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Commit 5bcdb734 authored by Neelay Shah's avatar Neelay Shah Committed by GitHub
Browse files

refactor: rename vllm_nixl to vllm and make default (#100)

parent a7c35dcf
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.github/workflows/pre-merge-python.yml
View file @ 5bcdb734
...@@ -30,8 +30,7 @@ jobs: ...@@ -30,8 +30,7 @@ jobs:
strategy: strategy:
matrix: matrix:
framework: framework:
- standard - vllm
- vllm_nixl
name: Build and Test - ${{ matrix.framework }} name: Build and Test - ${{ matrix.framework }}
env: env:
CONTAINER_ID: test_${{ github.run_id }}_${{ github.run_attempt }}_${{ github.job }}_${{ matrix.framework }} CONTAINER_ID: test_${{ github.run_id }}_${{ github.run_attempt }}_${{ github.job }}_${{ matrix.framework }}
......
container/Dockerfile.vllm
View file @ 5bcdb734
...@@ -8,6 +8,133 @@ FROM ${BASE_IMAGE}:${BASE_IMAGE_TAG} AS dev ...@@ -8,6 +8,133 @@ FROM ${BASE_IMAGE}:${BASE_IMAGE_TAG} AS dev
USER root USER root
### NIXL SETUP ###
ARG MOFED_VERSION=24.10-1.1.4.0
ARG PYTHON_VERSION=3.12
ARG NSYS_URL=https://developer.nvidia.com/downloads/assets/tools/secure/nsight-systems/2025_1/
ARG NSYS_PKG=NsightSystems-linux-cli-public-2025.1.1.131-3554042.deb
RUN apt-get update -y && apt-get -y install curl \
git \
libnuma-dev \
numactl \
wget \
autotools-dev \
automake \
libtool \
libz-dev \
libiberty-dev \
flex \
build-essential \
cmake \
libibverbs-dev \
libgoogle-glog-dev \
libgtest-dev \
libjsoncpp-dev \
libpython3-dev \
libboost-all-dev \
libssl-dev \
libgrpc-dev \
libgrpc++-dev \
libprotobuf-dev \
protobuf-compiler-grpc \
pybind11-dev \
python3-full \
python3-pip \
python3-numpy \
etcd-server \
net-tools \
pciutils \
libpci-dev \
vim \
tmux \
screen \
ibverbs-utils \
libibmad-dev
RUN apt-get install -y linux-tools-common linux-tools-generic ethtool iproute2
RUN apt-get install -y dkms linux-headers-generic
RUN apt-get install -y meson ninja-build uuid-dev gdb
RUN apt-get update && apt install -y wget libglib2.0-0
RUN wget ${NSYS_URL}${NSYS_PKG} && dpkg -i $NSYS_PKG && rm $NSYS_PKG
RUN cd /usr/local/src && \
curl -fSsL "https://content.mellanox.com/ofed/MLNX_OFED-${MOFED_VERSION}/MLNX_OFED_LINUX-${MOFED_VERSION}-ubuntu24.04-x86_64.tgz" -o mofed.tgz && \
tar -xf /usr/local/src/mofed.tgz && \
cd MLNX_OFED_LINUX-* && \
apt-get update && apt-get install -y --no-install-recommends \
./DEBS/libibverbs* ./DEBS/ibverbs-providers* ./DEBS/librdmacm* ./DEBS/libibumad* && \
rm -rf /var/lib/apt/lists/* /usr/local/src/*
ENV LIBRARY_PATH=$LIBRARY_PATH:/usr/local/cuda/lib64 \
LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64
ENV LIBRARY_PATH=$LIBRARY_PATH:/usr/local/lib \
LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
WORKDIR /workspace
RUN git clone https://github.com/NVIDIA/gdrcopy.git
RUN PREFIX=/usr/local DESTLIB=/usr/local/lib make -C /workspace/gdrcopy lib_install
RUN cp gdrcopy/src/libgdrapi.so.2.* /usr/lib/x86_64-linux-gnu/
RUN ldconfig
ARG UCX_VERSION=v1.18.0
RUN cd /usr/local/src && \
curl -fSsL "https://github.com/openucx/ucx/tarball/${UCX_VERSION}" | tar xz && \
cd openucx-ucx* && \
./autogen.sh && ./configure \
--enable-shared \
--disable-static \
--disable-doxygen-doc \
--enable-optimizations \
--enable-cma \
--enable-devel-headers \
--with-cuda=/usr/local/cuda \
--with-verbs \
--with-dm \
--with-gdrcopy=/usr/local \
--enable-mt \
--with-mlx5-dv && \
make -j && \
make -j install-strip && \
ldconfig
ENV LD_LIBRARY_PATH=/usr/lib:$LD_LIBRARY_PATH
ENV CPATH=/usr/include:$CPATH
ENV PATH=/usr/bin:$PATH
ENV PKG_CONFIG_PATH=/usr/lib/pkgconfig:$PKG_CONFIG_PATH
SHELL ["/bin/bash", "-c"]
WORKDIR /workspace
ENV LD_LIBRARY_PATH=/usr/local/ompi/lib:$LD_LIBRARY_PATH
ENV CPATH=/usr/local/ompi/include:$CPATH
ENV PATH=/usr/local/ompi/bin:$PATH
ENV PKG_CONFIG_PATH=/usr/local/ompi/lib/pkgconfig:$PKG_CONFIG_PATH
COPY --from=nixl . /opt/nixl
RUN cd /opt/nixl && \
mkdir build && \
meson setup build/ --prefix=/usr/local/nixl && \
cd build/ && \
ninja && \
ninja install
ENV LD_LIBRARY_PATH=/usr/local/nixl/lib/x86_64-linux-gnu/:$LD_LIBRARY_PATH
ENV PYTHONPATH=/usr/local/nixl/lib/python3/dist-packages/:/opt/nixl/test/python/:$PYTHONPATH
ENV UCX_TLS=^cuda_ipc
ENV NIXL_PLUGIN_DIR=/usr/local/nixl/lib/x86_64-linux-gnu/plugins
RUN ls -l /usr/local/nixl/
RUN ls -l /usr/local/nixl/include/
RUN ls -l /usr/local/nixl/include/internal/
RUN ls /opt/nixl
# Install utilities # Install utilities
RUN apt update -y && apt install -y git wget curl nvtop tmux vim RUN apt update -y && apt install -y git wget curl nvtop tmux vim
# nats # nats
...@@ -31,6 +158,10 @@ RUN mkdir /opt/dynamo && \ ...@@ -31,6 +158,10 @@ RUN mkdir /opt/dynamo && \
ENV VIRTUAL_ENV=/opt/dynamo/venv ENV VIRTUAL_ENV=/opt/dynamo/venv
ENV PATH="${VIRTUAL_ENV}/bin:${PATH}" ENV PATH="${VIRTUAL_ENV}/bin:${PATH}"
# Common dependencies
RUN --mount=type=bind,source=./container/deps/requirements.txt,target=/tmp/requirements.txt \
uv pip install --requirement /tmp/requirements.txt
# Install patched vllm - keep this early in Dockerfile to avoid # Install patched vllm - keep this early in Dockerfile to avoid
# rebuilds from unrelated source code changes # rebuilds from unrelated source code changes
ARG VLLM_REF="v0.7.2" ARG VLLM_REF="v0.7.2"
...@@ -39,7 +170,6 @@ RUN --mount=type=bind,source=./container/deps/,target=/tmp/deps \ ...@@ -39,7 +170,6 @@ RUN --mount=type=bind,source=./container/deps/,target=/tmp/deps \
bash /tmp/deps/vllm/install.sh --patch /tmp/deps/vllm/${VLLM_PATCH} --ref ${VLLM_REF} --install-cmd "uv pip install --editable" --use-precompiled --installation-dir /opt/vllm bash /tmp/deps/vllm/install.sh --patch /tmp/deps/vllm/${VLLM_PATCH} --ref ${VLLM_REF} --install-cmd "uv pip install --editable" --use-precompiled --installation-dir /opt/vllm
# Install genai-perf for benchmarking # Install genai-perf for benchmarking
# TODO: Move to tag when fix for genai-perf will be released
ARG GENAI_PERF_TAG="25d0188713adc47868d6b3f22426375237a90529" ARG GENAI_PERF_TAG="25d0188713adc47868d6b3f22426375237a90529"
RUN uv pip install "git+https://github.com/triton-inference-server/perf_analyzer.git@${GENAI_PERF_TAG}#subdirectory=genai-perf" RUN uv pip install "git+https://github.com/triton-inference-server/perf_analyzer.git@${GENAI_PERF_TAG}#subdirectory=genai-perf"
...@@ -47,7 +177,7 @@ RUN uv pip install "git+https://github.com/triton-inference-server/perf_analyzer ...@@ -47,7 +177,7 @@ RUN uv pip install "git+https://github.com/triton-inference-server/perf_analyzer
RUN --mount=type=bind,source=./container/deps/requirements.test.txt,target=/tmp/requirements.txt \ RUN --mount=type=bind,source=./container/deps/requirements.test.txt,target=/tmp/requirements.txt \
uv pip install --requirement /tmp/requirements.txt uv pip install --requirement /tmp/requirements.txt
### MISC UTILITY SETUP ### # ### MISC UTILITY SETUP ###
# Finish pyright install # Finish pyright install
RUN pyright --help > /dev/null 2>&1 RUN pyright --help > /dev/null 2>&1
...@@ -103,11 +233,6 @@ COPY lib/bindings /workspace/lib/bindings ...@@ -103,11 +233,6 @@ COPY lib/bindings /workspace/lib/bindings
RUN cd lib/bindings/c && \ RUN cd lib/bindings/c && \
cargo build --release --locked && cargo doc --no-deps cargo build --release --locked && cargo doc --no-deps
# Generate C bindings for kv cache routing in vLLM
COPY lib/bindings /workspace/lib/bindings
RUN cd lib/bindings/c && \
cargo build --release --locked && cargo doc --no-deps
COPY deploy/dynamo/sdk /workspace/deploy/dynamo/sdk COPY deploy/dynamo/sdk /workspace/deploy/dynamo/sdk
# Build dynamo wheel # Build dynamo wheel
RUN source /opt/dynamo/venv/bin/activate && \ RUN source /opt/dynamo/venv/bin/activate && \
...@@ -135,50 +260,5 @@ ENTRYPOINT ["/opt/nvidia/nvidia_entrypoint.sh"] ...@@ -135,50 +260,5 @@ ENTRYPOINT ["/opt/nvidia/nvidia_entrypoint.sh"]
CMD [] CMD []
### Lean Runtime Image Stage ### ### TODO Lean Runtime Image Stage ###
# FIXME: Separate build and runtime images
FROM ${BASE_IMAGE}:${BASE_IMAGE_TAG} AS runtime
USER root
# Install tools for interactive convenience
RUN apt update -y && \
apt install -y curl tmux vim && \
echo "set -g mouse on" >> /root/.tmux.conf
# Set environment variables
ENV VIRTUAL_ENV=/opt/dynamo/venv
ENV PATH="${VIRTUAL_ENV}/bin:${PATH}"
ENV RAPIDS_LIBUCX_PREFER_SYSTEM_LIBRARY=true
ENV VLLM_KV_CAPI_PATH="/opt/dynamo/bindings/lib/libdynamo_llm_capi.so"
# Copy binaries
COPY --from=dev /usr/local/bin/http /usr/local/bin/http
COPY --from=dev /usr/local/bin/llmctl /usr/local/bin/llmctl
COPY --from=dev /usr/local/bin/etcd/etcd /usr/local/bin/etcd
COPY --from=dev /usr/bin/nats-server /usr/local/bin/nats-server
COPY --from=dev /bin/uv /usr/local/bin/uv
COPY --from=dev /bin/uvx /usr/local/bin/uvx
# Copy venv with installed packages
RUN uv python install 3.12
COPY --from=dev /opt/vllm /opt/vllm
COPY --from=dev ${VIRTUAL_ENV} ${VIRTUAL_ENV}
# Copy minimal set of files for testing. May consider separate stage for testing
# if test dependencies start to negatively impact deployment environment/size.
COPY pyproject.toml /workspace/pyproject.toml
COPY container/deps/vllm /workspace/container/deps/vllm
# Add library for KV routing
COPY --from=dev ${VLLM_KV_CAPI_PATH} ${VLLM_KV_CAPI_PATH}
# Copy minimal set of files for deployment/examples
# FIXME: Use a more consolidated path after directory restructure
COPY examples/python_rs/llm/vllm /workspace/examples/python_rs/llm/vllm
WORKDIR /workspace
# FIXME: May want a modification with dynamo banner on entry
ENTRYPOINT ["/opt/nvidia/nvidia_entrypoint.sh"]
CMD []
container/Dockerfile.vllm_nixl deleted 100644 → 0
View file @ a7c35dcf
# SPDX-FileCopyrightText: Copyright (c) 2024-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
ARG BASE_IMAGE="nvcr.io/nvidia/cuda-dl-base"
ARG BASE_IMAGE_TAG="25.01-cuda12.8-devel-ubuntu24.04"
FROM ${BASE_IMAGE}:${BASE_IMAGE_TAG} AS dev
USER root
### NIXL SETUP ###
ARG MOFED_VERSION=24.10-1.1.4.0
ARG PYTHON_VERSION=3.12
ARG NSYS_URL=https://developer.nvidia.com/downloads/assets/tools/secure/nsight-systems/2025_1/
ARG NSYS_PKG=NsightSystems-linux-cli-public-2025.1.1.131-3554042.deb
RUN apt-get update -y && apt-get -y install curl \
git \
libnuma-dev \
numactl \
wget \
autotools-dev \
automake \
libtool \
libz-dev \
libiberty-dev \
flex \
build-essential \
cmake \
libibverbs-dev \
libgoogle-glog-dev \
libgtest-dev \
libjsoncpp-dev \
libpython3-dev \
libboost-all-dev \
libssl-dev \
libgrpc-dev \
libgrpc++-dev \
libprotobuf-dev \
protobuf-compiler-grpc \
pybind11-dev \
python3-full \
python3-pip \
python3-numpy \
etcd-server \
net-tools \
pciutils \
libpci-dev \
vim \
tmux \
screen \
ibverbs-utils \
libibmad-dev
RUN apt-get install -y linux-tools-common linux-tools-generic ethtool iproute2
RUN apt-get install -y dkms linux-headers-generic
RUN apt-get install -y meson ninja-build uuid-dev gdb
RUN apt-get update && apt install -y wget libglib2.0-0
RUN wget ${NSYS_URL}${NSYS_PKG} && dpkg -i $NSYS_PKG && rm $NSYS_PKG
RUN cd /usr/local/src && \
curl -fSsL "https://content.mellanox.com/ofed/MLNX_OFED-${MOFED_VERSION}/MLNX_OFED_LINUX-${MOFED_VERSION}-ubuntu24.04-x86_64.tgz" -o mofed.tgz && \
tar -xf /usr/local/src/mofed.tgz && \
cd MLNX_OFED_LINUX-* && \
apt-get update && apt-get install -y --no-install-recommends \
./DEBS/libibverbs* ./DEBS/ibverbs-providers* ./DEBS/librdmacm* ./DEBS/libibumad* && \
rm -rf /var/lib/apt/lists/* /usr/local/src/*
ENV LIBRARY_PATH=$LIBRARY_PATH:/usr/local/cuda/lib64 \
LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64
ENV LIBRARY_PATH=$LIBRARY_PATH:/usr/local/lib \
LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
WORKDIR /workspace
RUN git clone https://github.com/NVIDIA/gdrcopy.git
RUN PREFIX=/usr/local DESTLIB=/usr/local/lib make -C /workspace/gdrcopy lib_install
RUN cp gdrcopy/src/libgdrapi.so.2.* /usr/lib/x86_64-linux-gnu/
RUN ldconfig
ARG UCX_VERSION=v1.18.0
RUN cd /usr/local/src && \
curl -fSsL "https://github.com/openucx/ucx/tarball/${UCX_VERSION}" | tar xz && \
cd openucx-ucx* && \
./autogen.sh && ./configure \
--enable-shared \
--disable-static \
--disable-doxygen-doc \
--enable-optimizations \
--enable-cma \
--enable-devel-headers \
--with-cuda=/usr/local/cuda \
--with-verbs \
--with-dm \
--with-gdrcopy=/usr/local \
--enable-mt \
--with-mlx5-dv && \
make -j && \
make -j install-strip && \
ldconfig
ENV LD_LIBRARY_PATH=/usr/lib:$LD_LIBRARY_PATH
ENV CPATH=/usr/include:$CPATH
ENV PATH=/usr/bin:$PATH
ENV PKG_CONFIG_PATH=/usr/lib/pkgconfig:$PKG_CONFIG_PATH
SHELL ["/bin/bash", "-c"]
WORKDIR /workspace
ENV LD_LIBRARY_PATH=/usr/local/ompi/lib:$LD_LIBRARY_PATH
ENV CPATH=/usr/local/ompi/include:$CPATH
ENV PATH=/usr/local/ompi/bin:$PATH
ENV PKG_CONFIG_PATH=/usr/local/ompi/lib/pkgconfig:$PKG_CONFIG_PATH
COPY --from=nixl . /opt/nixl
RUN cd /opt/nixl && \
mkdir build && \
meson setup build/ --prefix=/usr/local/nixl && \
cd build/ && \
ninja && \
ninja install
ENV LD_LIBRARY_PATH=/usr/local/nixl/lib/x86_64-linux-gnu/:$LD_LIBRARY_PATH
ENV PYTHONPATH=/usr/local/nixl/lib/python3/dist-packages/:/opt/nixl/test/python/:$PYTHONPATH
ENV UCX_TLS=^cuda_ipc
ENV NIXL_PLUGIN_DIR=/usr/local/nixl/lib/x86_64-linux-gnu/plugins
RUN ls -l /usr/local/nixl/
RUN ls -l /usr/local/nixl/include/
RUN ls -l /usr/local/nixl/include/internal/
RUN ls /opt/nixl
# Install utilities
RUN apt update -y && apt install -y git wget curl nvtop tmux vim
# nats
RUN wget https://github.com/nats-io/nats-server/releases/download/v2.10.24/nats-server-v2.10.24-amd64.deb && dpkg -i nats-server-v2.10.24-amd64.deb
# etcd
ENV ETCD_VERSION="v3.5.18"
RUN wget https://github.com/etcd-io/etcd/releases/download/$ETCD_VERSION/etcd-$ETCD_VERSION-linux-amd64.tar.gz -O /tmp/etcd.tar.gz && \
mkdir -p /usr/local/bin/etcd && \
tar -xvf /tmp/etcd.tar.gz -C /usr/local/bin/etcd --strip-components=1
ENV PATH=/usr/local/bin/etcd/:$PATH
### VIRTUAL ENVIRONMENT SETUP ###
# Install uv and create virtualenv
COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/
RUN mkdir /opt/dynamo && \
uv venv /opt/dynamo/venv --python 3.12
# Activate virtual environment
ENV VIRTUAL_ENV=/opt/dynamo/venv
ENV PATH="${VIRTUAL_ENV}/bin:${PATH}"
# Common dependencies
RUN --mount=type=bind,source=./container/deps/requirements.txt,target=/tmp/requirements.txt \
uv pip install --requirement /tmp/requirements.txt
# Install patched vllm - keep this early in Dockerfile to avoid
# rebuilds from unrelated source code changes
ARG VLLM_REF="v0.7.2"
ARG VLLM_PATCH="vllm_${VLLM_REF}-dynamo-kv-disagg-patch.patch"
RUN --mount=type=bind,source=./container/deps/,target=/tmp/deps \
bash /tmp/deps/vllm/install.sh --patch /tmp/deps/vllm/${VLLM_PATCH} --ref ${VLLM_REF} --install-cmd "uv pip install --editable" --use-precompiled --installation-dir /opt/vllm
# Install genai-perf for benchmarking
ARG GENAI_PERF_TAG="25d0188713adc47868d6b3f22426375237a90529"
RUN uv pip install "git+https://github.com/triton-inference-server/perf_analyzer.git@${GENAI_PERF_TAG}#subdirectory=genai-perf"
# Install test dependencies
RUN --mount=type=bind,source=./container/deps/requirements.test.txt,target=/tmp/requirements.txt \
uv pip install --requirement /tmp/requirements.txt
# ### MISC UTILITY SETUP ###
# Finish pyright install
RUN pyright --help > /dev/null 2>&1
# Enable Git operations in the /workspace directory
RUN printf "[safe]\n directory=/workspace\n" > /root/.gitconfig
RUN ln -sf /bin/bash /bin/sh
### BUILDS ###
# Rust build/dev dependencies
RUN apt update -y && \
apt install -y \
build-essential \
protobuf-compiler \
cmake \
libssl-dev \
pkg-config && \
curl https://sh.rustup.rs -sSf | bash -s -- -y
ENV PATH="/root/.cargo/bin:${PATH}"
RUN rustup toolchain install 1.85.0-x86_64-unknown-linux-gnu
# Working directory
WORKDIR /workspace
# Copy Python wheel configuration files
COPY pyproject.toml /workspace/
COPY README.md /workspace/
COPY LICENSE /workspace/
# Build Rust runtime
COPY lib/runtime /workspace/lib/runtime
RUN cd lib/runtime && \
cargo build --release --locked && cargo doc --no-deps
# Build OpenAI HTTP Service binaries
COPY lib/llm /workspace/lib/llm
COPY components /workspace/components
RUN cd components && \
cargo build --release && \
cp target/release/http /usr/local/bin/
# Build Dynamo Run binaries
COPY launch /workspace/launch
RUN cd launch && \
cargo build --release --features mistralrs,sglang,vllm,python && \
cp target/release/dynamo-run /usr/local/bin/ && \
cp target/release/llmctl /usr/local/bin/
# Generate C bindings for kv cache routing in vLLM
COPY lib/bindings /workspace/lib/bindings
RUN cd lib/bindings/c && \
cargo build --release --locked && cargo doc --no-deps
COPY deploy/dynamo/sdk /workspace/deploy/dynamo/sdk
# Build dynamo wheel
RUN source /opt/dynamo/venv/bin/activate && \
uv build --wheel --out-dir /workspace/dist && \
uv pip install /workspace/dist/ai_dynamo*cp312*.whl && \
cd /workspace/deploy/dynamo/sdk && \
uv build --wheel --out-dir /workspace/dist && \
uv pip install /workspace/dist/ai_dynamo_sdk*any.whl
# Package the bindings
RUN mkdir -p /opt/dynamo/bindings/wheels && \
mkdir /opt/dynamo/bindings/lib && \
cp dist/ai_dynamo*cp312*.whl /opt/dynamo/bindings/wheels/. && \
cp lib/bindings/c/target/release/libdynamo_llm_capi.so /opt/dynamo/bindings/lib/. && \
cp -r lib/bindings/c/include /opt/dynamo/bindings/.
# Tell vllm to use the Dynamo LLM C API for KV Cache Routing
ENV VLLM_KV_CAPI_PATH="/opt/dynamo/bindings/lib/libdynamo_llm_capi.so"
# FIXME: Copy more specific folders in for dev/debug after directory restructure
COPY . /workspace
# FIXME: May want a modification with dynamo banner on entry
ENTRYPOINT ["/opt/nvidia/nvidia_entrypoint.sh"]
CMD []
### TODO Lean Runtime Image Stage ###
container/build.sh
View file @ 5bcdb734
...@@ -44,7 +44,7 @@ PYTHON_PACKAGE_VERSION=${current_tag:-$latest_tag.dev+$commit_id} ...@@ -44,7 +44,7 @@ PYTHON_PACKAGE_VERSION=${current_tag:-$latest_tag.dev+$commit_id}
# installed within framework specific sections of the Dockerfile. # installed within framework specific sections of the Dockerfile.
declare -A FRAMEWORKS=(["STANDARD"]=1 ["TENSORRTLLM"]=2 ["VLLM"]=3 ["VLLM_NIXL"]=4) declare -A FRAMEWORKS=(["STANDARD"]=1 ["TENSORRTLLM"]=2 ["VLLM"]=3 ["VLLM_NIXL"]=4)
DEFAULT_FRAMEWORK=STANDARD DEFAULT_FRAMEWORK=VLLM
SOURCE_DIR=$(dirname "$(readlink -f "$0")") SOURCE_DIR=$(dirname "$(readlink -f "$0")")
DOCKERFILE=${SOURCE_DIR}/Dockerfile DOCKERFILE=${SOURCE_DIR}/Dockerfile
...@@ -64,9 +64,6 @@ TENSORRTLLM_PIP_WHEEL_PATH="" ...@@ -64,9 +64,6 @@ TENSORRTLLM_PIP_WHEEL_PATH=""
VLLM_BASE_IMAGE="nvcr.io/nvidia/cuda-dl-base" VLLM_BASE_IMAGE="nvcr.io/nvidia/cuda-dl-base"
VLLM_BASE_IMAGE_TAG="25.01-cuda12.8-devel-ubuntu24.04" VLLM_BASE_IMAGE_TAG="25.01-cuda12.8-devel-ubuntu24.04"
VLLM_NIXL_BASE_IMAGE="nvcr.io/nvidia/cuda-dl-base"
VLLM_NIXL_BASE_IMAGE_TAG="25.01-cuda12.8-devel-ubuntu24.04"
NIXL_COMMIT=3ce6a673b266b4f293909ceb17ca7975f1ba5cd7 NIXL_COMMIT=3ce6a673b266b4f293909ceb17ca7975f1ba5cd7
NIXL_REPO=ai-dynamo/nixl.git NIXL_REPO=ai-dynamo/nixl.git
...@@ -197,6 +194,10 @@ get_options() { ...@@ -197,6 +194,10 @@ get_options() {
FRAMEWORK=$DEFAULT_FRAMEWORK FRAMEWORK=$DEFAULT_FRAMEWORK
fi fi
if [[ ${FRAMEWORK^^} == "VLLM_NIXL" ]]; then
FRAMEWORK="VLLM"
fi
if [ ! -z "$FRAMEWORK" ]; then if [ ! -z "$FRAMEWORK" ]; then
FRAMEWORK=${FRAMEWORK^^} FRAMEWORK=${FRAMEWORK^^}
...@@ -283,17 +284,14 @@ error() { ...@@ -283,17 +284,14 @@ error() {
get_options "$@" get_options "$@"
# Update DOCKERFILE if framework is VLLM # Update DOCKERFILE if framework is VLLM
if [[ $FRAMEWORK == "VLLM" ]]; then if [[ $FRAMEWORK == "VLLM" ]]; then
DOCKERFILE=${SOURCE_DIR}/Dockerfile.vllm DOCKERFILE=${SOURCE_DIR}/Dockerfile.vllm
elif [[ $FRAMEWORK == "VLLM_NIXL" ]]; then
DOCKERFILE=${SOURCE_DIR}/Dockerfile.vllm_nixl
elif [[ $FRAMEWORK == "TENSORRTLLM" ]]; then elif [[ $FRAMEWORK == "TENSORRTLLM" ]]; then
DOCKERFILE=${SOURCE_DIR}/Dockerfile.tensorrt_llm DOCKERFILE=${SOURCE_DIR}/Dockerfile.tensorrt_llm
fi fi
if [[ $FRAMEWORK == "VLLM_NIXL" ]]; then if [[ $FRAMEWORK == "VLLM" ]]; then
TEMP_DIR=$(mktemp -d) TEMP_DIR=$(mktemp -d)
# Clean up temp directory on script exit # Clean up temp directory on script exit
......
container/run.sh
View file @ 5bcdb734
...@@ -23,7 +23,7 @@ RUN_PREFIX= ...@@ -23,7 +23,7 @@ RUN_PREFIX=
# installed within framework specific sections of the Dockerfile. # installed within framework specific sections of the Dockerfile.
declare -A FRAMEWORKS=(["STANDARD"]=1 ["TENSORRTLLM"]=2 ["VLLM"]=3 ["VLLM_NIXL"]=4) declare -A FRAMEWORKS=(["STANDARD"]=1 ["TENSORRTLLM"]=2 ["VLLM"]=3 ["VLLM_NIXL"]=4)
DEFAULT_FRAMEWORK=STANDARD DEFAULT_FRAMEWORK=VLLM
SOURCE_DIR=$(dirname "$(readlink -f "$0")") SOURCE_DIR=$(dirname "$(readlink -f "$0")")
...@@ -170,6 +170,10 @@ get_options() { ...@@ -170,6 +170,10 @@ get_options() {
FRAMEWORK=$DEFAULT_FRAMEWORK FRAMEWORK=$DEFAULT_FRAMEWORK
fi fi
if [[ ${FRAMEWORK^^} == "VLLM_NIXL" ]]; then
FRAMEWORK="VLLM"
fi
if [ ! -z "$FRAMEWORK" ]; then if [ ! -z "$FRAMEWORK" ]; then
FRAMEWORK=${FRAMEWORK^^} FRAMEWORK=${FRAMEWORK^^}
if [[ ! -n "${FRAMEWORKS[$FRAMEWORK]}" ]]; then if [[ ! -n "${FRAMEWORKS[$FRAMEWORK]}" ]]; then
......
examples/python_rs/llm/vllm/README.md
View file @ 5bcdb734
...@@ -15,9 +15,7 @@ See the License for the specific language governing permissions and ...@@ -15,9 +15,7 @@ See the License for the specific language governing permissions and
limitations under the License. limitations under the License.
--> -->
# vLLM Integration with Dynamo > **NOTE**: This example is based on an internal NVIDIA library that will soon be publicly released. The example won't work until the official release.
This example demonstrates how to use Dynamo to serve large language models with the vLLM engine, enabling efficient model serving with both monolithic and disaggregated deployment options.
## Prerequisites ## Prerequisites
...@@ -25,7 +23,7 @@ Start required services (etcd and NATS): ...@@ -25,7 +23,7 @@ Start required services (etcd and NATS):
Option A: Using [Docker Compose](/deploy/docker-compose.yml) (Recommended) Option A: Using [Docker Compose](/deploy/docker-compose.yml) (Recommended)
```bash ```bash
docker compose -f ./deploy/docker-compose.yml up -d docker compose -f deploy/docker-compose.yml up -d
``` ```
Option B: Manual Setup Option B: Manual Setup
...@@ -35,370 +33,268 @@ Start required services (etcd and NATS): ...@@ -35,370 +33,268 @@ Start required services (etcd and NATS):
- [etcd](https://etcd.io) server - [etcd](https://etcd.io) server
- follow instructions in [etcd installation](https://etcd.io/docs/v3.5/install/) to start an `etcd-server` locally - follow instructions in [etcd installation](https://etcd.io/docs/v3.5/install/) to start an `etcd-server` locally
## Build docker
## Building the Environment
The example is designed to run in a containerized environment using Dynamo, vLLM, and associated dependencies. To build the container:
```bash
# Build image
./container/build.sh --framework VLLM
```
## Launching the Environment
``` ```
# Run image interactively ./container/build.sh
./container/run.sh --framework VLLM -it
```
## Deployment
### 1. HTTP Server
Run the server logging (with debug level logging):
```bash
DYN_LOG=DEBUG http
``` ```
By default the server will run on port 8080.
Add model to the server: ## Run container
```bash
llmctl http add chat deepseek-ai/DeepSeek-R1-Distill-Llama-8B dynamo.vllm.chat/completions
llmctl http add completions deepseek-ai/DeepSeek-R1-Distill-Llama-8B dynamo.vllm.completions
```
##### Example Output
``` ```
+------------+------------------------------------------+-----------+-----------+----------+ ./container/run.sh -it
| MODEL TYPE | MODEL NAME | NAMESPACE | COMPONENT | ENDPOINT |
+------------+------------------------------------------+-----------+-----------+----------+
| chat | deepseek-ai/DeepSeek-R1-Distill-Llama-8B | dynamo | vllm | generate |
+------------+------------------------------------------+-----------+-----------+----------+
``` ```
### 2. Workers All of the commands below are run inside the same container.
#### 2.1. Monolithic Deployment
In a separate terminal run the vllm worker: ## Run deployment
```bash This figure shows an overview of the major components to deploy:
# Activate virtual environment
source /opt/dynamo/venv/bin/activate
# Launch worker
cd /workspace/examples/python_rs/llm/vllm
python3 -m monolith.worker \
--model deepseek-ai/DeepSeek-R1-Distill-Llama-8B \
--enforce-eager
``` ```
+----------------+
##### Example Output +------| prefill worker |-------+
notify | | (optional) | |
finished | +----------------+ | pull
v v
+------+ +-----------+ +------------------+ push +---------------+
| HTTP |----->| processor |----->| decode/monolith |------------>| prefill queue |
| |<-----| |<-----| worker | (if disagg) | (optional) |
+------+ +-----------+ +------------------+ +---------------+
| ^ |
query best | | return | publish kv events
worker | | worker_id v
| | +------------------+
| +---------| kv-router |
+------------->| (optional) |
+------------------+
``` ```
INFO 03-02 05:30:36 __init__.py:190] Automatically detected platform cuda.
WARNING 03-02 05:30:36 nixl.py:43] NIXL is not available
INFO 03-02 05:30:43 config.py:542] This model supports multiple tasks: {'embed', 'score', 'generate', 'classify', 'reward'}. Defaulting to 'generate'.
INFO 03-02 05:30:43 base_engine.py:43] Initializing engine client
INFO 03-02 05:30:43 api_server.py:206] Started engine process with PID 1151
INFO 03-02 05:30:44 config.py:542] This model supports multiple tasks: {'embed', 'score', 'generate', 'classify', 'reward'}. Defaulting to 'generate'.
<SNIP>
INFO 03-02 05:32:20 llm_engine.py:476] init engine (profile, create kv cache, warmup model) took 4.22 seconds
Add model to dynamo and start http server.
```
llmctl http add chat-models deepseek-ai/DeepSeek-R1-Distill-Llama-8B dynamo-init.process.chat/completions
TRT_LOG=DEBUG http --port 8181
``` ```
#### 2.2. Disaggregated Deployment ### Processor
This deployment option splits the model serving across prefill and decode workers, enabling more efficient resource utilization.
**Terminal 1 - Prefill Worker:** Processor routes the requests to the (decode) workers. Three scheduling strategies are supported: 1. random, 2. round-robin, 3. kv (see [Kv Router](#kv-router)).
```bash
# Activate virtual environment
source /opt/dynamo/venv/bin/activate
# Launch prefill worker ```
cd /workspace/examples/python_rs/llm/vllm # Processor must take the same args as the (decoder) worker
VLLM_WORKER_MULTIPROC_METHOD=spawn CUDA_VISIBLE_DEVICES=0 python3 -m disaggregated.prefill_worker \ # This is temporary until we communicate the ModelDeploymentCard over etcd
RUST_LOG=info python3 processor.py \
--model deepseek-ai/DeepSeek-R1-Distill-Llama-8B \ --model deepseek-ai/DeepSeek-R1-Distill-Llama-8B \
--gpu-memory-utilization 0.8 \ --tokenizer deepseek-ai/DeepSeek-R1-Distill-Llama-8B \
--enforce-eager \ --block-size 64 \
--tensor-parallel-size 1 \ --max-model-len 16384 \
--kv-transfer-config \ --router <random/round-robin/kv>
'{"kv_connector":"DynamoNcclConnector","kv_role":"kv_producer","kv_rank":0,"kv_parallel_size":2}'
``` ```
##### Example Output Alternatively, the processor can be bypassed by directly hitting the worker endpoints:
```
INFO 03-02 05:59:44 worker.py:269] the current vLLM instance can use total_gpu_memory (47.54GiB) x gpu_memory_utilization (0.40) = 19.01GiB
INFO 03-02 05:59:44 worker.py:269] model weights take 14.99GiB; non_torch_memory takes 0.06GiB; PyTorch activation peak memory takes 1.19GiB; the rest of the memory reserved for KV Cache is 2.78GiB.
INFO 03-02 05:59:44 executor_base.py:110] # CUDA blocks: 1423, # CPU blocks: 2048
INFO 03-02 05:59:44 executor_base.py:115] Maximum concurrency for 10 tokens per request: 2276.80x
INFO 03-02 05:59:47 llm_engine.py:476] init engine (profile, create kv cache, warmup model) took 3.41 seconds
``` ```
llmctl http add chat-models deepseek-ai/DeepSeek-R1-Distill-Llama-8B dynamo-init.vllm.generate
**Terminal 2 - Decode Worker:** # monolithic
```bash CUDA_VISIBLE_DEVICES=0 python3 routerless/worker.py \
# Activate virtual environment --model deepseek-ai/DeepSeek-R1-Distill-Llama-8B \
source /opt/dynamo/venv/bin/activate --enforce-eager
# Launch decode worker # disaggregated
cd /workspace/examples/python_rs/llm/vllm CUDA_VISIBLE_DEVICES=0 python routerless/prefill_worker.py \
VLLM_WORKER_MULTIPROC_METHOD=spawn CUDA_VISIBLE_DEVICES=1,2 python3 -m disaggregated.decode_worker \
--model deepseek-ai/DeepSeek-R1-Distill-Llama-8B \ --model deepseek-ai/DeepSeek-R1-Distill-Llama-8B \
--gpu-memory-utilization 0.8 \
--enforce-eager \ --enforce-eager \
--tensor-parallel-size 2 \ --kv-transfer-config '{"kv_connector":"DynamoNixlConnector"}'
--kv-transfer-config \ CUDA_VISIBLE_DEVICES=1 python3 routerless/worker.py \
'{"kv_connector":"DynamoNcclConnector","kv_role":"kv_consumer","kv_rank":1,"kv_parallel_size":2}' --remote-prefill \
``` --model deepseek-ai/DeepSeek-R1-Distill-Llama-8B \
--enforce-eager \
The disaggregated deployment utilizes separate GPUs for prefill and decode operations, allowing for optimized resource allocation and improved performance. For more details on the disaggregated deployment, please refer to the [vLLM documentation](https://docs.vllm.ai/en/latest/features/disagg_prefill.html). --kv-transfer-config '{"kv_connector":"DynamoNixlConnector"}'
##### Example Output
```
INFO 03-02 05:59:44 worker.py:269] the current vLLM instance can use total_gpu_memory (47.54GiB) x gpu_memory_utilization (0.40) = 19.01GiB
INFO 03-02 05:59:44 worker.py:269] model weights take 14.99GiB; non_torch_memory takes 0.06GiB; PyTorch activation peak memory takes 1.19GiB; the rest of the memory reserved for KV Cache is 2.78GiB.
INFO 03-02 05:59:44 executor_base.py:110] # CUDA blocks: 1423, # CPU blocks: 2048
INFO 03-02 05:59:44 executor_base.py:115] Maximum concurrency for 10 tokens per request: 2276.80x
INFO 03-02 05:59:47 llm_engine.py:476] init engine (profile, create kv cache, warmup model) took 3.41 seconds
``` ```
### Kv Router
### 3. Client The KV Router is a component that aggregates KV Events from all the workers and maintains
a prefix tree of the cached tokens. It makes decisions on which worker to route requests
```bash to based on the length of the prefix match and the load on the workers.
curl localhost:8080/v1/chat/completions \ There are three steps needed to enable the kv router:
-H "Content-Type: application/json" \ 1. Use `--router kv` in the processor.
-d '{ 2. Use `--router kv` and `--enable-prefix-caching` in all the (decode) workers.
"model": "deepseek-ai/DeepSeek-R1-Distill-Llama-8B", 3. Launch the kv router in a separate terminal.
"messages": [ ```
{"role": "user", "content": "What is the capital of France?"} RUST_LOG=info python3 kv_router.py \
] --model-name deepseek-ai/DeepSeek-R1-Distill-Llama-8B \
}' --block-size 64 \
``` --min-workers 1
```
where `--min-workers` is the number of (decode) workers.
##### Example Output You can choose only the prefix strategy for now:
- `prefix`: Route requests to the worker that has the longest prefix match.
```json
{
"id": "5b04e7b0-0dcd-4c45-baa0-1d03d924010c",
"choices": [{
"message": {
"role": "assistant",
"content": "The capital of France is Paris. Paris is a major city known for iconic landmarks like the Eiffel Tower and the Louvre Museum."
},
"index": 0,
"finish_reason": "stop"
}],
"created": 1739548787,
"model": "vllm",
"object": "chat.completion",
"usage": null,
"system_fingerprint": null
}
```
### 4. Multi-Node Deployment ### Disaggregated Router
The vLLM workers can be deployed across multiple nodes by configuring the NATS and etcd connection endpoints through environment variables. This enables distributed inference across a cluster. The disaggregated router determines whether a request should be send to a
remote prefill engine or a local prefill engine for prefilling based on the
prefill length. If kv router is enabled, the disaggregated router will use
the absolute prefill length (actual prefill length - prefix hit length) to make
the decision.
Set the following environment variables on each node before running the workers: When prefilling locally, the vllm scheduler will prioritize
prefill request and pause any ongoing decode requests.
```bash To enable the disaggregated router, add the following commands in the decode workers:
export NATS_SERVER="nats://<nats-server-host>:<nats-server-port>"
export ETCD_ENDPOINTS="http://<etcd-server-host1>:<etcd-server-port>,http://<etcd-server-host2>:<etcd-server-port>",...
``` ```
python worker.py \
For disaggregated deployment, you will also need to pass the `kv_ip` and `kv_port` to the workers in the `kv_transfer_config` argument:
```bash
... ...
--kv-transfer-config \ --conditional-disagg \
'{"kv_connector":"PyNcclConnector","kv_role":"kv_producer","kv_rank":<rank>,"kv_parallel_size":2,"kv_ip":<master_node_ip>,"kv_port":<kv_port>}' --max-local-prefill-length <length>
``` ```
### Worker
### 5. KV Router Deployment #### Monolithic
The KV Router is a component that aggregates KV Events from all the workers and maintains a prefix tree of the cached tokens. It makes decisions on which worker to route requests to based on the length of the prefix match and the load on the workers.
You can run the router and workers in separate terminal sessions or use the `kv-router-run.sh` script to launch them all at once in their own tmux sessions.
#### Deploying using tmux
The helper script `kv-router-run.sh` will launch the router and workers in their own tmux sessions.
kv-router-run.sh <number_of_workers> <routing_strategy> Optional[<model_name>]
Example: Only kv router is supported for monolithic deployment.
```bash
# Launch 8 workers with prefix routing strategy and use deepseek-ai/DeepSeek-R1-Distill-Llama-8B as the model
bash /workspace/examples/python_rs/llm/vllm/scripts/kv-router-run.sh 8 test deepseek-ai/DeepSeek-R1-Distill-Llama-8B
# List tmux sessions
tmux ls
# Attach to the tmux sessions
tmux a -t v-1 # worker 1 - use cmd + b, d to detach
tmux a -t v-router # kv router - use cmd + b, d to detach
# Close the tmux sessions
tmux ls | grep 'v-' | cut -d: -f1 | xargs -I{} tmux kill-session -t {}
``` ```
CUDA_VISIBLE_DEVICES=0 python3 worker.py \
#### Deploying using separate terminals --model deepseek-ai/DeepSeek-R1-Distill-Llama-8B \
--enforce-eager \
**Terminal 1 - Router:** --block-size 64 \
```bash --max-model-len 16384 \
# Activate virtual environment <optional kv router args: --router kv --enable-prefix-caching>
source /opt/dynamo/venv/bin/activate
# Launch prefill worker
cd /workspace/examples/python_rs/llm/vllm
RUST_LOG=info python3 -m kv_router.router \
--routing-strategy prefix \
--model-name deepseek-ai/DeepSeek-R1-Distill-Llama-8B \
--min-workers 1 \
--block-size 64
``` ```
You can choose only the prefix strategy for now: #### Disaggregated
- `prefix`: Route requests to the worker that has the longest prefix match.
**Terminal 2 - Processor:** Kv router and disaggregated router are supported and can be turned on/off individually.
```bash
# Activate virtual environment
source /opt/dynamo/venv/bin/activate
# Processor must take the same args as the worker ```
# This is temporary until we communicate the ModelDeploymentCard over etcd # start prefill worker in one terminal
cd /workspace/examples/python_rs/llm/vllm # Note: prefix caching is not supported in the prefill for now
RUST_LOG=info python3 -m kv_router.processor \ CUDA_VISIBLE_DEVICES=0 python3 prefill_worker.py \
--model deepseek-ai/DeepSeek-R1-Distill-Llama-8B \ --model deepseek-ai/DeepSeek-R1-Distill-Llama-8B \
--tokenizer deepseek-ai/DeepSeek-R1-Distill-Llama-8B \ --enforce-eager \
--enable-prefix-caching \ --kv-transfer-config '{"kv_connector":"DynamoNixlConnector"}' \
--block-size 64 \ --block-size 64 \
--max-num-batched-tokens 16384 \
--max-model-len 16384 --max-model-len 16384
```
**Terminal 3 and 4 - Workers:**
```bash
# Activate virtual environment
source /opt/dynamo/venv/bin/activate
# Launch Worker 1 and Worker 2 with same command # start decode worker in another terminal
cd /workspace/examples/python_rs/llm/vllm CUDA_VISIBLE_DEVICES=1 python3 worker.py \
RUST_LOG=info python3 -m kv_router.worker \ --remote-prefill \
--model deepseek-ai/DeepSeek-R1-Distill-Llama-8B \ --model deepseek-ai/DeepSeek-R1-Distill-Llama-8B \
--tokenizer deepseek-ai/DeepSeek-R1-Distill-Llama-8B \ --enforce-eager \
--enable-prefix-caching \ --tensor-parallel-size 1 \
--kv-transfer-config '{"kv_connector":"DynamoNixlConnector"}' \
--block-size 64 \ --block-size 64 \
--max-model-len 16384 --max-num-batched-tokens 16384 \
--max-model-len 16384 \
<optional kv router args: --router kv --enable-prefix-caching>
<optional disaggregated router args: --conditional-disagg --max-local-prefill-length <length>>
``` ```
Note: Must enable prefix caching for KV Router to work ### Multi-Node Deployment
Note: block-size must be 64, otherwise Router won't work (accepts only 64 tokens)
**Terminal 5 - Client:** For multi-node deployment, etcd, nats, processor, and kv router
Don't forget to add the model to the server: are only required on the head node. The only components that need
```bash to be deployed on all nodes are the workers.
llmctl http add chat-models deepseek-ai/DeepSeek-R1-Distill-Llama-8B dynamo.process.chat/completions
```
Set the following environment variables on each node before running the workers:
```bash ```bash
curl localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{ export NATS_SERVER="nats://<nats-server-host>:<nats-server-port>"
"model": "deepseek-ai/DeepSeek-R1-Distill-Llama-8B", export ETCD_ENDPOINTS="http://<etcd-server-host>:<etcd-server-port>"
"messages": [
{
"role": "user",
"content": "In the heart of Eldoria, an ancient land of boundless magic and mysterious creatures, lies the long-forgotten city of Aeloria. Once a beacon of knowledge and power, Aeloria was buried beneath the shifting sands of time, lost to the world for centuries. You are an intrepid explorer, known for your unparalleled curiosity and courage, who has stumbled upon an ancient map hinting at ests that Aeloria holds a secret so profound that it has the potential to reshape the very fabric of reality. Your journey will take you through treacherous deserts, enchanted forests, and across perilous mountain ranges. Your Task: Character Background: Develop a detailed background for your character. Describe their motivations for seeking out Aeloria, their skills and weaknesses, and any personal connections to the ancient city or its legends. Are they driven by a quest for knowledge, a search for lost familt clue is hidden."
}
],
"stream":false,
"max_tokens": 30
}'
```
##### Example Output
```json
{
"id": "f435d1aa-d423-40a0-a616-00bc428a3e32",
"choices": [
{
"message": {
"role": "assistant",
"content": "Alright, the user is playing a character in a D&D setting. They want a detailed background for their character, set in the world of Eldoria, particularly in the city of Aeloria. The user mentioned it's about an intrepid explorer"
},
"index": 0,
"finish_reason": "length"
}
],
"created": 1740020570,
"model": "deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
"object": "chat.completion",
"usage": null,
"system_fingerprint": null
}
``` ```
### 6. Preprocessor and backend
This deployment splits the pre-processing and backend for model serving.
Run following commands in 4 terminals: ### Common Issues
**Terminal 1 - vLLM Worker:** If torch GLOO backend is complaining about file name too long, set
```bash
# Activate virtual environment
source /opt/dynamo/venv/bin/activate
cd /workspace/examples/python_rs/llm/vllm
RUST_LOG=info python3 -m preprocessor.worker --model deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
``` ```
export GLOO_SOCKET_IFNAME=lo
**Terminal 2 - preprocessor:**
```bash
# Activate virtual environment
source /opt/dynamo/venv/bin/activate
cd /workspace/examples/python_rs/llm/vllm
RUST_LOG=info python3 -m preprocessor.processor --model deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
``` ```
**Terminal 3 - HTTP Server** ## Client
Run the server logging (with debug level logging): In another terminal:
```bash
DYN_LOG=DEBUG http
``` ```
By default the server will run on port 8080. # this test request has around 200 tokens isl
curl localhost:8181/v1/chat/completions -H "Content-Type: application/json" -d '{
Add model to the server: "model": "deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
```bash
llmctl http add chat-models deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B dynamo.preprocessor.generate
```
**Terminal 4 - client**
```bash
curl localhost:8080/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"messages": [ "messages": [
{"role": "user", "content": "What is the capital of France?"} {
] "role": "user",
"content": "In the heart of Eldoria, an ancient land of boundless magic and mysterious creatures, lies the long-forgotten city of Aeloria. Once a beacon of knowledge and power, Aeloria was buried beneath the shifting sands of time, lost to the world for centuries. You are an intrepid explorer, known for your unparalleled curiosity and courage, who has stumbled upon an ancient map hinting at ests that Aeloria holds a secret so profound that it has the potential to reshape the very fabric of reality. Your journey will take you through treacherous deserts, enchanted forests, and across perilous mountain ranges. Your Task: Character Background: Develop a detailed background for your character. Describe their motivations for seeking out Aeloria, their skills and weaknesses, and any personal connections to the ancient city or its legends. Are they driven by a quest for knowledge, a search for lost familt clue is hidden."
}
],
"stream":false,
"max_tokens": 30
}' }'
``` ```
### 7. Known Issues and Limitations ## Run genai-perf
- vLLM is not working well with the `fork` method for multiprocessing and TP > 1. This is a known issue and a workaround is to use the `spawn` method instead. See [vLLM issue](https://github.com/vllm-project/vllm/issues/6152). `genai-perf` is a tool for profiling and benchmarking LLM servers. It is already installed in the container. For more details, please refer to the [genai-perf README](https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/perf_analyzer/genai-perf/README.html).
- `kv_rank` of `kv_producer` must be smaller than of `kv_consumer`.
- Instances with the same `kv_role` must have the same `--tensor-parallel-size`. ```
- Currently only `--pipeline-parallel-size 1` is supported for XpYd disaggregated deployment. genai-perf profile \
-m deepseek-ai/DeepSeek-R1-Distill-Llama-8B \
--url localhost:8181 \
--endpoint-type chat \
--streaming \
--service-kind openai \
--endpoint v1/chat/completions \
--warmup-request-count 10 \
--random-seed 123 \
--synthetic-input-tokens-stddev 0 \
--output-tokens-stddev 0 \
--tokenizer deepseek-ai/DeepSeek-R1-Distill-Llama-8B \
--synthetic-input-tokens-mean 3000 \
--output-tokens-mean 150 \
--extra-inputs min_tokens:150 \
--extra-inputs max_tokens:150 \
--profile-export-file my_profile_export.json \
--artifact-dir artifacts/ \
--concurrency 10 \
--request-count 40 \
-- -v \
--async
```
## Close deployment
Kill all python processes and clean up metadata files:
```
pkill -9 -f python
```
## TODOs, limitations, known issues
- [ ] Add etcd for discovery
- [ ] Multi-node deployment support
- [ ] Enable chunked prefill
- [ ] Process many remote prefill in one iteration
- [ ] Support recompute preemption
- [ ] Make sure decode does not preempt blocks before xfer finishes
- [ ] Layer wise transfer
- [ ] Non blocking send in prefill (cache manager should check xfer status)
- [ ] Test under load
- [ ] Support pp > 1
- [ ] Check why adding extra seed input is crashing vllm with remote prefill
- [ ] Unified worker for both prefill and decode
- [x] Support mixed tp
- [x] Require sending two parallel requests to start decode for the first time
- [x] Concurrency > 2 is not working
- [x] Parse cmdline args
- [x] Manual nixl example with tp1
- [x] Zero copy
- [x] Conditional remote prefill
- [x] Manual example with tp > 1
- [x] Run on dynamo distributed runtime
- [x] add oai http endpoint
- [x] Sample only on decode, do note return remote prefill response
- [x] Check if all transfers finished before moving to decode
- [x] Enable async output processing - could be working
examples/python_rs/llm/vllm/common/base_engine.py deleted 100644 → 0
View file @ a7c35dcf
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import abc
import logging
from common.chat_processor import ChatProcessor
from vllm.engine.arg_utils import AsyncEngineArgs
from vllm.entrypoints.openai.api_server import (
build_async_engine_client_from_engine_args,
)
logger = logging.getLogger("vllm")
class BaseVllmEngine:
"""
Request handler for the generate endpoint
"""
def __init__(self, engine_args: AsyncEngineArgs):
self.engine_args = engine_args
self.model_config = self.engine_args.create_model_config()
self.engine_client = None
self.chat_processor: ChatProcessor | None = None
self._engine_context = None
async def initialize(self):
"""Initialize the engine client and related components."""
logger.info("Initializing engine client")
self._engine_context = build_async_engine_client_from_engine_args(
self.engine_args
)
if self._engine_context is not None:
self.engine_client = await self._engine_context.__aenter__()
self.tokenizer = await self.engine_client.get_tokenizer()
self.chat_processor = ChatProcessor(self.tokenizer, self.model_config)
else:
raise RuntimeError("Failed to initialize engine client")
async def cleanup(self):
"""Cleanup resources."""
print("Cleaning up engine client")
if self._engine_context is not None:
await self._engine_context.__aexit__(None, None, None)
self._engine_context = None
self.engine_client = None
self.chat_processor = None
async def __aenter__(self):
await self.initialize()
"""Initialize with context manager syntax."""
return self
async def __aexit__(self, exc_type, exc_value, traceback):
await self.cleanup()
@abc.abstractmethod
async def generate(self, raw_request):
pass
examples/python_rs/llm/vllm/common/chat_processor.py deleted 100644 → 0
View file @ a7c35dcf
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import time
from typing import AsyncIterator, List, Optional, Protocol, Union, runtime_checkable
from vllm.config import ModelConfig
from vllm.engine.arg_utils import AsyncEngineArgs
from vllm.entrypoints.chat_utils import ConversationMessage
from vllm.entrypoints.openai.protocol import (
ChatCompletionRequest,
CompletionRequest,
RequestResponseMetadata,
)
from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
from vllm.entrypoints.openai.serving_completion import OpenAIServingCompletion
from vllm.entrypoints.openai.serving_engine import RequestPrompt
from vllm.inputs.data import TokensPrompt
from vllm.transformers_utils.tokenizer import AnyTokenizer
@runtime_checkable
class ProcessMixInRequired(Protocol):
engine_args: AsyncEngineArgs
chat_processor: "ChatProcessor | None"
completions_processor: "CompletionsProcessor | None"
model_config: ModelConfig
class ProcessMixIn(ProcessMixInRequired):
"""
Mixin for pre and post processing for vLLM
Requires engine_args, engine_client, processor, model_config to be initialized
"""
engine_args: AsyncEngineArgs
chat_processor: "ChatProcessor | None"
completions_processor: "CompletionsProcessor | None"
model_config: ModelConfig
def __init__(self):
pass
def _get_processor(
self, raw_request: Union[CompletionRequest, ChatCompletionRequest]
):
# Determine the processor type based on the request structure
return (
self.chat_processor
if isinstance(raw_request, ChatCompletionRequest)
else self.completions_processor
)
async def _parse_raw_request(
self, raw_request: Union[CompletionRequest, ChatCompletionRequest]
):
processor = self._get_processor(raw_request)
if processor is None:
raise RuntimeError("Processor has not been initialized")
request = processor.parse_raw_request(raw_request)
preprocess_result = await processor.preprocess(raw_request)
default_max_tokens = self.model_config.max_model_len - len(
preprocess_result.engine_prompt["prompt_token_ids"]
)
default_sampling_params = self.model_config.get_diff_sampling_param()
sampling_params = request.to_sampling_params(
default_max_tokens,
self.model_config.logits_processor_pattern,
default_sampling_params,
)
return (
request,
preprocess_result.conversation,
preprocess_result.request_prompt,
preprocess_result.engine_prompt,
sampling_params,
)
async def _stream_response(self, request, generator, request_id, conversation):
processor = self._get_processor(request)
if processor is None:
raise RuntimeError("processor has not been initialized")
return processor.stream_response(
request,
generator,
request_id,
conversation,
)
class PreprocessResult:
def __init__(
self,
conversation: Optional[ConversationMessage],
request_prompt: RequestPrompt,
engine_prompt: TokensPrompt,
):
self.conversation = conversation
self.request_prompt = request_prompt
self.engine_prompt = engine_prompt
class ChatProcessor:
def __init__(self, tokenizer: AnyTokenizer, model_config: ModelConfig):
self.tokenizer = tokenizer
self.model_config = model_config
self.openai_serving = OpenAIServingChat(
engine_client=None,
model_config=model_config,
models=None,
request_logger=None,
response_role="assistant",
chat_template=None,
chat_template_content_format="auto",
)
def parse_raw_request(
self, raw_request: ChatCompletionRequest
) -> ChatCompletionRequest:
return ChatCompletionRequest.parse_obj(raw_request)
async def preprocess(self, raw_request: ChatCompletionRequest) -> PreprocessResult:
request = self.parse_raw_request(raw_request)
(
conversation,
request_prompts,
engine_prompts,
) = await self.openai_serving._preprocess_chat(
request,
self.tokenizer,
request.messages,
chat_template=request.chat_template or self.tokenizer.chat_template,
chat_template_content_format=self.openai_serving.chat_template_content_format,
add_generation_prompt=request.add_generation_prompt,
continue_final_message=request.continue_final_message,
tool_dicts=None,
documents=request.documents,
chat_template_kwargs=request.chat_template_kwargs,
tool_parser=self.openai_serving.tool_parser,
truncate_prompt_tokens=request.truncate_prompt_tokens,
add_special_tokens=request.add_special_tokens,
)
return PreprocessResult(conversation[0], request_prompts[0], engine_prompts[0])
async def stream_response(
self,
request: ChatCompletionRequest,
result_generator: AsyncIterator,
request_id: str,
conversation: List,
):
request_metadata = RequestResponseMetadata(request_id=request_id)
if not request.stream:
raise ValueError("Only streaming responses are supported")
async for raw_response in self.openai_serving.chat_completion_stream_generator(
request,
result_generator,
request_id,
request.model,
conversation,
self.tokenizer,
request_metadata,
):
if raw_response.startswith("data: [DONE]"):
break
response = json.loads(raw_response.lstrip("data: "))
yield response
class CompletionsProcessor:
def __init__(self, tokenizer: AnyTokenizer, model_config: ModelConfig):
self.tokenizer = tokenizer
self.model_config = model_config
self.openai_serving = OpenAIServingCompletion(
engine_client=None,
model_config=model_config,
models=None,
request_logger=None,
)
def parse_raw_request(self, raw_request: CompletionRequest) -> CompletionRequest:
return CompletionRequest.parse_obj(raw_request)
async def preprocess(self, raw_request: CompletionRequest) -> PreprocessResult:
request = self.parse_raw_request(raw_request)
(
request_prompts,
engine_prompts,
) = await self.openai_serving._preprocess_completion(
request,
self.tokenizer,
input_or_inputs=request.prompt,
truncate_prompt_tokens=request.truncate_prompt_tokens,
add_special_tokens=request.add_special_tokens,
)
return PreprocessResult(None, request_prompts[0], engine_prompts[0])
async def stream_response(
self,
request: CompletionRequest,
result_generator: AsyncIterator,
request_id: str,
conversation: Optional[List[ConversationMessage]] = None,
):
request_metadata = RequestResponseMetadata(request_id=request_id)
if not request.stream:
raise ValueError("Only streaming responses are supported")
async for raw_response in self.openai_serving.completion_stream_generator(
request,
result_generator,
request_id,
int(time.time()), # created_time
request.model,
1, # num_prompts
self.tokenizer,
request_metadata,
):
if raw_response.startswith("data: [DONE]"):
break
response = json.loads(raw_response.lstrip("data: "))
yield response
examples/python_rs/llm/vllm/common/client.py deleted 100644 → 0
View file @ a7c35dcf
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import asyncio
import uvloop
from dynamo.runtime import DistributedRuntime, dynamo_worker
from .protocol import Request
@dynamo_worker()
async def worker(
runtime: DistributedRuntime,
component: str,
prompt: str,
max_tokens: int,
temperature: float,
):
"""
Instantiate a `backend` client and call the `generate` endpoint
"""
# get endpoint
endpoint = runtime.namespace("dynamo").component(component).endpoint("generate")
# create client
client = await endpoint.client()
# issue request
tasks = []
for _ in range(1):
tasks.append(
client.generate(
Request(
prompt=prompt,
sampling_params={
"temperature": temperature,
"max_tokens": max_tokens,
},
).model_dump_json()
)
)
streams = await asyncio.gather(*tasks)
# process response
for stream in streams:
async for resp in stream:
print(resp)
if __name__ == "__main__":
uvloop.install()
parser = argparse.ArgumentParser()
parser.add_argument("--prompt", type=str, default="what is the capital of france?")
parser.add_argument("--component", type=str, default="vllm")
parser.add_argument("--max-tokens", type=int, default=10)
parser.add_argument("--temperature", type=float, default=0.5)
args = parser.parse_args()
asyncio.run(worker(args.component, args.prompt, args.max_tokens, args.temperature))
examples/python_rs/llm/vllm/common/parser.py deleted 100644 → 0
View file @ a7c35dcf
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from vllm.engine.arg_utils import AsyncEngineArgs
from vllm.utils import FlexibleArgumentParser
def parse_vllm_args() -> AsyncEngineArgs:
parser = FlexibleArgumentParser()
parser = AsyncEngineArgs.add_cli_args(parser)
args = parser.parse_args()
return AsyncEngineArgs.from_cli_args(args)
examples/python_rs/llm/vllm/common/protocol.py deleted 100644 → 0
View file @ a7c35dcf
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
from typing import Any, List, Optional
import msgspec
from pydantic import BaseModel, ConfigDict, field_validator
from pydantic_core import core_schema
from typing_extensions import NotRequired
from vllm.inputs.data import TokensPrompt
from vllm.outputs import CompletionOutput
from vllm.sampling_params import SamplingParams
from vllm.sequence import PromptLogprobs, RequestMetrics
class Request(BaseModel):
prompt: str
sampling_params: dict
class Tokens(BaseModel):
tokens: list[int]
class PrefillRequest(Request):
request_id: str
class Response(BaseModel):
text: str
class PrefillResponse(BaseModel):
prefilled: bool
# Hack to override the type of multi_modal_data in TokensPrompt
# as pydantic doesn't understand generic types
# TokensPrompt is defined here: https://github.com/vllm-project/vllm/blob/a4c402a756fa3213caf9d2cde0e4ceb2d57727f2/vllm/inputs/data.py#L38
# multi_modal_data is defined here: https://github.com/vllm-project/vllm/blob/main/vllm/multimodal/inputs.py#L103
# ModalityData is defined here: https://github.com/vllm-project/vllm/blob/main/vllm/multimodal/inputs.py#L80
class PatchedTokensPrompt(TokensPrompt):
multi_modal_data: NotRequired[Optional[Any]] # type: ignore
# Monkey-patch the SamplingParams type to add a dummy core schema so pydantic can validate it
# Sampling params is a mspspec struct
# SamplingParams is defined here: https://github.com/vllm-project/vllm/blob/a4c402a756fa3213caf9d2cde0e4ceb2d57727f2/vllm/sampling_params.py#L88
SamplingParams.__get_pydantic_core_schema__ = classmethod(
lambda cls, source, handler: core_schema.any_schema()
)
class vLLMGenerateRequest(BaseModel):
"""
Serializable class of all the fields vLLM engine requires for inference
"""
model_config = ConfigDict(arbitrary_types_allowed=True)
engine_prompt: PatchedTokensPrompt
sampling_params: SamplingParams
request_id: str
@field_validator("sampling_params", mode="before")
@classmethod
def parse_sampling_params(cls, v: Any) -> SamplingParams:
if isinstance(v, str):
v = json.loads(v)
if isinstance(v, dict):
return SamplingParams(**v)
return v
model_config = ConfigDict(
json_encoders={SamplingParams: lambda v: msgspec.json.encode(v)}
)
class MyRequestOutput(BaseModel):
"""
RequestOutput from vLLM is not serializable by default
https://github.com/vllm-project/vllm/blob/a4c402a756fa3213caf9d2cde0e4ceb2d57727f2/vllm/outputs.py#L85
This class is used to serialize the RequestOutput and any recursively defined types
We can do this because PromptLogprobs, RequestMetrics, and CompletionOutput are all serializable dataclasses
"""
model_config = ConfigDict(arbitrary_types_allowed=True)
request_id: str
prompt: Optional[str] = None
prompt_token_ids: Optional[List[int]] = None
prompt_logprobs: Optional[PromptLogprobs] = None
outputs: List[CompletionOutput]
finished: bool
metrics: Optional[RequestMetrics] = None
# lora_request: Optional[LoRARequest] = None
# encoder_prompt: Optional[str] = None
# encoder_prompt_token_ids: Optional[List[int]] = None
# num_cached_tokens: Optional[int] = None
# multi_modal_placeholders: Optional[MultiModalPlaceholderDict] = None
examples/python_rs/llm/vllm/disaggregated/decode_worker.py deleted 100644 → 0
View file @ a7c35dcf
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import asyncio
import socket
import uuid
import msgspec
import uvloop
from common.base_engine import BaseVllmEngine
from common.chat_processor import ProcessMixIn
from common.parser import parse_vllm_args
from common.protocol import PrefillRequest
from vllm.engine.arg_utils import AsyncEngineArgs
from vllm.entrypoints.openai.protocol import (
ChatCompletionRequest,
ChatCompletionStreamResponse,
)
from vllm.logger import logger as vllm_logger
from dynamo.runtime import DistributedRuntime, dynamo_endpoint, dynamo_worker
class VllmDecodeEngine(BaseVllmEngine, ProcessMixIn):
"""
Request handler for the generate endpoint
"""
def __init__(self, engine_args: AsyncEngineArgs, prefill):
assert (
engine_args.kv_transfer_config.is_kv_consumer
), "Decode worker must be a KV consumer"
if engine_args.enable_chunked_prefill is not False:
vllm_logger.info(
"Chunked prefill is not supported in disaggregated mode, disabling it"
)
engine_args.enable_chunked_prefill = False
super().__init__(engine_args)
self.prefill = prefill
self.kv_transfer_config = engine_args.create_engine_config().kv_transfer_config
self.kv_rank = self.kv_transfer_config.kv_rank
@dynamo_endpoint(ChatCompletionRequest, ChatCompletionStreamResponse)
async def generate(self, raw_request):
if self.engine_client is None:
await self.initialize()
vllm_logger.debug(f"Got raw request: {raw_request}")
(
request,
conversation,
request_prompt,
engine_prompt,
sampling_params,
) = await self._parse_raw_request(raw_request)
# TODO: pass decode info through a separate request param
request_id = f"{uuid.uuid4()}___decode_hostname_{socket.gethostname()}___decode_kv_rank_{self.kv_rank}"
prefill_sampling_params = {**msgspec.to_builtins(sampling_params)}
prefill_sampling_params["max_tokens"] = 1
prefill_sampling_params["min_tokens"] = 1
prefill_request = PrefillRequest(
prompt=request_prompt, # TODO: we should use engine prompt to avoid extra tokenization
sampling_params=prefill_sampling_params,
request_id=request_id,
)
vllm_logger.debug(f"Prefill request: {prefill_request}")
prefill_output = self.prefill.generate(
prefill_request.model_dump_json(),
)
vllm_logger.debug(
f"Running generate with engine_prompt: {engine_prompt}, sampling_params: {sampling_params}, request_id: {request_id}"
)
if self.engine_client is None:
raise RuntimeError("Engine client not initialized")
else:
generator = self.engine_client.generate(
engine_prompt, sampling_params, request_id
)
async for response in await self._stream_response(
request, generator, request_id, conversation
):
vllm_logger.debug(f"Generated response: {response}")
yield response
await prefill_output
@dynamo_worker()
async def worker(runtime: DistributedRuntime, engine_args: AsyncEngineArgs):
"""
Instantiate a `backend` component and serve the `generate` endpoint
A `Component` can serve multiple endpoints
"""
component = runtime.namespace("dynamo").component("vllm")
await component.create_service()
prefill = (
await runtime.namespace("dynamo")
.component("prefill")
.endpoint("generate")
.client()
)
async with VllmDecodeEngine(engine_args, prefill) as decode_engine:
endpoint = component.endpoint("generate")
await endpoint.serve_endpoint(decode_engine.generate)
if __name__ == "__main__":
uvloop.install()
engine_args = parse_vllm_args()
asyncio.run(worker(engine_args))
examples/python_rs/llm/vllm/disaggregated/prefill_worker.py deleted 100644 → 0
View file @ a7c35dcf
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import asyncio
import uvloop
import vllm
from common.base_engine import BaseVllmEngine
from common.parser import parse_vllm_args
from common.protocol import PrefillRequest, PrefillResponse
from vllm.engine.arg_utils import AsyncEngineArgs
from vllm.logger import logger as vllm_logger
from dynamo.runtime import DistributedRuntime, dynamo_endpoint, dynamo_worker
class VllmPrefillEngine(BaseVllmEngine):
"""
Request handler for the generate endpoint
"""
def __init__(self, engine_args: AsyncEngineArgs):
assert (
engine_args.kv_transfer_config.is_kv_producer
), "Prefill worker must be a KV producer"
if engine_args.enable_chunked_prefill is not False:
vllm_logger.info(
"Chunked prefill is not supported in disaggregated mode, disabling it"
)
engine_args.enable_chunked_prefill = False
super().__init__(engine_args)
self.kv_transfer_config = engine_args.create_engine_config().kv_transfer_config
self.kv_rank = self.kv_transfer_config.kv_rank
@dynamo_endpoint(PrefillRequest, PrefillResponse)
async def generate(self, request):
if self.engine_client is None:
await self.initialize()
vllm_logger.debug(f"Received prefill request: {request}")
sampling_params = vllm.sampling_params.SamplingParams(**request.sampling_params)
if self.engine_client is None:
raise RuntimeError("Engine client not initialized")
else:
async for response in self.engine_client.generate(
request.prompt, sampling_params, request.request_id
):
vllm_logger.debug(f"Generated response: {response}")
yield True
@dynamo_worker()
async def worker(runtime: DistributedRuntime, engine_args: AsyncEngineArgs):
"""
Instantiate a `backend` component and serve the `generate` endpoint
A `Component` can serve multiple endpoints
"""
component = runtime.namespace("dynamo").component("prefill")
await component.create_service()
async with VllmPrefillEngine(engine_args) as prefill_engine:
endpoint = component.endpoint("generate")
await endpoint.serve_endpoint(prefill_engine.generate)
if __name__ == "__main__":
uvloop.install()
engine_args = parse_vllm_args()
asyncio.run(worker(engine_args))
examples/python_rs/llm/vllm/kv_router/processor.py deleted 100644 → 0
View file @ a7c35dcf
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import asyncio
import uuid
from enum import Enum
from typing import AsyncIterator, Tuple, Union
import uvloop
from common.chat_processor import ChatProcessor, CompletionsProcessor, ProcessMixIn
from common.parser import parse_vllm_args
from common.protocol import MyRequestOutput, Tokens, vLLMGenerateRequest
from transformers import AutoTokenizer
from vllm.engine.arg_utils import AsyncEngineArgs
from vllm.entrypoints.openai.protocol import (
ChatCompletionRequest,
ChatCompletionStreamResponse,
CompletionRequest,
CompletionStreamResponse,
)
from vllm.logger import logger as vllm_logger
from vllm.outputs import RequestOutput
from vllm.transformers_utils.tokenizer import AnyTokenizer
from dynamo.runtime import Client, DistributedRuntime, dynamo_endpoint, dynamo_worker
class RequestType(Enum):
CHAT = "chat"
COMPLETION = "completion"
class Processor(ProcessMixIn):
"""
vLLM pre and post processing
"""
def __init__(
self,
engine_args: AsyncEngineArgs,
router_client: Client,
workers_client: Client,
):
self.engine_args = engine_args
self.model_config = self.engine_args.create_model_config()
self.tokenizer = self._create_tokenizer(engine_args)
self.chat_processor = ChatProcessor(self.tokenizer, self.model_config)
self.completions_processor = CompletionsProcessor(
self.tokenizer, self.model_config
)
self.router_client = router_client
self.workers_client = workers_client
def _create_tokenizer(self, engine_args: AsyncEngineArgs) -> AnyTokenizer:
"""Create a TokenizerGroup using engine arguments similar to VLLM's approach"""
model_path = engine_args.model
# Create the base tokenizer with VLLM's typical settings
base_tokenizer = AutoTokenizer.from_pretrained(
model_path,
trust_remote_code=True,
padding_side="left",
truncation_side="left",
use_fast=True, # VLLM might use the fast tokenizer for efficiency
)
return base_tokenizer
async def _generate(
self,
raw_request: Union[CompletionRequest, ChatCompletionRequest],
request_type: RequestType,
):
request_id = str(uuid.uuid4())
vllm_logger.debug(f"Got raw request: {raw_request}")
(
request,
conversation,
prompt,
engine_prompt,
sampling_params,
) = await self._parse_raw_request(raw_request)
worker_id_generator: AsyncIterator = await self.router_client.generate(
Tokens(tokens=engine_prompt["prompt_token_ids"]).model_dump_json()
)
worker_id = (
await worker_id_generator.__anext__()
) # only one worker id is returned
worker_id = worker_id.data()
vllm_logger.info(f"Worker ID: {worker_id}")
if worker_id == "":
engine_generator = await self.workers_client.random(
vLLMGenerateRequest(
engine_prompt=engine_prompt,
sampling_params=sampling_params,
request_id=request_id,
).model_dump_json()
)
else:
engine_generator = await self.workers_client.direct(
vLLMGenerateRequest(
engine_prompt=engine_prompt,
sampling_params=sampling_params,
request_id=request_id,
).model_dump_json(),
int(worker_id),
)
output = self._generate_responses(engine_generator, request_type)
async for response in await self._stream_response(
request, output, request_id, conversation
):
yield response
async def _generate_responses(
self, engine_generator: AsyncIterator[RequestOutput], request_type: RequestType
) -> AsyncIterator[Union[RequestOutput, Tuple[int, RequestOutput]]]:
prompt_idx = 0
async for resp in engine_generator:
# Deserialize the response from the engine
# Creates correct vLLM objects for each field
output = MyRequestOutput.model_validate_json(resp.data())
# OpenAIServingChat.chat_completion_stream_generator() method expects a RequestOutput object
request_output = RequestOutput(
request_id=output.request_id,
prompt=output.prompt,
prompt_token_ids=output.prompt_token_ids,
prompt_logprobs=output.prompt_logprobs,
outputs=output.outputs,
finished=output.finished,
metrics=output.metrics,
)
if request_type == RequestType.CHAT:
# For chat requests, yield the request_output directly.
yield request_output
elif request_type == RequestType.COMPLETION:
# Completion requests can have multiple prompts and stream generator requires the prompt index
yield (prompt_idx, request_output)
else:
raise NotImplementedError(
f"Request type {request_type} not implemented"
)
@dynamo_endpoint(ChatCompletionRequest, ChatCompletionStreamResponse)
async def generate_chat(self, raw_request):
async for response in self._generate(raw_request, RequestType.CHAT):
yield response
@dynamo_endpoint(CompletionRequest, CompletionStreamResponse)
async def generate_completions(self, raw_request):
async for response in self._generate(raw_request, RequestType.COMPLETION):
yield response
@dynamo_worker()
async def worker(runtime: DistributedRuntime, engine_args: AsyncEngineArgs):
"""
Set up clients to the router and workers.
Serve the dynamo.process.chat/completions endpoint.
"""
workers_client = (
await runtime.namespace("dynamo")
.component("vllm")
.endpoint("generate")
.client()
)
router_client = (
await runtime.namespace("dynamo")
.component("router")
.endpoint("generate")
.client()
)
preprocess_component = runtime.namespace("dynamo").component("process")
await preprocess_component.create_service()
chat_endpoint = preprocess_component.endpoint("chat/completions")
completions_endpoint = preprocess_component.endpoint("completions")
processor = Processor(engine_args, router_client, workers_client)
await asyncio.gather(
chat_endpoint.serve_endpoint(processor.generate_chat),
completions_endpoint.serve_endpoint(processor.generate_completions),
)
if __name__ == "__main__":
uvloop.install()
engine_args = parse_vllm_args()
asyncio.run(worker(engine_args))
examples/python_rs/llm/vllm/kv_router/router.py deleted 100644 → 0
View file @ a7c35dcf
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import asyncio
import random
from argparse import Namespace
from typing import AsyncIterator
import uvloop
from common.protocol import Tokens
from vllm.logger import logger as vllm_logger
from dynamo.llm import AggregatedMetrics, KvIndexer, KvMetricsAggregator, OverlapScores
from dynamo.runtime import DistributedRuntime, dynamo_endpoint, dynamo_worker
WorkerId = str
class CustomRouter:
"""
Request handler for the generate endpoint
"""
def __init__(
self,
workers_client,
indexer: KvIndexer,
metrics_aggregator: KvMetricsAggregator,
):
vllm_logger.info("Initializing Custom Router")
self.indexer = indexer
self.metrics_aggregator = metrics_aggregator
self.workers_client = workers_client
def _cost_function(
self,
scores: OverlapScores | None,
metrics: AggregatedMetrics | None,
token_length: int,
):
worker_scores = {}
if scores:
for worker_id, score in scores.scores.items():
# score is number of matching blocks we multiply by block_size to get tokens
# and compare to token_length. The larger the cache hit the better
worker_scores[worker_id] = (
score * self.indexer.block_size() / token_length
)
worker_metrics = {}
# pull metrics for each worker
max_waiting = 0.0
if metrics:
for endpoint in metrics.endpoints:
worker_id = endpoint.worker_id
worker_metrics[worker_id] = {
"gpu_cache_usage_perc": endpoint.gpu_cache_usage_perc
if hasattr(endpoint, "gpu_cache_usage_perc")
else 0.0,
"num_requests_waiting": endpoint.num_requests_waiting
if hasattr(endpoint, "num_requests_waiting")
else 0.0,
"gpu_prefix_cache_hit_rate": endpoint.gpu_prefix_cache_hit_rate
if hasattr(endpoint, "gpu_prefix_cache_hit_rate")
else 0.0,
}
max_waiting = max(
max_waiting, worker_metrics[worker_id]["num_requests_waiting"]
)
# Get all worker IDs from the client. This is needed because scores / metrics may not have values for all workers
# and we want all workers to be considered in the logit calculation
worker_ids = self.workers_client.endpoint_ids()
worker_logits = {}
for worker_id in worker_ids:
# Use default values if worker not in scores or metrics
score = worker_scores.get(worker_id, 0.0)
metrics_dict = worker_metrics.get(
worker_id,
{
"gpu_cache_usage_perc": 0.0,
"num_requests_waiting": 0.0,
"gpu_prefix_cache_hit_rate": 0.0,
},
)
normalized_waiting = (
metrics_dict["num_requests_waiting"] / max_waiting
if max_waiting > 0
else 0.0
)
# Have 1 metric that weights towards cache hit
# 2 metrics that penalize overloaded worker and queuing
worker_logits[worker_id] = (
2 * score - metrics_dict["gpu_cache_usage_perc"] - normalized_waiting
)
vllm_logger.info(
f"Formula for {worker_id}: {worker_logits[worker_id]:.3f} = 2.0 * {score:.3f} - {metrics_dict['gpu_cache_usage_perc']:.3f} - {normalized_waiting:.3f}"
)
if not worker_logits or all(logit == 0 for logit in worker_logits.values()):
return ""
# Select the worker with the highest logit
if worker_logits:
max_logit = max(worker_logits.values())
best_workers = [
wid for wid, logit in worker_logits.items() if logit == max_logit
]
best_worker_id = random.choice(best_workers)
else:
best_worker_id = ""
# Log the metrics for the selected worker
if best_worker_id:
vllm_logger.info(
f"Selected worker: {best_worker_id}, logit: {worker_logits[best_worker_id]:.3f}"
)
vllm_logger.info(
f"Score: {scores.scores.get(best_worker_id, 0.0) if scores else 0.0:.3f}"
)
metrics_dict = worker_metrics.get(best_worker_id, {})
vllm_logger.info(
f"GPU Cache Hit Rate: {metrics_dict.get('gpu_prefix_cache_hit_rate', 0.0):.3f}"
)
vllm_logger.info(
f"GPU Cache Usage: {metrics_dict.get('gpu_cache_usage_perc', 0.0):.3f}"
)
vllm_logger.info(
f"Requests Waiting: {metrics_dict.get('num_requests_waiting', 0.0) / max_waiting if max_waiting > 0 else 0.0:.3f}"
)
return best_worker_id
@dynamo_endpoint(Tokens, WorkerId)
async def generate(self, request) -> AsyncIterator[WorkerId]:
lora_id = 0
worker_id = ""
try:
scores = await self.indexer.find_matches_for_request(
request.tokens, lora_id
)
except Exception as e:
scores = {}
vllm_logger.exception(f"Error finding matches: {e}")
token_length = len(request.tokens)
metrics = await self.metrics_aggregator.get_metrics()
worker_id = self._cost_function(scores, metrics, token_length)
vllm_logger.info(f"Scheduling to worker_id: {worker_id}")
vllm_logger.info("########")
yield str(worker_id)
@dynamo_worker()
async def worker(runtime: DistributedRuntime, args: Namespace):
"""
Set up the worker clients.
Serve the dynamo.router.generate endpoint.
"""
workers_client = (
await runtime.namespace("dynamo")
.component("vllm")
.endpoint("generate")
.client()
)
while len(workers_client.endpoint_ids()) < args.min_workers:
vllm_logger.info(
f"Waiting for more workers... Current: {len(workers_client.endpoint_ids())}, Required: {args.min_workers}"
)
await asyncio.sleep(5)
vllm_logger.info(
f"Required number of workers ({args.min_workers}) are ready:\n"
+ "\n".join(f"id: {id}" for id in workers_client.endpoint_ids())
)
kv_listener = runtime.namespace("dynamo").component("vllm")
await kv_listener.create_service()
router_component = runtime.namespace("dynamo").component("router")
await router_component.create_service()
endpoint = router_component.endpoint("generate")
indexer = KvIndexer(kv_listener, args.block_size)
metrics_aggregator = KvMetricsAggregator(kv_listener)
await endpoint.serve_endpoint(
CustomRouter(workers_client, indexer, metrics_aggregator).generate
)
if __name__ == "__main__":
uvloop.install()
import argparse
parser = argparse.ArgumentParser()
parser.add_argument(
"--min-workers",
type=int,
default=1,
help="Minimum number of workers required before proceeding",
)
# TODO: Read block size
parser.add_argument(
"--block-size",
type=int,
default=64,
help="Block size for the KV Indexer",
)
args = parser.parse_args()
asyncio.run(worker(args))
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