We're hiring! If you are interested in working on Triton at OpenAI, we have roles open for Compiler Engineers and Kernel Engineers.
Documentation |
Nightly Wheels |
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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.
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
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
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.
-
Find the version of LLVM that Triton builds against. Check
cmake/llvm-hash.txt
to see the current version. For example, if it says: 49af6502c6dcb4a7f7520178bd14df396f78240cThis means that the version of Triton you have builds against LLVM 49af6502.
-
git checkout
LLVM at this revision. Optionally, make additional modifications to LLVM. -
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
-
Grab a snack, this will take a while.
-
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
-
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
topip install
to make nop builds faster. Without this, every invocation ofpip install
uses a different symlink to cmake, and this forces ninja to rebuild most of the.a
files. -
vscode intellisense has some difficulty figuring out how to build Triton's C++ (probably because, in our build, users don't invoke cmake directly, but instead use setup.py). Teach vscode how to compile Triton as follows.
- Do a local build.
- Get the full path to the
compile_commands.json
file produced by the build:find python/build -name 'compile_commands.json | xargs readlink -f'
- In vscode, install the
C/C++
extension,
then open the command palette (
Shift + Command + P
on Mac, orShift + Ctrl + P
on Windows/Linux) and openC/C++: Edit Configurations (UI)
. - Open "Advanced Settings" and paste the full path to
compile_commands.json
into the "Compile Commands" textbox.
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
You may find it helpful to make a symlink to the builddir and tell your local git to ignore it.
$ ln -s python/build/cmake<...> build
$ echo build >> .git/info/exclude
Then you can e.g. rebuild and run lit with the following command.
$ ninja -C build && ( cd build ; lit test )
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)