[Mlir-commits] [mlir] [mlir][linalg] Vectorize unpack op without masking (PR #89067)

[Andrzej Warzyński]

================
@@ -1560,40 +1575,61 @@ vectorizeAsTensorUnpackOp(RewriterBase &rewriter, tensor::UnPackOp unpackOp,
 
   ArrayRef<int64_t> innerDimPos = unpackOp.getInnerDimsPos();
   ArrayRef<int64_t> innerTiles = unpackOp.getStaticInnerTiles();
-
-  SmallVector<int64_t> readMaskShape(inputVectorSizes.begin(),
-                                     inputVectorSizes.end());
-  ArrayRef<int64_t> outerDimsPerm = unpackOp.getOuterDimsPerm();
   ArrayRef<int64_t> sourceShape = unpackTensorType.getShape();
+  bool useInBoundsInsteadOfMasking = false;
+  ArrayRef<int64_t> outerDimsPerm = unpackOp.getOuterDimsPerm();
+
+  auto destSize = unpackOp.getDestRank();
 
-  // ReadMask is the size of tensor used to read and apply mask. It is
+  // vectorSizes is the shape of the vector that will be used to do final
+  // write on the destination tensor. It is set like this: Let's say the
+  // sourceShape is 'M' and the vectorSize (VS) array is size 'N' where N <= M.
+  // Thus:
+  // - vectorSizes = sourceShape.take_front(N)
+  // - if outer_dims_perms is present: do that permutation on initVectorShape.
+  // - Multiply all the locations pointed by innerDimPos by the innerTileSize
+  //  attribute value.
----------------
banach-space wrote:

```suggestion
  // vectorSizes is the shape of the vector that will be used to do final
  // write on the destination tensor. It is set like this: Let's say the
  // source tensor is rank 'M' and the dest tensor is rank 'N', where N <= M.
  // Thus:
  // 1. vectorSizes = sourceShape.take_front(N)
  // 2. if outer_dims_perms is present: do that permutation on vectorSizes.
  // 3. multiply all the locations in VectorSize pointed by innerDimPos by the innerTiles
  //  attribute value.
```

1. Remove references to `initVectorShape`
2. This sentence doesn't make sense when `vectorSizes` is empty: "the vectorSize (VS) array is size 'N'". That's fine - I think what's more important is the rank of the source tensor (`M`) and the output tensor (`N`).
3. Consistent capitalisation (nit)
4. Use numbering to highlight that these are consecutive steps (nit)

https://github.com/llvm/llvm-project/pull/89067