merge main

.github/workflows/publish.yml

+# This workflow will upload a Python Package to Release asset
+# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions
+
+name: Create Release
+
+on:
+  push:
+    tags:
+      - v*
+
+# Needed to create release and upload assets
+permissions:
+  contents: write
+
+jobs:
+  release:
+    # Retrieve tag and create release
+    name: Create Release
+    runs-on: ubuntu-latest
+    outputs:
+      upload_url: ${{ steps.create_release.outputs.upload_url }}
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+
+      - name: Extract branch info
+        shell: bash
+        run: |
+          echo "release_tag=${GITHUB_REF#refs/*/}" >> $GITHUB_ENV
+
+      - name: Create Release
+        id: create_release
+        uses: "actions/github-script@v6"
+        env:
+          RELEASE_TAG: ${{ env.release_tag }}
+        with:
+          github-token: "${{ secrets.GITHUB_TOKEN }}"
+          script: |
+            const script = require('.github/workflows/scripts/create_release.js')
+            await script(github, context, core)
+
+  wheel:
+    name: Build Wheel
+    runs-on: ${{ matrix.os }}
+    needs: release
+
+    strategy:
+      fail-fast: false
+      matrix:
+          os: ['ubuntu-20.04']
+          python-version: ['3.8', '3.9', '3.10', '3.11']
+          pytorch-version: ['2.1.2']  # Must be the most recent version that meets requirements.txt.
+          cuda-version: ['11.8', '12.1']
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+
+      - name: Set up Linux Env
+        if: ${{ runner.os == 'Linux' }}
+        run: |
+          bash -x .github/workflows/scripts/env.sh
+
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+            python-version: ${{ matrix.python-version }}
+
+      - name: Install CUDA ${{ matrix.cuda-version }}
+        run: |
+          bash -x .github/workflows/scripts/cuda-install.sh ${{ matrix.cuda-version }} ${{ matrix.os }}
+
+      - name: Install PyTorch ${{ matrix.pytorch-version }} with CUDA ${{ matrix.cuda-version }}
+        run: |
+          bash -x .github/workflows/scripts/pytorch-install.sh ${{ matrix.python-version }} ${{ matrix.pytorch-version }} ${{ matrix.cuda-version }}
+
+      - name: Build wheel
+        shell: bash
+        run: |
+          bash -x .github/workflows/scripts/build.sh ${{ matrix.python-version }} ${{ matrix.cuda-version }}
+          wheel_name=$(ls dist/*whl | xargs -n 1 basename)
+          asset_name=${wheel_name//"linux"/"manylinux1"}
+          echo "wheel_name=${wheel_name}" >> $GITHUB_ENV
+          echo "asset_name=${asset_name}" >> $GITHUB_ENV
+
+      - name: Upload Release Asset
+        uses: actions/upload-release-asset@v1
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        with:
+          upload_url: ${{ needs.release.outputs.upload_url }}
+          asset_path: ./dist/${{ env.wheel_name }}
+          asset_name: ${{ env.asset_name }}
+          asset_content_type: application/*
+
+      # (Danielkinz): This last step will publish the .whl to pypi. Warning: untested
+      # - name: Publish package
+      #   uses: pypa/gh-action-pypi-publish@release/v1.8
+      #   with:
+      #     repository-url: https://test.pypi.org/legacy/
+      #     password: ${{ secrets.PYPI_API_TOKEN }}
+      #     skip-existing: true

.github/workflows/ruff.yml

+name: ruff
+
+on:
+  # Trigger the workflow on push or pull request,
+  # but only for the main branch
+  push:
+    branches:
+      - main
+  pull_request:
+    branches:
+      - main
+
+jobs:
+  ruff:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.10"]
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install ruff==0.1.5
+    - name: Analysing the code with ruff
+      run: |
+        ruff vllm tests

.github/workflows/scripts/build.sh

+#!/bin/bash
+
+python_executable=python$1
+cuda_home=/usr/local/cuda-$2
+
+# Update paths
+PATH=${cuda_home}/bin:$PATH
+LD_LIBRARY_PATH=${cuda_home}/lib64:$LD_LIBRARY_PATH
+
+# Install requirements
+$python_executable -m pip install wheel packaging
+$python_executable -m pip install -r requirements.txt
+
+# Limit the number of parallel jobs to avoid OOM
+export MAX_JOBS=1
+
+# Build
+$python_executable setup.py bdist_wheel --dist-dir=dist

.github/workflows/scripts/create_release.js

+// Uses Github's API to create the release and wait for result.
+// We use a JS script since github CLI doesn't provide a way to wait for the release's creation and returns immediately.
+
+module.exports = async (github, context, core) => {
+	try {
+		const response = await github.rest.repos.createRelease({
+			draft: false,
+			generate_release_notes: true,
+			name: process.env.RELEASE_TAG,
+			owner: context.repo.owner,
+			prerelease: false,
+			repo: context.repo.repo,
+			tag_name: process.env.RELEASE_TAG,
+		});
+
+		core.setOutput('upload_url', response.data.upload_url);
+	} catch (error) {
+		core.setFailed(error.message);
+	}
+}
\ No newline at end of file

.github/workflows/scripts/cuda-install.sh

+#!/bin/bash
+
+# Replace '.' with '-' ex: 11.8 -> 11-8
+cuda_version=$(echo $1 | tr "." "-")
+# Removes '-' and '.' ex: ubuntu-20.04 -> ubuntu2004
+OS=$(echo $2 | tr -d ".\-")
+
+# Installs CUDA
+wget -nv https://developer.download.nvidia.com/compute/cuda/repos/${OS}/x86_64/cuda-keyring_1.1-1_all.deb
+sudo dpkg -i cuda-keyring_1.1-1_all.deb
+rm cuda-keyring_1.1-1_all.deb
+sudo apt -qq update
+sudo apt -y install cuda-${cuda_version} cuda-nvcc-${cuda_version} cuda-libraries-dev-${cuda_version}
+sudo apt clean
+
+# Test nvcc
+PATH=/usr/local/cuda-$1/bin:${PATH}
+nvcc --version
+
+# Log gcc, g++, c++ versions
+gcc --version
+g++ --version
+c++ --version

.github/workflows/scripts/env.sh

+#!/bin/bash
+
+# This file installs common linux environment tools
+
+export LANG C.UTF-8
+
+# python_version=$1
+
+sudo    apt-get update && \
+sudo    apt-get install -y --no-install-recommends \
+        software-properties-common \
+
+sudo    apt-get install -y --no-install-recommends \
+        build-essential \
+        apt-utils \
+        ca-certificates \
+        wget \
+        git \
+        vim \
+        libssl-dev \
+        curl \
+        unzip \
+        unrar \
+        cmake \
+        net-tools \
+        sudo \
+        autotools-dev \
+        rsync \
+        jq \
+        openssh-server \
+        tmux \
+        screen \
+        htop \
+        pdsh \
+        openssh-client \
+        lshw \
+        dmidecode \
+        util-linux \
+        automake \
+        autoconf \
+        libtool \
+        net-tools \
+        pciutils \
+        libpci-dev \
+        libaio-dev \
+        libcap2 \
+        libtinfo5 \
+        fakeroot \
+        devscripts \
+        debhelper \
+        nfs-common
+
+# Remove github bloat files to free up disk space
+sudo rm -rf "/usr/local/share/boost"
+sudo rm -rf "$AGENT_TOOLSDIRECTORY"
+sudo rm -rf "/usr/share/dotnet"

.github/workflows/scripts/pytorch-install.sh

+#!/bin/bash
+
+python_executable=python$1
+pytorch_version=$2
+cuda_version=$3
+
+# Install torch
+$python_executable -m pip install numpy pyyaml scipy ipython mkl mkl-include ninja cython typing pandas typing-extensions dataclasses setuptools && conda clean -ya
+$python_executable -m pip install torch==${pytorch_version}+cu${cuda_version//./} --extra-index-url https://download.pytorch.org/whl/cu${cuda_version//./}
+
+# Print version information
+$python_executable --version
+$python_executable -c "import torch; print('PyTorch:', torch.__version__)"
+$python_executable -c "import torch; print('CUDA:', torch.version.cuda)"
+$python_executable -c "from torch.utils import cpp_extension; print (cpp_extension.CUDA_HOME)"

.github/workflows/yapf.yml

+name: yapf
+
+on:
+  # Trigger the workflow on push or pull request,
+  # but only for the main branch
+  push:
+    branches:
+      - main
+  pull_request:
+    branches:
+      - main
+jobs:
+  yapf:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.10"]
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install yapf==0.32.0
+        pip install toml==0.10.2
+    - name: Running yapf
+      run: |
+        yapf --diff --recursive vllm tests

.gitignore

+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+.idea/
+
+# VSCode
+.vscode/
+
+# DS Store
+.DS_Store
+
+# Results
+*.csv
+
+# Python pickle files
+*.pkl
+
+# Sphinx documentation
+_build/
+
+# vim swap files
+*.swo
+*.swp
+
+# hip files generated by PyTorch
+*.hip
+*_hip*

.readthedocs.yaml

+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+version: 2
+
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.8"
+
+sphinx:
+   configuration: docs/source/conf.py
+
+# If using Sphinx, optionally build your docs in additional formats such as PDF
+formats:
+   - pdf
+
+# Optionally declare the Python requirements required to build your docs
+python:
+   install:
+   - requirements: docs/requirements-docs.txt

CONTRIBUTING.md

+# Contributing to vLLM
+
+Thank you for your interest in contributing to vLLM!
+Our community is open to everyone and welcomes all kinds of contributions, no matter how small or large.
+There are several ways you can contribute to the project:
+
+- Identify and report any issues or bugs.
+- Request or add a new model.
+- Suggest or implement new features.
+
+However, remember that contributions aren't just about code.
+We believe in the power of community support; thus, answering queries, assisting others, and enhancing the documentation are highly regarded and beneficial contributions.
+
+Finally, one of the most impactful ways to support us is by raising awareness about vLLM.
+Talk about it in your blog posts, highlighting how it's driving your incredible projects.
+Express your support on Twitter if vLLM aids you, or simply offer your appreciation by starring our repository.
+
+
+## Setup for development
+
+### Build from source
+
+```bash
+pip install -r requirements.txt
+pip install -e .  # This may take several minutes.
+```
+
+### Testing
+
+```bash
+pip install -r requirements-dev.txt
+
+# Static type checking
+mypy
+# Unit tests
+pytest tests/
+```
+**Note:** Currently, the repository does not pass the mypy tests.
+
+
+## Contributing Guidelines
+
+### Issue Reporting
+
+If you encounter a bug or have a feature request, please check our issues page first to see if someone else has already reported it.
+If not, please file a new issue, providing as much relevant information as possible.
+
+### Coding Style Guide
+
+In general, we adhere to [Google Python style guide](https://google.github.io/styleguide/pyguide.html) and [Google C++ style guide](https://google.github.io/styleguide/cppguide.html).
+
+We include a formatting script [`format.sh`](./format.sh) to format the code.
+
+### Pull Requests
+
+When submitting a pull request:
+
+1. Make sure your code has been rebased on top of the latest commit on the main branch.
+2. Ensure code is properly formatted by running [`format.sh`](./format.sh).
+3. Include a detailed description of the changes in the pull request.
+Explain why you made the changes you did.
+If your pull request fixes an open issue, please include a reference to it in the description.
+
+### Code Reviews
+
+All submissions, including submissions by project members, require a code review.
+To make the review process as smooth as possible, please:
+
+1. Keep your changes as concise as possible.
+If your pull request involves multiple unrelated changes, consider splitting it into separate pull requests.
+2. Respond to all comments within a reasonable time frame.
+If a comment isn't clear or you disagree with a suggestion, feel free to ask for clarification or discuss the suggestion.
+
+### Thank You
+
+Finally, thank you for taking the time to read these guidelines and for your interest in contributing to vLLM.
+Your contributions make vLLM a great tool for everyone!

Dockerfile

+FROM nvidia/cuda:12.1.0-devel-ubuntu22.04 AS dev
+
+RUN apt-get update -y \
+    && apt-get install -y python3-pip
+
+WORKDIR /workspace
+
+# install build and runtime dependencies
+COPY requirements.txt requirements.txt
+RUN --mount=type=cache,target=/root/.cache/pip \
+    pip install -r requirements.txt
+
+# install development dependencies
+COPY requirements-dev.txt requirements-dev.txt
+RUN --mount=type=cache,target=/root/.cache/pip \
+    pip install -r requirements-dev.txt
+
+# image to build pytorch extensions
+FROM dev AS build
+
+# install build dependencies
+COPY requirements-build.txt requirements-build.txt
+RUN --mount=type=cache,target=/root/.cache/pip \
+    pip install -r requirements-build.txt
+
+# copy input files
+COPY csrc csrc
+COPY setup.py setup.py
+COPY requirements.txt requirements.txt
+COPY pyproject.toml pyproject.toml
+COPY vllm/__init__.py vllm/__init__.py
+
+ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
+ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
+# max jobs used by Ninja to build extensions
+ARG max_jobs=2
+ENV MAX_JOBS=${max_jobs}
+# number of threads used by nvcc
+ARG nvcc_threads=8
+ENV NVCC_THREADS=$nvcc_threads
+
+RUN python3 setup.py build_ext --inplace
+
+# image to run unit testing suite
+FROM dev AS test
+
+# copy pytorch extensions separately to avoid having to rebuild
+# when python code changes
+COPY --from=build /workspace/vllm/*.so /workspace/vllm/
+COPY tests tests
+COPY vllm vllm
+
+ENTRYPOINT ["python3", "-m", "pytest", "tests"]
+
+# use CUDA base as CUDA runtime dependencies are already installed via pip
+FROM nvidia/cuda:12.1.0-base-ubuntu22.04 AS vllm-base
+
+# libnccl required for ray
+RUN apt-get update -y \
+    && apt-get install -y python3-pip
+
+WORKDIR /workspace
+COPY requirements.txt requirements.txt
+RUN --mount=type=cache,target=/root/.cache/pip \
+    pip install -r requirements.txt
+
+FROM vllm-base AS vllm
+COPY --from=build /workspace/vllm/*.so /workspace/vllm/
+COPY vllm vllm
+
+EXPOSE 8000
+ENTRYPOINT ["python3", "-m", "vllm.entrypoints.api_server"]
+
+# openai api server alternative
+FROM vllm-base AS vllm-openai
+# install additional dependencies for openai api server
+RUN --mount=type=cache,target=/root/.cache/pip \
+    pip install accelerate
+
+COPY --from=build /workspace/vllm/*.so /workspace/vllm/
+COPY vllm vllm
+
+ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
+

Dockerfile.rocm

+FROM rocm/pytorch:rocm5.7_ubuntu22.04_py3.10_pytorch_2.0.1
+
+# Install some basic utilities
+RUN apt-get update && apt-get install python3 python3-pip -y
+
+# Install some basic utilities
+RUN apt-get update && apt-get install -y \
+    curl \
+    ca-certificates \
+    sudo \
+    git \
+    bzip2 \
+    libx11-6 \
+    build-essential \
+    wget \
+    unzip \
+    nvidia-cuda-toolkit \
+    tmux \
+ && rm -rf /var/lib/apt/lists/*
+
+### Mount Point ###
+# When launching the container, mount the code directory to /app
+ARG APP_MOUNT=/app
+VOLUME [ ${APP_MOUNT} ]
+WORKDIR ${APP_MOUNT}
+
+RUN python3 -m pip install --upgrade pip
+RUN python3 -m pip install --no-cache-dir fastapi ninja tokenizers pandas
+
+ENV LLVM_SYMBOLIZER_PATH=/opt/rocm/llvm/bin/llvm-symbolizer
+ENV PATH=$PATH:/opt/rocm/bin:/libtorch/bin:
+ENV LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/rocm/lib/:/libtorch/lib:
+ENV CPLUS_INCLUDE_PATH=$CPLUS_INCLUDE_PATH:/libtorch/include:/libtorch/include/torch/csrc/api/include/:/opt/rocm/include/:
+
+# Install ROCm flash-attention
+RUN mkdir libs \
+    && cd libs \
+    && git clone https://github.com/ROCmSoftwarePlatform/flash-attention.git \
+    && cd flash-attention \
+    && git checkout 3d2b6f5 \
+    && git submodule update --init \
+    && export GPU_ARCHS=$(/opt/rocm/llvm/bin/amdgpu-offload-arch) \
+    && patch /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/utils/hipify/hipify_python.py hipify_patch.patch \
+    && python3 setup.py install \
+    && cd ..
+
+COPY ./ /app/vllm
+
+RUN python3 -m pip install --upgrade pip
+RUN pip install xformers==0.0.23 --no-deps
+
+RUN cd /app \
+    && cd vllm \
+    && pip install -U -r requirements-rocm.txt \
+    && bash patch_xformers.rocm.sh \
+    && python3 setup.py install \
+    && cd ..
+
+RUN python3 -m pip install --upgrade pip
+RUN python3 -m pip install --no-cache-dir ray[all]
+
+CMD ["/bin/bash"]

LICENSE

+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
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+   See the License for the specific language governing permissions and
+   limitations under the License.

MANIFEST.in

+include LICENSE
+include requirements.txt
+
+recursive-include csrc *

README.md

-# vllm
+<p align="center">
+  <picture>
+    <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/logos/vllm-logo-text-dark.png">
+    <img alt="vLLM" src="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/logos/vllm-logo-text-light.png" width=55%>
+  </picture>
+</p>
 
+<h3 align="center">
+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://discord.gg/jz7wjKhh6g"><b>Discord</b></a> |
+
+</p>
+
+---
+
+*Latest News* 🔥
+- [2023/12] Added ROCm support to vLLM.
+- [2023/10] We hosted [the first vLLM meetup](https://lu.ma/first-vllm-meetup) in SF! Please find the meetup slides [here](https://docs.google.com/presentation/d/1QL-XPFXiFpDBh86DbEegFXBXFXjix4v032GhShbKf3s/edit?usp=sharing).
+- [2023/09] We created our [Discord server](https://discord.gg/jz7wjKhh6g)! Join us to discuss vLLM and LLM serving! We will also post the latest announcements and updates there.
+- [2023/09] We released our [PagedAttention paper](https://arxiv.org/abs/2309.06180) on arXiv!
+- [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.
+- [2023/07] Added support for LLaMA-2! You can run and serve 7B/13B/70B LLaMA-2s on vLLM with a single command!
+- [2023/06] Serving vLLM On any Cloud with SkyPilot. Check out a 1-click [example](https://github.com/skypilot-org/skypilot/blob/master/llm/vllm) to start the vLLM demo, and the [blog post](https://blog.skypilot.co/serving-llm-24x-faster-on-the-cloud-with-vllm-and-skypilot/) for the story behind vLLM development on the clouds.
+- [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).
+
+---
+
+vLLM is a fast and easy-to-use library for LLM inference and serving.
+
+vLLM is fast with:
+
+- State-of-the-art serving throughput
+- Efficient management of attention key and value memory with **PagedAttention**
+- Continuous batching of incoming requests
+- Fast model execution with CUDA/HIP graph
+- Quantization: [GPTQ](https://arxiv.org/abs/2210.17323), [AWQ](https://arxiv.org/abs/2306.00978), [SqueezeLLM](https://arxiv.org/abs/2306.07629)
+- Optimized CUDA kernels
+
+vLLM is flexible and easy to use with:
+
+- Seamless integration with popular Hugging Face models
+- High-throughput serving with various decoding algorithms, including *parallel sampling*, *beam search*, and more
+- Tensor parallelism support for distributed inference
+- Streaming outputs
+- OpenAI-compatible API server
+- Support NVIDIA GPUs and AMD GPUs
+
+vLLM seamlessly supports many Hugging Face models, including the following architectures:
+
+- Aquila & Aquila2 (`BAAI/AquilaChat2-7B`, `BAAI/AquilaChat2-34B`, `BAAI/Aquila-7B`, `BAAI/AquilaChat-7B`, etc.)
+- Baichuan & Baichuan2 (`baichuan-inc/Baichuan2-13B-Chat`, `baichuan-inc/Baichuan-7B`, etc.)
+- BLOOM (`bigscience/bloom`, `bigscience/bloomz`, etc.)
+- ChatGLM (`THUDM/chatglm2-6b`, `THUDM/chatglm3-6b`, etc.)
+- DeciLM (`Deci/DeciLM-7B`, `Deci/DeciLM-7B-instruct`, etc.)
+- Falcon (`tiiuae/falcon-7b`, `tiiuae/falcon-40b`, `tiiuae/falcon-rw-7b`, etc.)
+- GPT-2 (`gpt2`, `gpt2-xl`, etc.)
+- GPT BigCode (`bigcode/starcoder`, `bigcode/gpt_bigcode-santacoder`, etc.)
+- GPT-J (`EleutherAI/gpt-j-6b`, `nomic-ai/gpt4all-j`, etc.)
+- GPT-NeoX (`EleutherAI/gpt-neox-20b`, `databricks/dolly-v2-12b`, `stabilityai/stablelm-tuned-alpha-7b`, etc.)
+- InternLM (`internlm/internlm-7b`, `internlm/internlm-chat-7b`, etc.)
+- LLaMA & LLaMA-2 (`meta-llama/Llama-2-70b-hf`, `lmsys/vicuna-13b-v1.3`, `young-geng/koala`, `openlm-research/open_llama_13b`, etc.)
+- Mistral (`mistralai/Mistral-7B-v0.1`, `mistralai/Mistral-7B-Instruct-v0.1`, etc.)
+- Mixtral (`mistralai/Mixtral-8x7B-v0.1`, `mistralai/Mixtral-8x7B-Instruct-v0.1`, etc.)
+- MPT (`mosaicml/mpt-7b`, `mosaicml/mpt-30b`, etc.)
+- OPT (`facebook/opt-66b`, `facebook/opt-iml-max-30b`, etc.)
+- Phi (`microsoft/phi-1_5`, `microsoft/phi-2`, etc.)
+- Qwen (`Qwen/Qwen-7B`, `Qwen/Qwen-7B-Chat`, etc.)
+- Yi (`01-ai/Yi-6B`, `01-ai/Yi-34B`, etc.)
+
+Install vLLM with pip or [from source](https://vllm.readthedocs.io/en/latest/getting_started/installation.html#build-from-source):
+
+```bash
+pip install vllm
+```
+
+## Getting Started
+
+Visit our [documentation](https://vllm.readthedocs.io/en/latest/) to get started.
+- [Installation](https://vllm.readthedocs.io/en/latest/getting_started/installation.html)
+- [Quickstart](https://vllm.readthedocs.io/en/latest/getting_started/quickstart.html)
+- [Supported Models](https://vllm.readthedocs.io/en/latest/models/supported_models.html)
+
+## Contributing
+
+We welcome and value any contributions and collaborations.
+Please check out [CONTRIBUTING.md](./CONTRIBUTING.md) for how to get involved.
+
+## Citation
+
+If you use vLLM for your research, please cite our [paper](https://arxiv.org/abs/2309.06180):
+```bibtex
+@inproceedings{kwon2023efficient,
+  title={Efficient Memory Management for Large Language Model Serving with PagedAttention},
+  author={Woosuk Kwon and Zhuohan Li and Siyuan Zhuang and Ying Sheng and Lianmin Zheng and Cody Hao Yu and Joseph E. Gonzalez and Hao Zhang and Ion Stoica},
+  booktitle={Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles},
+  year={2023}
+}
+```

benchmarks/README.md

+# Benchmarking vLLM
+
+## Downloading the ShareGPT dataset
+
+You can download the dataset by running:
+```bash
+wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
+```

benchmarks/benchmark_latency.py

+"""Benchmark the latency of processing a single batch of requests."""
+import argparse
+import time
+from pathlib import Path
+from typing import Optional
+
+import numpy as np
+import torch
+from tqdm import tqdm
+
+from vllm import LLM, SamplingParams
+
+
+def main(args: argparse.Namespace):
+    print(args)
+
+    # NOTE(woosuk): If the request cannot be processed in a single batch,
+    # the engine will automatically process the request in multiple batches.
+    llm = LLM(
+        model=args.model,
+        tokenizer=args.tokenizer,
+        quantization=args.quantization,
+        tensor_parallel_size=args.tensor_parallel_size,
+        trust_remote_code=args.trust_remote_code,
+        dtype=args.dtype,
+        enforce_eager=args.enforce_eager,
+    )
+
+    sampling_params = SamplingParams(
+        n=args.n,
+        temperature=0.0 if args.use_beam_search else 1.0,
+        top_p=1.0,
+        use_beam_search=args.use_beam_search,
+        ignore_eos=True,
+        max_tokens=args.output_len,
+    )
+    print(sampling_params)
+    dummy_prompt_token_ids = [[0] * args.input_len] * args.batch_size
+
+    def run_to_completion(profile_dir: Optional[str] = None):
+        if profile_dir:
+            with torch.profiler.profile(
+                    activities=[
+                        torch.profiler.ProfilerActivity.CPU,
+                        torch.profiler.ProfilerActivity.CUDA,
+                    ],
+                    on_trace_ready=torch.profiler.tensorboard_trace_handler(
+                        str(profile_dir))) as p:
+                llm.generate(prompt_token_ids=dummy_prompt_token_ids,
+                             sampling_params=sampling_params,
+                             use_tqdm=False)
+            print(p.key_averages())
+        else:
+            start_time = time.perf_counter()
+            llm.generate(prompt_token_ids=dummy_prompt_token_ids,
+                         sampling_params=sampling_params,
+                         use_tqdm=False)
+            end_time = time.perf_counter()
+            latency = end_time - start_time
+            return latency
+
+    print("Warming up...")
+    run_to_completion(profile_dir=None)
+
+    if args.profile:
+        profile_dir = args.profile_result_dir
+        if not profile_dir:
+            profile_dir = Path(".") / "vllm_benchmark_result" / f"latency_result_{time.time()}"
+        print(f"Profiling (results will be saved to '{profile_dir}')...")
+        run_to_completion(profile_dir=args.profile_result_dir)
+        return
+
+    # Benchmark.
+    latencies = []
+    for _ in tqdm(range(args.num_iters), desc="Profiling iterations"):
+        latencies.append(run_to_completion(profile_dir=None))
+    print(f'Avg latency: {np.mean(latencies)} seconds')
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(
+        description='Benchmark the latency of processing a single batch of '
+        'requests till completion.')
+    parser.add_argument('--model', type=str, default='facebook/opt-125m')
+    parser.add_argument('--tokenizer', type=str, default=None)
+    parser.add_argument('--quantization',
+                        '-q',
+                        choices=['awq', 'gptq', 'squeezellm', None],
+                        default=None)
+    parser.add_argument('--tensor-parallel-size', '-tp', type=int, default=1)
+    parser.add_argument('--input-len', type=int, default=32)
+    parser.add_argument('--output-len', type=int, default=128)
+    parser.add_argument('--batch-size', type=int, default=8)
+    parser.add_argument('--n',
+                        type=int,
+                        default=1,
+                        help='Number of generated sequences per prompt.')
+    parser.add_argument('--use-beam-search', action='store_true')
+    parser.add_argument('--num-iters',
+                        type=int,
+                        default=3,
+                        help='Number of iterations to run.')
+    parser.add_argument('--trust-remote-code',
+                        action='store_true',
+                        help='trust remote code from huggingface')
+    parser.add_argument(
+        '--dtype',
+        type=str,
+        default='auto',
+        choices=['auto', 'half', 'float16', 'bfloat16', 'float', 'float32'],
+        help='data type for model weights and activations. '
+        'The "auto" option will use FP16 precision '
+        'for FP32 and FP16 models, and BF16 precision '
+        'for BF16 models.')
+    parser.add_argument('--enforce-eager',
+                        action='store_true',
+                        help='enforce eager mode and disable CUDA graph')
+    parser.add_argument(
+        '--profile',
+        action='store_true',
+        help='profile the generation process of a single batch')
+    parser.add_argument(
+        '--profile-result-dir',
+        type=str,
+        default=None,
+        help=(
+            'path to save the pytorch profiler output. Can be visualized '
+            'with ui.perfetto.dev or Tensorboard.'
+        ))
+    args = parser.parse_args()
+    main(args)

benchmarks/benchmark_serving.py

+"""Benchmark online serving throughput.
+
+On the server side, run one of the following commands:
+    (vLLM backend)
+    python -m vllm.entrypoints.api_server \
+        --model <your_model> --swap-space 16 \
+        --disable-log-requests
+
+    (TGI backend)
+    ./launch_hf_server.sh <your_model>
+
+On the client side, run:
+    python benchmarks/benchmark_serving.py \
+        --backend <backend> \
+        --tokenizer <your_model> --dataset <target_dataset> \
+        --request-rate <request_rate>
+"""
+import argparse
+import asyncio
+import json
+import random
+import time
+from typing import AsyncGenerator, List, Tuple
+
+import aiohttp
+import numpy as np
+from transformers import PreTrainedTokenizerBase
+from vllm.transformers_utils.tokenizer import get_tokenizer
+
+# (prompt len, output len, latency)
+REQUEST_LATENCY: List[Tuple[int, int, float]] = []
+
+
+def sample_requests(
+    dataset_path: str,
+    num_requests: int,
+    tokenizer: PreTrainedTokenizerBase,
+) -> List[Tuple[str, int, int]]:
+    # Load the dataset.
+    with open(dataset_path) as f:
+        dataset = json.load(f)
+    # Filter out the conversations with less than 2 turns.
+    dataset = [
+        data for data in dataset
+        if len(data["conversations"]) >= 2
+    ]
+    # Only keep the first two turns of each conversation.
+    dataset = [
+        (data["conversations"][0]["value"], data["conversations"][1]["value"])
+        for data in dataset
+    ]
+
+    # Tokenize the prompts and completions.
+    prompts = [prompt for prompt, _ in dataset]
+    prompt_token_ids = tokenizer(prompts).input_ids
+    completions = [completion for _, completion in dataset]
+    completion_token_ids = tokenizer(completions).input_ids
+    tokenized_dataset = []
+    for i in range(len(dataset)):
+        output_len = len(completion_token_ids[i])
+        tokenized_dataset.append((prompts[i], prompt_token_ids[i], output_len))
+
+    # Filter out too long sequences.
+    filtered_dataset: List[Tuple[str, int, int]] = []
+    for prompt, prompt_token_ids, output_len in tokenized_dataset:
+        prompt_len = len(prompt_token_ids)
+        if prompt_len < 4 or output_len < 4:
+            # Prune too short sequences.
+            # This is because TGI causes errors when the input or output length
+            # is too short.
+            continue
+        if prompt_len > 1024 or prompt_len + output_len > 2048:
+            # Prune too long sequences.
+            continue
+        filtered_dataset.append((prompt, prompt_len, output_len))
+
+    # Sample the requests.
+    sampled_requests = random.sample(filtered_dataset, num_requests)
+    return sampled_requests
+
+
+async def get_request(
+    input_requests: List[Tuple[str, int, int]],
+    request_rate: float,
+) -> AsyncGenerator[Tuple[str, int, int], None]:
+    input_requests = iter(input_requests)
+    for request in input_requests:
+        yield request
+
+        if request_rate == float("inf"):
+            # If the request rate is infinity, then we don't need to wait.
+            continue
+        # Sample the request interval from the exponential distribution.
+        interval = np.random.exponential(1.0 / request_rate)
+        # The next request will be sent after the interval.
+        await asyncio.sleep(interval)
+
+
+async def send_request(
+    backend: str,
+    api_url: str,
+    prompt: str,
+    prompt_len: int,
+    output_len: int,
+    best_of: int,
+    use_beam_search: bool,
+) -> None:
+    request_start_time = time.perf_counter()
+
+    headers = {"User-Agent": "Benchmark Client"}
+    if backend == "vllm":
+        pload = {
+            "prompt": prompt,
+            "n": 1,
+            "best_of": best_of,
+            "use_beam_search": use_beam_search,
+            "temperature": 0.0 if use_beam_search else 1.0,
+            "top_p": 1.0,
+            "max_tokens": output_len,
+            "ignore_eos": True,
+            "stream": False,
+        }
+    elif backend == "tgi":
+        assert not use_beam_search
+        params = {
+            "best_of": best_of,
+            "max_new_tokens": output_len,
+            "do_sample": True,
+        }
+        pload = {
+            "inputs": prompt,
+            "parameters": params,
+        }
+    else:
+        raise ValueError(f"Unknown backend: {backend}")
+
+    timeout = aiohttp.ClientTimeout(total=3 * 3600)
+    async with aiohttp.ClientSession(timeout=timeout) as session:
+        while True:
+            async with session.post(api_url, headers=headers, json=pload) as response:
+                chunks = []
+                async for chunk, _ in response.content.iter_chunks():
+                    chunks.append(chunk)
+            output = b"".join(chunks).decode("utf-8")
+            output = json.loads(output)
+
+            # Re-send the request if it failed.
+            if "error" not in output:
+                break
+
+    request_end_time = time.perf_counter()
+    request_latency = request_end_time - request_start_time
+    REQUEST_LATENCY.append((prompt_len, output_len, request_latency))
+
+
+async def benchmark(
+    backend: str,
+    api_url: str,
+    input_requests: List[Tuple[str, int, int]],
+    best_of: int,
+    use_beam_search: bool,
+    request_rate: float,
+) -> None:
+    tasks: List[asyncio.Task] = []
+    async for request in get_request(input_requests, request_rate):
+        prompt, prompt_len, output_len = request
+        task = asyncio.create_task(send_request(backend, api_url, prompt,
+                                                prompt_len, output_len,
+                                                best_of, use_beam_search))
+        tasks.append(task)
+    await asyncio.gather(*tasks)
+
+
+def main(args: argparse.Namespace):
+    print(args)
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+
+    api_url = f"http://{args.host}:{args.port}/generate"
+    tokenizer = get_tokenizer(args.tokenizer, trust_remote_code=args.trust_remote_code)
+    input_requests = sample_requests(args.dataset, args.num_prompts, tokenizer)
+
+    benchmark_start_time = time.perf_counter()
+    asyncio.run(benchmark(args.backend, api_url, input_requests, args.best_of,
+                          args.use_beam_search, args.request_rate))
+    benchmark_end_time = time.perf_counter()
+    benchmark_time = benchmark_end_time - benchmark_start_time
+    print(f"Total time: {benchmark_time:.2f} s")
+    print(f"Throughput: {args.num_prompts / benchmark_time:.2f} requests/s")
+
+    # Compute the latency statistics.
+    avg_latency = np.mean([latency for _, _, latency in REQUEST_LATENCY])
+    print(f"Average latency: {avg_latency:.2f} s")
+    avg_per_token_latency = np.mean([
+        latency / (prompt_len + output_len)
+        for prompt_len, output_len, latency in REQUEST_LATENCY
+    ])
+    print(f"Average latency per token: {avg_per_token_latency:.2f} s")
+    avg_per_output_token_latency = np.mean([
+        latency / output_len
+        for _, output_len, latency in REQUEST_LATENCY
+    ])
+    print("Average latency per output token: "
+          f"{avg_per_output_token_latency:.2f} s")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Benchmark the online serving throughput.")
+    parser.add_argument("--backend", type=str, default="vllm",
+                        choices=["vllm", "tgi"])
+    parser.add_argument("--host", type=str, default="localhost")
+    parser.add_argument("--port", type=int, default=8000)
+    parser.add_argument("--dataset", type=str, required=True,
+                        help="Path to the dataset.")
+    parser.add_argument("--tokenizer", type=str, required=True,
+                        help="Name or path of the tokenizer.")
+    parser.add_argument("--best-of", type=int, default=1,
+                        help="Generates `best_of` sequences per prompt and "
+                             "returns the best one.")
+    parser.add_argument("--use-beam-search", action="store_true")
+    parser.add_argument("--num-prompts", type=int, default=1000,
+                        help="Number of prompts to process.")
+    parser.add_argument("--request-rate", type=float, default=float("inf"),
+                        help="Number of requests per second. If this is inf, "
+                             "then all the requests are sent at time 0. "
+                             "Otherwise, we use Poisson process to synthesize "
+                             "the request arrival times.")
+    parser.add_argument("--seed", type=int, default=0)
+    parser.add_argument('--trust-remote-code', action='store_true',
+                        help='trust remote code from huggingface')
+    args = parser.parse_args()
+    main(args)

benchmarks/benchmark_throughput.py

+"""Benchmark offline inference throughput."""
+import argparse
+import json
+import random
+import time
+from typing import List, Optional, Tuple
+
+import torch
+from transformers import (AutoModelForCausalLM, AutoTokenizer,
+                          PreTrainedTokenizerBase)
+from tqdm import tqdm
+
+
+def sample_requests(
+    dataset_path: str,
+    num_requests: int,
+    tokenizer: PreTrainedTokenizerBase,
+    fixed_output_len: Optional[int],
+) -> List[Tuple[str, int, int]]:
+    if fixed_output_len is not None and fixed_output_len < 4:
+        raise ValueError("output_len too small")
+
+    # Load the dataset.
+    with open(dataset_path) as f:
+        dataset = json.load(f)
+    # Filter out the conversations with less than 2 turns.
+    dataset = [data for data in dataset if len(data["conversations"]) >= 2]
+    # Only keep the first two turns of each conversation.
+    dataset = [(data["conversations"][0]["value"],
+                data["conversations"][1]["value"]) for data in dataset]
+
+    # Tokenize the prompts and completions.
+    prompts = [prompt for prompt, _ in dataset]
+    prompt_token_ids = tokenizer(prompts).input_ids
+    completions = [completion for _, completion in dataset]
+    completion_token_ids = tokenizer(completions).input_ids
+    tokenized_dataset = []
+    for i in range(len(dataset)):
+        output_len = len(completion_token_ids[i])
+        if fixed_output_len is not None:
+            output_len = fixed_output_len
+        tokenized_dataset.append((prompts[i], prompt_token_ids[i], output_len))
+
+    # Filter out too long sequences.
+    filtered_dataset: List[Tuple[str, int, int]] = []
+    for prompt, prompt_token_ids, output_len in tokenized_dataset:
+        prompt_len = len(prompt_token_ids)
+        if prompt_len < 4 or output_len < 4:
+            # Prune too short sequences.
+            continue
+        if prompt_len > 1024 or prompt_len + output_len > 2048:
+            # Prune too long sequences.
+            continue
+        filtered_dataset.append((prompt, prompt_len, output_len))
+
+    # Sample the requests.
+    sampled_requests = random.sample(filtered_dataset, num_requests)
+    return sampled_requests
+
+
+def run_vllm(
+    requests: List[Tuple[str, int, int]],
+    model: str,
+    tokenizer: str,
+    quantization: Optional[str],
+    tensor_parallel_size: int,
+    seed: int,
+    n: int,
+    use_beam_search: bool,
+    trust_remote_code: bool,
+    dtype: str,
+    max_model_len: Optional[int],
+    enforce_eager: bool,
+) -> float:
+    from vllm import LLM, SamplingParams
+    llm = LLM(
+        model=model,
+        tokenizer=tokenizer,
+        quantization=quantization,
+        tensor_parallel_size=tensor_parallel_size,
+        seed=seed,
+        trust_remote_code=trust_remote_code,
+        dtype=dtype,
+        max_model_len=max_model_len,
+        enforce_eager=enforce_eager,
+    )
+
+    # Add the requests to the engine.
+    for prompt, _, output_len in requests:
+        sampling_params = SamplingParams(
+            n=n,
+            temperature=0.0 if use_beam_search else 1.0,
+            top_p=1.0,
+            use_beam_search=use_beam_search,
+            ignore_eos=True,
+            max_tokens=output_len,
+        )
+        # FIXME(woosuk): Do not use internal method.
+        llm._add_request(
+            prompt=prompt,
+            prompt_token_ids=None,
+            sampling_params=sampling_params,
+        )
+
+    start = time.perf_counter()
+    # FIXME(woosuk): Do not use internal method.
+    llm._run_engine(use_tqdm=True)
+    end = time.perf_counter()
+    return end - start
+
+
+def run_hf(
+    requests: List[Tuple[str, int, int]],
+    model: str,
+    tokenizer: PreTrainedTokenizerBase,
+    n: int,
+    use_beam_search: bool,
+    max_batch_size: int,
+    trust_remote_code: bool,
+) -> float:
+    assert not use_beam_search
+    llm = AutoModelForCausalLM.from_pretrained(
+        model, torch_dtype=torch.float16, trust_remote_code=trust_remote_code)
+    if llm.config.model_type == "llama":
+        # To enable padding in the HF backend.
+        tokenizer.pad_token = tokenizer.eos_token
+    llm = llm.cuda()
+
+    pbar = tqdm(total=len(requests))
+    start = time.perf_counter()
+    batch: List[str] = []
+    max_prompt_len = 0
+    max_output_len = 0
+    for i in range(len(requests)):
+        prompt, prompt_len, output_len = requests[i]
+        # Add the prompt to the batch.
+        batch.append(prompt)
+        max_prompt_len = max(max_prompt_len, prompt_len)
+        max_output_len = max(max_output_len, output_len)
+        if len(batch) < max_batch_size and i != len(requests) - 1:
+            # Check if we can add more requests to the batch.
+            _, next_prompt_len, next_output_len = requests[i + 1]
+            if (max(max_prompt_len, next_prompt_len) +
+                    max(max_output_len, next_output_len)) <= 2048:
+                # We can add more requests to the batch.
+                continue
+
+        # Generate the sequences.
+        input_ids = tokenizer(batch, return_tensors="pt",
+                              padding=True).input_ids
+        llm_outputs = llm.generate(
+            input_ids=input_ids.cuda(),
+            do_sample=not use_beam_search,
+            num_return_sequences=n,
+            temperature=1.0,
+            top_p=1.0,
+            use_cache=True,
+            max_new_tokens=max_output_len,
+        )
+        # Include the decoding time.
+        tokenizer.batch_decode(llm_outputs, skip_special_tokens=True)
+        pbar.update(len(batch))
+
+        # Clear the batch.
+        batch = []
+        max_prompt_len = 0
+        max_output_len = 0
+    end = time.perf_counter()
+    return end - start
+
+
+def run_mii(
+    requests: List[Tuple[str, int, int]],
+    model: str,
+    tensor_parallel_size: int,
+    output_len: int,
+) -> float:
+    from mii import pipeline
+    llm = pipeline(model, tensor_parallel=tensor_parallel_size)
+    prompts = [prompt for prompt, _, _ in requests]
+
+    start = time.perf_counter()
+    llm(prompts, max_new_tokens=output_len)
+    end = time.perf_counter()
+    return end - start
+
+
+def main(args: argparse.Namespace):
+    print(args)
+    random.seed(args.seed)
+
+    # Sample the requests.
+    tokenizer = AutoTokenizer.from_pretrained(
+        args.tokenizer, trust_remote_code=args.trust_remote_code)
+    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)]
+    else:
+        requests = sample_requests(args.dataset, args.num_prompts, tokenizer,
+                                   args.output_len)
+
+    if args.backend == "vllm":
+        elapsed_time = run_vllm(requests, args.model, args.tokenizer,
+                                args.quantization, args.tensor_parallel_size,
+                                args.seed, args.n, args.use_beam_search,
+                                args.trust_remote_code, args.dtype,
+                                args.max_model_len, args.enforce_eager)
+    elif args.backend == "hf":
+        assert args.tensor_parallel_size == 1
+        elapsed_time = run_hf(requests, args.model, tokenizer, args.n,
+                              args.use_beam_search, args.hf_max_batch_size,
+                              args.trust_remote_code)
+    elif args.backend == "mii":
+        elapsed_time = run_mii(requests, args.model, args.tensor_parallel_size,
+                               args.output_len)
+    else:
+        raise ValueError(f"Unknown backend: {args.backend}")
+    total_num_tokens = sum(prompt_len + output_len
+                           for _, prompt_len, output_len in requests)
+    print(f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
+          f"{total_num_tokens / elapsed_time:.2f} tokens/s")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Benchmark the throughput.")
+    parser.add_argument("--backend",
+                        type=str,
+                        choices=["vllm", "hf", "mii"],
+                        default="vllm")
+    parser.add_argument("--dataset",
+                        type=str,
+                        default=None,
+                        help="Path to the dataset.")
+    parser.add_argument("--input-len",
+                        type=int,
+                        default=None,
+                        help="Input prompt length for each request")
+    parser.add_argument("--output-len",
+                        type=int,
+                        default=None,
+                        help="Output length for each request. Overrides the "
+                        "output length from the dataset.")
+    parser.add_argument("--model", type=str, default="facebook/opt-125m")
+    parser.add_argument("--tokenizer", type=str, default=None)
+    parser.add_argument('--quantization',
+                        '-q',
+                        choices=['awq', 'gptq', 'squeezellm', None],
+                        default=None)
+    parser.add_argument("--tensor-parallel-size", "-tp", type=int, default=1)
+    parser.add_argument("--n",
+                        type=int,
+                        default=1,
+                        help="Number of generated sequences per prompt.")
+    parser.add_argument("--use-beam-search", action="store_true")
+    parser.add_argument("--num-prompts",
+                        type=int,
+                        default=1000,
+                        help="Number of prompts to process.")
+    parser.add_argument("--seed", type=int, default=0)
+    parser.add_argument("--hf-max-batch-size",
+                        type=int,
+                        default=None,
+                        help="Maximum batch size for HF backend.")
+    parser.add_argument('--trust-remote-code',
+                        action='store_true',
+                        help='trust remote code from huggingface')
+    parser.add_argument(
+        '--max-model-len',
+        type=int,
+        default=None,
+        help='Maximum length of a sequence (including prompt and output). '
+        'If None, will be derived from the model.')
+    parser.add_argument(
+        '--dtype',
+        type=str,
+        default='auto',
+        choices=['auto', 'half', 'float16', 'bfloat16', 'float', 'float32'],
+        help='data type for model weights and activations. '
+        'The "auto" option will use FP16 precision '
+        'for FP32 and FP16 models, and BF16 precision '
+        'for BF16 models.')
+    parser.add_argument("--enforce-eager",
+                        action="store_true",
+                        help="enforce eager execution")
+    args = parser.parse_args()
+    if args.tokenizer is None:
+        args.tokenizer = args.model
+    if args.dataset is None:
+        assert args.input_len is not None
+        assert args.output_len is not None
+    else:
+        assert args.input_len is None
+
+    if args.backend == "vllm":
+        if args.hf_max_batch_size is not None:
+            raise ValueError("HF max batch size is only for HF backend.")
+    elif args.backend == "hf":
+        if args.hf_max_batch_size is None:
+            raise ValueError("HF max batch size is required for HF backend.")
+        if args.quantization is not None:
+            raise ValueError("Quantization is only for vLLM backend.")
+    elif args.backend == "mii":
+        if args.dtype != "auto":
+            raise ValueError("dtype must be auto for MII backend.")
+        if args.n != 1:
+            raise ValueError("n must be 1 for MII backend.")
+        if args.use_beam_search:
+            raise ValueError("Beam search is not supported for MII backend.")
+        if args.quantization is not None:
+            raise ValueError("Quantization is only for vLLM backend.")
+        if args.hf_max_batch_size is not None:
+            raise ValueError("HF max batch size is only for HF backend.")
+        if args.tokenizer != args.model:
+            raise ValueError("Tokenizer must be the same as the model for MII "
+                             "backend.")
+    main(args)