Dockerfile.s390x

# Base UBI image for s390x architecture
ARG BASE_UBI_IMAGE_TAG=9.5-1736404155
ARG PYTHON_VERSION=3.12
FROM registry.access.redhat.com/ubi9/ubi-minimal:${BASE_UBI_IMAGE_TAG} AS base

# Install basic dependencies
ARG PYTHON_VERSION
ENV PYTHON_VERSION=${PYTHON_VERSION}

WORKDIR /workspace

ENV LANG=C.UTF-8 \
    LC_ALL=C.UTF-8

# Install development utilities
RUN microdnf install -y \
    which procps findutils tar vim git gcc gcc-gfortran g++ make patch zlib-devel \
    libjpeg-turbo-devel libtiff-devel libpng-devel libwebp-devel freetype-devel harfbuzz-devel \
    openssl-devel openblas openblas-devel autoconf automake libtool cmake && \
    microdnf clean all

# Python Installation
FROM base AS python-install
ARG PYTHON_VERSION

ENV VIRTUAL_ENV=/opt/vllm
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
ENV PYTHON_VERSION=${PYTHON_VERSION}
RUN microdnf install -y \
    python${PYTHON_VERSION}-devel python${PYTHON_VERSION}-pip python${PYTHON_VERSION}-wheel  && \
    python${PYTHON_VERSION} -m venv $VIRTUAL_ENV && pip install --no-cache -U pip wheel uv && microdnf clean all

FROM python-install AS pyarrow

# Build Apache Arrow
WORKDIR /tmp
RUN --mount=type=cache,target=/root/.cache/uv \
    git clone https://github.com/apache/arrow.git && \
    cd arrow/cpp && \
    mkdir release && cd release && \
    cmake -DCMAKE_BUILD_TYPE=Release \
          -DCMAKE_INSTALL_PREFIX=/usr/local \
          -DARROW_PYTHON=ON \
          -DARROW_PARQUET=ON \
          -DARROW_ORC=ON \
          -DARROW_FILESYSTEM=ON \
          -DARROW_WITH_LZ4=ON \
          -DARROW_WITH_ZSTD=ON \
          -DARROW_WITH_SNAPPY=ON \
          -DARROW_JSON=ON \
          -DARROW_CSV=ON \
          -DARROW_DATASET=ON \
          -DPROTOBUF_PROTOC_EXECUTABLE=/usr/bin/protoc \
          -DARROW_DEPENDENCY_SOURCE=BUNDLED \
          .. && \
    make -j$(nproc) && \
    make install && \
    cd ../../python && \
    export PYARROW_PARALLEL=4 && \
    export ARROW_BUILD_TYPE=release && \
    uv pip install -r requirements/build.txt && \
    python setup.py build_ext --build-type=$ARROW_BUILD_TYPE --bundle-arrow-cpp bdist_wheel

FROM python-install AS numa-build
# Install numactl (needed for numa.h dependency)
WORKDIR /tmp
RUN curl -LO https://github.com/numactl/numactl/archive/refs/tags/v2.0.16.tar.gz && \
    tar -xvzf v2.0.16.tar.gz && \
    cd numactl-2.0.16 && \
    ./autogen.sh && \
    ./configure && \
    make

# Set include path
ENV C_INCLUDE_PATH="/usr/local/include:$C_INCLUDE_PATH"

FROM python-install AS rust
ENV CARGO_HOME=/root/.cargo
ENV RUSTUP_HOME=/root/.rustup
ENV PATH="$CARGO_HOME/bin:$RUSTUP_HOME/bin:$PATH"

RUN curl https://sh.rustup.rs -sSf | sh -s -- -y && \
    . "$CARGO_HOME/env" && \
    rustup default stable && \
    rustup show

FROM python-install AS torch-vision
# Install torchvision
ARG TORCH_VERSION=2.7.0.dev20250304
ARG TORCH_VISION_VERSION=v0.20.1
WORKDIR /tmp
RUN --mount=type=cache,target=/root/.cache/uv \
    git clone https://github.com/pytorch/vision.git && \
    cd vision && \
    git checkout $TORCH_VISION_VERSION && \
    uv pip install -v torch==${TORCH_VERSION} --extra-index-url https://download.pytorch.org/whl/nightly/cpu && \
    python setup.py bdist_wheel

# Final build stage
FROM python-install AS vllm-cpu
ARG PYTHON_VERSION

# Set correct library path for torch and numactl
ENV LD_LIBRARY_PATH="/opt/vllm/lib64/python${PYTHON_VERSION}/site-packages/torch/lib:/usr/local/lib:$LD_LIBRARY_PATH"
ENV C_INCLUDE_PATH="/usr/local/include:$C_INCLUDE_PATH"
ENV UV_LINK_MODE=copy
ENV CARGO_HOME=/root/.cargo
ENV RUSTUP_HOME=/root/.rustup
ENV PATH="$CARGO_HOME/bin:$RUSTUP_HOME/bin:$PATH"

COPY . /workspace/vllm
WORKDIR /workspace/vllm

RUN --mount=type=bind,from=numa-build,src=/tmp/numactl-2.0.16,target=/numactl \
    make -C /numactl install

# Install dependencies, including PyTorch and Apache Arrow
RUN --mount=type=cache,target=/root/.cache/uv \
    --mount=type=bind,from=rust,source=/root/.cargo,target=/root/.cargo,rw \
    --mount=type=bind,from=rust,source=/root/.rustup,target=/root/.rustup,rw \
    --mount=type=bind,from=pyarrow,source=/tmp/arrow/python/dist,target=/tmp/arrow-wheels \
    --mount=type=bind,from=torch-vision,source=/tmp/vision/dist,target=/tmp/vision-wheels/ \
     sed -i '/^torch/d' requirements/build.txt && \
     ARROW_WHL_FILE=$(ls /tmp/arrow-wheels/pyarrow-*.whl | head -n 1) && \
     VISION_WHL_FILE=$(ls /tmp/vision-wheels/*.whl | head -n 1) && \
    uv pip install -v \    
        $ARROW_WHL_FILE  \
        $VISION_WHL_FILE \
        --extra-index-url https://download.pytorch.org/whl/nightly/cpu \
        --index-strategy unsafe-best-match \
        -r requirements/build.txt \
        -r requirements/cpu.txt 

# Build and install vllm
RUN --mount=type=cache,target=/root/.cache/uv \
    VLLM_TARGET_DEVICE=cpu python setup.py bdist_wheel && \
    uv pip install "$(echo dist/*.whl)[tensorizer]"

# setup non-root user for vllm
RUN umask 002 && \
    useradd --uid 2000 --gid 0 vllm && \
    mkdir -p /home/vllm && \
    chmod g+rwx /home/vllm

COPY LICENSE /licenses/vllm.md
COPY examples/*.jinja /app/data/template/

USER 2000
WORKDIR /home/vllm

# Set the default entrypoint
ENTRYPOINT ["python", "-m", "vllm.entrypoints.openai.api_server"]