Precompiled XLA binaries for EXLA.

Currently supports UNIX systems, including macOS (although no built-in support for Apple Metal). Windows platforms are only supported upstream via WSL.

Usage

EXLA already depends on this package, so you generally don't need to install it yourself. There is however a number of environment variables that you may want to use in order to customize the variant of XLA binary.

The binaries are always built/downloaded to match the current configuration, so you should set the environment variables in .bash_profile or a similar configuration file so you don't need to export it in every shell session.

XLA_TARGET

The default value is usually cpu, which implies the final the binary supports targeting only the host CPU. If a matching CUDA version is detected, the target is set to CUDA accordingly.

To use XLA with NVidia GPU you need CUDA and cuDNN compatible with your GPU drivers. See the installation instructions and the cuDNN support matrix for version compatibility. To use precompiled XLA binaries specify a target matching your CUDA version (like cuda12). You can find your CUDA version by running nvcc --version (note that nvidia-smi shows the highest supported CUDA version, not the installed one). When building from source it's enough to specify cuda as the target.

Note that all precompiled Linux binaries assume glibc 2.31 or newer.

Notes for ROCm

For GPU support, we primarily rely on CUDA, because of the popularity and availability in the cloud. In case you use ROCm and it does not work, please open up an issue and we will be happy to help.

In addition to building in a local environment, you can build the ROCm binary using the Docker-based scripts in builds/. You may want to adjust the ROCm version in rocm.Dockerfile accordingly.

When you encounter errors at runtime, you may want to set ROCM_PATH=/opt/rocm-5.7.0 and LD_LIBRARY_PATH="/opt/rocm-5.7.0/lib" (with your respective version). For further issues, feel free to open an issue.

XLA_BUILD

Defaults to false. If true the binary is built locally, which may be intended if no precompiled binary is available for your target environment. Once set, you must run mix deps.clean xla --build explicitly to force XLA to recompile. Building has a number of dependencies, see Building from source below.

XLA_ARCHIVE_URL

A URL pointing to a specific build of the .tar.gz archive. When using this option you need to make sure the build matches your OS, CPU architecture and the XLA target.

XLA_CACHE_DIR

The directory to store the downloaded and built archives in. Defaults to the standard cache location for the given operating system.

XLA_TARGET_PLATFORM

The target triplet describing the target platform, such as aarch64-linux-gnu. By default this target is inferred for the host, however you may want to override this when cross-compiling the project using Nerves.

Building from source

Note: currently only macOS and Linux is supported. When on Windows, the best option to use XLA and EXLA is by running inside WSL.

To build the XLA binaries locally you need to set XLA_BUILD=true and possibly XLA_TARGET. Keep in mind that the compilation usually takes a very long time.

You will need the following installed in your system for the compilation:

• Git for fetching XLA source
• Bazel v6.5.0 for compiling XLA
• Python3 with NumPy installed for compiling XLA

Common issues

Bazel version

Use bazel --version to check your Bazel version, make sure you are using v6.5.0. Most binaries are available on Github, but it can also be installed with asdf:

asdf plugin-add bazel
asdf install bazel 6.5.0
asdf global bazel 6.5.0

GCC

You may have issues with newer and older versions of GCC. XLA builds are known to work with GCC versions between 7.5 and 9.3. If your system uses a newer GCC version, you can install an older version and tell Bazel to use it with export CC=/path/to/gcc-{version} where version is the GCC version you installed.

Python and asdf

Bazel cannot find python installed via the asdf version manager by default. asdf uses a function to lookup the specified version of a given binary, this approach prevents Bazel from being able to correctly build XLA. The error is unknown command: python. Perhaps you have to reshim?. There are two known workarounds:

1. Explicitly change your $PATH to point to a Python installation (note the build process looks for python, not python3). For example:

    # Point directly to a specific Python version
    export PATH=$HOME/.asdf/installs/python/3.10.8/bin:$PATH
   

2. Use the asdf direnv plugin to install direnv 2.20.0. direnv along with the asdf-direnv plugin will explicitly set the paths for any binary specified in your project's .tool-versions files.

If you still get the error, you can also try setting PYTHON_BIN_PATH, like export PYTHON_BIN_PATH=/usr/bin/python3.9.

After doing any of the steps above, it may be necessary to clear the build cache by removing ~/.cache/xla_build (or the corresponding OS-specific cache location).

GPU support

To build binaries with GPU support, you need all the GPU-specific dependencies (CUDA, ROCm), then you can build with either XLA_TARGET=cuda or XLA_TARGET=rocm. See the XLA_TARGET for more details.

TPU support

All you need is setting XLA_TARGET=tpu.

Compilation-specific environment variables

You can use the following env vars to customize your build:

• BUILD_CACHE - controls where to store XLA source and builds

• BUILD_FLAGS - additional flags passed to Bazel

• BUILD_MODE - controls to compile opt (default) artifacts or dbg, example: BUILD_MODE=dbg

Runtime flags

You can further configure XLA runtime options with XLA_FLAGS, see: xla/debug_options_flags.cc for the list of available flags.