[mlir][NFC] Simplify type checks with isa predicates

mlir/lib/Conversion/GPUCommon/GPUOpsLowering.cpp

@@ -545,8 +545,7 @@ LogicalResult impl::scalarizeVectorOp(Operation *op, ValueRange operands,
                                       ConversionPatternRewriter &rewriter,
                                       const LLVMTypeConverter &converter) {
   TypeRange operandTypes(operands);
-  if (llvm::none_of(operandTypes,
-                    [](Type type) { return isa<VectorType>(type); })) {
+  if (llvm::none_of(operandTypes, llvm::IsaPred<VectorType>)) {
     return rewriter.notifyMatchFailure(op, "expected vector operand");
   }
   if (op->getNumRegions() != 0 || op->getNumSuccessors() != 0)

mlir/lib/Conversion/VectorToGPU/VectorToGPU.cpp

@@ -202,9 +202,7 @@ template <typename ExtOpTy>
 static bool integerExtendSupportsMMAMatrixType(ExtOpTy extOp) {
   if (!isa<vector::TransferReadOp>(extOp.getOperand().getDefiningOp()))
     return false;
-  return llvm::all_of(extOp->getUsers(), [](Operation *user) {
-    return isa<vector::ContractionOp>(user);
-  });
+  return llvm::all_of(extOp->getUsers(), llvm::IsaPred<vector::ContractionOp>);
 }
 
 static bool fpExtendSupportsMMAMatrixType(arith::ExtFOp extOp) { return true; }
@@ -345,15 +343,13 @@ getSliceContract(Operation *op,
 static SetVector<Operation *> getOpToConvert(mlir::Operation *op,
                                              bool useNvGpu) {
   auto hasVectorDest = [](Operation *op) {
-    return llvm::any_of(op->getResultTypes(),
-                        [](Type t) { return isa<VectorType>(t); });
+    return llvm::any_of(op->getResultTypes(), llvm::IsaPred<VectorType>);
   };
   BackwardSliceOptions backwardSliceOptions;
   backwardSliceOptions.filter = hasVectorDest;
 
   auto hasVectorSrc = [](Operation *op) {
-    return llvm::any_of(op->getOperandTypes(),
-                        [](Type t) { return isa<VectorType>(t); });
+    return llvm::any_of(op->getOperandTypes(), llvm::IsaPred<VectorType>);
   };
   ForwardSliceOptions forwardSliceOptions;
   forwardSliceOptions.filter = hasVectorSrc;

mlir/lib/Dialect/Affine/Analysis/AffineAnalysis.cpp

@@ -136,8 +136,7 @@ static bool isLocallyDefined(Value v, Operation *enclosingOp) {
 
 bool mlir::affine::isLoopMemoryParallel(AffineForOp forOp) {
   // Any memref-typed iteration arguments are treated as serializing.
-  if (llvm::any_of(forOp.getResultTypes(),
-                   [](Type type) { return isa<BaseMemRefType>(type); }))
+  if (llvm::any_of(forOp.getResultTypes(), llvm::IsaPred<BaseMemRefType>))
     return false;
 
   // Collect all load and store ops in loop nest rooted at 'forOp'.

mlir/lib/Dialect/Affine/Transforms/SuperVectorize.cpp

@@ -609,9 +609,8 @@ makePattern(const DenseSet<Operation *> &parallelLoops, int vectorRank,
 }
 
 static NestedPattern &vectorTransferPattern() {
-  static auto pattern = affine::matcher::Op([](Operation &op) {
-    return isa<vector::TransferReadOp, vector::TransferWriteOp>(op);
-  });
+  static auto pattern = affine::matcher::Op(
+      llvm::IsaPred<vector::TransferReadOp, vector::TransferWriteOp>);
   return pattern;
 }
 

mlir/lib/Dialect/Affine/Utils/LoopFusionUtils.cpp

@@ -211,8 +211,7 @@ static unsigned getMaxLoopDepth(ArrayRef<Operation *> srcOps,
   unsigned loopDepth = getInnermostCommonLoopDepth(targetDstOps);
 
   // Return common loop depth for loads if there are no store ops.
-  if (all_of(targetDstOps,
-             [&](Operation *op) { return isa<AffineReadOpInterface>(op); }))
+  if (all_of(targetDstOps, llvm::IsaPred<AffineReadOpInterface>))
     return loopDepth;
 
   // Check dependences on all pairs of ops in 'targetDstOps' and store the

mlir/lib/Dialect/Bufferization/Transforms/FuncBufferizableOpInterfaceImpl.cpp

@@ -326,7 +326,7 @@ struct FuncOpInterface
   static bool supportsUnstructuredControlFlow() { return true; }
 
   bool hasTensorSemantics(Operation *op) const {
-    auto isaTensor = [](Type type) { return isa<TensorType>(type); };
+    auto isaTensor = llvm::IsaPred<TensorType>;
 
     // A function has tensor semantics if it has tensor arguments/results.
     auto funcOp = cast<FuncOp>(op);

mlir/lib/Dialect/Bufferization/Transforms/OneShotModuleBufferize.cpp

@@ -67,6 +67,7 @@
 #include "mlir/Dialect/Bufferization/Transforms/Transforms.h"
 #include "mlir/Dialect/Func/IR/FuncOps.h"
 #include "mlir/Dialect/MemRef/IR/MemRef.h"
+#include "mlir/IR/BuiltinTypes.h"
 #include "mlir/IR/Operation.h"
 
 using namespace mlir;
@@ -277,9 +278,10 @@ static void equivalenceAnalysis(func::FuncOp funcOp,
 
 /// Return "true" if the given function signature has tensor semantics.
 static bool hasTensorSignature(func::FuncOp funcOp) {
-  auto isaTensor = [](Type t) { return isa<TensorType>(t); };
-  return llvm::any_of(funcOp.getFunctionType().getInputs(), isaTensor) ||
-         llvm::any_of(funcOp.getFunctionType().getResults(), isaTensor);
+  return llvm::any_of(funcOp.getFunctionType().getInputs(),
+                      llvm::IsaPred<TensorType>) ||
+         llvm::any_of(funcOp.getFunctionType().getResults(),
+                      llvm::IsaPred<TensorType>);
 }
 
 /// Store all functions of the `moduleOp` in `orderedFuncOps`, sorted by

mlir/lib/Dialect/EmitC/IR/EmitC.cpp

@@ -224,8 +224,7 @@ LogicalResult emitc::CallOpaqueOp::verify() {
     }
   }
 
-  if (llvm::any_of(getResultTypes(),
-                   [](Type type) { return isa<ArrayType>(type); })) {
+  if (llvm::any_of(getResultTypes(), llvm::IsaPred<ArrayType>)) {
     return emitOpError() << "cannot return array type";
   }
 

mlir/lib/Dialect/GPU/TransformOps/GPUTransformOps.cpp

@@ -296,22 +296,14 @@ checkMappingAttributeTypes(std::optional<TransformOpInterface> transformOp,
                                  "scf.forall op requires a mapping attribute");
   }
 
-  bool hasBlockMapping =
-      llvm::any_of(forallOp.getMapping().value(), [](Attribute attr) {
-        return isa<GPUBlockMappingAttr>(attr);
-      });
-  bool hasWarpgroupMapping =
-      llvm::any_of(forallOp.getMapping().value(), [](Attribute attr) {
-        return isa<GPUWarpgroupMappingAttr>(attr);
-      });
-  bool hasWarpMapping =
-      llvm::any_of(forallOp.getMapping().value(), [](Attribute attr) {
-        return isa<GPUWarpMappingAttr>(attr);
-      });
-  bool hasThreadMapping =
-      llvm::any_of(forallOp.getMapping().value(), [](Attribute attr) {
-        return isa<GPUThreadMappingAttr>(attr);
-      });
+  bool hasBlockMapping = llvm::any_of(forallOp.getMapping().value(),
+                                      llvm::IsaPred<GPUBlockMappingAttr>);
+  bool hasWarpgroupMapping = llvm::any_of(
+      forallOp.getMapping().value(), llvm::IsaPred<GPUWarpgroupMappingAttr>);
+  bool hasWarpMapping = llvm::any_of(forallOp.getMapping().value(),
+                                     llvm::IsaPred<GPUWarpMappingAttr>);
+  bool hasThreadMapping = llvm::any_of(forallOp.getMapping().value(),
+                                       llvm::IsaPred<GPUThreadMappingAttr>);
   int64_t countMappingTypes = 0;
   countMappingTypes += hasBlockMapping ? 1 : 0;
   countMappingTypes += hasWarpgroupMapping ? 1 : 0;

mlir/lib/Dialect/GPU/Transforms/AsyncRegionRewriter.cpp

@@ -232,9 +232,8 @@ struct GpuAsyncRegionPass::DeferWaitCallback {
   // control flow code.
   static bool areAllUsersExecuteOrAwait(Value token) {
     return !token.use_empty() &&
-           llvm::all_of(token.getUsers(), [](Operation *user) {
-             return isa<async::ExecuteOp, async::AwaitOp>(user);
-           });
+           llvm::all_of(token.getUsers(),
+                        llvm::IsaPred<async::ExecuteOp, async::AwaitOp>);
   }
 
   // Add the `asyncToken` as dependency as needed after `op`.

mlir/lib/Dialect/LLVMIR/IR/LLVMDialect.cpp

@@ -2786,10 +2786,8 @@ LogicalResult LLVM::BitcastOp::verify() {
   if (!resultType)
     return success();
 
-  auto isVector = [](Type type) {
-    return llvm::isa<VectorType, LLVMScalableVectorType, LLVMFixedVectorType>(
-        type);
-  };
+  auto isVector =
+      llvm::IsaPred<VectorType, LLVMScalableVectorType, LLVMFixedVectorType>;
 
   // Due to bitcast requiring both operands to be of the same size, it is not
   // possible for only one of the two to be a pointer of vectors.

mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp

@@ -28,6 +28,7 @@
 #include "mlir/IR/AffineExprVisitor.h"
 #include "mlir/IR/AffineMap.h"
 #include "mlir/IR/BuiltinAttributes.h"
+#include "mlir/IR/BuiltinTypeInterfaces.h"
 #include "mlir/IR/Matchers.h"
 #include "mlir/IR/OpImplementation.h"
 #include "mlir/IR/OperationSupport.h"
@@ -119,8 +120,7 @@ static void fillStructuredOpRegion(OpBuilder &opBuilder, Region &region,
                                    TypeRange inputTypes, TypeRange outputTypes,
                                    ArrayRef<NamedAttribute> attrs,
                                    RegionBuilderFn regionBuilder) {
-  assert(llvm::all_of(outputTypes,
-                      [](Type t) { return llvm::isa<ShapedType>(t); }));
+  assert(llvm::all_of(outputTypes, llvm::IsaPred<ShapedType>));
 
   SmallVector<Type, 8> argTypes;
   SmallVector<Location, 8> argLocs;
@@ -162,7 +162,7 @@ static void buildStructuredOp(OpBuilder &b, OperationState &state,
       resultTensorTypes.value_or(TypeRange());
   if (!resultTensorTypes)
     copy_if(outputs.getTypes(), std::back_inserter(derivedResultTypes),
-            [](Type type) { return llvm::isa<RankedTensorType>(type); });
+            llvm::IsaPred<RankedTensorType>);
 
   state.addOperands(inputs);
   state.addOperands(outputs);

mlir/lib/Dialect/Linalg/Transforms/ElementwiseToLinalg.cpp

@@ -27,8 +27,7 @@ static bool isElementwiseMappableOpOnRankedTensors(Operation *op) {
 
   // TODO: The conversion pattern can be made to work for `any_of` here, but
   // it's more complex as it requires tracking which operands are scalars.
-  return llvm::all_of(op->getOperandTypes(),
-                      [](Type type) { return isa<RankedTensorType>(type); });
+  return llvm::all_of(op->getOperandTypes(), llvm::IsaPred<RankedTensorType>);
 }
 
 /// Given `op` assumed `isElementwiseMappableOpOnRankedTensors`, iterate over

mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp

@@ -3537,15 +3537,14 @@ struct Conv1DGenerator
   // Otherwise, check for one or zero `ext` predecessor. The `ext` operands
   // must be block arguments or extension of block arguments.
   bool setOperKind(Operation *reduceOp) {
-    int numBlockArguments = llvm::count_if(
-        reduceOp->getOperands(), [](Value v) { return isa<BlockArgument>(v); });
+    int numBlockArguments =
+        llvm::count_if(reduceOp->getOperands(), llvm::IsaPred<BlockArgument>);
     switch (numBlockArguments) {
     case 1: {
       // Will be convolution if feeder is a MulOp.
       // Otherwise, if it can be pooling.
-      auto feedValIt = llvm::find_if(reduceOp->getOperands(), [](Value v) {
-        return !isa<BlockArgument>(v);
-      });
+      auto feedValIt = llvm::find_if_not(reduceOp->getOperands(),
+                                         llvm::IsaPred<BlockArgument>);
       Operation *feedOp = (*feedValIt).getDefiningOp();
       if (isCastOfBlockArgument(feedOp)) {
         oper = Pool;

mlir/lib/Dialect/OpenACC/IR/OpenACC.cpp

@@ -457,7 +457,7 @@ static ParseResult parseRegions(OpAsmParser &parser, OperationState &state,
 }
 
 static bool isComputeOperation(Operation *op) {
-  return isa<acc::ParallelOp>(op) || isa<acc::LoopOp>(op);
+  return isa<acc::ParallelOp, acc::LoopOp>(op);
 }
 
 namespace {

mlir/lib/Dialect/SPIRV/IR/CooperativeMatrixOps.cpp

@@ -125,8 +125,7 @@ LogicalResult KHRCooperativeMatrixMulAddOp::verify() {
   if (getMatrixOperands()) {
     Type elementTypes[] = {typeA.getElementType(), typeB.getElementType(),
                            typeC.getElementType()};
-    if (!llvm::all_of(elementTypes,
-                      [](Type ty) { return isa<IntegerType>(ty); })) {
+    if (!llvm::all_of(elementTypes, llvm::IsaPred<IntegerType>)) {
       return emitOpError("Matrix Operands require all matrix element types to "
                          "be Integer Types");
     }

mlir/lib/Dialect/Shape/IR/Shape.cpp

@@ -65,9 +65,8 @@ LogicalResult shape::getShapeVec(Value input,
 }
 
 static bool isErrorPropagationPossible(TypeRange operandTypes) {
-  return llvm::any_of(operandTypes, [](Type ty) {
-    return llvm::isa<SizeType, ShapeType, ValueShapeType>(ty);
-  });
+  return llvm::any_of(operandTypes,
+                      llvm::IsaPred<SizeType, ShapeType, ValueShapeType>);
 }
 
 static LogicalResult verifySizeOrIndexOp(Operation *op) {

mlir/lib/Dialect/Traits.cpp

@@ -188,9 +188,8 @@ Type OpTrait::util::getBroadcastedType(Type type1, Type type2,
 /// Returns a tuple corresponding to whether range has tensor or vector type.
 template <typename iterator_range>
 static std::tuple<bool, bool> hasTensorOrVectorType(iterator_range types) {
-  return std::make_tuple(
-      llvm::any_of(types, [](Type t) { return isa<TensorType>(t); }),
-      llvm::any_of(types, [](Type t) { return isa<VectorType>(t); }));
+  return {llvm::any_of(types, llvm::IsaPred<TensorType>),
+          llvm::any_of(types, llvm::IsaPred<VectorType>)};
 }
 
 static bool isCompatibleInferredReturnShape(ArrayRef<int64_t> inferred,
@@ -202,7 +201,7 @@ static bool isCompatibleInferredReturnShape(ArrayRef<int64_t> inferred,
   };
   if (inferred.size() != existing.size())
     return false;
-  for (auto [inferredDim, existingDim] : llvm::zip(inferred, existing))
+  for (auto [inferredDim, existingDim] : llvm::zip_equal(inferred, existing))
     if (!isCompatible(inferredDim, existingDim))
       return false;
   return true;
@@ -238,8 +237,8 @@ LogicalResult OpTrait::impl::verifyCompatibleOperandBroadcast(Operation *op) {
        std::get<1>(resultsHasTensorVectorType)))
     return op->emitError("cannot broadcast vector with tensor");
 
-  auto rankedOperands = make_filter_range(
-      op->getOperandTypes(), [](Type t) { return isa<RankedTensorType>(t); });
+  auto rankedOperands =
+      make_filter_range(op->getOperandTypes(), llvm::IsaPred<RankedTensorType>);
 
   // If all operands are unranked, then all result shapes are possible.
   if (rankedOperands.empty())
@@ -257,8 +256,8 @@ LogicalResult OpTrait::impl::verifyCompatibleOperandBroadcast(Operation *op) {
       return op->emitOpError("operands don't have broadcast-compatible shapes");
   }
 
-  auto rankedResults = make_filter_range(
-      op->getResultTypes(), [](Type t) { return isa<RankedTensorType>(t); });
+  auto rankedResults =
+      make_filter_range(op->getResultTypes(), llvm::IsaPred<RankedTensorType>);
 
   // If all of the results are unranked then no further verification.
   if (rankedResults.empty())

mlir/lib/Dialect/Transform/IR/TransformOps.cpp

@@ -819,7 +819,7 @@ bool transform::CastOp::areCastCompatible(TypeRange inputs, TypeRange outputs) {
   assert(outputs.size() == 1 && "expected one output");
   return llvm::all_of(
       std::initializer_list<Type>{inputs.front(), outputs.front()},
-      [](Type ty) { return isa<transform::TransformHandleTypeInterface>(ty); });
+      llvm::IsaPred<transform::TransformHandleTypeInterface>);
 }
 
 //===----------------------------------------------------------------------===//

mlir/lib/Dialect/Vector/IR/VectorOps.cpp

@@ -898,13 +898,12 @@ static LogicalResult verifyOutputShape(
 
     AffineMap resMap = op.getIndexingMapsArray()[2];
     auto extentsMap = AffineMap::get(/*dimCount=*/extents.size(),
-                                     /*symCount=*/0, extents, ctx);
+                                     /*symbolCount=*/0, extents, ctx);
     // Compose the resMap with the extentsMap, which is a constant map.
     AffineMap expectedMap = simplifyAffineMap(resMap.compose(extentsMap));
-    assert(
-        llvm::all_of(expectedMap.getResults(),
-                     [](AffineExpr e) { return isa<AffineConstantExpr>(e); }) &&
-        "expected constant extent along all dimensions.");
+    assert(llvm::all_of(expectedMap.getResults(),
+                        llvm::IsaPred<AffineConstantExpr>) &&
+           "expected constant extent along all dimensions.");
     // Extract the expected shape and build the type.
     auto expectedShape = llvm::to_vector<4>(
         llvm::map_range(expectedMap.getResults(), [](AffineExpr e) {