We're hiring! If you are interested in working on Triton at OpenAI, we have roles open for Compiler Engineers and Kernel Engineers.

`Documentation`

Triton Developer Conference Registration Now Closed

The Triton Developer Conference will be held in a hybrid mode at the Microsoft Silicon Valley Campus in Mountain View, California. The conference will be held on September 20th from 10am to 4pm, followed by a reception till 5:30 pm.

Tentative Agenda for the conference (subject to change):

Time	Title	Speaker
10:00 AM	Welcome	Kevin Scott (Microsoft)
10:20 AM	The Triton Compiler: Past, Present and Future	Phil Tillet (OpenAI)
11:00 AM	Break
11:20 AM	Hopper support in Triton	Gustav Zhu (Nvidia)
11:40 AM	Bringing Triton to AMD GPUs	Jason Furmanek, Lixun Zhang (AMD)
12:00 PM	Intel XPU Backend for Triton	Eikan Wang (Intel)
12:20 PM	Vectorization of Triton Kernels for Qualcomm Hexagon Backend	Javed Absar (Qualcomm)
12:30 PM	Lunch
1:40 PM	Triton for MTIA	Roman Levenstein et al, (Meta)
2:00 PM	Using Triton IR for high-performance fusions in XLA	George Karpenkov (Google)
2:20 PM	Triton for All: Triton as a device-independent language	Ian Bearman (Microsoft)
2:40 PM	Break
3:00 PM	PyTorch 2.0 and TorchInductor	Jason Ansel, Horace He (Meta)
3:20 PM	Pallas: A JAX Kernel Language	Sharad Vikram (Google)
3:40 PM	Writing Grouped GEMMs in Triton	Vinod Grover (Nvidia)
4:00 PM	Reception

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/openai/triton.git;
cd triton;

pip install ninja cmake; # 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; # 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.

Find the version of LLVM that Triton builds against. Check python/setup.py for a line like
```
version = "llvm-17.0.0-c5dede880d17"
```
This means that the version of Triton you have builds against LLVM c5dede880d17.
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"
$ 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>/python
$ LLVM_INCLUDE_DIRS=$LLVM_BUILD_DIR/include \
  LLVM_LIBRARY_DIR=$LLVM_BUILD_DIR \
  LLVM_SYSPATH=$LLVM_BUILD_DIR \
  pip install -e python

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

khushi-411/triton