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sync release with main @ v0.5.0.post1-99-g8720c92e
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import os | ||
import zipfile | ||
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MAX_SIZE_MB = 150 | ||
MAX_SIZE_MB = 200 | ||
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def print_top_10_largest_files(zip_file): | ||
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# vLLM benchmark suite | ||
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## Introduction | ||
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This directory contains the performance benchmarking CI for vllm. | ||
The goal is to help developers know the impact of their PRs on the performance of vllm. | ||
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This benchmark will be *triggered* upon: | ||
- A PR being merged into vllm. | ||
- Every commit for those PRs with `perf-benchmarks` label. | ||
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**Benchmarking Coverage**: latency, throughput and fix-qps serving on A100 (the support for more GPUs is comming later), 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 less than 1.5 hr so that it won't take forever to run. | ||
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## Configuring the workload | ||
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The benchmarking workload contains three parts: | ||
- Latency tests in `latency-tests.json`. | ||
- Throughput tests in `throughput-tests.json`. | ||
- Serving tests in `serving-tests.json`. | ||
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See [descriptions.md](tests/descriptions.md) for detailed descriptions. | ||
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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 | ||
[ | ||
{ | ||
"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 | ||
} | ||
}, | ||
] | ||
``` | ||
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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-benchmarks-suite.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` | ||
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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 | ||
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`. | ||
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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 | ||
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: | ||
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``` | ||
[ | ||
{ | ||
"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 | ||
} | ||
}, | ||
] | ||
``` | ||
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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` | ||
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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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## Visualizing the results | ||
The `convert-results-json-to-markdown.py` helps you put the benchmarking results inside a markdown table, by formatting [descriptions.md](tests/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. |
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steps: | ||
- label: "Wait for container to be ready" | ||
agents: | ||
queue: A100 | ||
plugins: | ||
- kubernetes: | ||
podSpec: | ||
containers: | ||
- image: badouralix/curl-jq | ||
command: | ||
- sh | ||
- .buildkite/nightly-benchmarks/scripts/wait-for-image.sh | ||
- wait | ||
- label: "A100 Benchmark" | ||
agents: | ||
queue: A100 | ||
plugins: | ||
- kubernetes: | ||
podSpec: | ||
priorityClassName: perf-benchmark | ||
containers: | ||
- image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT | ||
command: | ||
- bash .buildkite/nightly-benchmarks/run-benchmarks-suite.sh | ||
resources: | ||
limits: | ||
nvidia.com/gpu: 8 | ||
volumeMounts: | ||
- name: devshm | ||
mountPath: /dev/shm | ||
env: | ||
- name: VLLM_USAGE_SOURCE | ||
value: ci-test | ||
- name: HF_TOKEN | ||
valueFrom: | ||
secretKeyRef: | ||
name: hf-token-secret | ||
key: token | ||
nodeSelector: | ||
nvidia.com/gpu.product: NVIDIA-A100-SXM4-80GB | ||
volumes: | ||
- name: devshm | ||
emptyDir: | ||
medium: Memory | ||
# - label: "H100: NVIDIA SMI" | ||
# agents: | ||
# queue: H100 | ||
# plugins: | ||
# - docker#v5.11.0: | ||
# image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT | ||
# command: | ||
# - bash | ||
# - .buildkite/nightly-benchmarks/run-benchmarks-suite.sh | ||
# mount-buildkite-agent: true | ||
# propagate-environment: true | ||
# propagate-uid-gid: false | ||
# ipc: host | ||
# gpus: all | ||
# environment: | ||
# - VLLM_USAGE_SOURCE | ||
# - HF_TOKEN | ||
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#!/usr/bin/env bash | ||
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# NOTE(simon): this script runs inside a buildkite agent with CPU only access. | ||
set -euo pipefail | ||
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# Install system packages | ||
apt update | ||
apt install -y curl jq | ||
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# Install minijinja for templating | ||
curl -sSfL https://github.com/mitsuhiko/minijinja/releases/latest/download/minijinja-cli-installer.sh | sh | ||
source $HOME/.cargo/env | ||
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# If BUILDKITE_PULL_REQUEST != "false", then we check the PR labels using curl and jq | ||
if [ "$BUILDKITE_PULL_REQUEST" != "false" ]; then | ||
PR_LABELS=$(curl -s "https://api.github.com/repos/vllm-project/vllm/pulls/$BUILDKITE_PULL_REQUEST" | jq -r '.labels[].name') | ||
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if [[ $PR_LABELS == *"perf-benchmarks"* ]]; then | ||
echo "This PR has the 'perf-benchmarks' label. Proceeding with the nightly benchmarks." | ||
else | ||
echo "This PR does not have the 'perf-benchmarks' label. Skipping the nightly benchmarks." | ||
exit 0 | ||
fi | ||
fi | ||
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# Upload sample.yaml | ||
buildkite-agent pipeline upload .buildkite/nightly-benchmarks/benchmark-pipeline.yaml |
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