LocalAI/backend/python/vllm/install.sh

#!/bin/bash
set -e

EXTRA_PIP_INSTALL_FLAGS="--no-build-isolation"

# Avoid to overcommit the CPU during build
# https://github.com/vllm-project/vllm/issues/20079
# https://docs.vllm.ai/en/v0.8.3/serving/env_vars.html
# https://docs.redhat.com/it/documentation/red_hat_ai_inference_server/3.0/html/vllm_server_arguments/environment_variables-server-arguments
export NVCC_THREADS=2
export MAX_JOBS=1

backend_dir=$(dirname $0)

if [ -d $backend_dir/common ]; then
    source $backend_dir/common/libbackend.sh
else
    source $backend_dir/../common/libbackend.sh
fi

# Intel XPU: torch==2.11.0+xpu lives on the PyTorch XPU index, transitive
# deps on PyPI — unsafe-best-match lets uv mix both. vllm-xpu-kernels only
# ships a python3.12 wheel per upstream docs, so bump the portable Python
# before installRequirements (matches the l4t13 pattern below).
# https://github.com/vllm-project/vllm/blob/main/docs/getting_started/installation/gpu.xpu.inc.md
if [ "x${BUILD_PROFILE}" == "xintel" ]; then
    PYTHON_VERSION="3.12"
    PYTHON_PATCH="11"
    EXTRA_PIP_INSTALL_FLAGS+=" --index-strategy=unsafe-best-match"
fi

# CPU builds need unsafe-best-match to pull torch==2.10.0+cpu from the
# pytorch test channel while still resolving transformers/vllm from pypi.
if [ "x${BUILD_PROFILE}" == "xcpu" ]; then
    EXTRA_PIP_INSTALL_FLAGS+=" --index-strategy=unsafe-best-match"
fi

# cublas13 pulls the vLLM wheel from a per-tag cu130 index (PyPI's vllm wheel
# is built against CUDA 12 and won't load on cu130). uv's default per-package
# first-match strategy would still pick the PyPI wheel, so allow it to consult
# every configured index when resolving.
if [ "x${BUILD_PROFILE}" == "xcublas13" ]; then
    EXTRA_PIP_INSTALL_FLAGS+=" --index-strategy=unsafe-best-match"
fi

# Apple Silicon (Metal/MLX) via vllm-metal.
# vllm-metal (github.com/vllm-project/vllm-metal) brings vLLM to macOS on Apple
# Silicon: it registers through vLLM's platform-plugin entry point
# (metal -> vllm_metal:register), MetalPlatform activates, and the vLLM v1
# AsyncLLM engine runs on the GPU through MLX. LocalAI's backend.py is UNCHANGED
# on darwin — AsyncEngineArgs(...) -> AsyncLLMEngine.from_engine_args transparently
# resolves to the MLX engine (proven on a real M4 / macOS 26.5 against Qwen3-0.6B).
#
# vllm-metal REQUIRES Python 3.12, so force the portable CPython before the venv
# is created (ensureVenv reads PYTHON_VERSION/PYTHON_PATCH/PY_STANDALONE_TAG).
# The patch + standalone tag mirror the l4t13 cp312 pin — a known-good
# python-build-standalone release that also ships an aarch64-apple-darwin asset.
if [ "$(uname -s)" = "Darwin" ]; then
    PYTHON_VERSION="3.12"
    PYTHON_PATCH="12"
    PY_STANDALONE_TAG="20251120"
fi

# JetPack 7 / L4T arm64 vllm + torch wheels come straight from PyPI now
# (torch 2.11+ ships aarch64 + cu130 manylinux wheels and vllm 0.20+ ships
# an aarch64 wheel pinned to that torch). They're cp312-only, so bump the
# venv Python accordingly. JetPack 6 keeps cp310 + USE_PIP=true.
# https://pytorch.org/blog/vllm-and-pytorch-work-together-to-improve-the-developer-experience-on-aarch64/
if [ "x${BUILD_PROFILE}" == "xl4t12" ]; then
    USE_PIP=true
fi
if [ "x${BUILD_PROFILE}" == "xl4t13" ]; then
    PYTHON_VERSION="3.12"
    PYTHON_PATCH="12"
    PY_STANDALONE_TAG="20251120"
fi

# ===================== Apple Silicon (Metal/MLX) =====================
# Reproduce vllm-metal's upstream installer
# (curl -fsSL https://raw.githubusercontent.com/vllm-project/vllm-metal/main/install.sh)
# but INTO LocalAI's managed venv (ensureVenv) instead of a throwaway
# ~/.venv-vllm-metal, so the backend integrates with LocalAI's venv lifecycle
# (portable CPython, _makeVenvPortable relocation, runtime activation). The
# normal CUDA/CPU installRequirements is skipped on darwin — there is no
# macOS/arm64 vLLM wheel on PyPI; vLLM is built from source and the MLX engine
# is layered on by the vllm-metal wheel.
if [ "$(uname -s)" = "Darwin" ]; then
    # Create/activate the portable 3.12 venv. On darwin USE_PIP=true and
    # PORTABLE_PYTHON=true (set by scripts/build/python-darwin.sh), so this is a
    # `python -m venv` based, relocatable venv.
    ensureVenv

    # vllm-metal's installer drives everything through `uv`: building vLLM from
    # the CPU requirements needs `--index-strategy unsafe-best-match` (mixes the
    # pytorch CPU channel with PyPI), a flag plain pip does not have. The darwin
    # venv is pip-based, so bootstrap uv into it. uv honours $VIRTUAL_ENV (set by
    # libbackend's _activateVenv) and installs into THIS venv — same pattern the
    # intel branch below relies on.
    pip install uv

    # vllm-metal version pins -- AUTO-BUMPED by .github/bump_vllm_metal.sh, which
    # tracks vllm-project/vllm-metal releases (NOT vllm/vllm latest). VLLM_METAL_VERSION
    # is the vllm-metal release tag (its prebuilt wheel); VLLM_VERSION is the vLLM
    # source version that release builds against (vllm-metal declares it as vllm_v=).
    # They move in lockstep, so darwin can lag the Linux vllm pin
    # (requirements-cublas13-after.txt, bumped independently against vllm/vllm) until
    # vllm-metal supports a newer vLLM. Keep both as plain double-quoted assignments
    # each on their own line so the bumper's sed can rewrite them.
    VLLM_METAL_VERSION="v0.3.0.dev20260622062346"
    VLLM_VERSION="0.23.0"

    _vllm_src=$(mktemp -d)
    trap 'rm -rf "${_vllm_src}"' EXIT
    pushd "${_vllm_src}"
        # 1) Build vLLM ${VLLM_VERSION} from the release source tarball against
        #    the CPU requirements. vllm-metal layers its MLX platform plugin on
        #    top of this exact build.
        curl -fsSL -o "vllm-${VLLM_VERSION}.tar.gz" \
            "https://github.com/vllm-project/vllm/releases/download/v${VLLM_VERSION}/vllm-${VLLM_VERSION}.tar.gz"
        tar -xzf "vllm-${VLLM_VERSION}.tar.gz"
        pushd "vllm-${VLLM_VERSION}"
            uv pip install -r requirements/cpu.txt --index-strategy unsafe-best-match
            # -Wno-parentheses: clang on macOS treats one of vLLM's C++ warnings
            # as an error without it (matches the upstream installer's CXXFLAGS).
            CXXFLAGS="-Wno-parentheses" uv pip install .
        popd
    popd

    # 2) Install the prebuilt vllm-metal wheel from the PINNED release
    #    (${VLLM_METAL_VERSION}). It pulls mlx / mlx-metal as deps and registers
    #    the `metal` platform plugin that backend.py resolves to at engine-init
    #    time. Pinning the tag (vs releases/latest) keeps the wheel and the vLLM
    #    source build above reproducible and coupled; .github/bump_vllm_metal.sh
    #    advances both together.
    _metal_wheel_url=$(curl -fsSL "https://api.github.com/repos/vllm-project/vllm-metal/releases/tags/${VLLM_METAL_VERSION}" \
        | grep -oE '"browser_download_url"[[:space:]]*:[[:space:]]*"[^"]+\.whl"' \
        | head -n1 | sed -E 's/.*"(https[^"]+)".*/\1/')
    if [ -z "${_metal_wheel_url}" ]; then
        echo "ERROR: could not resolve a vllm-metal wheel URL for release ${VLLM_METAL_VERSION}" >&2
        exit 1
    fi
    echo "Installing vllm-metal wheel: ${_metal_wheel_url}"
    uv pip install "${_metal_wheel_url}"

    # Generate the gRPC stubs (backend_pb2*). installRequirements normally does
    # this via runProtogen at the end; we skipped installRequirements on darwin,
    # so call it explicitly here.
    runProtogen

# Intel XPU has no upstream-published vllm wheels, so we always build vllm
# from source against torch-xpu and replace the default triton with
# triton-xpu (matching torch 2.11). Mirrors the upstream procedure:
# https://github.com/vllm-project/vllm/blob/main/docs/getting_started/installation/gpu.xpu.inc.md
elif [ "x${BUILD_TYPE}" == "xintel" ]; then
    # Hide requirements-intel-after.txt so installRequirements doesn't
    # try `pip install vllm` (would either fail or grab a non-XPU wheel).
    _intel_after="${backend_dir}/requirements-intel-after.txt"
    _intel_after_bak=""
    if [ -f "${_intel_after}" ]; then
        _intel_after_bak="${_intel_after}.xpu.bak"
        mv "${_intel_after}" "${_intel_after_bak}"
    fi
    installRequirements
    if [ -n "${_intel_after_bak}" ]; then
        mv "${_intel_after_bak}" "${_intel_after}"
    fi

    # vllm's CMake build needs the Intel oneAPI dpcpp/sycl compiler — the
    # base image (intel/oneapi-basekit) has it but the env isn't sourced.
    if [ -f /opt/intel/oneapi/setvars.sh ]; then
        set +u
        source /opt/intel/oneapi/setvars.sh --force
        set -u
    fi

    _vllm_src=$(mktemp -d)
    trap 'rm -rf "${_vllm_src}"' EXIT
    git clone --depth 1 https://github.com/vllm-project/vllm "${_vllm_src}/vllm"
    pushd "${_vllm_src}/vllm"
        # Install vllm's own runtime deps (torch-xpu, vllm_xpu_kernels,
        # pydantic, fastapi, …) from upstream's requirements/xpu.txt — the
        # canonical source of truth. Avoids re-pinning everything ourselves.
        uv pip install ${EXTRA_PIP_INSTALL_FLAGS:-} -r requirements/xpu.txt
        # Stock triton (NVIDIA-only) may have come in transitively; replace
        # with triton-xpu==3.7.0 which matches torch 2.11.
        uv pip uninstall triton triton-xpu 2>/dev/null || true
        uv pip install ${EXTRA_PIP_INSTALL_FLAGS:-} \
            --extra-index-url https://download.pytorch.org/whl/xpu \
            triton-xpu==3.7.0
        export CMAKE_PREFIX_PATH="$(python -c 'import site; print(site.getsitepackages()[0])'):${CMAKE_PREFIX_PATH:-}"
        VLLM_TARGET_DEVICE=xpu uv pip install ${EXTRA_PIP_INSTALL_FLAGS:-} --no-deps .
    popd
# FROM_SOURCE=true on a CPU build skips the prebuilt vllm wheel in
# requirements-cpu-after.txt and compiles vllm locally against the host's
# actual CPU. Not used by default because it takes ~30-40 minutes, but
# kept here for hosts where the prebuilt wheel SIGILLs (CPU without the
# required SIMD baseline, e.g. AVX-512 VNNI/BF16). Default CI uses a
# bigger-runner with compatible hardware instead.
elif [ "x${BUILD_TYPE}" == "x" ] && [ "x${FROM_SOURCE:-}" == "xtrue" ]; then
    # Temporarily hide the prebuilt wheel so installRequirements doesn't
    # pull it — the rest of the requirements files (base deps, torch,
    # transformers) are still installed normally.
    _cpu_after="${backend_dir}/requirements-cpu-after.txt"
    _cpu_after_bak=""
    if [ -f "${_cpu_after}" ]; then
        _cpu_after_bak="${_cpu_after}.from-source.bak"
        mv "${_cpu_after}" "${_cpu_after_bak}"
    fi
    installRequirements
    if [ -n "${_cpu_after_bak}" ]; then
        mv "${_cpu_after_bak}" "${_cpu_after}"
    fi

    # Build vllm from source against the installed torch.
    # https://docs.vllm.ai/en/latest/getting_started/installation/cpu/
    _vllm_src=$(mktemp -d)
    trap 'rm -rf "${_vllm_src}"' EXIT
    git clone --depth 1 https://github.com/vllm-project/vllm "${_vllm_src}/vllm"
    pushd "${_vllm_src}/vllm"
        uv pip install ${EXTRA_PIP_INSTALL_FLAGS:-} wheel packaging ninja "setuptools>=49.4.0" numpy typing-extensions pillow setuptools-scm
        # Respect pre-installed torch version — skip vllm's own requirements-build.txt torch pin.
        VLLM_TARGET_DEVICE=cpu uv pip install ${EXTRA_PIP_INSTALL_FLAGS:-} --no-deps .
    popd
else
    installRequirements
fi