What is done in this PR:
- [x] Add `ConvertLayout`, `getSizePerThread` and `getShapePerCTA`
implementation for `SliceEncodingAttr`
- [x] Split `emitIndices` into two phases:
`emitBaseIndexForBlockedLayout` and `emitOffsetForBlockedLayout`
- [x] Add `ReduceOpConversion::matchAndRewriteBasic` implementation
- [x] Add `ReduceOpConversion::matchAndRewriteFast` implementation with
ptx instruction `shfl.sync`
- [x] Add support for scalar value in `StoreOpConversion`
- [x] Add Reduce1d and Reduce2d unit tests and pass all unit tests
Co-authored-by: Qingyi Liu <liuqingyi1993@gmail.com>
1, Disable static loop unrolling in the frontend by default;
2, A minor fix in axisAnalysis in order to support scf;
3, A minor fix in TritonGPUToLLVM to support swizzling.
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 the following things:
- Code refactoring on Load and Store op codegen, rewrite with same logic
and share much code
- Support the vectorized load/store
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>
