Thomas bd900e0a6f [BACKEND] Fix reductions when number of unique element is smaller than layout (#1913)
Fix calculation of unique number of threads within a warp. We need to
consider the number of elements per thread in the calculation. Also
change the layout test to integer sum in order to catch bugs with unique
data as max reduction may hide those kind of problems.
2023-07-07 19:48:13 -07:00

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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/python;
pip install cmake; # build-time dependency
pip install -e .

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.

If youre interested in joining our team and working on Triton & GPU kernels, were hiring!

Compatibility

Supported Platforms:

  • Linux

Supported Hardware:

  • NVIDIA GPUs (Compute Capability 7.0+)
  • Under development: AMD GPUs, CPUs
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