[Mlir-commits] [mlir] 1fd9c02 - [mlir] Adopt cast function objects. NFC. (#168228)

[llvmlistbot at llvm.org]

Author: Jakub Kuderski
Date: 2025-11-15T14:51:14-05:00
New Revision: 1fd9c02513c22271ff16b3091d6849aba96e5407

URL: https://github.com/llvm/llvm-project/commit/1fd9c02513c22271ff16b3091d6849aba96e5407
DIFF: https://github.com/llvm/llvm-project/commit/1fd9c02513c22271ff16b3091d6849aba96e5407.diff

LOG: [mlir] Adopt cast function objects. NFC. (#168228)

These were added in https://github.com/llvm/llvm-project/pull/165803.

Added: 
    

Modified: 
    mlir/lib/CAPI/Dialect/LLVM.cpp
    mlir/lib/Dialect/Tensor/IR/TensorOps.cpp
    mlir/lib/Dialect/Transform/Interfaces/TransformInterfaces.cpp
    mlir/lib/Dialect/XeGPU/Transforms/XeGPUSubgroupDistribute.cpp
    mlir/lib/IR/TypeUtilities.cpp

Removed: 
    


################################################################################
diff  --git a/mlir/lib/CAPI/Dialect/LLVM.cpp b/mlir/lib/CAPI/Dialect/LLVM.cpp
index eaad8a87aab9b..6636f0ea73ec9 100644
--- a/mlir/lib/CAPI/Dialect/LLVM.cpp
+++ b/mlir/lib/CAPI/Dialect/LLVM.cpp
@@ -159,9 +159,8 @@ MlirAttribute mlirLLVMDIExpressionAttrGet(MlirContext ctx, intptr_t nOperations,
 
   return wrap(DIExpressionAttr::get(
       unwrap(ctx),
-      llvm::map_to_vector(
-          unwrapList(nOperations, operations, attrStorage),
-          [](Attribute a) { return cast<DIExpressionElemAttr>(a); })));
+      llvm::map_to_vector(unwrapList(nOperations, operations, attrStorage),
+                          llvm::CastTo<DIExpressionElemAttr>)));
 }
 
 MlirAttribute mlirLLVMDINullTypeAttrGet(MlirContext ctx) {
@@ -202,7 +201,7 @@ MlirAttribute mlirLLVMDICompositeTypeAttrGet(
       cast<DIExpressionAttr>(unwrap(allocated)),
       cast<DIExpressionAttr>(unwrap(associated)),
       llvm::map_to_vector(unwrapList(nElements, elements, elementsStorage),
-                          [](Attribute a) { return cast<DINodeAttr>(a); })));
+                          llvm::CastTo<DINodeAttr>)));
 }
 
 MlirAttribute mlirLLVMDIDerivedTypeAttrGet(
@@ -308,7 +307,7 @@ MlirAttribute mlirLLVMDISubroutineTypeAttrGet(MlirContext ctx,
   return wrap(DISubroutineTypeAttr::get(
       unwrap(ctx), callingConvention,
       llvm::map_to_vector(unwrapList(nTypes, types, attrStorage),
-                          [](Attribute a) { return cast<DITypeAttr>(a); })));
+                          llvm::CastTo<DITypeAttr>)));
 }
 
 MlirAttribute mlirLLVMDISubprogramAttrGetRecSelf(MlirAttribute recId) {
@@ -338,10 +337,10 @@ MlirAttribute mlirLLVMDISubprogramAttrGet(
       cast<DISubroutineTypeAttr>(unwrap(type)),
       llvm::map_to_vector(
           unwrapList(nRetainedNodes, retainedNodes, nodesStorage),
-          [](Attribute a) { return cast<DINodeAttr>(a); }),
+          llvm::CastTo<DINodeAttr>),
       llvm::map_to_vector(
           unwrapList(nAnnotations, annotations, annotationsStorage),
-          [](Attribute a) { return cast<DINodeAttr>(a); })));
+          llvm::CastTo<DINodeAttr>)));
 }
 
 MlirAttribute mlirLLVMDISubprogramAttrGetScope(MlirAttribute diSubprogram) {
@@ -398,7 +397,7 @@ MlirAttribute mlirLLVMDIImportedEntityAttrGet(
       cast<DINodeAttr>(unwrap(entity)), cast<DIFileAttr>(unwrap(file)), line,
       cast<StringAttr>(unwrap(name)),
       llvm::map_to_vector(unwrapList(nElements, elements, elementsStorage),
-                          [](Attribute a) { return cast<DINodeAttr>(a); })));
+                          llvm::CastTo<DINodeAttr>)));
 }
 
 MlirAttribute mlirLLVMDIAnnotationAttrGet(MlirContext ctx, MlirAttribute name,

diff  --git a/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp b/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp
index 110bfdce72ea4..b1893f0868ac5 100644
--- a/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp
+++ b/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp
@@ -551,9 +551,7 @@ void CastOp::getCanonicalizationPatterns(RewritePatternSet &results,
 RankedTensorType ConcatOp::inferResultType(int64_t dim, TypeRange inputTypes) {
   assert(!inputTypes.empty() && "cannot concatenate 0 tensors");
   auto tensorTypes =
-      llvm::to_vector<4>(llvm::map_range(inputTypes, [](Type type) {
-        return llvm::cast<RankedTensorType>(type);
-      }));
+      llvm::map_to_vector<4>(inputTypes, llvm::CastTo<RankedTensorType>);
   int64_t concatRank = tensorTypes[0].getRank();
 
   // The concatenation dim must be in the range [0, rank).

diff  --git a/mlir/lib/Dialect/Transform/Interfaces/TransformInterfaces.cpp b/mlir/lib/Dialect/Transform/Interfaces/TransformInterfaces.cpp
index 8859541c78c91..24b048795b136 100644
--- a/mlir/lib/Dialect/Transform/Interfaces/TransformInterfaces.cpp
+++ b/mlir/lib/Dialect/Transform/Interfaces/TransformInterfaces.cpp
@@ -1495,8 +1495,7 @@ transform::detail::checkApplyToOne(Operation *transformOp,
 
 template <typename T>
 static SmallVector<T> castVector(ArrayRef<transform::MappedValue> range) {
-  return llvm::to_vector(llvm::map_range(
-      range, [](transform::MappedValue value) { return cast<T>(value); }));
+  return llvm::map_to_vector(range, llvm::CastTo<T>);
 }
 
 void transform::detail::setApplyToOneResults(

diff  --git a/mlir/lib/Dialect/XeGPU/Transforms/XeGPUSubgroupDistribute.cpp b/mlir/lib/Dialect/XeGPU/Transforms/XeGPUSubgroupDistribute.cpp
index bbd7733e89c29..4455811a2e681 100644
--- a/mlir/lib/Dialect/XeGPU/Transforms/XeGPUSubgroupDistribute.cpp
+++ b/mlir/lib/Dialect/XeGPU/Transforms/XeGPUSubgroupDistribute.cpp
@@ -926,8 +926,7 @@ static SmallVector<Value> computeDistributedCoordinatesForMatrixOp(
   SmallVector<OpFoldResult> ofrVec = xegpu::addWithRightAligned(
       rewriter, loc, getAsOpFoldResult(maybeCoords.value()[0]),
       getAsOpFoldResult(origOffsets));
-  newCoods = llvm::to_vector(llvm::map_range(
-      ofrVec, [&](OpFoldResult ofr) -> Value { return cast<Value>(ofr); }));
+  newCoods = llvm::map_to_vector(ofrVec, llvm::CastTo<Value>);
   return newCoods;
 }
 

diff  --git a/mlir/lib/IR/TypeUtilities.cpp b/mlir/lib/IR/TypeUtilities.cpp
index e438631ffe1f5..199744d208143 100644
--- a/mlir/lib/IR/TypeUtilities.cpp
+++ b/mlir/lib/IR/TypeUtilities.cpp
@@ -118,8 +118,7 @@ LogicalResult mlir::verifyCompatibleDims(ArrayRef<int64_t> dims) {
 /// have compatible dimensions. Dimensions are compatible if all non-dynamic
 /// dims are equal. The element type does not matter.
 LogicalResult mlir::verifyCompatibleShapes(TypeRange types) {
-  auto shapedTypes = llvm::map_to_vector<8>(
-      types, [](auto type) { return llvm::dyn_cast<ShapedType>(type); });
+  auto shapedTypes = llvm::map_to_vector<8>(types, llvm::DynCastTo<ShapedType>);
   // Return failure if some, but not all are not shaped. Return early if none
   // are shaped also.
   if (llvm::none_of(shapedTypes, [](auto t) { return t; }))