189491727a
This change attaches builtin-ness as an explicit attribute, rather than a module prefix expectation. This permits us to source those builtins from multiple sub-modules (useful when some builtins are part of the true cyclic implementation core, and some are just useful library additions); but also prevents accidental inclusion of non-builtins that happen to be in the right library. Once the flag exists, and the compiler is using `is_builtin()` for decision making; the existence of the current `@extern` interface becomes isomorphic to `@builtin`; and the interface can be unified. Leaving `@extern` a thin-wrapper, and encouraging continued use of it, establishes future-proofing towards adding additional extern tracing, metric hooks, or scanning in the future. * Add `triton.impl` package to hold the core, order dependent impl details. * Extract `@builtin` and unify `@extern`; add `is_builtin()` * Add sense bit so that `@builtin` detection is less fragile. * Modify the compiler to use `is_builtin()`
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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 3.6-3.9 and PyPy 3.6-3.7.
And the latest nightly release:
pip install -U --pre triton
Changelog
Version 1.1 is out! New features include:
- Many, many bugfixes
- More documentation
- Automatic on-disk caching of compiled binary objects
- Random Number Generation
- Faster (up to 2x on A100), cleaner blocksparse ops
Contributing
Community contributions are more than welcome, whether it be to fix bugs or to add new features. Feel free to open GitHub issues about your contribution ideas, and we will review them. A contributor's guide containing general guidelines is coming soon!
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
Disclaimer
Triton is a fairly recent project, and it is under active development. We expect it to be pretty useful in a wide variety of cases, but don't be surprised if it's a bit rough around the edges :)