[MLIR][NFC] Fix minor spelling issues

[ashermancinelli]

Fixes minor spelling issues in the shape dialect's passes tablegen file.

[llvmbot]

@llvm/pr-subscribers-mlir

Author: Asher Mancinelli (ashermancinelli)

Changes

Fixes minor spelling issues in the shape dialect's passes tablegen file.

---

Full diff: https://github.com/llvm/llvm-project/pull/167606.diff

1 Files Affected:

• (modified) mlir/include/mlir/Dialect/Shape/Transforms/Passes.td (+4-4)

diff --git a/mlir/include/mlir/Dialect/Shape/Transforms/Passes.td b/mlir/include/mlir/Dialect/Shape/Transforms/Passes.td
index 588b5ebc5e37a..0b8c465fdcbbe 100644
--- a/mlir/include/mlir/Dialect/Shape/Transforms/Passes.td
+++ b/mlir/include/mlir/Dialect/Shape/Transforms/Passes.td
@@ -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.
 
@@ -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)
     ```

[llvmbot]

@llvm/pr-subscribers-mlir-shape

Author: Asher Mancinelli (ashermancinelli)

Changes

Fixes minor spelling issues in the shape dialect's passes tablegen file.

---

Full diff: https://github.com/llvm/llvm-project/pull/167606.diff

1 Files Affected:

• (modified) mlir/include/mlir/Dialect/Shape/Transforms/Passes.td (+4-4)

diff --git a/mlir/include/mlir/Dialect/Shape/Transforms/Passes.td b/mlir/include/mlir/Dialect/Shape/Transforms/Passes.td
index 588b5ebc5e37a..0b8c465fdcbbe 100644
--- a/mlir/include/mlir/Dialect/Shape/Transforms/Passes.td
+++ b/mlir/include/mlir/Dialect/Shape/Transforms/Passes.td
@@ -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.
 
@@ -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)
     ```