[all-commits] [llvm/llvm-project] be06c7: [mlir][linalg] Enable Vectorization of 0-D tensor....

[Andrzej Warzyński via All-commits]

  Branch: refs/heads/main
  Home:   https://github.com/llvm/llvm-project
  Commit: be06c79c4591a64bbd6b50cb711b450712b04d9c
      https://github.com/llvm/llvm-project/commit/be06c79c4591a64bbd6b50cb711b450712b04d9c
  Author: Andrzej Warzyński <andrzej.warzynski at arm.com>
  Date:   2024-12-11 (Wed, 11 Dec 2024)

  Changed paths:
    M mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
    M mlir/test/Dialect/Linalg/vectorization-with-patterns.mlir
    M mlir/test/Dialect/Linalg/vectorize-tensor-extract.mlir

  Log Message:
  -----------
  [mlir][linalg] Enable Vectorization of 0-D tensor.extract (#119079)

This patch removes an assert in `vectorizeTensorExtract` that was
blocking
the vectorization of 0-D tensor.extract operations, e.g.:

```mlir
  %1 = tensor.extract %src[] : tensor<f32>
```

As demonstrated by the included tests, this case is already effectively
supported.

**Context**
The removed assert was introduced in #109580 as a guard, pending proper
support
and testing for 0-D tensors. This PR addresses that previously
undocumented
TODO. Apologies for the oversight!

**Updates and Tests**
* Revised the existing test `@negative_no_loop` to ensure the
  `vectorize_nd_extract` attribute is included, allowing the vectorizer
  to process it. The test was renamed and variables updated for clarity.
* Added a new test `@extract_scalar_from_0d_into_1d` to cover "mixed"
  0-D/1-D tensor extraction, e.g.:
```mlir
  %res = linalg.generic {
    indexing_maps = [#map],
    iterator_types = ["parallel"]
  } outs(%init : tensor<1xf32>) {
  ^bb0(%in: f32):
    %1 = tensor.extract %src[] : tensor<f32>
    linalg.yield %1 : f32
  } -> tensor<1xf32>

  return %res : tensor<1xf32>
```

**Additional updates**
I also took the liberty and improved test coverage for 0-D tensor in the
vectorizer tests:
* Added a specific test for "0D linalg.generic" in
  "vectorization-with-patterns.mlir".
* Renamed several tests in "vectorization-with-patterns.mlir" to clarify
  that the 0-D case is now covered.



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