169 lines
6.8 KiB
Bash
Executable File
169 lines
6.8 KiB
Bash
Executable File
#!/bin/bash
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set -ex
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# Simple build script wrapping uv/pip
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# Usage:
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# ./build.sh # local build (torch-based arch detection, TK only on SM90)
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# Environment overrides (if set, they win over auto-detection):
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# TORCH_CUDA_ARCH_LIST
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# CMAKE_ARGS (for FASTVIDEO_KERNEL_BUILD_TK / CMAKE_CUDA_ARCHITECTURES / GPU_BACKEND)
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echo "Building fastvideo-kernel..."
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# ---------------------------------------------------------------------------
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# Neutralise conda-injected compiler toolchains.
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#
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# Conda compiler packages (gcc_linux-aarch64, gxx_linux-64, etc.) set
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# CMAKE_ARGS, CFLAGS, CXXFLAGS, and LDFLAGS on activation. When multiple
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# toolchains are installed the variables can reference a *cross*-compiler
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# that doesn't match the host (e.g. aarch64-conda-linux-gnu-c++ on x86_64).
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# Even when the correct toolchain is active, the flags it injects
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# (-march=nocona, -mtune=haswell, …) can conflict with nvcc's host-compiler
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# expectations. Clear them so CMake discovers the system compiler instead.
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# ---------------------------------------------------------------------------
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if [[ -n "${CONDA_PREFIX:-}" ]]; then
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_need_clean=0
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# Detect conda cross-compiler that doesn't match the host.
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_host_arch="$(uname -m)"
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if [[ "${CXX:-}" == *"conda"* ]] || [[ "${CC:-}" == *"conda"* ]]; then
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_need_clean=1
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fi
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if [[ "${CMAKE_ARGS:-}" == *"conda"* ]]; then
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_need_clean=1
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fi
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if (( _need_clean )); then
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echo "NOTE: Clearing conda-injected compiler settings (CC/CXX/CMAKE_ARGS/CFLAGS/...)"
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echo " to use the system compiler for CUDA extension builds."
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unset CC CXX CMAKE_ARGS CFLAGS CXXFLAGS LDFLAGS
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fi
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unset _need_clean _host_arch
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fi
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# Ensure only the kernel's required headers are initialized. A repository-wide
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# update also clones the unrelated VBench evaluation submodule. Skip outside a
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# git checkout (e.g. Docker contexts that exclude .git), where the submodule
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# contents must already be present.
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if git rev-parse --git-dir >/dev/null 2>&1; then
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git submodule update --init --recursive include/cutlass include/tk
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fi
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# Fail fast with a clear message if the headers are still missing (e.g. a
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# Docker context that excluded .git AND the submodule contents) instead of
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# dying later in a wall of nvcc include errors. CUTLASS is consumed by the
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# always-built turbodiffusion sources, so it is a hard error; ThunderKittens
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# only feeds the TK-gated Hopper kernels, so a missing tree just warns (the
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# TK gate resolves later, and non-SM90/ROCm builds never touch it).
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if [ ! -d include/cutlass/include ]; then
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echo "ERROR: include/cutlass/include is missing. Outside a git checkout the" >&2
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echo " CUTLASS sources must already be present (run" >&2
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echo " 'git submodule update --init --recursive include/cutlass include/tk'" >&2
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echo " in the source checkout, or include them in the build context)." >&2
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exit 1
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fi
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if [ ! -d include/tk/include ]; then
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echo "WARNING: include/tk/include is missing; ThunderKittens (Hopper sm_90a)" >&2
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echo " kernels cannot be built. Fine for non-SM90/ROCm targets." >&2
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fi
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# Install build dependencies
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uv pip install scikit-build-core cmake ninja
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RELEASE=0
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GPU_BACKEND=CUDA
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for arg in "$@"; do
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case "$arg" in
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--rocm)
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GPU_BACKEND=ROCM
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;;
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esac
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done
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has_cmake_arg() {
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local key="$1"
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[[ "${CMAKE_ARGS:-}" =~ (^|[[:space:]])-D${key}(=|$) ]]
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}
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detect_with_torch() {
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# Prefer the active venv's python directly over `uv run --active --no-project`,
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# which on some uv versions provisions its own interpreter and misses packages
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# installed into VIRTUAL_ENV.
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local py
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if [[ -n "${VIRTUAL_ENV:-}" && -x "${VIRTUAL_ENV}/bin/python" ]]; then
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py="${VIRTUAL_ENV}/bin/python"
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else
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py="$(command -v python3 || command -v python)"
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fi
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"${py}" -c "import torch
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if not torch.cuda.is_available():
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raise RuntimeError('torch.cuda.is_available() is false')
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mj, mn = torch.cuda.get_device_capability(0)
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print(f'{mj}.{mn}')"
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}
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if [ "${GPU_BACKEND}" = "CUDA" ]; then
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# Compute capability drives the arch/TK defaults below. Prefer an explicit
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# TORCH_CUDA_ARCH_LIST (works on GPU-less build machines such as CI/Docker);
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# only probe a live GPU via torch when no arch was provided.
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if [ -n "${TORCH_CUDA_ARCH_LIST:-}" ]; then
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echo "Using TORCH_CUDA_ARCH_LIST=${TORCH_CUDA_ARCH_LIST} (skipping torch GPU probe)"
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first_arch="${TORCH_CUDA_ARCH_LIST%%[;, ]*}" # first entry, e.g. 9.0a
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first_arch="${first_arch%[af]}" # strip trailing a/f suffix
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cc_major="${first_arch%%.*}"
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cc_minor="${first_arch##*.}"
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else
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detected_cc="$(detect_with_torch)" || {
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echo "ERROR: torch-based CUDA arch detection failed and TORCH_CUDA_ARCH_LIST is unset." >&2
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echo " Set TORCH_CUDA_ARCH_LIST (e.g. 9.0a) for GPU-less builds, or build where CUDA is available." >&2
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exit 1
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}
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cc_major="${detected_cc%%.*}"
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cc_minor="${detected_cc##*.}"
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echo "Detected compute capability via torch: ${detected_cc}"
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fi
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cmake_arch="${cc_major}${cc_minor}"
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# Respect explicit overrides.
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if [ -z "${TORCH_CUDA_ARCH_LIST:-}" ]; then
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if [ "${cc_major}" = "9" ] && [ "${cc_minor}" = "0" ]; then
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export TORCH_CUDA_ARCH_LIST="9.0a"
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elif [ "${cc_major}" = "12" ] && [ "${cc_minor}" = "0" ]; then
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# Blackwell sm_120 needs the arch-conditional 'a' suffix so CMake's
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# AUTO gate (matches 12.0a/120a/sm_120a) builds the attn_qat_infer
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# (modified SageAttention3 FP4) kernels instead of silently skipping.
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export TORCH_CUDA_ARCH_LIST="12.0a"
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else
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export TORCH_CUDA_ARCH_LIST="${cc_major}.${cc_minor}"
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fi
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fi
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# ThunderKittens build targeting:
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# - SM90: compile Hopper/TK kernels with 90a.
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# - Others (e.g., SM100): compile non-TK path with detected arch.
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if ! has_cmake_arg "CMAKE_CUDA_ARCHITECTURES"; then
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if [ "${cc_major}" = "9" ] && [ "${cc_minor}" = "0" ]; then
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CMAKE_ARGS="${CMAKE_ARGS:-} -DCMAKE_CUDA_ARCHITECTURES=90a"
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else
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CMAKE_ARGS="${CMAKE_ARGS:-} -DCMAKE_CUDA_ARCHITECTURES=${cmake_arch}"
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fi
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fi
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if ! has_cmake_arg "FASTVIDEO_KERNEL_BUILD_TK"; then
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if [ "${cc_major}" = "9" ] && [ "${cc_minor}" = "0" ]; then
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CMAKE_ARGS="${CMAKE_ARGS:-} -DFASTVIDEO_KERNEL_BUILD_TK=ON"
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else
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CMAKE_ARGS="${CMAKE_ARGS:-} -DFASTVIDEO_KERNEL_BUILD_TK=OFF"
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fi
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fi
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fi
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if ! has_cmake_arg "GPU_BACKEND"; then
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CMAKE_ARGS="${CMAKE_ARGS:-} -DGPU_BACKEND=${GPU_BACKEND}"
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fi
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export CMAKE_ARGS
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echo "TORCH_CUDA_ARCH_LIST: ${TORCH_CUDA_ARCH_LIST:-<unset>}"
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echo "CMAKE_ARGS: ${CMAKE_ARGS:-<unset>}"
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echo "GPU_BACKEND: ${GPU_BACKEND:-<unset>}"
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# Build and install
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# Use -v for verbose output
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uv pip install . -v --no-build-isolation
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