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 custom ops at higher productivity than CUDA, but also with much higher flexibility than TVM.

The main scope 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 using numpy-like extensions of the C language.
Triton-IR: An intermediate-representation for optimizing multi-dimensional array operations in linear algebra programs
Triton-JIT: An optimizing just-in-time compiler for Triton-C, which generates GPU code on par with state-of-the-art CUDA-C (e.g., CUTLASS) and PTX (e.g., ISAAC). 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 cite us if you use our work!

Installation

Triton is a fairly self-contained package and uses its own parser (forked from wgtcc) and LLVM code-generator. However, at the moment it relies on LLVM-8.0+ for PTX code generation.

sudo apt-get install llvm-8-dev
git clone https://github.com/ptillet/triton.git;
cd triton/python/;
python setup.py develop;
cd examples;
python einsum.py

Tutorials

The Triton-C language
The PyTriton API
Extended Einstein Summations (coming soon...)
The Triton-IR representation (coming soon...)
The Triton-JIT compiler (coming soon...)