Getting Started with Building from Source

This document describes how to build the TT-XLA project on your local machine. You must build from source if you want to develop for TT-XLA. If you only want to run models, please choose one of the following sets of instructions instead:

• Installing a Wheel and Running an Example - You should choose this option if you want to run models.
• Using a Docker Container to Run an Example - Choose this option if you want to keep the environment for running models separate from your existing environment.

The following topics are covered:

• Configuring Hardware
• System Dependencies
• Build Process
• Testing
• Common Build Errors

NOTE: If you encounter issues, please request assistance on the TT-XLA Issues page.

Configuring Hardware

Before setup can happen, you must configure your hardware. You can skip this section if you already completed the configuration steps. Otherwise, follow the instructions on the Getting Started page.

System Dependencies

TT-XLA has the following system dependencies:

• Ubuntu 22.04
• Python 3.10
• python3.10-venv
• Clang 17
• GCC 11
• Ninja
• CMake 4.0.3

Installing Python

If your system already has Python installed, make sure it is Python 3.10:

python3 --version

If not, install Python:

sudo apt install python3.10

Installing CMake 4.0.3

To install CMake 4 or higher, do the following:

1. Install CMake 4.0.3:

pip install cmake==4.0.3

2. Check that the correct version of CMake is installed:

cmake --version

If you see cmake version 4.0.3 you are ready for the next section.

Installing Clang 17

To install Clang 17, do the following:

1. Install Clang 17:

wget https://apt.llvm.org/llvm.sh
chmod u+x llvm.sh
sudo ./llvm.sh 17
sudo apt install -y libc++-17-dev libc++abi-17-dev
sudo ln -s /usr/bin/clang-17 /usr/bin/clang
sudo ln -s /usr/bin/clang++-17 /usr/bin/clang++

2. Check that the selected GCC candidate using Clang 17 is using 11:

clang -v

3. Look for the line that starts with: Selected GCC installation:. If it is something other than GCC 11, and you do not see GCC 11 listed as an option, please install GCC 11 using:

sudo apt-get install gcc-11 lib32stdc++-11-dev lib32gcc-11-dev

4. If you see GCC 12 listed as installed and listed as the default choice, uninstall it with:

sudo rm -rf /usr/bin/../lib/gcc/x86_64-linux-gnu/12

Installing Ninja

To install Ninja, do the following:

sudo apt install ninja-build

Installing OpenMPI

To install OpenMPI, do the following:

sudo wget -q https://github.com/dmakoviichuk-tt/mpi-ulfm/releases/download/v5.0.7-ulfm/openmpi-ulfm_5.0.7-1_amd64.deb -O /tmp/openmpi-ulfm.deb && sudo apt install /tmp/openmpi-ulfm.deb

Installing Additional Dependencies

TT-XLA additionally requires the following libraries:

sudo apt install protobuf-compiler libprotobuf-dev
sudo apt install ccache
sudo apt install libnuma-dev
sudo apt install libhwloc-dev
sudo apt install libboost-all-dev

Build Process

TT-XLA integration with the TT-MLIR compiler is still in progress. Currently TT-XLA depends on the TT-MLIR toolchain to build from source. This build flow provides an easy way to experiment with TT-XLA, StableHLO, and the TT-MLIR infrastructure. The build process will be updated in the future to enhance the user experience.

Building the TT-MLIR Toolchain

Before compiling TT-XLA, the TT-MLIR toolchain needs to be built:

• Clone the tt-mlir repo.
• Follow the TT-MLIR build instructions to set up the environment and build the toolchain.

Building TT-XLA

Before running these commands to build TT-XLA, please ensure that the environment variable TTMLIR_TOOLCHAIN_DIR is set to point to the TT-MLIR toolchain directory created above as part of the TT-MLIR environment setup (for example export TTMLIR_TOOLCHAIN_DIR=/opt/ttmlir-toolchain/). You can also set export LOGGER_LEVEL=DEBUG in order to enable debug logs. To build TT-XLA do the following:

1. Make sure you are not in the TT-MLIR build directory, and you are in the location where you want TT-XLA to install.

2. Clone TT-XLA:

git clone https://github.com/tenstorrent/tt-xla.git

3. Navigate into the TT-XLA folder:

cd tt-xla

4. Run the following set of commands to build TT-XLA (this will build the PJRT plugin and install it into venv):

source venv/activate
cmake -G Ninja -B build # -DCMAKE_BUILD_TYPE=Debug in case you want debug build
cmake --build build

5. To verify that everything is working correctly, run the following command:

python -c "import jax; print(jax.devices('tt'))"

The command should output all available TT devices, e.g. [TTDevice(id=0, arch=Wormhole_b0)]

6. (optional) If you want to build the TT-XLA wheel, run the following command:

cd python_package
python setup.py bdist_wheel

The above command outputs a python_package/dist/pjrt_plugin_tt*.whl file which is self-contained. To install the created wheel, run:

pip install dist/pjrt_plugin_tt*.whl

The wheel has the following structure:

pjrt_plugin_tt/                     # PJRT plugin package
    |-- __init__.py
    |-- pjrt_plugin_tt.so               # PJRT plugin binary
    |-- tt-metal/                       # tt-metal runtime dependencies (kernels, riscv compiler/linker, etc.)
    `-- lib/                            # shared library dependencies (tt-mlir, tt-metal)
jax_plugin_tt/                      # Thin JAX wrapper
    `-- __init__.py                     # imports and sets up pjrt_plugin_tt for XLA
torch_plugin_tt                     # Thin PyTorch/XLA wrapper
    `-- __init__.py                     # imports and sets up pjrt_plugin_tt for PyTorch/XLA

It contains a custom Tenstorrent PJRT plugin (pjrt_plugin_tt.so) and its dependencies (tt-mlir and tt-metal). Additionally, there are thin wrappers for JAX (jax_plugin_tt) and PyTorch/XLA (torch_plugin_tt) that import the PJRT plugin and set it up for use with the respective frameworks.

Testing

The TT-XLA repo contains various tests in the tests directory. To run an individual test, pytest -svv is recommended in order to capture all potential error messages down the line. Multi-chip tests can be run only on specific Tenstorrent hardware, therefore these tests are structured in folders named by the Tenstorrent cards/systems they can be run on. For example, you can run pytest -v tests/jax/multi_chip/n300 only on a system with an n300 Tenstorrent card. Single-chip tests can be run on any system with the command pytest -v tests/jax/single_chip.

Common Build Errors

• Building TT-XLA requires clang-17. Please make sure that clang-17 is installed on the system and clang/clang++ links to the correct version of the respective tools.
• Please also see the TT-MLIR docs for common build errors.

Pre-commit

Pre-commit applies a git hook to the local repository such that linting is checked and applied on every git commit action. Install it from the root of the repository using:

source venv/activate
pre-commit install

If you have already committed something locally before installing the pre-commit hooks, you can run this command to check all files:

pre-commit run --all-files

For more information please visit pre-commit.