[mlir][tensor] Add TilingInterface support for fusing tensor.pad (#105892)

This adds implementations for the two TilingInterface methods required
for fusion to `tensor.pad`: `getIterationDomainTileFromResultTile` and
`generateResultTileValue`, allowing fusion of pad with a tiled consumer.

Author: qedawkins

mlir/lib/Dialect/Tensor/IR/TensorTilingInterfaceImpl.cpp

@@ -67,6 +67,23 @@ struct PadOpTiling : public TilingInterface::ExternalModel<PadOpTiling, PadOp> {
     resultSizes.assign(sizes.begin(), sizes.end());
     return success();
   }
+
+  LogicalResult getIterationDomainTileFromResultTile(
+      Operation *op, OpBuilder &b, unsigned resultNumber,
+      ArrayRef<OpFoldResult> offsets, ArrayRef<OpFoldResult> sizes,
+      SmallVectorImpl<OpFoldResult> &iterDomainOffsets,
+      SmallVectorImpl<OpFoldResult> &iterDomainSizes) const {
+    iterDomainOffsets.assign(offsets.begin(), offsets.end());
+    iterDomainSizes.assign(sizes.begin(), sizes.end());
+    return success();
+  }
+
+  FailureOr<TilingResult>
+  generateResultTileValue(Operation *op, OpBuilder &b, unsigned resultNumber,
+                          ArrayRef<OpFoldResult> offsets,
+                          ArrayRef<OpFoldResult> sizes) const {
+    return getTiledImplementation(op, b, offsets, sizes);
+  }
 };
 
 template <typename OpTy>

mlir/test/Dialect/Tensor/tiling.mlir

@@ -116,6 +116,47 @@ module attributes {transform.with_named_sequence} {
 
 // -----
 
+// CHECK-LABEL: func @fuse_static_pad_tensor_3_4(
+//  CHECK-SAME:     %[[IN:.*]]: tensor<7x9xf32>
+//   CHECK-DAG:   %[[C0:.*]] = arith.constant 0 : index
+//   CHECK-DAG:   %[[C2:.*]] = arith.constant 2 : index
+//   CHECK-DAG:   %[[C3:.*]] = arith.constant 3 : index
+//   CHECK-DAG:   %[[C15:.*]] = arith.constant 15 : index
+//   CHECK-DAG:   %[[C16:.*]] = arith.constant 16 : index
+//       CHECK:   %[[RESULT:.*]] = scf.for {{.*}} = %[[C0]] to %[[C15]] step %[[C2]]
+//       CHECK:     scf.for {{.*}} = %[[C0]] to %[[C16]] step %[[C3]] iter_args(%[[INNER_OUT:.*]] =
+//       CHECK:       %[[SWAP_RESULT:.*]] = scf.if
+//       CHECK:         tensor.generate
+//       CHECK:       else
+//       CHECK:         %[[SLICE:.*]] = tensor.extract_slice %[[IN]][{{.*}}, {{.*}}] [{{.*}}, {{.*}}] [1, 1]
+//       CHECK:         %[[PAD:.*]] = tensor.pad %[[SLICE]]
+//       CHECK:       %[[COPY:.*]] = linalg.copy ins(%[[SWAP_RESULT:.*]]
+//       CHECK:       tensor.insert_slice %[[COPY]] into %[[INNER_OUT]][{{.*}}, {{.*}}] [{{.*}}, {{.*}}] [1, 1]
+//       CHECK:   return %[[RESULT]]
+
+func.func @fuse_static_pad_tensor_3_4(%input_tensor: tensor<7x9xf32>,
+                        %pad_value: f32) -> tensor<15x16xf32> {
+  %0 = tensor.pad %input_tensor low[3, 4] high[5, 3] {
+    ^bb0(%arg1: index, %arg2: index):
+      tensor.yield %pad_value : f32
+    } : tensor<7x9xf32> to tensor<15x16xf32>
+  %empty = tensor.empty() : tensor<15x16xf32>
+  %1 = linalg.copy ins(%0 : tensor<15x16xf32>) outs(%empty : tensor<15x16xf32>) -> tensor<15x16xf32>
+  return %1 : tensor<15x16xf32>
+}
+
+module attributes {transform.with_named_sequence} {
+  transform.named_sequence @__transform_main(%arg1 : !transform.any_op {transform.readonly}) {
+    %copy = transform.structured.match ops{["linalg.copy"]} in %arg1
+      : (!transform.any_op) -> !transform.any_op
+    %a, %b, %c = transform.structured.fuse %copy [2, 3]
+      : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
+    transform.yield
+  }
+}
+
+// -----
+
 // CHECK-LABEL: func @static_pad_tensor_0_3(
 //  CHECK-SAME:     %[[IN:.*]]: tensor<7x9xf32>
 //   CHECK-DAG:   %[[C0:.*]] = arith.constant 0 : index