Dockerfile.nightly_torch

#######
#
# THIS FILE IS DEPRECATED AND WILL BE REMOVED SHORTLY
#
# Please use the standard Dockerfile with PYTORCH_NIGHTLY=1 instead
#
#######

# The vLLM Dockerfile is used to construct vLLM image against torch nightly that can be directly used for testing

# for torch nightly, cuda >=12.6 is required,
# use 12.8 due to FlashAttention issue with cuda 12.6 (https://github.com/vllm-project/vllm/issues/15435#issuecomment-2775924628)
ARG CUDA_VERSION=12.8.0
#
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS base
ARG CUDA_VERSION=12.8.0
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN apt-get update -y \
    && apt-get install -y ccache software-properties-common git curl sudo \
    && for i in 1 2 3; do \
        add-apt-repository -y ppa:deadsnakes/ppa && break || \
        { echo "Attempt $i failed, retrying in 5s..."; sleep 5; }; \
    done \
    && apt-get update -y \
    && apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
    && update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
    && update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
    && ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
    && curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
    && python3 --version \
    && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
    python3 -m pip install uv

# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500

# 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
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/

WORKDIR /workspace

# install build and runtime dependencies
COPY requirements/common.txt requirements/common.txt
COPY use_existing_torch.py use_existing_torch.py
COPY pyproject.toml pyproject.toml

# install build and runtime dependencies without stable torch version
RUN python3 use_existing_torch.py

# install torch nightly
ARG PINNED_TORCH_VERSION
RUN --mount=type=cache,target=/root/.cache/uv \
    if [ -n "$PINNED_TORCH_VERSION" ]; then \
      pkgs="$PINNED_TORCH_VERSION"; \
    else \
      pkgs="torch torchaudio torchvision"; \
    fi && \
    uv pip install --system $pkgs --index-url https://download.pytorch.org/whl/nightly/cu128

RUN --mount=type=cache,target=/root/.cache/uv \
    uv pip install --system numba==0.61.2

RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/common.txt

# build can take a long time, and the torch nightly version fetched from url can be different in next docker stage.
# track the nightly torch version used in the build, when we set up runtime environment we can make sure the version is the same
RUN uv pip freeze | grep -i '^torch\|^torchvision\|^torchaudio' > torch_build_versions.txt
RUN cat torch_build_versions.txt

# cuda arch list used by torch
# can be useful for `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243

#################### BASE BUILD IMAGE ####################

#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM

# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500

COPY . .

RUN python3 use_existing_torch.py

RUN --mount=type=cache,target=/root/.cache/uv \
    uv pip install --system -r requirements/build.txt

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=16
ENV MAX_JOBS=${max_jobs}
ARG nvcc_threads=2
ENV NVCC_THREADS=$nvcc_threads

ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0

# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
    --mount=type=bind,source=.git,target=.git \
    if [ "$USE_SCCACHE" = "1" ]; then \
        echo "Installing sccache..." \
        && curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
        && tar -xzf sccache.tar.gz \
        && sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
        && rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
        && export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
        && export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
        && export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
        && export SCCACHE_IDLE_TIMEOUT=0 \
        && export CMAKE_BUILD_TYPE=Release \
        && sccache --show-stats \
        && python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
        && sccache --show-stats; \
    fi

ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
    --mount=type=cache,target=/root/.cache/uv \
    --mount=type=bind,source=.git,target=.git  \
    if [ "$USE_SCCACHE" != "1" ]; then \
        # Clean any existing CMake artifacts
        rm -rf .deps && \
        mkdir -p .deps && \
        python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
    fi

#################### WHEEL BUILD IMAGE ####################

################### VLLM INSTALLED IMAGE ####################
# Setup clean environment for vLLM and its dependencies for test and api server using ubuntu22.04 with AOT flashinfer
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
# prepare for environment starts
ARG CUDA_VERSION=12.8.0
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM

RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
    echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment

# Install Python and other dependencies
RUN apt-get update -y \
    && apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
    && apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
    && for i in 1 2 3; do \
        add-apt-repository -y ppa:deadsnakes/ppa && break || \
        { echo "Attempt $i failed, retrying in 5s..."; sleep 5; }; \
    done \
    && apt-get update -y \
    && apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
    && update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
    && update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
    && ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
    && curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
    && python3 --version && python3 -m pip --version

RUN --mount=type=cache,target=/root/.cache/uv \
    python3 -m pip install uv

# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500

# 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
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/

# get the nightly torch version used in the build to make sure the version is the same
COPY --from=base /workspace/torch_build_versions.txt ./torch_build_versions.txt

RUN --mount=type=cache,target=/root/.cache/uv \
    uv pip install --system $(cat torch_build_versions.txt | xargs) --index-url https://download.pytorch.org/whl/nightly/cu128

# install the vllm wheel
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/vllm-dist \
    --mount=type=cache,target=/root/.cache/uv \
    uv pip install --system vllm-dist/*.whl --verbose

ARG torch_cuda_arch_list='8.0;8.6;8.9;9.0'

# install package for build flashinfer
# see issue: https://github.com/flashinfer-ai/flashinfer/issues/738
RUN pip install setuptools==75.6.0 packaging==23.2 ninja==1.11.1.3 build==1.2.2.post1


# build flashinfer for torch nightly from source around 10 mins
# release version: v0.6.4
# todo(elainewy): cache flashinfer build result for faster build
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
    --mount=type=cache,target=/root/.cache/uv \
    echo "git clone flashinfer..." \
    && git clone --depth 1 --branch v0.6.4 --recursive https://github.com/flashinfer-ai/flashinfer.git \
    && cd flashinfer \
    && git submodule update --init --recursive \
    && echo "finish git clone flashinfer..." \
    && rm -rf build \
    && export TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list} \
    && FLASHINFER_ENABLE_AOT=1 python3 setup.py bdist_wheel --dist-dir=../flashinfer-dist --verbose \
    && cd .. \
    && rm -rf flashinfer

# install flashinfer
RUN --mount=type=cache,target=/root/.cache/uv \
    uv pip install --system flashinfer-dist/*.whl --verbose

# install common packages
COPY requirements/common.txt requirements/common.txt
COPY use_existing_torch.py use_existing_torch.py
COPY pyproject.toml pyproject.toml

COPY examples examples
COPY benchmarks benchmarks
COPY ./vllm/collect_env.py .

RUN python3 use_existing_torch.py
RUN --mount=type=cache,target=/root/.cache/uv \
    uv pip install --system -r requirements/common.txt

################### VLLM INSTALLED IMAGE ####################


#################### UNITTEST IMAGE #############################
FROM vllm-base as test
COPY tests/ tests/

# install build and runtime dependencies without stable torch version
COPY requirements/nightly_torch_test.txt requirements/nightly_torch_test.txt

# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500

# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
    uv pip install --system -e tests/vllm_test_utils

# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
    uv pip install --system hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1

RUN --mount=type=cache,target=/root/.cache/uv \
    uv pip install --system -r requirements/nightly_torch_test.txt

# Logging to confirm the torch versions
RUN pip freeze | grep -E 'torch|vllm|flashinfer'

# Logging to confirm all the packages are installed
RUN pip freeze

#################### UNITTEST IMAGE #############################