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Documentation

Triton

This is the development repository of Triton, a language and compiler for writing highly efficient custom Deep-Learning primitives. The aim of Triton is to provide an open-source environment to write fast code at higher productivity than CUDA, but also with higher flexibility than other existing DSLs.

The foundations of this project are described in the following MAPL2019 publication: Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations. Please consider citing this work if you use Triton!

The official documentation contains installation instructions and tutorials.

Quick Installation

You can install the latest stable release of Triton from pip:

pip install triton

Binary wheels are available for CPython 3.7-3.11 and PyPy 3.8-3.9.

And the latest nightly release:

pip install -U --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/Triton-Nightly/pypi/simple/ triton-nightly

Install from source

git clone https://github.com/ROCmSoftwarePlatform/triton.git
cd triton
git checkout triton-mlir

Build

cd python
pip3 install ninja cmake; # build time dependencies
pip3 install -e .

Run tests:

# Run the Python tests
pytest
# Run the ctest
cd build/temp.linux-x86_64-3.7
ctest
# Run the lit tests
lit -v test

Install from source

git clone https://github.com/openai/triton.git;
cd triton;

pip install ninja cmake wheel; # build-time dependencies
pip install -e python

Or with a virtualenv:

git clone https://github.com/openai/triton.git;
cd triton;

python -m venv .venv --prompt triton;
source .venv/bin/activate;

pip install ninja cmake wheel; # build-time dependencies
pip install -e python

Building with a custom LLVM

Triton uses LLVM to generate code for GPUs and CPUs. Normally, the Triton build downloads a prebuilt LLVM, but you can also build LLVM from source and use that.

LLVM does not have a stable API, so the Triton build will not work at an arbitrary LLVM version.

1. Find the version of LLVM that Triton builds against. Check python/setup.py for a line like

   version = "llvmorg-18-init-7000-g76ce4736721a"
   

   This means that the version of Triton you have builds against LLVM 76ce4736721a.

2. git checkout LLVM at this revision. Optionally, make additional modifications to LLVM.

3. Build LLVM. For example, you might run

   $ cd $HOME/llvm-project  # your clone of LLVM.
   $ mkdir build
   $ cd build
   $ cmake -G Ninja -DCMAKE_BUILD_TYPE=Release -DLLVM_ENABLE_ASSERTIONS=ON  ../llvm -DLLVM_ENABLE_PROJECTS="mlir;llvm"
   $ ninja
   

4. Grab a snack, this will take a while.

5. Build Triton as above, but set the following environment variables.

   # Modify as appropriate to point to your LLVM build.
   $ export LLVM_BUILD_DIR=$HOME/llvm-project/build
   
   $ cd <triton install>
   $ LLVM_INCLUDE_DIRS=$LLVM_BUILD_DIR/include \
     LLVM_LIBRARY_DIR=$LLVM_BUILD_DIR/lib \
     LLVM_SYSPATH=$LLVM_BUILD_DIR \
     pip install -e python
   

Tips for building

• Set TRITON_BUILD_WITH_CLANG_LLD=true as an environment variable to use clang and lld. lld in particular results in faster builds.

• Set TRITON_BUILD_WITH_CCACHE=true to build with ccache.

• Pass --no-build-isolation to pip install to make nop builds faster. Without this, every invocation of pip install uses a different symlink to cmake, and this forces ninja to rebuild most of the .a files.

Running tests

There currently isn't a turnkey way to run all the Triton tests, but you can follow the following recipe.

# One-time setup.  Note we have to reinstall local Triton because torch
# overwrites it with the public version.
$ pip install scipy numpy torch pytest lit && pip install -e python

# Run Python tests using your local GPU.
$ python3 -m pytest python/test/unit

# Move to builddir.  Fill in <...> with the full path, e.g.
# `cmake.linux-x86_64-cpython-3.11`.
$ cd python/build/cmake<...>

# Run C++ unit tests.
$ ninja test

# Run lit tests.
$ lit test

Changelog

Version 2.0 is out! New features include:

• Many, many bug fixes
• Performance improvements
• Backend rewritten to use MLIR
• Support for kernels that contain back-to-back matmuls (e.g., flash attention)

Contributing

Community contributions are more than welcome, whether it be to fix bugs or to add new features at github. For more detailed instructions, please visit our contributor's guide.

Compatibility

Supported Platforms:

• Linux

Supported Hardware:

• NVIDIA GPUs (Compute Capability 7.0+)
• Under development: AMD GPUs, CPUs