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The benchmark values can be overridden with:
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- `SERVER_BENCH_URL` server url prefix for chat completions, default `http://localhost:8080/v1`
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- `SERVER_BENCH_N_PROMPTS` total prompts to randomly select in the benchmark, default `480`
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- `SERVER_BENCH_MODEL_ALIAS` model alias to pass in the completion request, default `my-model`
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- `SERVER_BENCH_MAX_TOKENS` max tokens to predict, default: `512`
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- `SERVER_BENCH_DATASET` path to the benchmark dataset file
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- `SERVER_BENCH_MAX_PROMPT_TOKENS` maximum prompt tokens to filter out in the dataset: default `1024`
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- `SERVER_BENCH_MAX_CONTEXT` maximum context size of the completions request to filter out in the dataset: prompt + predicted tokens, default `2048`
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Note: the local tokenizer is just a string space split, real number of tokens will differ.
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Or with [k6 options](https://k6.io/docs/using-k6/k6-options/reference/):
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```shell
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SERVER_BENCH_N_PROMPTS=500 k6 run script.js --duration 10m --iterations 500 --vus 8
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```
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To [debug http request](https://k6.io/docs/using-k6/http-debugging/) use `--http-debug="full"`.
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#### Metrics
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Following metrics are available computed from the OAI chat completions response `usage`:
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- `llamacpp_tokens_second` Trend of `usage.total_tokens / request duration`
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- `llamacpp_prompt_tokens` Trend of `usage.prompt_tokens`
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- `llamacpp_prompt_tokens_total_counter` Counter of `usage.prompt_tokens`
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- `llamacpp_completion_tokens` Trend of `usage.completion_tokens`
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- `llamacpp_completion_tokens_total_counter` Counter of `usage.completion_tokens`
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- `llamacpp_completions_truncated_rate` Rate of completions truncated, i.e. if `finish_reason === 'length'`
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- `llamacpp_completions_stop_rate` Rate of completions stopped by the model, i.e. if `finish_reason === 'stop'`
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The script will fail if too many completions are truncated, see `llamacpp_completions_truncated_rate`.
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K6 metrics might be compared against [server metrics](../README.md), with:
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```shell
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curl http://localhost:8080/metrics
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```
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### Using the CI python script
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The `bench.py` script does several steps:
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- start the server
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- define good variable for k6
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- run k6 script
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- extract metrics from prometheus
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It aims to be used in the CI, but you can run it manually:
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```shell
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LLAMA_SERVER_BIN_PATH=../../../cmake-build-release/bin/llama-server python bench.py \
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--runner-label local \
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--name local \
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--branch `git rev-parse --abbrev-ref HEAD` \
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--commit `git rev-parse HEAD` \
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--scenario script.js \
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--duration 5m \
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--hf-repo ggml-org/models \
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--hf-file phi-2/ggml-model-q4_0.gguf \
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--model-path-prefix models \
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--parallel 4 \
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-ngl 33 \
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--batch-size 2048 \
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--ubatch-size 256 \
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--ctx-size 4096 \
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--n-prompts 200 \
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--max-prompt-tokens 256 \
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--max-tokens 256
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```
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