[mlir][tensor] Fold identity reshape of 0d-tensors (#146375)

Just like 1d-tensors, reshapes of 0d-tensors (aka scalars) are always
no-folds as they only have one possible layout. This PR adds logic to
the `fold` implementation to optimize these away as is currently
implemented for 1d tensors.

Author: zero9178

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

@@ -1872,9 +1872,9 @@ OpFoldResult ReshapeOp::fold(FoldAdaptor adaptor) {
   if (!sourceTy || !resultTy || sourceTy != resultTy)
     return {};
 
-  // If the source and result are both 1D tensors and have the same type, the
-  // reshape has no effect, even if the tensor is dynamically shaped.
-  if (sourceTy.getRank() == 1)
+  // If the source and result are both 0D or 1D tensors and have the same type,
+  // the reshape has no effect, even if the tensor is dynamically shaped.
+  if (sourceTy.getRank() <= 1)
     return source;
 
   if (auto fromElements = getShape().getDefiningOp<tensor::FromElementsOp>()) {

mlir/test/Dialect/Tensor/canonicalize.mlir

@@ -971,6 +971,17 @@ func.func @fold_reshape_1d(%input: tensor<?xf32>, %shape: tensor<1xindex>) -> te
 
 // -----
 
+// CHECK-LABEL: func @fold_reshape_0d
+//  CHECK-SAME: %[[INPUT:[a-zA-Z0-9_]+]]: tensor<f32>
+//  CHECK-SAME: %[[SHAPE:[a-zA-Z0-9_]+]]: tensor<0xindex>
+//       CHECK: return %[[INPUT]]
+func.func @fold_reshape_0d(%input: tensor<f32>, %shape: tensor<0xindex>) -> tensor<f32> {
+  %0 = tensor.reshape %input(%shape) : (tensor<f32>, tensor<0xindex>) -> tensor<f32>
+  return %0 : tensor<f32>
+}
+
+// -----
+
 // CHECK-LABEL: func @fold_extract_constant_splat
 //   CHECK-NOT: tensor.extract_slice
 //       CHECK: arith.constant dense<42> : tensor<4x4xi32>