llvm-project

39ad84e4 - [mlir][linalg] Split GenericPadOpVectorizationPattern into two patterns (#111349)

[mlir][linalg] Split GenericPadOpVectorizationPattern into two patterns (#111349) At the moment, `GenericPadOpVectorizationPattern` implements two orthogonal transformations: 1. Rewrites `tensor::PadOp` into a sequence of `tensor::EmptyOp`, `linalg::FillOp` and `tensor::InsertSliceOp`. 2. Vectorizes (where possible) `tensor::InsertSliceOp` (see `tryVectorizeCopy`). This patch splits `GenericPadOpVectorizationPattern` into two separate patterns: 1. `GeneralizePadOpPattern` for the first transformation (note that currently `GenericPadOpVectorizationPattern` inherits from `GeneralizePadOpPattern`). 2. `InsertSliceVectorizePattern` to vectorize `tensor::InsertSliceOp`. With this change, we gain the following: * a clear separation between pre-processing and vectorization transformations/stages, * a path to support masked vectorisation for `tensor.insert_slice` (with a dedicated pattern for vectorization, it is much easier to specify the input vector sizes used in masking), * more opportunities to vectorize `tensor.insert_slice`. Note for downstream users: -------------------------- If you were using `populatePadOpVectorizationPatterns`, following this change you will also have to add `populateInsertSliceVectorizationPatterns`. Finer implementation details: ----------------------------- 1. The majority of changes in this patch are copy & paste + some edits. 1.1. The only functional change is that the vectorization of `tensor.insert_slice` is now broadly available (as opposed to being constrained to the pad vectorization pattern: `GenericPadOpVectorizationPattern`). 1.2. Following-on from the above, `@pad_and_insert_slice_dest` is updated. As expected, the input `tensor.insert_slice` Op is no longer "preserved" and instead gets vectorized successfully. 2. The `linalg.fill` case in `getConstantPadVal` works under the assumption that only _scalar_ source values can be used. That's consistent with the definition of the Op, but it's not tested at the moment. Hence a test case in Linalg/invalid.mlir is added. 3. The behaviour of the two TD vectorization Ops, `transform.structured.vectorize_children_and_apply_patterns` and `transform.structured.vectorize` is preserved.