Build Instructions¶

Note: The most up-to-date build instructions are embedded in a set of scripts bundled in the FBGEMM repo under setup_env.bash.

The general steps for building FBGEMM_GPU are as follows:

Set up an isolated build environment.
Set up the toolchain.
Run the build script.

FBGEMM Requirements¶

Hardware Requirements¶

Building and running FBGEMM requires a CPU with support for AVX2 instruction set or higher.

In general, FBGEMM does not have any dependency on Intel MKL. However, for performance comparisons, some benchmarks use MKL functions. If MKL is found or the MKL path is provided through the INTEL_MKL_DIR environment variable, the benchmarks will be built with MKL and performance numbers will be reported for MKL functions. Otherwise, this subset of benchmarks will not built.

Software Dependencies¶

All three dependencies are provided through the FBGEMM repo’s git submodules. However, if a custom version is desired, they can be set in the build using the environment variables ASMJIT_SRC_DIR, CPUINFO_SRC_DIR, and GOOGLETEST_SOURCE_DIR.

asmjit¶

With inner kernels, FBGEMM takes a “one size doesn’t fit all” approach, so the implementation dynamically generates efficient matrix-shape specific vectorized code using a third-party library called asmjit.

cpuinfo¶

FBGEMM detects CPU instruction set support at runtime using the cpuinfo library provided by the PyTorch project, and dispatches optimized kernels for the detected instruction set.

GoogleTest¶

GoogleTest is required to build and run FBGEMM’s tests. However, GoogleTest is not required if you don’t want to run FBGEMM tests. Tests are built together with the library by default; to turn this off, simply set FBGEMM_BUILD_TESTS=0.

Set Up an Isolated Build Environment¶

Follow the instructions for setting up the Conda environment at Set Up an Isolated Build Environment.

Install the Build Tools¶

C/C++ Compiler¶

For Linux and macOS platforms, follow the instructions in C/C++ Compiler (GCC) to install the GCC toolchain. For Clang-based builds, follow the instructions in C/C++ Compiler (Clang) to install the Clang toolchain.

For builds on Windows machines, Microsoft Visual Studio 2019 or newer is recommended. Follow the installation instructions provided by Microsoft here.

Other Build Tools¶

Install the other necessary build tools such as ninja, cmake, etc:

conda install -n ${env_name} -y \
    bazel \
    cmake \
    doxygen \
    make \
    ninja \
    openblas

Note that the bazel package is only necessary for Bazel builds, and the ninja package is only necessary for Windows builds.

Build the FBGEMM Library¶

Preparing the Build¶

Clone the repo along with its submodules:

# !! Run inside the Conda environment !!

# Clone the repo and its submodules
git clone --recurse-submodules https://github.com/pytorch/FBGEMM.git
cd FBGEMM

Building on Linux and macOS (CMake + GCC)¶

Assuming a Conda environment with all the tools installed, the CMake build process is straightforward:

# !! Run inside the Conda environment !!

# Create a build directory
mkdir build
cd build

# Set CMake build arguments
build_args=(
  -DUSE_SANITIZER=address
  -DFBGEMM_LIBRARY_TYPE=shared
  -DPYTHON_EXECUTABLE=`which python3`

  # OPTIONAL: Set to generate Doxygen documentation
  -DFBGEMM_BUILD_DOCS=ON
)

# Set up the build
cmake ${build_args[@]} ..

# Build the library
make -j VERBOSE=1

# Run all tests
make test

# Install the library
make install

Build Issues with GCC 12+¶

As of time of writing, compilation of FBGEMM on GCC 12+ will fail due to a known compiler regression. To work around the issue, append the following exports prior to running CMake:

# !! Run inside the Conda environment !!

export CFLAGS+=" -Wno-error=maybe-uninitialized -Wno-error=uninitialized -Wno-error=restrict"
export CXXFLAGS+=" -Wno-error=maybe-uninitialized -Wno-error=uninitialized -Wno-error=restrict"

Please see GitHub issues 77939, 1094, and 1666 for more details.

Building on Linux and macOS (CMake + Clang)¶

The steps for building FBGEMM using Clang are exactly the same as that for building using GCC. However, extra build arguments need to be added to the CMake invocation to specify the Clang path, the LLVM-based C++ standard library (libc++), and the LLVM-based OpenMP implementation (libomp):

# !! Run inside the Conda environment !!

# Locate Clang
cc_path=$(which clang)
cxx_path=$(which clang++)

# Append to the CMake build arguments
build_args+=(
  -DCMAKE_C_COMPILER="${cc_path}"
  -DCMAKE_CXX_COMPILER="${cxx_path}"
  -DCMAKE_C_FLAGS=\"-fopenmp=libomp -stdlib=libc++ -I $CONDA_PREFIX/include\"
  -DCMAKE_CXX_FLAGS=\"-fopenmp=libomp -stdlib=libc++ -I $CONDA_PREFIX/include\"
)

Building on Linux (Bazel)¶

Likewise, a Bazel build is also very straightforward:

# !! Run inside the Conda environment !!

# Build the library
bazel build -s :*

# Run all tests
bazel test -s :*

Building on Windows¶

# Specify the target architecture to bc x64
call "C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" x64

# Create a build directory
mkdir %BUILD_DIR%
cd %BUILD_DIR%

cmake -G Ninja -DFBGEMM_BUILD_BENCHMARKS=OFF -DFBGEMM_LIBRARY_TYPE=${{ matrix.library-type }} -DCMAKE_BUILD_TYPE=Release -DCMAKE_C_COMPILER="cl.exe" -DCMAKE_CXX_COMPILER="cl.exe" ..
ninja -v all