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621e86b331
* server: bench: Init a bench scenario with K6 See #5827 * server: bench: EOL EOF * server: bench: PR feedback and improved k6 script configuration * server: bench: remove llamacpp_completions_tokens_seconds as it include prompt processing time and it's misleading server: bench: add max_tokens from SERVER_BENCH_MAX_TOKENS server: bench: increase truncated rate to 80% before failing * server: bench: fix doc * server: bench: change gauge custom metrics to trend * server: bench: change gauge custom metrics to trend server: bench: add trend custom metrics for total tokens per second average * server: bench: doc add an option to debug http request * server: bench: filter dataset too short and too long sequences * server: bench: allow to filter out conversation in the dataset based on env variable * server: bench: fix assistant message sent instead of user message * server: bench: fix assistant message sent instead of user message * server : add defrag thold parameter * server: bench: select prompts based on the current iteration id not randomly to make the bench more reproducible --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
89 lines
3.1 KiB
Markdown
89 lines
3.1 KiB
Markdown
### Server benchmark tools
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Benchmark is using [k6](https://k6.io/).
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##### Install k6
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Follow instruction from: https://k6.io/docs/get-started/installation/
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Example for ubuntu:
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```shell
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snap install k6
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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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--log-format text \
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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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