From abbb6b7d952bb99643bc60a1fdcbaa262678864b Mon Sep 17 00:00:00 2001 From: Asher Mancinelli Date: Tue, 11 Nov 2025 16:01:54 -0800 Subject: [PATCH] [MLIR][Doc] Fix minor spelling issues --- mlir/include/mlir/Dialect/Shape/Transforms/Passes.td | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) 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) -> tensor :: @shape_cal_0( of type 'tensor' at index: 0) // - Shape for: %1 = "test.concat"(%0, %arg1) {axis = 0 : i64} : (tensor, tensor<2x4x?xf32>) -> tensor :: @shape_cal_1( of type 'tensor' at index: 0) ```