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vLLM benchmark suite

Introduction

This directory contains two sets of benchmark for vllm.

  • Performance benchmark: benchmark vllm's performance under various workload, for developers to gain clarity on whether their PR improves/degrades vllm's performance
  • Nightly benchmark: compare vllm's performance against alternatives (tgi, trt-llm and lmdeploy), for the public to know when to choose vllm.

See vLLM performance dashboard for the latest performance benchmark results and vLLM GitHub README for latest nightly benchmark results.

Performance benchmark quick overview

Benchmarking Coverage: latency, throughput and fix-qps serving on A100 (the support for FP8 benchmark on H100 is coming!), 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.

Nightly benchmark quick overview

Benchmarking Coverage: Fix-qps serving on A100 (the support for FP8 benchmark on H100 is coming!) on Llama-3 8B, 70B and Mixtral 8x7B.

Benchmarking engines: vllm, TGI, trt-llm and lmdeploy.

Benchmarking Duration: about 3.5hrs.

Trigger the benchmark

Performance benchmark will be triggered when:

  • A PR being merged into vllm.
  • Every commit for those PRs with perf-benchmarks label AND ready label.

Nightly benchmark will be triggered when:

  • Every commit for those PRs with perf-benchmarks label and nightly-benchmarks label.

Performance benchmark details

See 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.

Latency test

Here is an example of one test inside latency-tests.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 benchmark_latency.py. 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 benchmark_latency.py. For example, the corresponding command line arguments for benchmark_latency.py 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 benchmark_throughput.py.

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 benchmark_serving.py with request rate = inf to cover the online serving overhead. The corresponding parameters are in serving-tests.json, and here is an example:

[
    {
        "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": "",
            "disable_log_requests": "",
            "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 benchmark_serving.py.
  • The qps_list controls the list of qps for test. It will be used to configure the --request-rate parameter in benchmark_serving.py

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 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.

Nightly test details

See nightly-descriptions.md for the detailed description on test workload, models and docker containers of benchmarking other llm engines.

Workflow

  • The nightly-pipeline.yaml specifies the docker containers for different LLM serving engines.
  • Inside each container, we run run-nightly-suite.sh, which will probe the serving engine of the current container.
  • The run-nightly-suite.sh will redirect the request to tests/run-[llm serving engine name]-nightly.sh, which parses the workload described in nightly-tests.json and performs the benchmark.
  • At last, we run scripts/plot-nightly-results.py to collect and plot the final benchmarking results, and update the results to buildkite.

Nightly tests

In nightly-tests.json, we include the command line arguments for benchmarking commands, together with the benchmarking test cases. The format is highly similar to performance benchmark.

Docker containers

The docker containers for benchmarking are specified in nightly-pipeline.yaml.

WARNING: the docker versions are HARD-CODED and SHOULD BE ALIGNED WITH nightly-descriptions.md. The docker versions need to be hard-coded as there are several version-specific bug fixes inside tests/run-[llm serving engine name]-nightly.sh.

WARNING: populating trt-llm to latest version is not easy, as it requires updating several protobuf files in tensorrt-demo.