[mlir][linalg] Add tests for tensor.unpack decomposition (llvm#118786)

This commit adds additional tests and documentation for
`DecomposeOuterUnitDimsUnPackOpPattern` to ensure symmetry with its
counterpart for `tensor.pack`, `DecomposeOuterUnitDimsPackOpPattern`.

The new tests aim to improve implementation, documentation, and test
coverage for tensor.unpack. They cover the following scenarios:

* Static tile sizes: A simple `tensor.unpack` case
  (`@simple_unpack_static_tiles`).
* Dynamic tile size: `tensor.unpack` with a single dynamic tile size
  (`@simple_unpack_dynamic_tile`).
* Transpose: `tensor.unpack` with dynamic tile size and transpose
  (`@simple_unpack_dynamic_tile_transpose`), currently commented out due
  to some missing logic (see below)
* Scalable tile size: `tensor.unpack` with a scalable inner tile size
  (@simple_unpack_scalable_tile).

Notes:

The test `@simple_unpack_dynamic_tile_transpose` is commented out
because the logic for capturing dynamic sizes for `tensor::EmptyOp` when
some tile sizes are dynamic is incomplete. This missing functionality
will be addressed in a follow-up patch.

Author: banach-space

mlir/include/mlir/Dialect/Linalg/Transforms/Transforms.h

@@ -1519,7 +1519,7 @@ struct DecomposePadOpPattern : public OpRewritePattern<tensor::PadOp> {
 ///   * tensor::PadOp + linalg::TransposeOp + tensor::EmptyOp +
 ///     tensor::InsertSliceOp ops.
 ///
-/// Required that all the outer dims of the input tensor::PackOp are 1.
+/// Requires that all the outer dims of the input tensor::PackOp are 1.
 ///
 /// Before:
 /// ```
@@ -1555,9 +1555,33 @@ struct DecomposeOuterUnitDimsPackOpPattern
                                 PatternRewriter &rewriter) const override;
 };
 
-/// Rewrites a tensor::UnPackOp into a sequence of rank-reduced extract_slice op
-/// + transpose op + insert_slice op, where the tensor::UnPackOp has outer dims
-/// being all 1s.
+/// Rewrites a tensor::UnPackOp into a sequence of rank-reduced
+///   * tensor::ExtractSliceOp + linalg::TransposeOp + tensor::InsertSliceOp
+///
+/// Requires that all the outer dims of the input tensor::PackOp are 1.
+///
+/// Before:
+/// ```
+/// %packed = tensor.unpack %input
+///   inner_dims_pos = [1, 0]
+///   inner_tiles = [2, 8]
+///   into %output : tensor<1x1x2x8xf32> -> tensor<5x1xf32>
+/// ```
+///
+/// After:
+/// ```
+///   // Rank-reduced extract to obtain the tile
+///   %slice = tensor.extract_slice %arg0[0, 0, 0, 0] [1, 1, 2, 8] [1, 1, 1, 1]
+///     : tensor<1x1x2x8xf32> to tensor<2x8xf32>
+///   // EmptyOp + TransposeOp
+///   %init = tensor.empty() : tensor<8x2xf32>
+///   %transposed = linalg.transpose
+///     ins(%extracted_slice : tensor<2x8xf32>)
+///     outs(%0 : tensor<8x2xf32>) permutation = [1, 0]
+///   // Extract a slice matching the specified output size
+///   %result = tensor.extract_slice %transposed[0, 0] [5, 1] [1, 1]
+///     : tensor<8x2xf32> to tensor<5x1xf32>
+/// ```
 struct DecomposeOuterUnitDimsUnPackOpPattern
     : public OpRewritePattern<tensor::UnPackOp> {
   using OpRewritePattern<tensor::UnPackOp>::OpRewritePattern;

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

@@ -67,6 +67,9 @@ func.func @simple_pad_and_pack_dynamic_tile(%input: tensor<5x1xf32>, %output: te
 // CHECK:           %[[RES:.*]] = tensor.insert_slice %[[PAD:.*]] into %[[DEST]][0, 0, 0, 0] [1, 1, %[[TILE_DIM_0]], 2] [1, 1, 1, 1] : tensor<?x2xf32> into tensor<1x1x?x2xf32>
 // CHECK:           return %[[RES]] : tensor<1x1x?x2xf32>
 
+/// Same as example above, but the dynamic tile size is a compile-time constant
+/// that's folded away.
+
 func.func @simple_pad_and_pack_dynamic_tile_cst(%input: tensor<5x1xf32>, %output: tensor<1x1x?x2xf32>, %pad: f32) -> tensor<1x1x?x2xf32> {
   %tile_dim_0 = arith.constant 8 : index
   %0 = tensor.pack %input padding_value(%pad : f32) inner_dims_pos = [0, 1] inner_tiles = [%tile_dim_0, 2] into %output : tensor<5x1xf32> -> tensor<1x1x?x2xf32>

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

@@ -19,11 +19,11 @@ func.func @simple_KCRSsr_to_KCRS(%arg0: tensor<1x1x1x1x8x32xf32>, %arg1: tensor<
 
 // -----
 
-func.func @simple_unpack_and_extract_slice(%input: tensor<1x1x8x2xf32>, %output: tensor<5x1xf32>) -> tensor<5x1xf32> {
+func.func @simple_unpack_static_tiles(%input: tensor<1x1x8x2xf32>, %output: tensor<5x1xf32>) -> tensor<5x1xf32> {
   %0 = tensor.unpack %input inner_dims_pos = [0, 1] inner_tiles = [8, 2] into %output : tensor<1x1x8x2xf32> -> tensor<5x1xf32>
   return %0 : tensor<5x1xf32>
 }
-// CHECK-LABEL: func.func @simple_unpack_and_extract_slice
+// CHECK-LABEL: func.func @simple_unpack_static_tiles
 // CHECK-SAME:    %[[SRC:[a-zA-Z0-9]+]]
 // CHECK-SAME:    %[[DEST:[a-zA-Z0-9]+]]
 // CHECK:         %[[TILE:.+]] = tensor.extract_slice %[[SRC]][0, 0, 0, 0] [1, 1, 8, 2] [1, 1, 1, 1]
@@ -33,6 +33,55 @@ func.func @simple_unpack_and_extract_slice(%input: tensor<1x1x8x2xf32>, %output:
 // CHECK:         %[[SLICE:.+]] = tensor.extract_slice %[[TILE]][0, 0] [5, 1] [1, 1]
 // CHECK:         return %[[SLICE]]
 
+/// 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>
+  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-NOT:     linalg.transpose
+//                They have the same type, so the insert_slice op is folded
+//                away.
+// CHECK:         %[[SLICE:.+]] = tensor.extract_slice %[[TILE]][0, 0] [5, 1] [1, 1]
+// CHECK:         return %[[SLICE]]
+
+/// 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(%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.
+
+func.func @simple_unpack_scalable_tile(%input: tensor<1x1x?x2xf32>, %output: tensor<5x1xf32>) -> tensor<5x1xf32> {
+  %c8 = arith.constant 8 : index
+  %vscale = vector.vscale
+  %c8_vscale = arith.muli %vscale, %c8 : index
+  %0 = tensor.unpack %input inner_dims_pos = [0, 1] inner_tiles = [%c8_vscale, 2] into %output : tensor<1x1x?x2xf32> -> tensor<5x1xf32>
+  return %0 : tensor<5x1xf32>
+}
+// CHECK-LABEL: func.func @simple_unpack_scalable_tile
+// CHECK-SAME:    %[[SRC:[a-zA-Z0-9]+]]
+// CHECK-SAME:    %[[DEST:[a-zA-Z0-9]+]]
+// CHECK-DAG:     %[[C8:.+]] = arith.constant 8 : index
+// CHECK-DAG:     %[[VS:.+]] = vector.vscale
+// CHECK:         %[[C8_VS:.+]] = arith.muli %[[VS]], %[[C8]] : index
+// CHECK:         %[[TILE:.+]] = tensor.extract_slice %[[SRC]][0, 0, 0, 0] [1, 1, %[[C8_VS]], 2] [1, 1, 1, 1]
+// CHECK-NOT:     linalg.transpose
+//                They have the same type, so the insert_slice op is folded
+//                away.
+// CHECK:         %[[SLICE:.+]] = tensor.extract_slice %[[TILE]][0, 0] [5, 1] [1, 1]
+// CHECK:         return %[[SLICE]]
+
 // -----
 
 func.func @simple_CNnc_to_NC(%arg0: tensor<1x1x32x8xf32>, %arg1: tensor<32x8xf32>) -> tensor<32x8xf32>{