Merge tag 'v0.6.3.post1' into v0.6.3.post1-dev

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:
+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.
+- Request or add support for a new model.
 - Suggest or implement new features.
+- Improve documentation or contribute a how-to guide. 
 
-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.
+We also believe in the power of community support; thus, answering queries, offering PR reviews, and assisting others are also 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.
+Finally, one of the most impactful ways to support us is by raising awareness about vLLM. Talk about it in your blog posts and highlight how it's driving your incredible projects. Express your support on social media if you're using vLLM, or simply offer your appreciation by starring our repository!
 
 
-## Setup for development
+## Developing
 
-### Build from source
+Depending on the kind of development you'd like to do (e.g. Python, CUDA), you can choose to build vLLM with or without compilation. Check out the [building from source](https://docs.vllm.ai/en/latest/getting_started/installation.html#build-from-source) documentation for details.
 
-```bash
-pip install -e .  # This may take several minutes.
-```
 
-### Testing
+## Testing
 
 ```bash
 pip install -r requirements-dev.txt
@@ -36,15 +29,16 @@ mypy
 # Unit tests
 pytest tests/
 ```
-**Note:** Currently, the repository does not pass the mypy tests.
+**Note:** Currently, the repository does not pass the ``mypy`` tests.
 
+## Contribution Guidelines
 
-## Contributing Guidelines
+### Issues
 
-### Issue Reporting
+If you encounter a bug or have a feature request, please [search existing issues](https://github.com/vllm-project/vllm/issues?q=is%3Aissue) first to see if it has already been reported. If not, please [file a new issue](https://github.com/vllm-project/vllm/issues/new/choose), providing as much relevant information as possible.
 
-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.
+> [!IMPORTANT]
+> If you discover a security vulnerability, please follow the instructions [here](/SECURITY.md#reporting-a-vulnerability).
 
 ### Pull Requests & Code Reviews
 
@@ -53,4 +47,4 @@ Please check the PR checklist in the [PR template](.github/PULL_REQUEST_TEMPLATE
 ### 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!
+All of your contributions help make vLLM a great tool and community for everyone!

Dockerfile

@@ -27,6 +27,14 @@ RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
     && curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
     && python3 --version && python3 -m pip --version
 
+# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
+# as it was causing spam when compiling the CUTLASS kernels
+RUN apt-get install -y gcc-10 g++-10
+RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
+RUN <<EOF
+gcc --version
+EOF
+
 # Workaround for https://github.com/openai/triton/issues/2507 and
 # https://github.com/pytorch/pytorch/issues/107960 -- hopefully
 # this won't be needed for future versions of this docker image
@@ -62,15 +70,10 @@ COPY requirements-build.txt requirements-build.txt
 RUN --mount=type=cache,target=/root/.cache/pip \
     python3 -m pip install -r requirements-build.txt
 
-# files and directories related to build wheels
-COPY csrc csrc
-COPY setup.py setup.py
-COPY cmake cmake
-COPY CMakeLists.txt CMakeLists.txt
-COPY requirements-common.txt requirements-common.txt
-COPY requirements-cuda.txt requirements-cuda.txt
-COPY pyproject.toml pyproject.toml
-COPY vllm vllm
+COPY . .
+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
 
 # max jobs used by Ninja to build extensions
 ARG max_jobs=2
@@ -135,7 +138,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
 #################### DEV IMAGE ####################
 #################### vLLM installation IMAGE ####################
 # image with vLLM installed
-FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu20.04 AS vllm-base
+FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu22.04 AS vllm-base
 ARG CUDA_VERSION=12.4.1
 ARG PYTHON_VERSION=3.12
 WORKDIR /vllm-workspace
@@ -173,6 +176,7 @@ RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist
 RUN --mount=type=cache,target=/root/.cache/pip \
     . /etc/environment && \
     python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.6/flashinfer-0.1.6+cu121torch2.4-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl
+COPY examples examples
 #################### vLLM installation IMAGE ####################
 
 

Dockerfile.cpu

@@ -22,29 +22,17 @@ ENV LD_PRELOAD="/usr/lib/x86_64-linux-gnu/libtcmalloc_minimal.so.4:/usr/local/li
 
 RUN echo 'ulimit -c 0' >> ~/.bashrc
 
-RUN pip install https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_dev/cpu/intel_extension_for_pytorch-2.4.0%2Bgitfbaa4bc-cp310-cp310-linux_x86_64.whl
+RUN pip install intel_extension_for_pytorch==2.4.0
 
 WORKDIR /workspace
 
-ENV PIP_EXTRA_INDEX_URL=https://download.pytorch.org/whl/cpu
+ARG PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu"
+ENV PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}
 RUN --mount=type=cache,target=/root/.cache/pip \
     --mount=type=bind,src=requirements-build.txt,target=requirements-build.txt \
     pip install --upgrade pip && \
     pip install -r requirements-build.txt
 
-# install oneDNN
-RUN git clone -b rls-v3.5 https://github.com/oneapi-src/oneDNN.git
-
-RUN --mount=type=cache,target=/root/.cache/ccache \
-    cmake -B ./oneDNN/build -S ./oneDNN -G Ninja -DONEDNN_LIBRARY_TYPE=STATIC \ 
-    -DONEDNN_BUILD_DOC=OFF \ 
-    -DONEDNN_BUILD_EXAMPLES=OFF \ 
-    -DONEDNN_BUILD_TESTS=OFF \ 
-    -DONEDNN_BUILD_GRAPH=OFF \ 
-    -DONEDNN_ENABLE_WORKLOAD=INFERENCE \ 
-    -DONEDNN_ENABLE_PRIMITIVE=MATMUL && \
-    cmake --build ./oneDNN/build --target install --config Release
-
 FROM cpu-test-1 AS build
 
 WORKDIR /workspace/vllm
@@ -54,7 +42,10 @@ RUN --mount=type=cache,target=/root/.cache/pip \
     --mount=type=bind,src=requirements-cpu.txt,target=requirements-cpu.txt \
     pip install -v -r requirements-cpu.txt
 
-COPY ./ ./
+COPY . .
+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
 
 # Support for building with non-AVX512 vLLM: docker build --build-arg VLLM_CPU_DISABLE_AVX512="true" ...
 ARG VLLM_CPU_DISABLE_AVX512

Dockerfile.neuron

@@ -17,7 +17,7 @@ RUN apt-get update && \
 # When launching the container, mount the code directory to /app
 ARG APP_MOUNT=/app
 VOLUME [ ${APP_MOUNT} ]
-WORKDIR ${APP_MOUNT}
+WORKDIR ${APP_MOUNT}/vllm
 
 RUN python3 -m pip install --upgrade pip
 RUN python3 -m pip install --no-cache-dir fastapi ninja tokenizers pandas
@@ -25,17 +25,17 @@ RUN python3 -m pip install sentencepiece transformers==4.36.2 -U
 RUN python3 -m pip install transformers-neuronx --extra-index-url=https://pip.repos.neuron.amazonaws.com -U
 RUN python3 -m pip install --pre neuronx-cc==2.15.* --extra-index-url=https://pip.repos.neuron.amazonaws.com -U
 
-COPY . /app/vllm
+COPY . .
+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
 
-RUN cd /app/vllm \
-    && python3 -m pip install -U \
+RUN python3 -m pip install -U \
         cmake>=3.26 ninja packaging setuptools-scm>=8 wheel jinja2 \
         -r requirements-neuron.txt
 
 ENV VLLM_TARGET_DEVICE neuron
 RUN --mount=type=bind,source=.git,target=.git \
-    cd /app/vllm \
-    && pip install --no-build-isolation -v -e . \
-    && cd ..
+    pip install --no-build-isolation -v -e . \
 
 CMD ["/bin/bash"]

Dockerfile.openvino

@@ -9,16 +9,10 @@ RUN apt-get update -y && \
         ffmpeg libsm6 libxext6 libgl1
 WORKDIR /workspace
 
-# copy requirements
-COPY requirements-build.txt /workspace/vllm/
-COPY requirements-common.txt /workspace/vllm/
-COPY requirements-openvino.txt /workspace/vllm/
-
-COPY vllm/ /workspace/vllm/vllm
-COPY csrc/core /workspace/vllm/csrc/core
-COPY cmake/utils.cmake /workspace/vllm/cmake/
-COPY CMakeLists.txt /workspace/vllm/
-COPY setup.py /workspace/vllm/
+COPY . .
+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
 
 # install build requirements
 RUN PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu" python3 -m pip install -r /workspace/vllm/requirements-build.txt

Dockerfile.ppc64le

@@ -14,6 +14,9 @@ RUN micromamba install -y -n base -c https://ftp.osuosl.org/pub/open-ce/1.11.0-p
 COPY ./ /workspace/vllm
 
 WORKDIR /workspace/vllm
+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
 
 # These packages will be in rocketce eventually
 RUN --mount=type=cache,target=/root/.cache/pip  \

Dockerfile.rocm

@@ -117,6 +117,9 @@ RUN --mount=type=cache,target=${CCACHE_DIR} \
 FROM base AS final
 # Import the vLLM development directory from the build context
 COPY . .
+ARG GIT_REPO_CHECK=0
+RUN --mount=type=bind,source=.git,target=.git \
+    if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
 
 # Package upgrades for useful functionality or to avoid dependency issues
 RUN --mount=type=cache,target=/root/.cache/pip \

Dockerfile.tpu

@@ -2,7 +2,7 @@ ARG NIGHTLY_DATE="20240828"
 ARG BASE_IMAGE="us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.10_tpuvm_$NIGHTLY_DATE"
 
 FROM $BASE_IMAGE
-WORKDIR /workspace
+WORKDIR /workspace/vllm
 
 # Install some basic utilities
 RUN apt-get update && apt-get install -y \
@@ -16,14 +16,17 @@ RUN --mount=type=cache,target=/root/.cache/pip \
     python3 -m pip install torch_xla[pallas] -f https://storage.googleapis.com/jax-releases/jax_nightly_releases.html -f https://storage.googleapis.com/jax-releases/jaxlib_nightly_releases.html
 
 # Build vLLM.
-COPY . /workspace/vllm
+COPY . .
+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
+
 ENV VLLM_TARGET_DEVICE="tpu"
 RUN --mount=type=cache,target=/root/.cache/pip \
     --mount=type=bind,source=.git,target=.git \
-     cd /workspace/vllm && \
     python3 -m pip install \
         cmake>=3.26 ninja packaging setuptools-scm>=8 wheel jinja2 \
         -r requirements-tpu.txt
-RUN cd /workspace/vllm && python3 setup.py develop
+RUN python3 setup.py develop
 
 CMD ["/bin/bash"]

Dockerfile.xpu

-FROM intel/oneapi-basekit:2024.2.1-0-devel-ubuntu22.04
+FROM intel/oneapi-basekit:2024.2.1-0-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 && \
@@ -7,20 +7,52 @@ RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRO
     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 apt-get update  -y && \
-    apt-get install -y curl libicu70 lsb-release git wget vim numactl python3 python3-pip ffmpeg libsm6 libxext6 libgl1
-
-COPY ./ /workspace/vllm
+RUN apt-get update -y && \
+    apt-get install -y --no-install-recommends --fix-missing \
+    curl \
+    ffmpeg \
+    git \
+    libsndfile1 \
+    libsm6 \
+    libxext6 \
+    libgl1 \
+    lsb-release \
+    numactl \
+    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
 
 RUN --mount=type=cache,target=/root/.cache/pip \
-    pip install -v --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ \
-        cmake>=3.26 ninja packaging setuptools-scm>=8 wheel jinja2 \
-        -r requirements-xpu.txt
+    pip install --no-cache-dir \
+    --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ \
+    -r requirements-xpu.txt
+
+COPY . .
+ARG GIT_REPO_CHECK
+RUN --mount=type=bind,source=.git,target=.git \
+    if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh; fi
+
+ENV VLLM_TARGET_DEVICE=xpu
 
 RUN --mount=type=cache,target=/root/.cache/pip \
     --mount=type=bind,source=.git,target=.git \
-    VLLM_TARGET_DEVICE=xpu python3 setup.py install
+    python3 setup.py install
 
 CMD ["/bin/bash"]
+
+FROM vllm-base AS vllm-openai
+
+# install additional dependencies for openai api server
+RUN --mount=type=cache,target=/root/.cache/pip \
+    pip install accelerate hf_transfer 'modelscope!=1.15.0'
+
+ENV VLLM_USAGE_SOURCE production-docker-image \
+    TRITON_XPU_PROFILE 1
+
+ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]

README.md

@@ -13,7 +13,7 @@ vLLM是一个快速且易于使用的LLM推理和服务库，使用PageAttention
 | LlamaForCausalLM      | LLaMA、LLaMA-2、LLaMA-3、Codellama、deepseek、Yi | Yes | Yes |
 | QWenLMHeadModel       | QWen、Qwen-VL                                   | Yes | Yes |
 | Qwen2ForCausalLM      | QWen1.5、CodeQwen1.5、QWen2                     | Yes | Yes |
-| ChatGLMModel          | chatglm2、chatglm3                              | Yes | Yes |
+| ChatGLMModel          | chatglm2、chatglm3、chatglm4、glm-4v-9b         | Yes | Yes |
 | BaiChuanForCausalLM   | Baichuan、Baichuan2                             | Yes | Yes |
 | BloomForCausalLM      | BLOOM                                           | Yes | Yes |
 | InternLMForCausalLM   | InternLM                                        | Yes | Yes |
@@ -29,6 +29,7 @@ vLLM支持
 + Python 3.9.
 + Python 3.10.
 + Python 3.11.
++ Python 3.12.
 
 ### 使用源码编译方式安装
 
@@ -75,7 +76,7 @@ VLLM_INSTALL_PUNICA_KERNELS=1 python3 setup.py install
 + 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0.6.2；
+- 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0.6.3.post1；
 
 ## Known Issue
 - 无

README_ORIGIN.md

@@ -10,22 +10,13 @@ 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> | <a href="https://x.com/vllm_project"><b>Twitter/X</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://discord.gg/jz7wjKhh6g"><b>Discord</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> |
 </p>
 
 
----
-
-**vLLM, AMD, Anyscale Meet & Greet at [Ray Summit 2024](http://raysummit.anyscale.com) (Monday, Sept 30th, 5-7pm PT) at Marriott Marquis San Francisco**
-
-We are excited to announce our special vLLM event in collaboration with AMD and Anyscale.
-Join us to learn more about recent advancements of vLLM on MI300X.
-Register [here](https://lu.ma/db5ld9n5) and be a part of the event!
-
----
-
 *Latest News* 🔥
+- [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://raysummit.anyscale.com/flow/anyscale/raysummit2024/landing/page/sessioncatalog?tab.day=20241001&search.sessiontracks=1719251906298001uzJ2) from other vLLM contributors and users!
 - [2024/09] We hosted [the sixth vLLM meetup](https://lu.ma/87q3nvnh) with NVIDIA! Please find the meetup slides [here](https://docs.google.com/presentation/d/1wrLGwytQfaOTd5wCGSPNhoaW3nq0E-9wqyP7ny93xRs/edit?usp=sharing).
 - [2024/07] We hosted [the fifth vLLM meetup](https://lu.ma/lp0gyjqr) with AWS! Please find the meetup slides [here](https://docs.google.com/presentation/d/1RgUD8aCfcHocghoP3zmXzck9vX3RCI9yfUAB2Bbcl4Y/edit?usp=sharing).
 - [2024/07] In partnership with Meta, vLLM officially supports Llama 3.1 with FP8 quantization and pipeline parallelism! Please check out our blog post [here](https://blog.vllm.ai/2024/07/23/llama31.html).
@@ -51,7 +42,7 @@ vLLM is fast with:
 - Speculative decoding
 - Chunked prefill
 
-**Performance benchmark**: We include a [performance benchmark](https://buildkite.com/vllm/performance-benchmark/builds/4068) that compares the performance of vLLM against other LLM serving engines ([TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM), [text-generation-inference](https://github.com/huggingface/text-generation-inference) and [lmdeploy](https://github.com/InternLM/lmdeploy)).
+**Performance benchmark**: We include a performance benchmark at the end of [our blog post](https://blog.vllm.ai/2024/09/05/perf-update.html). It compares the performance of vLLM against other LLM serving engines ([TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM), [SGLang](https://github.com/sgl-project/sglang) and [LMDeploy](https://github.com/InternLM/lmdeploy)). The implementation is under [nightly-benchmarks folder](.buildkite/nightly-benchmarks/) and you can [reproduce](https://github.com/vllm-project/vllm/issues/8176) this benchmark using our one-click runnable script. 
 
 vLLM is flexible and easy to use with:
 
@@ -136,5 +127,6 @@ If you use vLLM for your research, please cite our [paper](https://arxiv.org/abs
 
 * For technical questions and feature requests, please use Github issues or discussions.
 * For discussing with fellow users, please use Discord.
+* For 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.
\ No newline at end of file

SECURITY.md

@@ -2,11 +2,10 @@
 
 ## Reporting a Vulnerability
 
-If you believe you have found a security vulnerability in vLLM, we encourage you to let us know right away. 
-We will investigate all legitimate reports and do our best to quickly fix the problem.
+If you believe you have found a security vulnerability in vLLM, we encourage you to let us know right away. We will investigate all legitimate reports and do our best to quickly fix the problem.
 
-Please report security issues using https://github.com/vllm-project/vllm/security/advisories/new
+Please report security issues privately using [the vulnerability submission form](https://github.com/vllm-project/vllm/security/advisories/new).
 
 ---
-Please see PyTorch Security for more information how to securely interact with models: https://github.com/pytorch/pytorch/blob/main/SECURITY.md
-This document mostly references the recommendation from PyTorch, thank you! 
+
+Please see [PyTorch's Security Policy](https://github.com/pytorch/pytorch/blob/main/SECURITY.md) for more information and recommendations on how to securely interact with models.

benchmarks/backend_request_func.py

@@ -23,9 +23,9 @@ class RequestFuncInput:
     output_len: int
     model: str
     best_of: int = 1
-    use_beam_search: bool = False
     logprobs: Optional[int] = None
     multi_modal_content: Optional[dict] = None
+    ignore_eos: bool = False
 
 
 @dataclass
@@ -48,13 +48,13 @@ async def async_request_tgi(
     assert api_url.endswith("generate_stream")
 
     async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
-        assert not request_func_input.use_beam_search
         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.
+            # TGI does not accept ignore_eos flag.
         }
         payload = {
             "inputs": request_func_input.prompt,
@@ -119,7 +119,6 @@ async def async_request_trt_llm(
     assert api_url.endswith("generate_stream")
 
     async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
-        assert not request_func_input.use_beam_search
         assert request_func_input.best_of == 1
         payload = {
             "accumulate_tokens": True,
@@ -129,6 +128,8 @@ async def async_request_trt_llm(
             "max_tokens": request_func_input.output_len,
             "stream": True,
         }
+        if request_func_input.ignore_eos:
+            payload["min_length"] = request_func_input.output_len
         output = RequestFuncOutput()
         output.prompt_len = request_func_input.prompt_len
 
@@ -183,7 +184,6 @@ async def async_request_deepspeed_mii(
 ) -> RequestFuncOutput:
     async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
         assert request_func_input.best_of == 1
-        assert not request_func_input.use_beam_search
 
         payload = {
             "prompt": request_func_input.prompt,
@@ -231,7 +231,6 @@ async def async_request_openai_completions(
     ), "OpenAI Completions API URL must end with 'completions' or 'profile'."
 
     async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
-        assert not request_func_input.use_beam_search
         payload = {
             "model": request_func_input.model,
             "prompt": request_func_input.prompt,
@@ -240,6 +239,7 @@ async def async_request_openai_completions(
             "max_tokens": request_func_input.output_len,
             "logprobs": request_func_input.logprobs,
             "stream": True,
+            "ignore_eos": request_func_input.ignore_eos,
         }
         headers = {
             "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"
@@ -312,7 +312,6 @@ async def async_request_openai_chat_completions(
     ), "OpenAI Chat Completions API URL must end with 'chat/completions'."
 
     async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
-        assert not request_func_input.use_beam_search
         content = [{"type": "text", "text": request_func_input.prompt}]
         if request_func_input.multi_modal_content:
             content.append(request_func_input.multi_modal_content)
@@ -327,6 +326,7 @@ async def async_request_openai_chat_completions(
             "temperature": 0.0,
             "max_tokens": request_func_input.output_len,
             "stream": True,
+            "ignore_eos": request_func_input.ignore_eos,
         }
         headers = {
             "Content-Type": "application/json",
@@ -430,4 +430,5 @@ ASYNC_REQUEST_FUNCS = {
     "openai-chat": async_request_openai_chat_completions,
     "tensorrt-llm": async_request_trt_llm,
     "scalellm": async_request_openai_completions,
+    "sglang": async_request_openai_completions,
 }

benchmarks/benchmark_latency.py

@@ -11,7 +11,7 @@ from tqdm import tqdm
 
 from vllm import LLM, SamplingParams
 from vllm.engine.arg_utils import DEVICE_OPTIONS, EngineArgs
-from vllm.inputs import PromptInputs
+from vllm.inputs import PromptType
 from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
 from vllm.utils import FlexibleArgumentParser
 
@@ -38,7 +38,6 @@ def main(args: argparse.Namespace):
         quantization_param_path=args.quantization_param_path,
         device=args.device,
         ray_workers_use_nsight=args.ray_workers_use_nsight,
-        use_v2_block_manager=args.use_v2_block_manager,
         enable_chunked_prefill=args.enable_chunked_prefill,
         download_dir=args.download_dir,
         block_size=args.block_size,
@@ -51,9 +50,8 @@ def main(args: argparse.Namespace):
 
     sampling_params = SamplingParams(
         n=args.n,
-        temperature=0.0 if args.use_beam_search else 1.0,
+        temperature=1.0,
         top_p=1.0,
-        use_beam_search=args.use_beam_search,
         ignore_eos=True,
         max_tokens=args.output_len,
     )
@@ -61,7 +59,7 @@ def main(args: argparse.Namespace):
     dummy_prompt_token_ids = np.random.randint(10000,
                                                size=(args.batch_size,
                                                      args.input_len))
-    dummy_inputs: List[PromptInputs] = [{
+    dummy_prompts: List[PromptType] = [{
         "prompt_token_ids": batch
     } for batch in dummy_prompt_token_ids.tolist()]
 
@@ -74,13 +72,13 @@ def main(args: argparse.Namespace):
                     ],
                     on_trace_ready=torch.profiler.tensorboard_trace_handler(
                         str(profile_dir))) as p:
-                llm.generate(dummy_inputs,
+                llm.generate(dummy_prompts,
                              sampling_params=sampling_params,
                              use_tqdm=False)
             print(p.key_averages())
         else:
             start_time = time.perf_counter()
-            llm.generate(dummy_inputs,
+            llm.generate(dummy_prompts,
                          sampling_params=sampling_params,
                          use_tqdm=False)
             end_time = time.perf_counter()
@@ -222,7 +220,6 @@ if __name__ == '__main__':
     parser.add_argument("--enable-prefix-caching",
                         action='store_true',
                         help="Enable automatic prefix caching")
-    parser.add_argument('--use-v2-block-manager', action='store_true')
     parser.add_argument(
         "--ray-workers-use-nsight",
         action='store_true',

benchmarks/benchmark_prefix_caching.py

@@ -113,7 +113,7 @@ def repeat_and_sort_requests(requests: List[Tuple[str, int, int]],
 def main(args):
     tokenizer = get_tokenizer(args.model, trust_remote_code=True)
     input_length_range = tuple(map(int, args.input_length_range.split(':')))
-
+    random.seed(args.seed)
     if args.dataset_path is not None:
         print(f"Start to sample {args.num_prompts} prompts"
               "from {args.dataset_path}")
@@ -133,7 +133,6 @@ def main(args):
               tokenizer_mode='auto',
               trust_remote_code=True,
               enforce_eager=True,
-              use_v2_block_manager=args.use_v2_block_manager,
               tensor_parallel_size=args.tensor_parallel_size,
               enable_prefix_caching=args.enable_prefix_caching)
 
@@ -175,9 +174,6 @@ if __name__ == "__main__":
     parser.add_argument('--enable-prefix-caching',
                         action='store_true',
                         help='enable prefix caching')
-    parser.add_argument('--use-v2-block-manager',
-                        action='store_true',
-                        help='Use BlockSpaceMangerV2')
     parser.add_argument('--num-prompts',
                         type=int,
                         default=1,
@@ -194,5 +190,9 @@ if __name__ == "__main__":
                         default='128:256',
                         help='Range of input lengths for sampling prompts,'
                         'specified as "min:max" (e.g., "128:256").')
+    parser.add_argument("--seed",
+                        type=int,
+                        default=0,
+                        help='Random seed for reproducibility')
     args = parser.parse_args()
     main(args)

benchmarks/benchmark_prioritization.py

@@ -68,7 +68,6 @@ def run_vllm(
     tensor_parallel_size: int,
     seed: int,
     n: int,
-    use_beam_search: bool,
     trust_remote_code: bool,
     dtype: str,
     max_model_len: Optional[int],
@@ -114,9 +113,8 @@ def run_vllm(
         sampling_params.append(
             SamplingParams(
                 n=n,
-                temperature=0.0 if use_beam_search else 1.0,
+                temperature=1.0,
                 top_p=1.0,
-                use_beam_search=use_beam_search,
                 ignore_eos=True,
                 max_tokens=output_len,
             ))
@@ -144,15 +142,16 @@ def main(args: argparse.Namespace):
                                    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, args.kv_cache_dtype,
-            args.quantization_param_path, args.device,
-            args.enable_prefix_caching, args.enable_chunked_prefill,
-            args.max_num_batched_tokens, args.gpu_memory_utilization,
-            args.download_dir)
+        elapsed_time = run_vllm(requests, args.model, args.tokenizer,
+                                args.quantization, args.tensor_parallel_size,
+                                args.seed, args.n, args.trust_remote_code,
+                                args.dtype, args.max_model_len,
+                                args.enforce_eager, args.kv_cache_dtype,
+                                args.quantization_param_path, args.device,
+                                args.enable_prefix_caching,
+                                args.enable_chunked_prefill,
+                                args.max_num_batched_tokens,
+                                args.gpu_memory_utilization, args.download_dir)
     else:
         raise ValueError(f"Unknown backend: {args.backend}")
     total_num_tokens = sum(prompt_len + output_len
@@ -203,7 +202,6 @@ if __name__ == "__main__":
                         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=200,

benchmarks/benchmark_serving.py

-"""Benchmark online serving throughput.
+r"""Benchmark online serving throughput.
 
 On the server side, run one of the following commands:
     vLLM OpenAI API server
@@ -89,10 +89,8 @@ def sample_sharegpt_requests(
     tokenizer: PreTrainedTokenizerBase,
     fixed_output_len: Optional[int] = None,
 ) -> List[Tuple[str, int, int, None]]:
-    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:
+    with open(dataset_path, encoding='utf-8') 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]
@@ -117,7 +115,7 @@ def sample_sharegpt_requests(
         prompt_len = len(prompt_token_ids)
         output_len = len(completion_token_ids
                          ) if fixed_output_len is None else fixed_output_len
-        if prompt_len < 4 or output_len < 4:
+        if prompt_len < 4 or (fixed_output_len is None and output_len < 4):
             # Prune too short sequences.
             continue
         if prompt_len > 1024 or prompt_len + output_len > 2048:
@@ -141,7 +139,7 @@ def sample_sonnet_requests(
     ), "'args.sonnet-input-len' must be greater than 'args.prefix-input-len'."
 
     # Load the dataset.
-    with open(dataset_path) as f:
+    with open(dataset_path, encoding='utf-8') as f:
         poem_lines = f.readlines()
 
     # Tokenize the poem lines.
@@ -178,9 +176,9 @@ def sample_sonnet_requests(
     # Sample the rest of lines per request.
     sampled_requests: List[Tuple[str, int, int]] = []
     for _ in range(num_requests):
-        sampled_lines = "".join(
-            prefix_lines +
-            random.sample(poem_lines, num_input_lines - num_prefix_lines))
+        num_lines_needed = num_input_lines - num_prefix_lines
+        sampled_lines = "".join(prefix_lines +
+                                random.choices(poem_lines, k=num_lines_needed))
 
         prompt = f"{base_prompt}{sampled_lines}"
         message = [
@@ -228,10 +226,11 @@ def sample_hf_requests(
         prompt_len = len(prompt_token_ids)
         output_len = len(completion_token_ids
                          ) if fixed_output_len is None else fixed_output_len
-        if prompt_len < 4 or output_len < 4:
+        if fixed_output_len is None and (prompt_len < 4 or output_len < 4):
             # Prune too short sequences.
             continue
-        if prompt_len > 1024 or prompt_len + output_len > 2048:
+        if fixed_output_len is None and \
+            (prompt_len > 1024 or prompt_len + output_len > 2048):
             # Prune too long sequences.
             continue
 
@@ -392,12 +391,12 @@ async def benchmark(
     input_requests: List[Tuple[str, int, int]],
     logprobs: Optional[int],
     best_of: int,
-    use_beam_search: bool,
     request_rate: float,
     disable_tqdm: bool,
     profile: bool,
     selected_percentile_metrics: List[str],
     selected_percentiles: List[str],
+    ignore_eos: bool,
 ):
     if backend in ASYNC_REQUEST_FUNCS:
         request_func = ASYNC_REQUEST_FUNCS[backend]
@@ -419,8 +418,8 @@ async def benchmark(
         output_len=test_output_len,
         logprobs=logprobs,
         best_of=best_of,
-        use_beam_search=use_beam_search,
         multi_modal_content=test_mm_content,
+        ignore_eos=ignore_eos,
     )
     test_output = await request_func(request_func_input=test_input)
     if not test_output.success:
@@ -432,17 +431,15 @@ async def benchmark(
 
     if profile:
         print("Starting profiler...")
-        profile_input = RequestFuncInput(
-            model=model_id,
-            prompt=test_prompt,
-            api_url=base_url + "/start_profile",
-            prompt_len=test_prompt_len,
-            output_len=test_output_len,
-            logprobs=logprobs,
-            best_of=best_of,
-            use_beam_search=use_beam_search,
-            multi_modal_content=test_mm_content,
-        )
+        profile_input = RequestFuncInput(model=model_id,
+                                         prompt=test_prompt,
+                                         api_url=base_url + "/start_profile",
+                                         prompt_len=test_prompt_len,
+                                         output_len=test_output_len,
+                                         logprobs=logprobs,
+                                         best_of=best_of,
+                                         multi_modal_content=test_mm_content,
+                                         ignore_eos=ignore_eos)
         profile_output = await request_func(request_func_input=profile_input)
         if profile_output.success:
             print("Profiler started")
@@ -455,17 +452,15 @@ async def benchmark(
     tasks: List[asyncio.Task] = []
     async for request in get_request(input_requests, request_rate):
         prompt, prompt_len, output_len, mm_content = request
-        request_func_input = RequestFuncInput(
-            model=model_id,
-            prompt=prompt,
-            api_url=api_url,
-            prompt_len=prompt_len,
-            output_len=output_len,
-            logprobs=logprobs,
-            best_of=best_of,
-            use_beam_search=use_beam_search,
-            multi_modal_content=mm_content,
-        )
+        request_func_input = RequestFuncInput(model=model_id,
+                                              prompt=prompt,
+                                              api_url=api_url,
+                                              prompt_len=prompt_len,
+                                              output_len=output_len,
+                                              logprobs=logprobs,
+                                              best_of=best_of,
+                                              multi_modal_content=mm_content,
+                                              ignore_eos=ignore_eos)
         tasks.append(
             asyncio.create_task(
                 request_func(request_func_input=request_func_input,
@@ -482,7 +477,6 @@ async def benchmark(
             output_len=test_output_len,
             logprobs=logprobs,
             best_of=best_of,
-            use_beam_search=use_beam_search,
         )
         profile_output = await request_func(request_func_input=profile_input)
         if profile_output.success:
@@ -540,7 +534,7 @@ async def benchmark(
         # E.g., "Time to First Token"
         metric_header: str,
     ):
-        # This function print and add statistics of the specified
+        # This function prints and adds statistics of the specified
         # metric.
         if metric_attribute_name not in selected_percentile_metrics:
             return
@@ -678,7 +672,6 @@ def main(args: argparse.Namespace):
             input_requests=input_requests,
             logprobs=args.logprobs,
             best_of=args.best_of,
-            use_beam_search=args.use_beam_search,
             request_rate=args.request_rate,
             disable_tqdm=args.disable_tqdm,
             profile=args.profile,
@@ -686,6 +679,7 @@ def main(args: argparse.Namespace):
             selected_percentiles=[
                 float(p) for p in args.metric_percentiles.split(",")
             ],
+            ignore_eos=args.ignore_eos,
         ))
 
     # Save config and results to json
@@ -699,7 +693,6 @@ def main(args: argparse.Namespace):
         result_json["model_id"] = model_id
         result_json["tokenizer_id"] = tokenizer_id
         result_json["best_of"] = args.best_of
-        result_json["use_beam_search"] = args.use_beam_search
         result_json["num_prompts"] = args.num_prompts
 
         # Metadata
@@ -727,7 +720,7 @@ def main(args: argparse.Namespace):
             file_name = args.result_filename
         if args.result_dir:
             file_name = os.path.join(args.result_dir, file_name)
-        with open(file_name, "w") as outfile:
+        with open(file_name, "w", encoding='utf-8') as outfile:
             json.dump(result_json, outfile)
 
 
@@ -864,6 +857,11 @@ if __name__ == "__main__":
         "{backend}-{args.request_rate}qps-{base_model_id}-{current_dt}.json"
         " format.",
     )
+    parser.add_argument(
+        "--ignore-eos",
+        action="store_true",
+        help="Set ignore_eos flag when sending the benchmark request."
+        "Warning: ignore_eos is not supported in deepspeed_mii and tgi.")
     parser.add_argument(
         "--percentile-metrics",
         type=str,
@@ -963,4 +961,4 @@ if __name__ == "__main__":
     )
 
     args = parser.parse_args()
-    main(args)
\ No newline at end of file
+    main(args)

benchmarks/benchmark_throughput.py

@@ -12,11 +12,11 @@ from tqdm import tqdm
 from transformers import (AutoModelForCausalLM, AutoTokenizer,
                           PreTrainedTokenizerBase)
 
-from vllm.inputs import PromptInputs
 from vllm.engine.arg_utils import DEVICE_OPTIONS, AsyncEngineArgs, EngineArgs
 from vllm.entrypoints.openai.api_server import (
     build_async_engine_client_from_engine_args)
 from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
+from vllm.sampling_params import BeamSearchParams
 from vllm.utils import FlexibleArgumentParser, merge_async_iterators
 
 
@@ -75,7 +75,6 @@ def run_vllm(
     tensor_parallel_size: int,
     seed: int,
     n: int,
-    use_beam_search: bool,
     trust_remote_code: bool,
     dtype: str,
     max_model_len: Optional[int],
@@ -89,11 +88,9 @@ def run_vllm(
     distributed_executor_backend: Optional[str],
     gpu_memory_utilization: float = 0.9,
     num_scheduler_steps: int = 1,
-    use_v2_block_manager: bool = False,
     download_dir: Optional[str] = None,
     load_format: str = EngineArgs.load_format,
     disable_async_output_proc: bool = False,
-    use_new_beam_search_impl: bool = False,
 ) -> float:
     from vllm import LLM, SamplingParams
     llm = LLM(
@@ -117,7 +114,6 @@ def run_vllm(
         distributed_executor_backend=distributed_executor_backend,
         load_format=load_format,
         num_scheduler_steps=num_scheduler_steps,
-        use_v2_block_manager=use_v2_block_manager,
         disable_async_output_proc=disable_async_output_proc,
     )
 
@@ -129,13 +125,12 @@ def run_vllm(
         sampling_params.append(
             SamplingParams(
                 n=n,
-                temperature=0.0 if use_beam_search else 1.0,
+                temperature=1.0,
                 top_p=1.0,
-                use_beam_search=use_beam_search,
                 ignore_eos=True,
                 max_tokens=output_len,
             ))
-
+        
     # warmup
     warmup_prompts = []
     warmup_sampling_params = []
@@ -144,9 +139,8 @@ def run_vllm(
         warmup_sampling_params.append(
             SamplingParams(
                 n=n,
-                temperature=0.0 if use_beam_search else 1.0,
+                temperature=1.0,
                 top_p=1.0,
-                use_beam_search=use_beam_search,
                 ignore_eos=True,
                 max_tokens=output_len,
             ))
@@ -158,7 +152,7 @@ def run_vllm(
     # dummy_prompt_token_ids = np.random.randint(10000,
     #                                            size=(args.num_prompts,
     #                                                  args.input_len))
-    # dummy_inputs: List[PromptInputs] = [{
+    # dummy_prompts: List[PromptType] = [{
     #     "prompt_token_ids": batch
     # } for batch in dummy_prompt_token_ids.tolist()]
 
@@ -171,22 +165,27 @@ def run_vllm(
     # for _ in tqdm(range(args.num_iters_warmup), desc="Warmup iterations"):
     #     run_to_completion()
 
-    if not use_new_beam_search_impl:
+    
+    use_beam_search = False
+
+    if not use_beam_search:
         start = time.perf_counter()
         llm.generate(prompts, sampling_params, use_tqdm=True)
         end = time.perf_counter()
     else:
-        assert use_beam_search
         prompts = [prompt for prompt, _, _ in requests]
         # output_len should be the same for all requests.
         output_len = requests[0][2]
         for prompt, input_len, _output_len in requests:
             assert _output_len == output_len
         start = time.perf_counter()
-        llm.beam_search(prompts,
-                        beam_width=n,
-                        max_tokens=output_len,
-                        ignore_eos=True)
+        llm.beam_search(
+            prompts,
+            BeamSearchParams(
+                beam_width=n,
+                max_tokens=output_len,
+                ignore_eos=True,
+            ))
         end = time.perf_counter()
     return end - start
 
@@ -199,7 +198,6 @@ async def run_vllm_async(
     tensor_parallel_size: int,
     seed: int,
     n: int,
-    use_beam_search: bool,
     trust_remote_code: bool,
     dtype: str,
     max_model_len: Optional[int],
@@ -213,7 +211,6 @@ async def run_vllm_async(
     distributed_executor_backend: Optional[str],
     gpu_memory_utilization: float = 0.9,
     num_scheduler_steps: int = 1,
-    use_v2_block_manager: bool = False,
     download_dir: Optional[str] = None,
     load_format: str = EngineArgs.load_format,
     disable_async_output_proc: bool = False,
@@ -241,7 +238,6 @@ async def run_vllm_async(
         distributed_executor_backend=distributed_executor_backend,
         load_format=load_format,
         num_scheduler_steps=num_scheduler_steps,
-        use_v2_block_manager=use_v2_block_manager,
         disable_async_output_proc=disable_async_output_proc,
         worker_use_ray=False,
         disable_log_requests=True,
@@ -258,9 +254,8 @@ async def run_vllm_async(
             sampling_params.append(
                 SamplingParams(
                     n=n,
-                    temperature=0.0 if use_beam_search else 1.0,
+                    temperature=1.0,
                     top_p=1.0,
-                    use_beam_search=use_beam_search,
                     ignore_eos=True,
                     max_tokens=output_len,
                 ))
@@ -282,11 +277,9 @@ def run_hf(
     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":
@@ -318,7 +311,7 @@ def run_hf(
                               padding=True).input_ids
         llm_outputs = llm.generate(
             input_ids=input_ids.cuda(),
-            do_sample=not use_beam_search,
+            do_sample=True,
             num_return_sequences=n,
             temperature=1.0,
             top_p=1.0,
@@ -378,40 +371,37 @@ def main(args: argparse.Namespace):
         if args.async_engine:
             run_args = [
                 requests, args.model, args.tokenizer, args.quantization,
-                args.tensor_parallel_size, args.seed, args.n, args.use_beam_search,
+                args.tensor_parallel_size, args.seed, args.n,
                 args.trust_remote_code, args.dtype, args.max_model_len,
                 args.enforce_eager, args.kv_cache_dtype,
                 args.quantization_param_path, args.device,
                 args.enable_prefix_caching, args.enable_chunked_prefill,
                 args.max_num_batched_tokens, args.distributed_executor_backend,
                 args.gpu_memory_utilization, args.num_scheduler_steps,
-                args.use_v2_block_manager, args.download_dir, args.load_format,
-                args.disable_async_output_proc
+                args.download_dir, args.load_format, args.disable_async_output_proc
             ]
         else:
             run_args = [
                 warmup_requests, requests, args.model, args.tokenizer, args.quantization,
-                args.tensor_parallel_size, args.seed, args.n, args.use_beam_search,
+                args.tensor_parallel_size, args.seed, args.n,
                 args.trust_remote_code, args.dtype, args.max_model_len,
                 args.enforce_eager, args.kv_cache_dtype,
                 args.quantization_param_path, args.device,
                 args.enable_prefix_caching, args.enable_chunked_prefill,
                 args.max_num_batched_tokens, args.distributed_executor_backend,
                 args.gpu_memory_utilization, args.num_scheduler_steps,
-                args.use_v2_block_manager, args.download_dir, args.load_format,
-                args.disable_async_output_proc
+                args.download_dir, args.load_format, args.disable_async_output_proc
             ]
 
         if args.async_engine:
             run_args.append(args.disable_frontend_multiprocessing)
             elapsed_time = uvloop.run(run_vllm_async(*run_args))
         else:
-            elapsed_time = run_vllm(*run_args, args.use_new_beam_search_impl)
+            elapsed_time = run_vllm(*run_args)
     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)
+                              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)
@@ -473,12 +463,10 @@ if __name__ == "__main__":
                         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-warmup',
                         type=int,
                         default=1,
                         help='Number of iterations to run for warmup.')
-    parser.add_argument("--use-new-beam-search-impl", action="store_true")
     parser.add_argument("--num-prompts",
                         type=int,
                         default=1000,
@@ -543,9 +531,6 @@ if __name__ == "__main__":
         type=int,
         default=1,
         help="Maximum number of forward steps per scheduler call.")
-    parser.add_argument("--use-v2-block-manager",
-                        action='store_true',
-                        help="Enable block manager v2.")
     parser.add_argument(
         "--enable-prefix-caching",
         action='store_true',
@@ -633,8 +618,6 @@ if __name__ == "__main__":
             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:

benchmarks/kernels/benchmark_rope.py

@@ -31,7 +31,7 @@ def benchmark_rope_kernels_multi_lora(
     # batched RoPE can take multiple scaling factors
     batched_rope = get_rope(head_size, rotary_dim, max_position, base,
                             is_neox_style, {
-                                "type": "linear",
+                                "rope_type": "linear",
                                 "factor": tuple(scaling_factors)
                             })
     # non-batched RoPE takes only one scaling factor, we create multiple
@@ -41,7 +41,7 @@ def benchmark_rope_kernels_multi_lora(
         non_batched_ropes.append(
             get_rope(head_size, rotary_dim, max_position, base, is_neox_style,
                      {
-                         "type": "linear",
+                         "rope_type": "linear",
                          "factor": (scaling_factor, )
                      }))
 

benchmarks/overheads/benchmark_hashing.py

@@ -16,7 +16,6 @@ def main(args):
         enforce_eager=True,
         enable_prefix_caching=True,
         tensor_parallel_size=args.tensor_parallel_size,
-        use_v2_block_manager=args.use_v2_block_manager,
     )
 
     sampling_params = SamplingParams(temperature=0, max_tokens=args.output_len)
@@ -56,8 +55,5 @@ if __name__ == "__main__":
     parser.add_argument('--enable-prefix-caching',
                         action='store_true',
                         help='enable prefix caching')
-    parser.add_argument('--use-v2-block-manager',
-                        action='store_true',
-                        help='Use BlockSpaceMangerV2')
     args = parser.parse_args()
     main(args)