mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2025-12-24 09:05:39 +08:00
135 lines
7.7 KiB
Markdown
135 lines
7.7 KiB
Markdown
# vLLM benchmark suite
|
|
|
|
## Introduction
|
|
|
|
This directory contains a benchmarking suite for **developers** to run locally and gain clarity on whether their PR improves/degrades vllm's performance.
|
|
vLLM also maintains a continuous performance benchmark under [perf.vllm.ai](https://perf.vllm.ai/), hosted under PyTorch CI HUD.
|
|
|
|
## Performance benchmark quick overview
|
|
|
|
**Benchmarking Coverage**: latency, throughput and fix-qps serving on B200, A100, H100, Intel® Xeon® Processors and Intel® Gaudi® 3 Accelerators with different models.
|
|
|
|
**Benchmarking Duration**: about 1hr.
|
|
|
|
**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.
|
|
|
|
## Trigger the benchmark
|
|
|
|
The benchmark needs to be triggered manually:
|
|
|
|
```bash
|
|
bash .buildkite/performance-benchmarks/scripts/run-performance-benchmarks.sh
|
|
```
|
|
|
|
Runtime environment variables:
|
|
|
|
- `ON_CPU`: set the value to '1' on Intel® Xeon® Processors. Default value is 0.
|
|
- `SERVING_JSON`: JSON file to use for the serving tests. Default value is empty string (use default file).
|
|
- `LATENCY_JSON`: JSON file to use for the latency tests. Default value is empty string (use default file).
|
|
- `THROUGHPUT_JSON`: JSON file to use for the throughout tests. Default value is empty string (use default file).
|
|
- `REMOTE_HOST`: IP for the remote vLLM service to benchmark. Default value is empty string.
|
|
- `REMOTE_PORT`: Port for the remote vLLM service to benchmark. Default value is empty string.
|
|
|
|
## Performance benchmark details
|
|
|
|
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.
|
|
> NOTE: For Intel® Xeon® Processors, use `tests/latency-tests-cpu.json`, `tests/throughput-tests-cpu.json`, `tests/serving-tests-cpu.json` instead.
|
|
For Intel® Gaudi® 3 Accelerators, use `tests/latency-tests-hpu.json`, `tests/throughput-tests-hpu.json`, `tests/serving-tests-hpu.json` instead.
|
|
>
|
|
### Latency test
|
|
|
|
Here is an example of one test inside `latency-tests.json`:
|
|
|
|
```json
|
|
[
|
|
{
|
|
"test_name": "latency_llama8B_tp1",
|
|
"parameters": {
|
|
"model": "meta-llama/Meta-Llama-3-8B",
|
|
"tensor_parallel_size": 1,
|
|
"load_format": "dummy",
|
|
"num_iters_warmup": 5,
|
|
"num_iters": 15
|
|
}
|
|
},
|
|
]
|
|
```
|
|
|
|
In this example:
|
|
|
|
- The `test_name` attributes is a unique identifier for the test. In `latency-tests.json`, it must start with `latency_`.
|
|
- 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`
|
|
|
|
Note that the performance numbers are highly sensitive to the value of the parameters. Please make sure the parameters are set correctly.
|
|
|
|
WARNING: The benchmarking script will save json results by itself, so please do not configure `--output-json` parameter in the json file.
|
|
|
|
### Throughput test
|
|
|
|
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`.
|
|
|
|
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.
|
|
|
|
### Serving test
|
|
|
|
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:
|
|
|
|
```json
|
|
[
|
|
{
|
|
"test_name": "serving_llama8B_tp1_sharegpt",
|
|
"qps_list": [1, 4, 16, "inf"],
|
|
"server_parameters": {
|
|
"model": "meta-llama/Meta-Llama-3-8B",
|
|
"tensor_parallel_size": 1,
|
|
"swap_space": 16,
|
|
"disable_log_stats": "",
|
|
"load_format": "dummy"
|
|
},
|
|
"client_parameters": {
|
|
"model": "meta-llama/Meta-Llama-3-8B",
|
|
"backend": "vllm",
|
|
"dataset_name": "sharegpt",
|
|
"dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
|
|
"num_prompts": 200
|
|
}
|
|
},
|
|
]
|
|
```
|
|
|
|
Inside this example:
|
|
|
|
- The `test_name` attribute is also a unique identifier for the test. It must start with `serving_`.
|
|
- The `server-parameters` includes the command line arguments for vLLM server.
|
|
- The `client-parameters` includes the command line arguments for `vllm bench serve`.
|
|
- The `qps_list` controls the list of qps for test. It will be used to configure the `--request-rate` parameter in `vllm bench serve`
|
|
|
|
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.
|
|
|
|
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`.
|
|
|
|
### Visualizing the results
|
|
|
|
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.
|
|
You can find the result presented as a table inside the `buildkite/performance-benchmark` job page.
|
|
If you do not see the table, please wait till the benchmark finish running.
|
|
The json version of the table (together with the json version of the benchmark) will be also attached to the markdown file.
|
|
The raw benchmarking results (in the format of json files) are in the `Artifacts` tab of the benchmarking.
|
|
|
|
The `compare-json-results.py` helps to compare benchmark results JSON files converted using `convert-results-json-to-markdown.py`.
|
|
When run, benchmark script generates results under `benchmark/results` folder, along with the `benchmark_results.md` and `benchmark_results.json`.
|
|
`compare-json-results.py` compares two `benchmark_results.json` files and provides performance ratio e.g. for Output Tput, Median TTFT and Median TPOT.
|
|
If only one benchmark_results.json is passed, `compare-json-results.py` compares different TP and PP configurations in the benchmark_results.json instead.
|
|
|
|
Here is an example using the script to compare result_a and result_b with Model, Dataset name, input/output length, max concurrency and qps.
|
|
`python3 compare-json-results.py -f results_a/benchmark_results.json -f results_b/benchmark_results.json`
|
|
|
|
| | Model | Dataset Name | Input Len | Output Len | # of max concurrency | qps | results_a/benchmark_results.json | results_b/benchmark_results.json | perf_ratio |
|
|
|----|---------------------------------------|--------|-----|-----|------|-----|-----------|----------|----------|
|
|
| 0 | meta-llama/Meta-Llama-3.1-8B-Instruct | random | 128 | 128 | 1000 | 1 | 142.633982 | 156.526018 | 1.097396 |
|
|
| 1 | meta-llama/Meta-Llama-3.1-8B-Instruct | random | 128 | 128 | 1000 | inf| 241.620334 | 294.018783 | 1.216863 |
|
|
|
|
A comparison diagram will be generated below the table.
|
|
Here is an example to compare between 96c/results_gnr_96c_091_tp2pp3 and 128c/results_gnr_128c_091_tp2pp3
|
|
<img width="1886" height="828" alt="image" src="https://github.com/user-attachments/assets/c02a43ef-25d0-4fd6-90e5-2169a28682dd" />
|