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Commit 469e903b authored by zhuwenwen's avatar zhuwenwen
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Merge tag 'v0.8.2' into v0.8.2-dev

parents 389ebcf7 25f560a6
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Dockerfile.xpu
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FROM intel/oneapi-basekit:2024.2.1-0-devel-ubuntu22.04 AS vllm-base
# oneapi 2025.0.2 docker base image use rolling 2448 package. https://dgpu-docs.intel.com/releases/packages.html?release=Rolling+2448.13&os=Ubuntu+22.04, and we don't need install driver manually.
FROM intel/deep-learning-essentials:2025.0.2-0-devel-ubuntu22.04 AS vllm-base
RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/intel-oneapi-archive-keyring.gpg > /dev/null && \
echo "deb [signed-by=/usr/share/keyrings/intel-oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main " | tee /etc/apt/sources.list.d/oneAPI.list && \
chmod 644 /usr/share/keyrings/intel-oneapi-archive-keyring.gpg && \
wget -O- https://repositories.intel.com/graphics/intel-graphics.key | gpg --dearmor | tee /usr/share/keyrings/intel-graphics.gpg > /dev/null && \
echo "deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/graphics/ubuntu jammy arc" | tee /etc/apt/sources.list.d/intel.gpu.jammy.list && \
chmod 644 /usr/share/keyrings/intel-graphics.gpg
RUN rm /etc/apt/sources.list.d/intel-graphics.list
RUN apt-get update -y && \
apt-get install -y --no-install-recommends --fix-missing \
......@@ -21,30 +17,20 @@ RUN apt-get update -y && \
python3 \
python3-dev \
python3-pip \
# vim \
wget
WORKDIR /workspace/vllm
COPY requirements-xpu.txt /workspace/vllm/requirements-xpu.txt
COPY requirements-common.txt /workspace/vllm/requirements-common.txt
COPY requirements/xpu.txt /workspace/vllm/requirements/xpu.txt
COPY requirements/common.txt /workspace/vllm/requirements/common.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install --no-cache-dir \
-r requirements-xpu.txt
RUN git clone https://github.com/intel/pti-gpu && \
cd pti-gpu/sdk && \
git checkout 6c491f07a777ed872c2654ca9942f1d0dde0a082 && \
mkdir build && \
cd build && \
cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_TOOLCHAIN_FILE=../cmake/toolchains/icpx_toolchain.cmake -DBUILD_TESTING=OFF .. && \
make -j && \
cmake --install . --config Release --prefix "/usr/local"
-r requirements/xpu.txt
ENV LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/lib/"
COPY . .
ARG GIT_REPO_CHECK
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh; fi
......@@ -54,6 +40,12 @@ RUN --mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
python3 setup.py install
# Please refer xpu doc, we need manually install intel-extension-for-pytorch 2.6.10+xpu due to there are some conflict dependencies with torch 2.6.0+xpu
# FIXME: This will be fix in ipex 2.7. just leave this here for awareness.
RUN --mount=type=cache,target=/root/.cache/pip \
pip install intel-extension-for-pytorch==2.6.10+xpu \
--extra-index-url=https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
CMD ["/bin/bash"]
FROM vllm-base AS vllm-openai
......
MANIFEST.in
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include LICENSE
include requirements-common.txt
include requirements-cuda.txt
include requirements-rocm.txt
include requirements-neuron.txt
include requirements-cpu.txt
include requirements/common.txt
include requirements/cuda.txt
include requirements/rocm.txt
include requirements/neuron.txt
include requirements/cpu.txt
include CMakeLists.txt
recursive-include cmake *
......
README.md
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......@@ -3,7 +3,7 @@
vLLM是一个快速且易于使用的LLM推理和服务库,使用PageAttention高效管理kv内存,Continuous batching传入请求,支持很多Hugging Face模型,如LLaMA & LLaMA-2、Qwen、Chatglm2 & Chatglm3等。
## 暂不支持的官方功能
- **量化推理**:目前支持fp16的推理和gptq,awq-int4推理,marlin的权重量化、kv-cache fp8推理方案暂不支持
- **量化推理**:目前不支持marlin的权重量化、kv-cache fp8推理方案
- **模块支持**:目前不支持Sliding window attention
......@@ -85,7 +85,7 @@ VLLM_INSTALL_PUNICA_KERNELS=1 python3 setup.py install （若调试，可使用V
+ 若使用 pip install 下载安装过慢,可添加源:-i https://pypi.tuna.tsinghua.edu.cn/simple/
## 验证
- python -c "import vllm; print(vllm.\_\_version__)",版本号与官方版本同步,查询该软件的版本号,例如0.7.3;
- python -c "import vllm; print(vllm.\_\_version__)",版本号与官方版本同步,查询该软件的版本号,例如0.8.2;
## Known Issue
- 无
......
README_ORIGIN.md
View file @ 469e903b
......@@ -10,20 +10,29 @@ Easy, fast, and cheap LLM serving for everyone
</h3>
<p align="center">
| <a href="https://docs.vllm.ai"><b>Documentation</b></a> | <a href="https://vllm.ai"><b>Blog</b></a> | <a href="https://arxiv.org/abs/2309.06180"><b>Paper</b></a> | <a href="https://x.com/vllm_project"><b>Twitter/X</b></a> | <a href="https://slack.vllm.ai"><b>Developer Slack</b></a> |
| <a href="https://docs.vllm.ai"><b>Documentation</b></a> | <a href="https://vllm.ai"><b>Blog</b></a> | <a href="https://arxiv.org/abs/2309.06180"><b>Paper</b></a> | <a href="https://x.com/vllm_project"><b>Twitter/X</b></a> | <a href="https://discuss.vllm.ai"><b>User Forum</b></a> | <a href="https://slack.vllm.ai"><b>Developer Slack</b></a> |
</p>
---
We are excited to invite you to our Menlo Park meetup with Meta, evening of Thursday, February 27! Meta engineers will discuss the improvements on top of vLLM, and vLLM contributors will share updates from the v0.7.x series of releases. [Register Now](https://lu.ma/h7g3kuj9)
[2025/03] We are collaborating with Ollama to host an [Inference Night](https://lu.ma/vllm-ollama) at Y Combinator in San Francisco on Thursday, March 27, at 6 PM. Discuss all things inference local or data center!
[2025/04] We're hosting our first-ever *vLLM Asia Developer Day* in Singapore on *April 3rd*! This is a full-day event (9 AM - 9 PM SGT) in partnership with SGInnovate, AMD, and Embedded LLM. Meet the vLLM team and learn about LLM inference for RL, MI300X, and more! [Register Now](https://www.sginnovate.com/event/limited-availability-morning-evening-slots-remaining-inaugural-vllm-asia-developer-day)
---
*Latest News* 🔥
- [2025/03] We hosted [the first vLLM China Meetup](https://mp.weixin.qq.com/s/n77GibL2corAtQHtVEAzfg)! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1REHvfQMKGnvz6p3Fd23HhSO4c8j5WPGZV0bKYLwnHyQ/edit?usp=sharing).
- [2025/03] We hosted [the East Coast vLLM Meetup](https://lu.ma/7mu4k4xx)! Please find the meetup slides [here](https://docs.google.com/presentation/d/1NHiv8EUFF1NLd3fEYODm56nDmL26lEeXCaDgyDlTsRs/edit#slide=id.g31441846c39_0_0).
- [2025/02] We hosted [the ninth vLLM meetup](https://lu.ma/h7g3kuj9) with Meta! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1jzC_PZVXrVNSFVCW-V4cFXb6pn7zZ2CyP_Flwo05aqg/edit?usp=sharing) and AMD [here](https://drive.google.com/file/d/1Zk5qEJIkTmlQ2eQcXQZlljAx3m9s7nwn/view?usp=sharing). The slides from Meta will not be posted.
- [2025/01] We are excited to announce the alpha release of vLLM V1: A major architectural upgrade with 1.7x speedup! Clean code, optimized execution loop, zero-overhead prefix caching, enhanced multimodal support, and more. Please check out our blog post [here](https://blog.vllm.ai/2025/01/27/v1-alpha-release.html).
- [2025/01] We hosted [the eighth vLLM meetup](https://lu.ma/zep56hui) with Google Cloud! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1epVkt4Zu8Jz_S5OhEHPc798emsYh2BwYfRuDDVEF7u4/edit?usp=sharing), and Google Cloud team [here](https://drive.google.com/file/d/1h24pHewANyRL11xy5dXUbvRC9F9Kkjix/view?usp=sharing).
- [2024/12] vLLM joins [pytorch ecosystem](https://pytorch.org/blog/vllm-joins-pytorch)! Easy, Fast, and Cheap LLM Serving for Everyone!
<details>
<summary>Previous News</summary>
- [2024/11] We hosted [the seventh vLLM meetup](https://lu.ma/h0qvrajz) with Snowflake! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1e3CxQBV3JsfGp30SwyvS3eM_tW-ghOhJ9PAJGK6KR54/edit?usp=sharing), and Snowflake team [here](https://docs.google.com/presentation/d/1qF3RkDAbOULwz9WK5TOltt2fE9t6uIc_hVNLFAaQX6A/edit?usp=sharing).
- [2024/10] We have just created a developer slack ([slack.vllm.ai](https://slack.vllm.ai)) focusing on coordinating contributions and discussing features. Please feel free to join us there!
- [2024/10] Ray Summit 2024 held a special track for vLLM! Please find the opening talk slides from the vLLM team [here](https://docs.google.com/presentation/d/1B_KQxpHBTRa_mDF-tR6i8rWdOU5QoTZNcEg2MKZxEHM/edit?usp=sharing). Learn more from the [talks](https://www.youtube.com/playlist?list=PLzTswPQNepXl6AQwifuwUImLPFRVpksjR) from other vLLM contributors and users!
......@@ -37,8 +46,9 @@ We are excited to invite you to our Menlo Park meetup with Meta, evening of Thur
- [2023/08] We would like to express our sincere gratitude to [Andreessen Horowitz](https://a16z.com/2023/08/30/supporting-the-open-source-ai-community/) (a16z) for providing a generous grant to support the open-source development and research of vLLM.
- [2023/06] We officially released vLLM! FastChat-vLLM integration has powered [LMSYS Vicuna and Chatbot Arena](https://chat.lmsys.org) since mid-April. Check out our [blog post](https://vllm.ai).
---
</details>
---
## About
vLLM is a fast and easy-to-use library for LLM inference and serving.
......@@ -86,7 +96,7 @@ pip install vllm
```
Visit our [documentation](https://docs.vllm.ai/en/latest/) to learn more.
- [Installation](https://docs.vllm.ai/en/latest/getting_started/installation/index.html)
- [Installation](https://docs.vllm.ai/en/latest/getting_started/installation.html)
- [Quickstart](https://docs.vllm.ai/en/latest/getting_started/quickstart.html)
- [List of Supported Models](https://docs.vllm.ai/en/latest/models/supported_models.html)
......@@ -146,10 +156,11 @@ If you use vLLM for your research, please cite our [paper](https://arxiv.org/abs
## Contact Us
- For technical questions and feature requests, please use Github issues or discussions.
- For discussing with fellow users and coordinating contributions and development, please use Slack.
- For security disclosures, please use Github's security advisory feature.
- For collaborations and partnerships, please contact us at vllm-questions AT lists.berkeley.edu.
- For technical questions and feature requests, please use GitHub [Issues](https://github.com/vllm-project/vllm/issues) or [Discussions](https://github.com/vllm-project/vllm/discussions)
- For discussing with fellow users, please use the [vLLM Forum](https://discuss.vllm.ai)
- coordinating contributions and development, please use [Slack](https://slack.vllm.ai)
- For security disclosures, please use GitHub's [Security Advisories](https://github.com/vllm-project/vllm/security/advisories) feature
- For collaborations and partnerships, please contact us at [vllm-questions@lists.berkeley.edu](mailto:vllm-questions@lists.berkeley.edu)
## Media Kit
......
RELEASE.md 0 → 100644
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# Releasing vLLM
vLLM releases offer a reliable version of the code base, packaged into a binary format that can be conveniently accessed via PyPI. These releases also serve as key milestones for the development team to communicate with the community about newly available features, improvements, and upcoming changes that could affect users, including potential breaking changes.
## Release Versioning
vLLM uses a “right-shifted” versioning scheme where a new patch release is out every 2 weeks. And patch releases contain features and bug fixes (as opposed to semver where patch release contains only backwards-compatible bug fixes). When critical fixes need to be made, special release post1 is released.
* _major_ major architectural milestone and when incompatible API changes are made, similar to PyTorch 2.0.
* _minor_ major features
* _patch_ features and backwards-compatible bug fixes
* _post1_ or _patch-1_ backwards-compatible bug fixes, either explicit or implicit post release
## Release Cadence
Patch release is released on bi-weekly basis. Post release 1-3 days after patch release and uses same branch as patch release.
Following is the release cadence for year 2025. All future release dates below are tentative. Please note: Post releases are optional.
| Release Date | Patch release versions | Post Release versions |
| --- | --- | --- |
| Jan 2025 | 0.7.0 | --- |
| Feb 2025 | 0.7.1, 0.7.2, 0.7.3 | --- |
| Mar 2025 | 0.7.4, 0.7.5 | --- |
| Apr 2025 | 0.7.6, 0.7.7 | --- |
| May 2025 | 0.7.8, 0.7.9 | --- |
| Jun 2025 | 0.7.10, 0.7.11 | --- |
| Jul 2025 | 0.7.12, 0.7.13 | --- |
| Aug 2025 | 0.7.14, 0.7.15 | --- |
| Sep 2025 | 0.7.16, 0.7.17 | --- |
| Oct 2025 | 0.7.18, 0.7.19 | --- |
| Nov 2025 | 0.7.20, 0.7.21 | --- |
| Dec 2025 | 0.7.22, 0.7.23 | --- |
## Release branch
Each release is built from a dedicated release branch.
* For _major_, _minor_, _patch_ releases, the release branch cut is performed 1-2 days before release is live.
* For post releases, previously cut release branch is reused
* Release builds are triggered via push to RC tag like vX.Y.Z-rc1 . This enables us to build and test multiple RCs for each release.
* Final tag : vX.Y.Z does not trigger the build but used for Release notes and assets.
* After branch cut is created we monitor the main branch for any reverts and apply these reverts to a release branch.
## Release Cherry-Pick Criteria
After branch cut, we approach finalizing the release branch with clear criteria on what cherry picks are allowed in. Note: a cherry pick is a process to land a PR in the release branch after branch cut. These are typically limited to ensure that the team has sufficient time to complete a thorough round of testing on a stable code base.
* Regression fixes - that address functional/performance regression against the most recent release (e.g. 0.7.0 for 0.7.1 release)
* Critical fixes - critical fixes for severe issue such as silent incorrectness, backwards compatibility, crashes, deadlocks, (large) memory leaks
* Fixes to new features introduced in the most recent release (e.g. 0.7.0 for 0.7.1 release)
* Documentation improvements
* Release branch specific changes (e.g. change version identifiers or CI fixes)
Please note: **No feature work allowed for cherry picks**. All PRs that are considered for cherry-picks need to be merged on trunk, the only exception are Release branch specific changes.
benchmarks/README.md
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# Benchmarking vLLM
## Downloading the ShareGPT dataset
This README guides you through running benchmark tests with the extensive
datasets supported on vLLM. It’s a living document, updated as new features and datasets
become available.
You can download the dataset by running:
## Dataset Overview
<table style="width:100%; border-collapse: collapse;">
<thead>
<tr>
<th style="width:15%; text-align: left;">Dataset</th>
<th style="width:10%; text-align: center;">Online</th>
<th style="width:10%; text-align: center;">Offline</th>
<th style="width:65%; text-align: left;">Data Path</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>ShareGPT</strong></td>
<td style="text-align: center;">✅</td>
<td style="text-align: center;">✅</td>
<td><code>wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json</code></td>
</tr>
<tr>
<td><strong>BurstGPT</strong></td>
<td style="text-align: center;">✅</td>
<td style="text-align: center;">✅</td>
<td><code>wget https://github.com/HPMLL/BurstGPT/releases/download/v1.1/BurstGPT_without_fails_2.csv</code></td>
</tr>
<tr>
<td><strong>Sonnet</strong></td>
<td style="text-align: center;">✅</td>
<td style="text-align: center;">✅</td>
<td>Local file: <code>benchmarks/sonnet.txt</code></td>
</tr>
<tr>
<td><strong>Random</strong></td>
<td style="text-align: center;">✅</td>
<td style="text-align: center;">✅</td>
<td><code>synthetic</code></td>
</tr>
<tr>
<td><strong>HuggingFace</strong></td>
<td style="text-align: center;">🟡</td>
<td style="text-align: center;">🟡</td>
<td>Specify your dataset path on HuggingFace</td>
</tr>
<tr>
<td><strong>VisionArena</strong></td>
<td style="text-align: center;">✅</td>
<td style="text-align: center;">✅</td>
<td><code>lmarena-ai/vision-arena-bench-v0.1</code> (a HuggingFace dataset)</td>
</tr>
</tbody>
</table>
✅: supported
🚧: to be supported
🟡: Partial support. Currently, HuggingFaceDataset only supports dataset formats
similar to `lmms-lab/LLaVA-OneVision-Data` and `Aeala/ShareGPT_Vicuna_unfiltered`.
If you need support for other dataset formats, please consider contributing.
**Note**: VisionArena’s `dataset-name` should be set to `hf`
---
## Example - Online Benchmark
First start serving your model
```bash
MODEL_NAME="NousResearch/Hermes-3-Llama-3.1-8B"
vllm serve ${MODEL_NAME} --disable-log-requests
```
Then run the benchmarking script
```bash
wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
# download dataset
# wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
MODEL_NAME="NousResearch/Hermes-3-Llama-3.1-8B"
NUM_PROMPTS=10
BACKEND="vllm"
DATASET_NAME="sharegpt"
DATASET_PATH="<your data path>/ShareGPT_V3_unfiltered_cleaned_split.json"
python3 vllm/benchmarks/benchmark_serving.py --backend ${BACKEND} --model ${MODEL_NAME} --endpoint /v1/completions --dataset-name ${DATASET_NAME} --dataset-path ${DATASET_PATH} --num-prompts ${NUM_PROMPTS}
```
If successful, you will see the following output
```
============ Serving Benchmark Result ============
Successful requests: 10
Benchmark duration (s): 5.78
Total input tokens: 1369
Total generated tokens: 2212
Request throughput (req/s): 1.73
Output token throughput (tok/s): 382.89
Total Token throughput (tok/s): 619.85
---------------Time to First Token----------------
Mean TTFT (ms): 71.54
Median TTFT (ms): 73.88
P99 TTFT (ms): 79.49
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 7.91
Median TPOT (ms): 7.96
P99 TPOT (ms): 8.03
---------------Inter-token Latency----------------
Mean ITL (ms): 7.74
Median ITL (ms): 7.70
P99 ITL (ms): 8.39
==================================================
```
### VisionArena Benchmark for Vision Language Models
```bash
# need a model with vision capability here
vllm serve Qwen/Qwen2-VL-7B-Instruct --disable-log-requests
```
```bash
MODEL_NAME="Qwen/Qwen2-VL-7B-Instruct"
NUM_PROMPTS=10
BACKEND="openai-chat"
DATASET_NAME="hf"
DATASET_PATH="lmarena-ai/vision-arena-bench-v0.1"
DATASET_SPLIT='train'
python3 vllm/benchmarks/benchmark_serving.py \
--backend "${BACKEND}" \
--model "${MODEL_NAME}" \
--endpoint "/v1/chat/completions" \
--dataset-name "${DATASET_NAME}" \
--dataset-path "${DATASET_PATH}" \
--hf-split "${DATASET_SPLIT}" \
--num-prompts "${NUM_PROMPTS}"
```
## Downloading the ShareGPT4V dataset
### HuggingFaceDataset Examples
The json file refers to several image datasets (coco, llava, etc.). The benchmark scripts
will ignore a datapoint if the referred image is missing.
Currently, HuggingFaceDataset only supports dataset formats
similar to `lmms-lab/LLaVA-OneVision-Data` and `Aeala/ShareGPT_Vicuna_unfiltered`. If you need support for other dataset
formats, please consider contributing.
```bash
wget https://huggingface.co/datasets/Lin-Chen/ShareGPT4V/resolve/main/sharegpt4v_instruct_gpt4-vision_cap100k.json
mkdir coco -p
wget http://images.cocodataset.org/zips/train2017.zip -O coco/train2017.zip
unzip coco/train2017.zip -d coco/
# need a model with vision capability here
vllm serve Qwen/Qwen2-VL-7B-Instruct --disable-log-requests
```
# Downloading the BurstGPT dataset
**`lmms-lab/LLaVA-OneVision-Data`**
You can download the BurstGPT v1.1 dataset by running:
```bash
MODEL_NAME="Qwen/Qwen2-VL-7B-Instruct"
NUM_PROMPTS=10
BACKEND="openai-chat"
DATASET_NAME="hf"
DATASET_PATH="lmms-lab/LLaVA-OneVision-Data"
DATASET_SPLIT='train'
DATASET_SUBSET='chart2text(cauldron)'
python3 vllm/benchmarks/benchmark_serving.py \
--backend "${BACKEND}" \
--model "${MODEL_NAME}" \
--endpoint "/v1/chat/completions" \
--dataset-name "${DATASET_NAME}" \
--dataset-path "${DATASET_PATH}" \
--hf-split "${DATASET_SPLIT}" \
--num-prompts "${NUM_PROMPTS}" \
--hf-subset "${DATASET_SUBSET}"
```
**`Aeala/ShareGPT_Vicuna_unfiltered`**
```bash
MODEL_NAME="Qwen/Qwen2-VL-7B-Instruct"
NUM_PROMPTS=10
BACKEND="openai-chat"
DATASET_NAME="hf"
DATASET_PATH="Aeala/ShareGPT_Vicuna_unfiltered"
DATASET_SPLIT='train'
python3 vllm/benchmarks/benchmark_serving.py \
--backend "${BACKEND}" \
--model "${MODEL_NAME}" \
--endpoint "/v1/chat/completions" \
--dataset-name "${DATASET_NAME}" \
--dataset-path "${DATASET_PATH}" \
--hf-split "${DATASET_SPLIT}" \
--num-prompts "${NUM_PROMPTS}" \
```
---
## Example - Offline Throughput Benchmark
```bash
wget https://github.com/HPMLL/BurstGPT/releases/download/v1.1/BurstGPT_without_fails_2.csv
MODEL_NAME="NousResearch/Hermes-3-Llama-3.1-8B"
NUM_PROMPTS=10
DATASET_NAME="sonnet"
DATASET_PATH="vllm/benchmarks/sonnet.txt"
python3 vllm/benchmarks/benchmark_throughput.py \
--model "${MODEL_NAME}" \
--dataset-name "${DATASET_NAME}" \
--dataset-path "${DATASET_PATH}" \
--num-prompts "${NUM_PROMPTS}"
```
If successful, you will see the following output
```
Throughput: 7.15 requests/s, 4656.00 total tokens/s, 1072.15 output tokens/s
Total num prompt tokens: 5014
Total num output tokens: 1500
```
### VisionArena Benchmark for Vision Language Models
``` bash
MODEL_NAME="Qwen/Qwen2-VL-7B-Instruct"
NUM_PROMPTS=10
DATASET_NAME="hf"
DATASET_PATH="lmarena-ai/vision-arena-bench-v0.1"
DATASET_SPLIT="train"
python3 vllm/benchmarks/benchmark_throughput.py \
--model "${MODEL_NAME}" \
--backend "vllm-chat" \
--dataset-name "${DATASET_NAME}" \
--dataset-path "${DATASET_PATH}" \
--num-prompts "${NUM_PROMPTS}" \
--hf-split "${DATASET_SPLIT}"
```
The `num prompt tokens` now includes image token counts
```
Throughput: 2.55 requests/s, 4036.92 total tokens/s, 326.90 output tokens/s
Total num prompt tokens: 14527
Total num output tokens: 1280
```
### Benchmark with LoRA Adapters
``` bash
# download dataset
# wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
MODEL_NAME="meta-llama/Llama-2-7b-hf"
BACKEND="vllm"
DATASET_NAME="sharegpt"
DATASET_PATH="<your data path>/ShareGPT_V3_unfiltered_cleaned_split.json"
NUM_PROMPTS=10
MAX_LORAS=2
MAX_LORA_RANK=8
ENABLE_LORA="--enable-lora"
LORA_PATH="yard1/llama-2-7b-sql-lora-test"
python3 vllm/benchmarks/benchmark_throughput.py \
--model "${MODEL_NAME}" \
--backend "${BACKEND}" \
--dataset_path "${DATASET_PATH}" \
--dataset_name "${DATASET_NAME}" \
--num-prompts "${NUM_PROMPTS}" \
--max-loras "${MAX_LORAS}" \
--max-lora-rank "${MAX_LORA_RANK}" \
${ENABLE_LORA} \
--lora-path "${LORA_PATH}"
```
benchmarks/backend_request_func.py
View file @ 469e903b
......@@ -6,7 +6,7 @@ import sys
import time
import traceback
from dataclasses import dataclass, field
from typing import List, Optional, Union
from typing import Optional, Union
import aiohttp
import huggingface_hub.constants
......@@ -14,6 +14,9 @@ from tqdm.asyncio import tqdm
from transformers import (AutoTokenizer, PreTrainedTokenizer,
PreTrainedTokenizerFast)
# NOTE(simon): do not import vLLM here so the benchmark script
# can run without vLLM installed.
AIOHTTP_TIMEOUT = aiohttp.ClientTimeout(total=6 * 60 * 60)
......@@ -25,7 +28,6 @@ class RequestFuncInput:
output_len: int
model: str
model_name: Optional[str] = None
best_of: int = 1
logprobs: Optional[int] = None
extra_body: Optional[dict] = None
multi_modal_content: Optional[dict] = None
......@@ -39,8 +41,8 @@ class RequestFuncOutput:
latency: float = 0.0
output_tokens: int = 0
ttft: float = 0.0 # Time to first token
itl: List[float] = field(
default_factory=list) # List of inter-token latencies
itl: list[float] = field(
default_factory=list) # list of inter-token latencies
tpot: float = 0.0 # avg next-token latencies
prompt_len: int = 0
error: str = ""
......@@ -56,13 +58,12 @@ async def async_request_tgi(
async with aiohttp.ClientSession(trust_env=True,
timeout=AIOHTTP_TIMEOUT) as session:
params = {
"best_of": request_func_input.best_of,
"max_new_tokens": request_func_input.output_len,
"do_sample": True,
"temperature": 0.01, # TGI does not accept 0.0 temperature.
"top_p": 0.99, # TGI does not accept 1.0 top_p.
"truncate": request_func_input.prompt_len,
# TGI does not accept ignore_eos flag.
"ignore_eos_token": request_func_input.ignore_eos,
}
payload = {
"inputs": request_func_input.prompt,
......@@ -70,6 +71,10 @@ async def async_request_tgi(
}
output = RequestFuncOutput()
output.prompt_len = request_func_input.prompt_len
if request_func_input.ignore_eos:
output.output_tokens = request_func_input.output_len
else:
output.output_tokens = None
ttft = 0.0
st = time.perf_counter()
......@@ -128,7 +133,6 @@ async def async_request_trt_llm(
async with aiohttp.ClientSession(trust_env=True,
timeout=AIOHTTP_TIMEOUT) as session:
assert request_func_input.best_of == 1
payload = {
"accumulate_tokens": True,
"text_input": request_func_input.prompt,
......@@ -193,7 +197,6 @@ async def async_request_deepspeed_mii(
) -> RequestFuncOutput:
async with aiohttp.ClientSession(trust_env=True,
timeout=AIOHTTP_TIMEOUT) as session:
assert request_func_input.best_of == 1
payload = {
"prompt": request_func_input.prompt,
......@@ -247,7 +250,6 @@ async def async_request_openai_completions(
if request_func_input.model_name else request_func_input.model,
"prompt": request_func_input.prompt,
"temperature": 0.0,
"best_of": request_func_input.best_of,
"max_tokens": request_func_input.output_len,
"logprobs": request_func_input.logprobs,
"stream": True,
......@@ -336,7 +338,7 @@ async def async_request_openai_chat_completions(
) -> RequestFuncOutput:
api_url = request_func_input.api_url
assert api_url.endswith(
"chat/completions"
("chat/completions", "profile")
), "OpenAI Chat Completions API URL must end with 'chat/completions'."
async with aiohttp.ClientSession(trust_env=True,
......@@ -430,12 +432,17 @@ def get_model(pretrained_model_name_or_path: str) -> str:
if os.getenv('VLLM_USE_MODELSCOPE', 'False').lower() == 'true':
from modelscope import snapshot_download
model_path = snapshot_download(
model_id=pretrained_model_name_or_path,
local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
ignore_file_pattern=[".*.pt", ".*.safetensors", ".*.bin"])
from vllm.model_executor.model_loader.weight_utils import get_lock
# Use file lock to prevent multiple processes from
# downloading the same model weights at the same time.
with get_lock(pretrained_model_name_or_path):
model_path = snapshot_download(
model_id=pretrained_model_name_or_path,
local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
ignore_file_pattern=[".*.pt", ".*.safetensors", ".*.bin"])
return model_path
return model_path
return pretrained_model_name_or_path
......
benchmarks/benchmark_dataset.py 0 → 100644
View file @ 469e903b
This diff is collapsed.
benchmarks/benchmark_latency.py
View file @ 469e903b
......@@ -7,11 +7,11 @@ import json
import os
import time
from pathlib import Path
from typing import Any, Dict, List, Optional
from typing import Any, Optional
import numpy as np
import torch
from benchmark_utils import convert_to_pytorch_benchmark_format
from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
from tqdm import tqdm
from vllm import LLM, SamplingParams
......@@ -22,7 +22,7 @@ from vllm.utils import FlexibleArgumentParser
def save_to_pytorch_benchmark_format(args: argparse.Namespace,
results: Dict[str, Any]) -> None:
results: dict[str, Any]) -> None:
pt_records = convert_to_pytorch_benchmark_format(
args=args,
metrics={"latency": results["latencies"]},
......@@ -30,8 +30,7 @@ def save_to_pytorch_benchmark_format(args: argparse.Namespace,
for k in ["avg_latency", "percentiles"]})
if pt_records:
pt_file = f"{os.path.splitext(args.output_json)[0]}.pytorch.json"
with open(pt_file, "w") as f:
json.dump(pt_records, f)
write_to_json(pt_file, pt_records)
def main(args: argparse.Namespace):
......@@ -42,6 +41,10 @@ def main(args: argparse.Namespace):
# NOTE(woosuk): If the request cannot be processed in a single batch,
# the engine will automatically process the request in multiple batches.
llm = LLM(**dataclasses.asdict(engine_args))
assert llm.llm_engine.model_config.max_model_len >= (
args.input_len +
args.output_len), ("Please ensure that max_model_len is greater than"
" the sum of input_len and output_len.")
sampling_params = SamplingParams(
n=args.n,
......@@ -49,12 +52,13 @@ def main(args: argparse.Namespace):
top_p=1.0,
ignore_eos=True,
max_tokens=args.output_len,
detokenize=not args.disable_detokenize,
)
print(sampling_params)
dummy_prompt_token_ids = np.random.randint(10000,
size=(args.batch_size,
args.input_len))
dummy_prompts: List[PromptType] = [{
dummy_prompts: list[PromptType] = [{
"prompt_token_ids": batch
} for batch in dummy_prompt_token_ids.tolist()]
......@@ -170,6 +174,12 @@ if __name__ == "__main__":
default=None,
help="Path to save the latency results in JSON format.",
)
parser.add_argument(
"--disable-detokenize",
action="store_true",
help=("Do not detokenize responses (i.e. do not include "
"detokenization time in the latency measurement)"),
)
parser = EngineArgs.add_cli_args(parser)
args = parser.parse_args()
......
benchmarks/benchmark_prefix_caching.py
View file @ 469e903b
......@@ -31,7 +31,7 @@ import dataclasses
import json
import random
import time
from typing import List, Optional, Tuple
from typing import Optional
from transformers import PreTrainedTokenizerBase
......@@ -92,9 +92,9 @@ def sample_requests_from_dataset(
dataset_path: str,
num_requests: int,
tokenizer: PreTrainedTokenizerBase,
input_length_range: Tuple[int, int],
input_length_range: tuple[int, int],
fixed_output_len: Optional[int],
) -> List[Request]:
) -> list[Request]:
if fixed_output_len is not None and fixed_output_len < 4:
raise ValueError("output_len too small")
......@@ -114,7 +114,7 @@ def sample_requests_from_dataset(
assert min_len >= 0 and max_len >= min_len, "input_length_range too small"
# Filter out sequences that are too long or too short
filtered_requests: List[Request] = []
filtered_requests: list[Request] = []
for i in range(len(dataset)):
if len(filtered_requests) == num_requests:
......@@ -137,10 +137,10 @@ def sample_requests_from_dataset(
def sample_requests_from_random(
num_requests: int,
tokenizer: PreTrainedTokenizerBase,
input_length_range: Tuple[int, int],
input_length_range: tuple[int, int],
fixed_output_len: Optional[int],
prefix_len: int,
) -> List[Request]:
) -> list[Request]:
requests = []
prefix_token_ids = sample_tokens(tokenizer, prefix_len)
......@@ -159,9 +159,9 @@ def sample_requests_from_random(
return requests
def repeat_and_sort_requests(requests: List[Request],
def repeat_and_sort_requests(requests: list[Request],
repeat_count: int,
sort: bool = False) -> List[str]:
sort: bool = False) -> list[str]:
repeated_requests = requests * repeat_count
if sort:
repeated_requests.sort(key=lambda x: x[1])
......@@ -209,7 +209,9 @@ def main(args):
llm = LLM(**dataclasses.asdict(engine_args))
sampling_params = SamplingParams(temperature=0, max_tokens=args.output_len)
sampling_params = SamplingParams(temperature=0,
max_tokens=args.output_len,
detokenize=not args.disable_detokenize)
print("Testing filtered requests")
prompts = repeat_and_sort_requests(filtered_requests,
......@@ -258,6 +260,12 @@ if __name__ == "__main__":
"subtract this length when filtering prompts. Only used "
"when dataset-path is not provided.",
)
parser.add_argument(
'--disable-detokenize',
action='store_true',
help=("Do not detokenize responses (i.e. do not include "
"detokenization time in the latency measurement)"),
)
parser = EngineArgs.add_cli_args(parser)
args = parser.parse_args()
......
benchmarks/benchmark_prioritization.py
View file @ 469e903b
......@@ -5,7 +5,7 @@ import dataclasses
import json
import random
import time
from typing import List, Optional, Tuple
from typing import Optional
from transformers import AutoTokenizer, PreTrainedTokenizerBase
......@@ -13,12 +13,17 @@ from vllm.engine.arg_utils import EngineArgs
from vllm.utils import FlexibleArgumentParser
#Select a equi-probable random priority
def get_random_flag():
return 0 if random.random() < 0.5 else 1
def sample_requests(
dataset_path: str,
num_requests: int,
tokenizer: PreTrainedTokenizerBase,
fixed_output_len: Optional[int],
) -> List[Tuple[str, int, int]]:
) -> list[tuple[str, int, int, int]]:
if fixed_output_len is not None and fixed_output_len < 4:
raise ValueError("output_len too small")
......@@ -35,7 +40,7 @@ def sample_requests(
random.shuffle(dataset)
# Filter out sequences that are too long or too short
filtered_dataset: List[Tuple[str, int, int]] = []
filtered_dataset: list[tuple[str, int, int]] = []
for i in range(len(dataset)):
if len(filtered_dataset) == num_requests:
break
......@@ -55,8 +60,7 @@ def sample_requests(
# Prune too long sequences.
continue
#Select a equi-probable random priority
priority = 0 if random.random() < 0.5 else 1
priority = get_random_flag()
filtered_dataset.append((prompt, prompt_len, output_len, priority))
......@@ -64,13 +68,20 @@ def sample_requests(
def run_vllm(
requests: List[Tuple[str, int, int]],
requests: list[tuple[str, int, int]],
n: int,
engine_args: EngineArgs,
disable_detokenize: bool = False,
) -> float:
from vllm import LLM, SamplingParams
llm = LLM(**dataclasses.asdict(engine_args))
assert all(
llm.llm_engine.model_config.max_model_len >= (request[1] + request[2])
for request in requests), (
"Please ensure that max_model_len is greater than the sum of"
" input_len and output_len for all requests.")
# Add the requests to the engine.
prompts = []
sampling_params = []
......@@ -85,6 +96,7 @@ def run_vllm(
top_p=1.0,
ignore_eos=True,
max_tokens=output_len,
detokenize=not disable_detokenize,
))
start = time.perf_counter()
......@@ -103,15 +115,16 @@ def main(args: argparse.Namespace):
if args.dataset is None:
# Synthesize a prompt with the given input length.
prompt = "hi" * (args.input_len - 1)
requests = [(prompt, args.input_len, args.output_len)
for _ in range(args.num_prompts)]
requests = [(prompt, args.input_len, args.output_len,
get_random_flag()) for _ in range(args.num_prompts)]
else:
requests = sample_requests(args.dataset, args.num_prompts, tokenizer,
args.output_len)
if args.backend == "vllm":
elapsed_time = run_vllm(requests, args.n,
EngineArgs.from_cli_args(args))
EngineArgs.from_cli_args(args),
args.disable_detokenize)
else:
raise ValueError(f"Unknown backend: {args.backend}")
total_num_tokens = sum(prompt_len + output_len
......@@ -164,6 +177,12 @@ if __name__ == "__main__":
type=str,
default=None,
help='Path to save the throughput results in JSON format.')
parser.add_argument(
'--disable-detokenize',
action='store_true',
help=("Do not detokenize responses (i.e. do not include "
"detokenization time in the latency measurement)"),
)
parser = EngineArgs.add_cli_args(parser)
args = parser.parse_args()
......
benchmarks/benchmark_serving.py
View file @ 469e903b
This diff is collapsed.
benchmarks/benchmark_throughput.py
View file @ 469e903b
This diff is collapsed.
benchmarks/benchmark_utils.py
View file @ 469e903b
# SPDX-License-Identifier: Apache-2.0
import argparse
import json
import math
import os
from typing import Any, Dict, List
from typing import Any
def convert_to_pytorch_benchmark_format(args: argparse.Namespace,
metrics: Dict[str, List],
extra_info: Dict[str, Any]) -> List:
metrics: dict[str, list],
extra_info: dict[str, Any]) -> list:
"""
Save the benchmark results in the format used by PyTorch OSS benchmark with
on metric per record
......@@ -34,6 +36,34 @@ def convert_to_pytorch_benchmark_format(args: argparse.Namespace,
"extra_info": extra_info,
},
}
tp = record["benchmark"]["extra_info"]["args"].get(
"tensor_parallel_size")
# Save tensor_parallel_size parameter if it's part of the metadata
if not tp and "tensor_parallel_size" in extra_info:
record["benchmark"]["extra_info"]["args"][
"tensor_parallel_size"] = extra_info["tensor_parallel_size"]
records.append(record)
return records
class InfEncoder(json.JSONEncoder):
def clear_inf(self, o: Any):
if isinstance(o, dict):
return {k: self.clear_inf(v) for k, v in o.items()}
elif isinstance(o, list):
return [self.clear_inf(v) for v in o]
elif isinstance(o, float) and math.isinf(o):
return "inf"
return o
def iterencode(self, o: Any, *args, **kwargs) -> Any:
return super().iterencode(self.clear_inf(o), *args, **kwargs)
def write_to_json(filename: str, records: list) -> None:
with open(filename, "w") as f:
json.dump(records, f, cls=InfEncoder)
benchmarks/cutlass_benchmarks/sparse_benchmarks.py
View file @ 469e903b
......@@ -5,7 +5,8 @@ import copy
import itertools
import pickle as pkl
import time
from typing import Callable, Iterable, List, Tuple
from collections.abc import Iterable
from typing import Callable
import torch
import torch.utils.benchmark as TBenchmark
......@@ -228,7 +229,7 @@ def print_timers(timers: Iterable[TMeasurement]):
def run(dtype: torch.dtype,
MKNs: Iterable[Tuple[int, int, int]]) -> Iterable[TMeasurement]:
MKNs: Iterable[tuple[int, int, int]]) -> Iterable[TMeasurement]:
results = []
for m, k, n in MKNs:
timers = bench(dtype, m, k, n, f"scaled-{dtype}-gemm",
......@@ -241,7 +242,7 @@ def run(dtype: torch.dtype,
# output makers
def make_output(data: Iterable[TMeasurement],
MKNs: Iterable[Tuple[int, int, int]],
MKNs: Iterable[tuple[int, int, int]],
base_description: str,
timestamp=None):
print(f"== All Results {base_description} ====")
......@@ -282,7 +283,7 @@ def run_model_bench(args):
for i, model in enumerate(args.models):
print(f"[{i}] {model}")
def model_shapes(model_name: str, tp_size: int) -> List[Tuple[int, int]]:
def model_shapes(model_name: str, tp_size: int) -> list[tuple[int, int]]:
KNs = []
for KN, tp_split_dim in copy.deepcopy(WEIGHT_SHAPES[model_name]):
KN[tp_split_dim] = KN[tp_split_dim] // tp_size
......
benchmarks/cutlass_benchmarks/utils.py
View file @ 469e903b
# SPDX-License-Identifier: Apache-2.0
# Cutlass bench utils
from typing import Iterable, Tuple
from collections.abc import Iterable
import torch
......@@ -27,7 +27,7 @@ def to_fp16(tensor: torch.Tensor) -> torch.Tensor:
def make_rand_tensors(dtype: torch.dtype, m: int, n: int,
k: int) -> Tuple[torch.Tensor, torch.Tensor]:
k: int) -> tuple[torch.Tensor, torch.Tensor]:
a = torch.randn((m, k), device='cuda') * 5
b = torch.randn((n, k), device='cuda').t() * 5
......@@ -63,7 +63,7 @@ def prune_to_2_4(tensor):
def make_rand_sparse_tensors(dtype: torch.dtype, m: int, n: int,
k: int) -> Tuple[torch.Tensor, torch.Tensor]:
k: int) -> tuple[torch.Tensor, torch.Tensor]:
a = torch.randn((m, k), device='cuda') * 5
b = torch.randn((n, k), device='cuda').t() * 5
......@@ -88,7 +88,7 @@ def make_rand_sparse_tensors(dtype: torch.dtype, m: int, n: int,
def make_n_rand_sparse_tensors(num_tensors: int, dtype: torch.dtype,
m: int, n: int, k: int) -> \
Tuple[Iterable[torch.Tensor], Iterable[torch.Tensor]]:
tuple[Iterable[torch.Tensor], Iterable[torch.Tensor]]:
ABs = []
for _ in range(num_tensors):
b_comp, e, a, b = make_rand_sparse_tensors(dtype, m, n, k)
......
benchmarks/cutlass_benchmarks/w8a8_benchmarks.py
View file @ 469e903b
......@@ -5,7 +5,8 @@ import copy
import itertools
import pickle as pkl
import time
from typing import Callable, Iterable, List, Optional, Tuple
from collections.abc import Iterable
from typing import Callable, Optional
import torch
import torch.utils.benchmark as TBenchmark
......@@ -49,7 +50,7 @@ def bench_int8(
n: int,
label: str,
sub_label: str,
bench_kernels: Optional[List[str]] = None) -> Iterable[TMeasurement]:
bench_kernels: Optional[list[str]] = None) -> Iterable[TMeasurement]:
"""Benchmark INT8-based kernels."""
assert dtype == torch.int8
a, b = make_rand_tensors(torch.int8, m, n, k)
......@@ -101,7 +102,7 @@ def bench_fp8(
n: int,
label: str,
sub_label: str,
bench_kernels: Optional[List[str]] = None) -> Iterable[TMeasurement]:
bench_kernels: Optional[list[str]] = None) -> Iterable[TMeasurement]:
"""Benchmark FP8-based kernels."""
assert dtype == torch.float8_e4m3fn
a, b = make_rand_tensors(torch.float8_e4m3fn, m, n, k)
......@@ -180,7 +181,7 @@ def bench(dtype: torch.dtype,
n: int,
label: str,
sub_label: str,
bench_kernels: Optional[List[str]] = None) -> Iterable[TMeasurement]:
bench_kernels: Optional[list[str]] = None) -> Iterable[TMeasurement]:
if dtype == torch.int8:
return bench_int8(dtype, m, k, n, label, sub_label, bench_kernels)
if dtype == torch.float8_e4m3fn:
......@@ -195,8 +196,8 @@ def print_timers(timers: Iterable[TMeasurement]):
def run(dtype: torch.dtype,
MKNs: Iterable[Tuple[int, int, int]],
bench_kernels: Optional[List[str]] = None) -> Iterable[TMeasurement]:
MKNs: Iterable[tuple[int, int, int]],
bench_kernels: Optional[list[str]] = None) -> Iterable[TMeasurement]:
results = []
for m, k, n in MKNs:
timers = bench(dtype,
......@@ -212,7 +213,7 @@ def run(dtype: torch.dtype,
def make_output(data: Iterable[TMeasurement],
MKNs: Iterable[Tuple[int, int, int]],
MKNs: Iterable[tuple[int, int, int]],
base_description: str,
timestamp=None):
print(f"== All Results {base_description} ====")
......@@ -248,7 +249,7 @@ def run_model_bench(args):
for i, model in enumerate(args.models):
print(f"[{i}] {model}")
def model_shapes(model_name: str, tp_size: int) -> List[Tuple[int, int]]:
def model_shapes(model_name: str, tp_size: int) -> list[tuple[int, int]]:
KNs = []
for KN, tp_split_dim in copy.deepcopy(WEIGHT_SHAPES[model_name]):
KN[tp_split_dim] = KN[tp_split_dim] // tp_size
......
benchmarks/fused_kernels/layernorm_rms_benchmarks.py
View file @ 469e903b
......@@ -2,9 +2,10 @@
import pickle as pkl
import time
from collections.abc import Iterable
from dataclasses import dataclass
from itertools import product
from typing import Callable, Iterable, List, Optional
from typing import Callable, Optional
import torch
import torch.utils.benchmark as TBenchmark
......@@ -29,7 +30,7 @@ class bench_params_t:
f'x DT {self.dtype}')
def get_bench_params() -> List[bench_params_t]:
def get_bench_params() -> list[bench_params_t]:
## Test Fixtures
NUM_TOKENS = [2**x for x in range(11)]
HIDDEN_SIZES = list(range(1024, 8129, 1024))
......
benchmarks/kernels/benchmark_layernorm.py
View file @ 469e903b
......@@ -40,7 +40,7 @@ def main(num_tokens: int,
end_time = time.perf_counter()
if profile:
torch.cuda.cudart().cudaProfilerStart()
torch.cuda.cudart().cudaProfilerStop()
return (end_time - start_time) / num_iters
# Warmup.
......
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