@@ -213,11 +213,11 @@ def Tensor_DimOp : Tensor_Op<"dim", [
213213
214214 // Return the dynamic dimension of %A.
215215 %c1 = arith.constant 1 : index
216- %y = tensor.dim %A, %c1 : memref <4x?xf32>
216+ %y = tensor.dim %A, %c1 : tensor <4x?xf32>
217217
218218 // Equivalent generic form:
219- %x = "tensor.dim"(%A, %c0) : (memref <4x?xf32>, index) -> index
220- %y = "tensor.dim"(%A, %c1) : (memref <4x?xf32>, index) -> index
219+ %x = "tensor.dim"(%A, %c0) : (tensor <4x?xf32>, index) -> index
220+ %y = "tensor.dim"(%A, %c1) : (tensor <4x?xf32>, index) -> index
221221 ```
222222 }];
223223
@@ -673,7 +673,7 @@ def Tensor_GatherOp : Tensor_Op<"gather", [
673673
674674 At the tensor-level, the index tensor is specified in an AoS form (i.e.
675675 coordinate tuple is the most minor). It is the responsibility of further
676- lowerings and bufferiation to implement various concrete layouts.
676+ lowerings and bufferization to implement various concrete layouts.
677677
678678 Note: As currently specified, the operation must lower to an abstraction that
679679 performs copies to the output tensor. This is because the buffer type system
@@ -710,7 +710,7 @@ def Tensor_GatherOp : Tensor_Op<"gather", [
710710
711711 let extraClassDeclaration = [{
712712 // TODO: InferTypeOpInterface once enough confidence is built with
713- // tensor<tensor> and its lwoering to memref<memref>.
713+ // tensor<tensor> and its lowering to memref<memref>.
714714 static RankedTensorType inferResultType(RankedTensorType sourceType,
715715 RankedTensorType indicesType,
716716 ArrayRef<int64_t> gatherDims,
@@ -1673,7 +1673,7 @@ def Tensor_ScatterOp : Tensor_Op<"scatter", [
16731673 source tensor has size `1`.
16741674 I.e. if the dest type is `axbxcxd` and the coordinates are [1, 3], then
16751675 the source type suffix is `ax1xcx1`.
1676- Sactter also allows rank-reducing semantics where the shape `ax1xcx1` can be
1676+ Scatter also allows rank-reducing semantics where the shape `ax1xcx1` can be
16771677 further simplified to `axc`.
16781678
16791679 The elemental type of the indices tensor can be any integer type.
@@ -1693,7 +1693,7 @@ def Tensor_ScatterOp : Tensor_Op<"scatter", [
16931693
16941694 At the tensor-level, the index tensor is specified in an AoS form (i.e.
16951695 coordinate tuple is the most minor). It is the responsibility of further
1696- lowerings and bufferiation to implement various concrete layouts.
1696+ lowerings and bufferization to implement various concrete layouts.
16971697
16981698 Note: As currently specified, the operation must lower to an abstraction that
16991699 performs copies to the output tensor. This is because the buffer type system
@@ -2115,7 +2115,7 @@ def Tensor_UnPackOp : Tensor_RelayoutOp<"unpack"> {
21152115 /// Check if this UnPackOp is like a simple unpad operation.
21162116 /// In other words, this operation:
21172117 /// 1. drops useless dimensions (dimension of size 1), and
2118- /// 2. reduces dimensions in place (i.e., no tranpose .)
2118+ /// 2. reduces dimensions in place (i.e., no transpose .)
21192119 bool isLikeUnPad();
21202120 }];
21212121
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