fb3f2d6feb
* refine the gemm tuning scripts to reduce tuning space and better perf numbers * added code to support tuning in full tuning space * add a function to get best tuning config * refine the matmul tutorial example to print out best tuning config for each input * added even_k to gemm kernel heuristic for better performance * address review comments
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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/ROCmSoftwarePlatform/triton.git
cd triton
git checkout triton-mlir
Build
cd python
pip3 install cmake; # build time dependency
pip3 install -e .
Run tests:
# Run the Python tests
pytest
# Run the ctest
cd build/temp.linux-x86_64-3.7
ctest
# Run the lit tests
lit -v test
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 you’re interested in joining our team and working on Triton & GPU kernels, we’re hiring!
Compatibility
Supported Platforms:
- Linux
Supported Hardware:
- NVIDIA GPUs (Compute Capability 7.0+)
- Under development: AMD GPUs, CPUs