[mlir][linalg] Fix and Refactor DecomposeOuterUnitDimsUnPackOpPattern

This PR fixes an issue identified in #118786, where the following
example triggers a verification error:

```mlir
func.func @foo(
    %src: tensor<1x1x2x?xf32>,
    %dest: tensor<5x1xf32>,
    %tile_dim: index) -> tensor<5x1xf32> {

  %0 = tensor.unpack %src
    inner_dims_pos = [1, 0]
    inner_tiles = [2, %tile_dim]
    into %dest : tensor<1x1x2x?xf32> -> tensor<5x1xf32>

  return %0 : tensor<5x1xf32>
}
```

The error occurs because of faulty logic when computing dynamic sizes
for `tensor::EmptyOp`, which initializes tensors for `linalg::transpose`.
This specific example fails due to:

  * Dynamic inner tile size, combined with
  * Non-identity permutation.

For reference, here's the verification error:

```bash
error: 'tensor.empty' op incorrect number of dynamic sizes, has 0, expected 1
```

and here's the problematic `tensor.empty` (printed in generic form):
```mlir
%1 = "tensor.empty"() : () -> tensor<?x2xf32>
```

**Fix**
This PR refactors how dynamic sizes for `tensor::EmptyOp` are computed. Instead
of generating a separate vector of dynamic sizes to complement the output
shape, this PR adopts a common MLIR idiom: passing a vector of `OpFoldResult`s
directly to the `EmptyOp` builder. Previously, only dynamic sizes corresponding
to outer dimensions were tracked (see `dynamicSizes`), while inner dimensions
were skipped, causing issues in certain cases.

**Refactoring changes**
Variable names have been updated for better readability:

* `readShape` → `readShapeForExtractSlice`
* `readSizes` → `extractSliceSizes`
* `readStrides` → `stridesForExtractSlice`

Additional comments have been added for clarity.

**Remaining inconsistencies**
Patterns for `tensor::PackOp` and `tensor::UnpackOp` remain partially inconsistent:

`DecomposeOuterUnitDimsPackOpPattern` enforces that all outer dimensions must be
unit-sized, while `DecomposeOuterUnitDimsUnPackOpPattern` does not. The two
implementations have diverged in logic. I plan to address these inconsistencies
in a follow-up PR to further unify these patterns.

Author: banach-space

mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp

@@ -1252,64 +1252,88 @@ LogicalResult DecomposeOuterUnitDimsUnPackOpPattern::matchAndRewrite(
         "require the tiled outer dimensions of the result are all 1s");
   }
 
-  // 1. Use rank-reduced tensor.extract_slice op to extract the tile.
+  // 1. Use rank-reduced tensor.extract_slice op to extract the tile:
+  //    %extracted_tile = tensor.extract_slice(%unpack_op_input)
   Location loc = unpackOp.getLoc();
   Value source = unpackOp.getSource();
   DenseMap<int64_t, OpFoldResult> dimAndTileMapping =
       unpackOp.getDimAndTileMapping();
   Attribute zeroIdxAttr = rewriter.getIndexAttr(0);
   Attribute oneIdxAttr = rewriter.getIndexAttr(1);
-  SmallVector<OpFoldResult> readOffsets(srcRank, zeroIdxAttr);
-  SmallVector<OpFoldResult> readStrides(srcRank, oneIdxAttr);
-  SmallVector<OpFoldResult> readSizes;
-  SmallVector<int64_t> readShape;
-  SmallVector<Value> dynamicDims;
+
+  // The sizes, affset and strides attributes for ExtractSliceOp.
+  SmallVector<OpFoldResult> extractSliceSizes;
+  SmallVector<OpFoldResult> extractSliceOffsets(srcRank, zeroIdxAttr);
+  SmallVector<OpFoldResult> extractSliceStrides(srcRank, oneIdxAttr);
+  // The shape for ExtractSliceOp (due to rank-reducing, this is likely !=
+  // extractSliceSizes).
+  SmallVector<int64_t> readShapeForExtractSlice;
+
+  // Shape for EmptyOp that's used as the init value for TransposeOp below.
+  // This should match tile size + transposition.
+  SmallVector<OpFoldResult> shapeForEmptyOp;
+
   for (auto i : llvm::seq<unsigned>(0, destRank)) {
+    // Given the assumption that all outer tiled dims are 1, the corresponding
+    // slice size to read is also 1. As this will be rank-reducing "extract
+    // slice" (i.e. the unit dims will be "collapsed"), there's no need to
+    // update:
+    //  * the output shape for ExtractSliceOp, nor
+    //  * the shape for EmptyOp.
     if (dimAndTileMapping.count(i)) {
-      readSizes.push_back(oneIdxAttr);
+      extractSliceSizes.push_back(oneIdxAttr);
       continue;
     }
 
+    // Compute sizes attribute for ExtractSliceOp + EmptyOp
     if (ShapedType::isDynamic(srcShape[i])) {
-      Value dynamicDim =
+      OpFoldResult dynamicDim =
           rewriter.create<tensor::DimOp>(loc, source, i).getResult();
-      readSizes.push_back(dynamicDim);
-      dynamicDims.push_back(dynamicDim);
+      extractSliceSizes.push_back(dynamicDim);
+      shapeForEmptyOp.push_back(dynamicDim);
     } else {
-      readSizes.push_back(rewriter.getIndexAttr(srcShape[i]));
+      extractSliceSizes.push_back(rewriter.getIndexAttr(srcShape[i]));
+      if (srcShape[i] != 1)
+        shapeForEmptyOp.push_back(rewriter.getIndexAttr(srcShape[i]));
+    }
+    // Compute the output shape for ExtractSliceOp (take into account
+    // rank-reducing)
+    if (srcShape[i] != 1) {
+      readShapeForExtractSlice.push_back(srcShape[i]);
     }
-    if (srcShape[i] != 1)
-      readShape.push_back(srcShape[i]);
   }
   auto mixedTiles = unpackOp.getMixedTiles();
-  readSizes.append(mixedTiles.begin(), mixedTiles.end());
+  // TODO: This effectively assumes that that tile sizes match the trailing
+  // sizes for ExtractSliceOp and EmptyOp - document this.
+  extractSliceSizes.append(mixedTiles.begin(), mixedTiles.end());
+  shapeForEmptyOp.append(mixedTiles.begin(), mixedTiles.end());
 
   // Explicitly create the type for extract_slice op because the inner tile
   // size could be 1. We want to represent the whole inner tile in this case.
   auto tileShape = srcShape.drop_front(destRank);
   // Append the inner tile shape to the permuted and rank-reduced outer shape.
-  readShape.append(tileShape.begin(), tileShape.end());
+  readShapeForExtractSlice.append(tileShape.begin(), tileShape.end());
   Type elemType = unpackOp.getSourceType().getElementType();
-  auto readType = RankedTensorType::get(readShape, elemType);
+  auto readType = RankedTensorType::get(readShapeForExtractSlice, elemType);
   Value innerTile = rewriter.create<tensor::ExtractSliceOp>(
-      loc, readType, unpackOp.getSource(), readOffsets, readSizes, readStrides);
+      loc, readType, unpackOp.getSource(), extractSliceOffsets,
+      extractSliceSizes, extractSliceStrides);
 
   // 2. Transpose the tile to match the outer corresponding tile order.
   SmallVector<int64_t> perm = getPackUnpackRankReducedPerm(
       srcShape.take_front(destRank), innerDimsPos, unpackOp.getOuterDimsPerm());
   // Unpack is a transition out of packed space so we invert the permutation.
   perm = invertPermutationVector(perm);
-  SmallVector<int64_t> transpShape(readShape);
-  applyPermutationToVector<int64_t>(transpShape, perm);
+  applyPermutationToVector<OpFoldResult>(shapeForEmptyOp, perm);
 
   Value empty =
-      rewriter.create<tensor::EmptyOp>(loc, transpShape, elemType, dynamicDims);
+      rewriter.create<tensor::EmptyOp>(loc, shapeForEmptyOp, elemType);
   auto transposedOp =
       rewriter.create<linalg::TransposeOp>(loc, innerTile, empty, perm);
 
   // 3. Handle in-complete tiles if needed. It truncates trailing data from the
   // transposed tile.
-  int numLoops = transpShape.size();
+  int numLoops = shapeForEmptyOp.size();
   SmallVector<OpFoldResult> tileStrides(numLoops, oneIdxAttr);
   SmallVector<OpFoldResult> tileOffsets(numLoops, zeroIdxAttr);
   SmallVector<OpFoldResult> tileSizes;

mlir/test/Dialect/Linalg/decompose-tensor-unpack.mlir

@@ -35,15 +35,15 @@ func.func @simple_unpack_static_tiles(%input: tensor<1x1x8x2xf32>, %output: tens
 
 /// Same as example above, but with 1 dynamic tile size.
 
-func.func @simple_unpack_dynamic_tile(%input: tensor<1x1x?x2xf32>, %output: tensor<5x1xf32>, %tile_dim_0: index) -> tensor<5x1xf32> {
-  %0 = tensor.unpack %input inner_dims_pos = [0, 1] inner_tiles = [%tile_dim_0, 2] into %output : tensor<1x1x?x2xf32> -> tensor<5x1xf32>
+func.func @simple_unpack_dynamic_tile(%input: tensor<1x1x?x2xf32>, %output: tensor<5x1xf32>, %tile_dim: index) -> tensor<5x1xf32> {
+  %0 = tensor.unpack %input inner_dims_pos = [0, 1] inner_tiles = [%tile_dim, 2] into %output : tensor<1x1x?x2xf32> -> tensor<5x1xf32>
   return %0 : tensor<5x1xf32>
 }
 // CHECK-LABEL: func.func @simple_unpack_dynamic_tile
 // CHECK-SAME:    %[[SRC:[a-zA-Z0-9]+]]
 // CHECK-SAME:    %[[DEST:[a-zA-Z0-9]+]]
-// CHECK-SAME:    %[[TILE_DIM_1:[a-zA-Z0-9]+]]
-// CHECK:         %[[TILE:.+]] = tensor.extract_slice %[[SRC]][0, 0, 0, 0] [1, 1, %[[TILE_DIM_1]], 2] [1, 1, 1, 1]
+// CHECK-SAME:    %[[TILE_DIM:[a-zA-Z0-9]+]]
+// CHECK:         %[[TILE:.+]] = tensor.extract_slice %[[SRC]][0, 0, 0, 0] [1, 1, %[[TILE_DIM]], 2] [1, 1, 1, 1]
 // CHECK-NOT:     linalg.transpose
 //                They have the same type, so the insert_slice op is folded
 //                away.
@@ -52,13 +52,23 @@ func.func @simple_unpack_dynamic_tile(%input: tensor<1x1x?x2xf32>, %output: tens
 
 /// Same as example above, but with 1 dynamic tile size and a trasnpose
 
-/// FIXME: This is currently broken:
-///   * 'tensor.empty' op incorrect number of dynamic sizes, has 0, expected 1
+func.func @simple_unpack_dynamic_tile_transpose(%src: tensor<1x1x2x?xf32>, %dest: tensor<5x1xf32>, %tile_dim: index) -> tensor<5x1xf32> {
+  %0 = tensor.unpack %src inner_dims_pos = [1, 0] inner_tiles = [2, %tile_dim] into %dest : tensor<1x1x2x?xf32> -> tensor<5x1xf32>
+  return %0 : tensor<5x1xf32>
+}
+// CHECK-LABEL:   func.func @simple_unpack_dynamic_tile_transpose
+// CHECK-SAME:    %[[SRC:[a-zA-Z0-9]+]]
+// CHECK-SAME:    %[[DEST:[a-zA-Z0-9]+]]
+// CHECK-SAME:    %[[TILE_DIM:[a-zA-Z0-9]+]]
+// CHECK:           %[[TILE:.*]] = tensor.extract_slice %[[SRC]][0, 0, 0, 0] [1, 1, 2, %[[TILE_DIM]]] [1, 1, 1, 1] : tensor<1x1x2x?xf32> to tensor<2x?xf32>
+// CHECK:           %[[EMPTY:.*]] = tensor.empty(%[[TILE_DIM]]) : tensor<?x2xf32>
+// CHECK:           %[[TRANSP:.*]] = linalg.transpose
+// CHECK-SAME:        ins(%[[TILE]] : tensor<2x?xf32>)
+// CHECK-SAME:        outs(%[[EMPTY]] : tensor<?x2xf32>)
+// CHECK-SAME:        permutation = [1, 0]
+// CHECK:           %[[SLICE:.*]] = tensor.extract_slice %[[TRANSP]][0, 0] [5, 1] [1, 1] : tensor<?x2xf32> to tensor<5x1xf32>
+// CHECK:           return %[[SLICE]] : tensor<5x1xf32>
 
-//func.func @simple_unpack_dynamic_tile_transpose(%input: tensor<1x1x2x?xf32>, %output: tensor<5x1xf32>, %tile_dim_0: index) -> tensor<5x1xf32> {
-//  %0 = tensor.unpack %input inner_dims_pos = [1, 0] inner_tiles = [2, %tile_dim_0] into %output : tensor<1x1x2x?xf32> -> tensor<5x1xf32>
-//  return %0 : tensor<5x1xf32>
-//}
 
 /// Same as example above, but with 1 scalable tile size.