構建說明¶

注意：最新的構建說明包含在 FBGEMM 倉庫下 setup_env.bash 捆綁的一組指令碼中。

當前可用的 FBGEMM GenAI 構建變體有

CUDA

構建 FBGEMM GenAI 的一般步驟如下

設定獨立的構建環境。
設定用於 CUDA 構建的工具鏈。
安裝 PyTorch。
執行構建指令碼。

設定獨立的構建環境¶

按照說明設定 Conda 環境

其他預構建設定¶

由於 FBGEMM GenAI 利用與 FBGEMM_GPU 相同的構建過程，請參閱準備構建以獲取更多預構建設定資訊。

準備構建¶

克隆倉庫及其子模組，並安裝 requirements_genai.txt

# !! Run inside the Conda environment !!

# Select a version tag
FBGEMM_VERSION=v1.2.0

# Clone the repo along with its submodules
git clone --recursive -b ${FBGEMM_VERSION} https://github.com/pytorch/FBGEMM.git fbgemm_${FBGEMM_VERSION}

# Install additional required packages for building and testing
cd fbgemm_${FBGEMM_VERSION}/fbgemm_gpu
pip install -r requirements_genai.txt

設定 Wheel 構建變數¶

構建 Python wheel 時，必須首先正確設定包名、Python 版本標籤和 Python 平臺名稱

# Set the package name depending on the build variant
export package_name=fbgemm_genai_{cuda}

# Set the Python version tag.  It should follow the convention `py<major><minor>`,
# e.g. Python 3.13 --> py313
export python_tag=py313

# Determine the processor architecture
export ARCH=$(uname -m)

# Set the Python platform name for the Linux case
export python_plat_name="manylinux_2_28_${ARCH}"
# For the macOS (x86_64) case
export python_plat_name="macosx_10_9_${ARCH}"
# For the macOS (arm64) case
export python_plat_name="macosx_11_0_${ARCH}"
# For the Windows case
export python_plat_name="win_${ARCH}"

CUDA 構建¶

為 CUDA 構建 FBGEMM GenAI 需要安裝 NVML 和 cuDNN，並透過環境變數使其可用於構建。但是，構建包不需要存在 CUDA 裝置。

與僅支援 CPU 的構建類似，透過將 --cxxprefix=$CONDA_PREFIX 附加到構建命令，可以啟用使用 Clang + libstdc++ 進行構建，前提是工具鏈已正確安裝。

# !! Run in fbgemm_gpu/ directory inside the Conda environment !!

# [OPTIONAL] Specify the CUDA installation paths
# This may be required if CMake is unable to find nvcc
export CUDACXX=/path/to/nvcc
export CUDA_BIN_PATH=/path/to/cuda/installation

# [OPTIONAL] Provide the CUB installation directory (applicable only to CUDA versions prior to 11.1)
export CUB_DIR=/path/to/cub

# [OPTIONAL] Allow NVCC to use host compilers that are newer than what NVCC officially supports
nvcc_prepend_flags=(
  -allow-unsupported-compiler
)

# [OPTIONAL] If clang is the host compiler, set NVCC to use libstdc++ since libc++ is not supported
nvcc_prepend_flags+=(
  -Xcompiler -stdlib=libstdc++
  -ccbin "/path/to/clang++"
)

# [OPTIONAL] Set NVCC_PREPEND_FLAGS as needed
export NVCC_PREPEND_FLAGS="${nvcc_prepend_flags[@]}"

# [OPTIONAL] Enable verbose NVCC logs
export NVCC_VERBOSE=1

# Specify cuDNN header and library paths
export CUDNN_INCLUDE_DIR=/path/to/cudnn/include
export CUDNN_LIBRARY=/path/to/cudnn/lib

# Specify NVML filepath
export NVML_LIB_PATH=/path/to/libnvidia-ml.so

# Specify NCCL filepath
export NCCL_LIB_PATH=/path/to/libnccl.so.2

# Build for SM70/80 (V100/A100 GPU); update as needed
# If not specified, only the CUDA architecture supported by current system will be targeted
# If not specified and no CUDA device is present either, all CUDA architectures will be targeted
cuda_arch_list=7.0;8.0

# Unset TORCH_CUDA_ARCH_LIST if it exists, bc it takes precedence over
# -DTORCH_CUDA_ARCH_LIST during the invocation of setup.py
unset TORCH_CUDA_ARCH_LIST

# Build the wheel artifact only
python setup.py bdist_wheel \
    --package_variant=genai \
    --python-tag="${python_tag}" \
    --plat-name="${python_plat_name}" \
    --nvml_lib_path=${NVML_LIB_PATH} \
    --nccl_lib_path=${NCCL_LIB_PATH} \
    -DTORCH_CUDA_ARCH_LIST="${cuda_arch_list}"

# Build and install the library into the Conda environment
python setup.py install \
    --package_variant=genai \
    --nvml_lib_path=${NVML_LIB_PATH} \
    --nccl_lib_path=${NCCL_LIB_PATH} \
    -DTORCH_CUDA_ARCH_LIST="${cuda_arch_list}"

構建後檢查（面向開發者）¶

由於 FBGEMM GenAI 利用與 FBGEMM_GPU 相同的構建過程，請參閱構建後檢查（面向開發者）以獲取有關額外構建後檢查的資訊。