This PR does the following:
- CUDA utilities (e.g., cuGetInfo) won't be compiled as part of libtriton.so anymore.
- Refactoring driver/llvm.cc to split it between PTX codegen and python.
- By extension this will also deprecate include/external so Triton won't have to live with a copy of some CUDA/Hip headers anymore.
- `triton-translate` becomes a `triton.tools.aot` Python utility that re-uses functions from the triton.compile sub-module.
LLVM Conversion for Dot op.
Due to the lack of `convert_layout`, currently, the dot only supports
the following combination of operands
- `$a` in shared layout
- `$b` in shared layout
- `$c` in MMA layout(but only Splat-like, leaving the generic cases to
`convert_layout`)
This PR focus on `mma.16816` related logic support, leaving the other
cases to the following PR.
Co-authored-by: Philippe Tillet <phil@openai.com>
This code in this branch assumes the `src` operand in
`insert_slice_async` always aliases the result, which shouldn't hold for
generally cases but is just a workaround to make the pipeline pass work.
I'm also working on the complete analysis in another
[branch](https://github.com/openai/triton-mlir/tree/keren/analyze-slice).
This PR does
1. Add some C++ tests for `PTXFormat`
2. Enhance the functionality of `PTXFormat`, make a `PTXInstr` instance
can be called multiple times similar as a C function.
This PR does the following things:
- Code refactoring on Load and Store op codegen, rewrite with same logic
and share much code
- Support the vectorized load/store
This PR both simplifies the layout conversion simplification algorithm, and also improves it to make it work with vectorized element-wise ops. The conversion optimizer still has a lot of room for improvements, and other PRs will address its limitations (ideally via some sort of explicit cost model)
This deprecates the use of release-build LLVM hosted by the LLVM project, which makes debugging harder for developers.
This PR implements the following solution:
1. Create LLVM release tarballs with assert enabled on our own (using Docker)
2. Host them in our own GitHub repositories
3. Use our LLVM for CI and/or development if `TRITON_USE_ASSERT_ENABLED_LLVM=1` is set.
The purpose of this PR is analyzing shared memory aliases so that we can
fix memory allocation bugs and save memory allocations in triton code
involving complex control flows.
Changes to memory bar and allocation are on the way.
Co-authored-by: Philippe Tillet <phil@openai.com>
* [BACKEND] two minor bugfix on StoreOpLowering and kernel launch & support optional other in LoadOpLowering
* Clean code
Co-authored-by: goostavz <gzhu@nvidia.com>
Co-authored-by: Yan Chunwei <yanchunwei@outlook.com>
Add backend support of arith::AddIOp, arith::AddFOp, GetProgramIdOp, GEPOp and bugfix for SplatOp, StoreOp, FuncOp
Co-authored-by: gzhu <gzhu@nvidia.com>
