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

[Andrzej Warzyński]

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@@ -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.
+  SmallVector<int64_t> vectorSizes(inputVectorSizes);
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banach-space wrote:

Yeah, looks like this condition is indeed checked in 2. above, thanks! 

That's a "pre-condition" though - no harm in adding an additional assert to document assumptions made in this method.

In any case, it's just a nice to have :)

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