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8 changes: 4 additions & 4 deletions mlir/include/mlir/Dialect/Shape/Transforms/Passes.td
Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@ def OutlineShapeComputationPass
let summary = "Using shape.func to preserve shape computation";
let description = [{
This pass outlines the shape computation part in high level IR by adding
shape.func and populate corresponding mapping infoemation into
shape.func and populate corresponding mapping information into
ShapeMappingAnalysis. The shape computation part is usually introduced by
shape reification, and each single dynamic shape is denoted by shape.with_shape.

Expand Down Expand Up @@ -80,12 +80,12 @@ def OutlineShapeComputationPass

For the above example, the shape computation is inlined in the input IR,
which is used for two values' (test.abs and test.concat) shape. And the shape
compuatation part is outlined in the output IR.
computation part is outlined in the output IR.

And the shape mapping infomation will be:
And the shape mapping information will be:

```
// ---- Shape Mapping Infomation -----
// ---- Shape Mapping Information -----
// - Shape for: %0 = "test.abs"(%arg0) : (tensor<?x4x?xf32>) -> tensor<?x4x?xf32> :: @shape_cal_0(<block argument> of type 'tensor<?x4x?xf32>' at index: 0)
// - Shape for: %1 = "test.concat"(%0, %arg1) {axis = 0 : i64} : (tensor<?x4x?xf32>, tensor<2x4x?xf32>) -> tensor<?x4x?xf32> :: @shape_cal_1(<block argument> of type 'tensor<?x4x?xf32>' at index: 0)
```
Expand Down
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