181 lines
7.7 KiB
Markdown
181 lines
7.7 KiB
Markdown
# vLLM benchmark suite
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## Introduction
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This directory contains a benchmarking suite for **developers** to run locally and gain clarity on whether their PR improves/degrades vllm's performance.
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vLLM also maintains a continuous performance benchmark under [perf.vllm.ai](https://perf.vllm.ai/), hosted under PyTorch CI HUD.
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## Performance benchmark quick overview
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**Benchmarking Coverage**: latency, throughput and fix-qps serving on B200, A100, H100, Intel® Xeon® Processors, Intel® Gaudi® 3 Accelerators and Arm® Neoverse™ with different models.
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**Benchmarking Duration**: about 1hr.
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**For benchmarking developers**: please try your best to constraint the duration of benchmarking to about 1 hr so that it won't take forever to run.
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## Trigger the benchmark
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The benchmark needs to be triggered manually:
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```bash
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bash .buildkite/performance-benchmarks/scripts/run-performance-benchmarks.sh
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```
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Runtime environment variables:
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- `ON_CPU`: set the value to '1' on Intel® Xeon® and Arm® Neoverse™ Processors. Default value is 0.
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- `SERVING_JSON`: JSON file to use for the serving tests. Default value is empty string (use default file).
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- `LATENCY_JSON`: JSON file to use for the latency tests. Default value is empty string (use default file).
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- `THROUGHPUT_JSON`: JSON file to use for the throughout tests. Default value is empty string (use default file).
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- `REMOTE_HOST`: IP for the remote vLLM service to benchmark. Default value is empty string.
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- `REMOTE_PORT`: Port for the remote vLLM service to benchmark. Default value is empty string.
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## Performance benchmark details
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See [performance-benchmarks-descriptions.md](performance-benchmarks-descriptions.md) for detailed descriptions, and use `tests/latency-tests.json`, `tests/throughput-tests.json`, `tests/serving-tests.json` to configure the test cases.
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> NOTE: For Intel® Xeon® Processors, use `tests/latency-tests-cpu.json`, `tests/throughput-tests-cpu.json`, `tests/serving-tests-cpu.json` instead.
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> For Intel® Gaudi® 3 Accelerators, use `tests/latency-tests-hpu.json`, `tests/throughput-tests-hpu.json`, `tests/serving-tests-hpu.json` instead.
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> For Arm® Neoverse™, use `tests/latency-tests-arm64-cpu.json`, `tests/throughput-tests-arm64-cpu.json`, `tests/serving-tests-arm64-cpu.json` instead.
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### Latency test
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Here is an example of one test inside `latency-tests.json`:
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```json
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[
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{
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"test_name": "latency_llama8B_tp1",
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"parameters": {
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"model": "meta-llama/Meta-Llama-3-8B",
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"tensor_parallel_size": 1,
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"load_format": "dummy",
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"num_iters_warmup": 5,
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"num_iters": 15
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}
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},
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]
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```
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In this example:
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- The `test_name` attributes is a unique identifier for the test. In `latency-tests.json`, it must start with `latency_`.
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- The `parameters` attribute control the command line arguments to be used for `vllm bench latency`. Note that please use underline `_` instead of the dash `-` when specifying the command line arguments, and `run-performance-benchmarks.sh` will convert the underline to dash when feeding the arguments to `vllm bench latency`. For example, the corresponding command line arguments for `vllm bench latency` will be `--model meta-llama/Meta-Llama-3-8B --tensor-parallel-size 1 --load-format dummy --num-iters-warmup 5 --num-iters 15`
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Note that the performance numbers are highly sensitive to the value of the parameters. Please make sure the parameters are set correctly.
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WARNING: The benchmarking script will save json results by itself, so please do not configure `--output-json` parameter in the json file.
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### Throughput test
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The tests are specified in `throughput-tests.json`. The syntax is similar to `latency-tests.json`, except for that the parameters will be fed forward to `vllm bench throughput`.
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The number of this test is also stable -- a slight change on the value of this number might vary the performance numbers by a lot.
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### Serving test
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We test the throughput by using `vllm bench serve` with request rate = inf to cover the online serving overhead. The corresponding parameters are in `serving-tests.json`, and here is an example:
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```json
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[
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{
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"test_name": "serving_llama8B_tp1_sharegpt",
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"qps_list": [1, 4, 16, "inf"],
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"server_parameters": {
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"model": "meta-llama/Meta-Llama-3-8B",
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"tensor_parallel_size": 1,
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"disable_log_stats": "",
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"load_format": "dummy"
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},
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"client_parameters": {
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"model": "meta-llama/Meta-Llama-3-8B",
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"backend": "vllm",
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"dataset_name": "sharegpt",
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"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
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"num_prompts": 200
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}
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},
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]
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```
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Inside this example:
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- The `test_name` attribute is also a unique identifier for the test. It must start with `serving_`.
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- The `server-parameters` includes the command line arguments for vLLM server.
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- The `client-parameters` includes the command line arguments for `vllm bench serve`.
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- The `qps_list` controls the list of qps for test. It will be used to configure the `--request-rate` parameter in `vllm bench serve`
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The number of this test is less stable compared to the delay and latency benchmarks (due to randomized sharegpt dataset sampling inside `benchmark_serving.py`), but a large change on this number (e.g. 5% change) still vary the output greatly.
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WARNING: The benchmarking script will save json results by itself, so please do not configure `--save-results` or other results-saving-related parameters in `serving-tests.json`.
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#### Default Parameters Field
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We can specify default parameters in a JSON field with key `defaults`. Parameters defined in the field are applied globally to all serving tests, and can be overridden in test case fields. Here is an example:
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<details>
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<summary> An Example of default parameters field </summary>
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```json
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{
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"defaults": {
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"qps_list": [
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"inf"
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],
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"server_environment_variables": {
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"VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1
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},
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"server_parameters": {
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"tensor_parallel_size": 1,
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"dtype": "bfloat16",
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"block_size": 128,
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"disable_log_stats": "",
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"load_format": "dummy"
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},
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"client_parameters": {
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"backend": "vllm",
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"dataset_name": "random",
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"random-input-len": 128,
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"random-output-len": 128,
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"num_prompts": 200,
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"ignore-eos": ""
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}
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},
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"tests": [
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{
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"test_name": "serving_llama3B_tp2_random_128_128",
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"server_parameters": {
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"model": "meta-llama/Llama-3.2-3B-Instruct",
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"tensor_parallel_size": 2,
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},
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"client_parameters": {
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"model": "meta-llama/Llama-3.2-3B-Instruct",
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}
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},
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{
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"test_name": "serving_qwen3_tp4_random_128_128",
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"server_parameters": {
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"model": "Qwen/Qwen3-14B",
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"tensor_parallel_size": 4,
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},
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"client_parameters": {
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"model": "Qwen/Qwen3-14B",
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}
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},
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]
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}
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```
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</details>
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### Visualizing the results
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The `convert-results-json-to-markdown.py` helps you put the benchmarking results inside a markdown table, by formatting [descriptions.md](performance-benchmarks-descriptions.md) with real benchmarking results.
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You can find the result presented as a table inside the `buildkite/performance-benchmark` job page.
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If you do not see the table, please wait till the benchmark finish running.
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The json version of the table (together with the json version of the benchmark) will be also attached to the markdown file.
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The raw benchmarking results (in the format of json files) are in the `Artifacts` tab of the benchmarking.
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#### Performance Results Comparison
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Follow the instructions in [performance results comparison](https://docs.vllm.ai/en/latest/benchmarking/dashboard/#performance-results-comparison) to analyze performance results and the sizing guide.
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