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[PYTHON][TESTS][DOC] Various improvement of the API and code quality:

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* Simplified `triton.kernel` API to achieve lower latency:
  > .data_ptr() must now be passed as kernel argument. No more implicit
conversion from torch.tensor
  > compilation options are now constant attributes, i.e., opt.d('VAR')
becomes opt.VAR
  > torch.device must now be passed explicitly to triton.kernel (no
longer inferred from torch.tensor arguments)
* C++ tests moved to `python/tests/`
* C++ tutorial created in `tutorials/`
* Python tutorial created in python/tutorials/
* Version changed to 1.0alpha
* No longer copying C++ headers into the Python package
* added python/triton/ops/ package for pre-written Triton ops
This commit is contained in:
Philippe Tillet
2021-01-29 17:27:16 -05:00
parent a5a477c36b
commit 269ebc12e5
63 changed files with 2255 additions and 3883 deletions
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19
README.md
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@@ -2,20 +2,11 @@
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:
- **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-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.
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!
The 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
Triton is a fairly self-contained package and uses its own parser (forked from [wgtcc](https://github.com/wgtdkp/wgtcc)) and LLVM-10+ for code generation.
You can install the latest release with pip as follows:
```
sudo apt-get install llvm-10-dev
@@ -30,6 +21,7 @@ or the latest development version with:
for the C++ package:
```
git clone https://github.com/ptillet/triton.git;
cd triton;
mkdir build;
cd build;
cmake ../;
@@ -39,9 +31,4 @@ make -j8;
## Getting Started
Please visit the [documentation](https://docs.triton-lang.org) to get started with Triton
## 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!
You can find tutorials for Triton for [Python](https://github.com/ptillet/triton/tree/master/tutorials) and [C++](https://github.com/ptillet/triton/tree/master/python/tutorials).