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 much higher flexibility than TVM and without having to manually specify compute schedules.

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. The whole compiler stack (~30k lines of C++ code) should take around 15 secs to compile.

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...)