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[README] Improved wording

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Philippe Tillet
2020-05-10 00:34:10 -04:00
committed by Philippe Tillet
parent f35b9100e2
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README.md
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# Triton # 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 much higher flexibility than [TVM](https://github.com/apache/incubator-tvm) and without having to manually specify compute schedules. 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: 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-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-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](https://github.com/NVIDIA/cutlass)). This includes transparent support for mixed-precision and Tensor Cores. - **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](https://github.com/NVIDIA/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. 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](http://www.eecs.harvard.edu/~htk/publication/2019-mapl-tillet-kung-cox.pdf). Please cite us if you use our work! The formal foundations of this project are described in the following MAPL2019 publication: [Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations](http://www.eecs.harvard.edu/~htk/publication/2019-mapl-tillet-kung-cox.pdf). Please consider citing us if you use our work!
## Installation ## Installation
@@ -29,6 +27,15 @@ or the latest development version with:
pip install -e "git+https://github.com/ptillet/triton.git#egg=triton&subdirectory=python" 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 ## Getting Started
@@ -38,7 +45,3 @@ Please visit the [documentation](https://docs.triton-lang.org) to get started wi
## Contributing ## 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! 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!
## ISAAC (deprecated) for fast inference
Before working on Triton, I wrote custom auto-tuned PTX code for fast, quantized inference on GPUs. While this project is now deprecated, you can use it at your own risk by checking out the "isaac" tag in this repository.