triton/README.md

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 main components of Triton at the moment are:

• Triton-C: An imperative, single-threaded language for writing highly efficient compute-kernels at a relatively high abstraction level (think numpy-like array operations in a C-like language).
• Triton-IR: A special-purpose intermediate representation (Triton-IR) for aiding array-level program analysis and optimizations in Triton-C programs.
• Triton-JIT: An optimizing just-in-time compiler for Triton-IR, which generates GPU code on par with state-of-the-art CUDA-C (e.g., CUTLASS). This includes transparent support for mixed-precision and Tensor Cores.

Bindings for automatic PyTorch custom op generations are included in PyTriton, along with a small DSL based on einsum that supports convolutions, shift-convolutions, direct einsums, etc.

The formal 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 us if you use our work!

Installation

Triton is a fairly self-contained package and uses its own parser (forked from wgtcc) and LLVM-10+ for code generation.

You can install the latest release with pip as follows:

sudo apt-get install llvm-10-dev
pip install triton

or the latest development version with:

 pip install -e "git+https://github.com/ptillet/triton.git#egg=triton&subdirectory=python"

for the C++ package:

git clone https://github.com/ptillet/triton.git;
mkdir build;
cd build;
cmake ../;
make -j8;

Getting Started

Please visit the documentation to get started with Triton

Contributing

Please keep in mind that this is a project I have been carrying out completely on my own as part of my Ph.D. thesis. While I am confident in the approach, there are still many things to fix and to polish. Please contact me (ptillet AT g.harvard.edu) or raise an issue if you want to contribute!