1baa4e125f
This PR decouples the operand loading from the mma codegen to make it ready for the ongoing `DotOperandEncodingAttr` migration. The existing DotOp conversion is composed of the following two procedures: 1. Loading the $a,$b,$c operand from smem to registers 2. Conducting the MMA instruction codegen. While in the latest design, the 1st stage should be part of the `convert_layout(shared_layout) -> dot_operand_layout`, that's why the decoupling is necessary. Some details, this PR introduces a `MMA16816ConversionHelper` class, it has `loadA`, `loadB` and `loadC` methods to help load $a, $b and $c from smem to registers, both `loadA` and `loadB` methods returns a `LLVM::Struct` which should be compatible with the new `DotOperandEncodingAttr` conversion. The conversion layout for $a and $b is as follows: ```c++ // The layout is a list of Value with coordinate of (i,j), the order is as // the follows: // [ // (0,0), (0,1), (1,0), (1,1), # i=0, j=0 // (0,2), (0,3), (1,2), (1,3), # i=0, j=1 // (0,4), (0,5), (1,4), (1,5), # i=0, j=2 // ... // (2,0), (2,1), (3,0), (3,1), # i=1, j=0 // (2,2), (2,3), (3,2), (3,3), # i=1, j=1 // (2,4), (2,5), (2,4), (2,5), # i=1, j=2 // ... // ] // i \in [0, n0) and j \in [0, n1) ``` In the `convertDot` method, it takes loaded $a, $b, $c($a and $b are type of `LLVM::Struct` while $c is a scalar Value), extract the elements from `LLVM::Struct` following the layout above, and pass the elements to MMA inline asm.
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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 :)