Demo fix for performance-unnecessary-value-param (WIP)

[Mogball]

mlir/lib/Dialect/StandardOps/IR/Ops.cpp

1049

TypeRangeRange is too large to be passed by value?

[mehdi_amini]

mlir/lib/Dialect/StandardOps/IR/Ops.cpp

1049

From the doc:

The check is only applied to parameters of types that are expensive to copy which means they are not trivially copyable or have a non-trivial copy constructor or destructor.

Maybe there is something to fix in the TypeRangeRange class?

[mehdi_amini]

Full repo run

[Mogball]

mlir/lib/Dialect/StandardOps/IR/Ops.cpp

1049

The class has two std::functions inside of it

[arjunp]

mlir/unittests/Analysis/Presburger/SimplexTest.cpp

98

Not sure if this should be done in this patch but I guess ArrayRef should be used instead of const SmallVector &.

[mehdi_amini]

Apply a few manual fixes, for example const std::function<> & -> llvm::function_ref<>

mlir/include/mlir/Analysis/NestedMatcher.h

~NestedPatternContext() {
     NestedPattern::allocator() = nullptr;
   }
   llvm::BumpPtrAllocator allocator;
 };
 
 namespace matcher {
 // Syntactic sugar NestedPattern builder functions.
 NestedPattern Op(FilterFunctionType filter = defaultFilterFunction);
-NestedPattern If(NestedPattern child);
-NestedPattern If(FilterFunctionType filter, NestedPattern child);
+NestedPattern If(const NestedPattern &child);
+NestedPattern If(const FilterFunctionType &filter, const NestedPattern &child);
 NestedPattern If(ArrayRef<NestedPattern> nested = {});
-NestedPattern If(FilterFunctionType filter,
+NestedPattern If(const FilterFunctionType &filter,
                  ArrayRef<NestedPattern> nested = {});
-NestedPattern For(NestedPattern child);
-NestedPattern For(FilterFunctionType filter, NestedPattern child);
+NestedPattern For(const NestedPattern &child);
+NestedPattern For(const FilterFunctionType &filter, const NestedPattern &child);
 NestedPattern For(ArrayRef<NestedPattern> nested = {});
-NestedPattern For(FilterFunctionType filter,
+NestedPattern For(const FilterFunctionType &filter,
                   ArrayRef<NestedPattern> nested = {});
 
 bool isParallelLoop(Operation &op);
 bool isReductionLoop(Operation &op);
 bool isLoadOrStore(Operation &op);
 
 } // namespace matcher
 } // namespace mlir
 
 #endif // MLIR_ANALYSIS_MLFUNCTIONMATCHER_H_

mlir/include/mlir/Dialect/Async/Transforms.h

 /// Must return an index type.
 using AsyncMinTaskSizeComputationFunction =
     std::function<Value(ImplicitLocOpBuilder, scf::ParallelOp)>;
 
 /// Add a pattern to the given pattern list to lower scf.parallel to async
 /// operations.
 void populateAsyncParallelForPatterns(
     RewritePatternSet &patterns, bool asyncDispatch, int32_t numWorkerThreads,
-    AsyncMinTaskSizeComputationFunction computeMinTaskSize);
+    const AsyncMinTaskSizeComputationFunction &computeMinTaskSize);
 
 } // namespace async
 } // namespace mlir
 
 #endif // MLIR_DIALECT_ASYNC_TRANSFORMS_H_

mlir/include/mlir/Dialect/Linalg/ComprehensiveBufferize/BufferizableOpInterface.h

public:
   ///              |
   ///              1
   ///
   /// In the above example, Values with a star satisfy the condition. When
   /// starting the traversal from Value 1, the resulting SetVector is:
   /// { 2, 7, 8, 5 }
   llvm::SetVector<Value>
   findValueInReverseUseDefChain(Value value,
-                                std::function<bool(Value)> condition);
+                                llvm::function_ref<bool(Value)> condition);
 
   /// Find the Value of the last preceding write of a given Value.
   ///
   /// Note: Unknown ops are handled conservatively and assumed to be writes.
   /// Furthermore, BlockArguments are also assumed to be writes. There is no
   /// analysis across block boundaries.
   ///
   /// Note: When reaching an end of the reverse SSA use-def chain, that value

mlir/include/mlir/Dialect/Linalg/Passes.h

 /// work on primitive types, if possible.
 std::unique_ptr<Pass> createLinalgDetensorizePass();
 
 //===----------------------------------------------------------------------===//
 /// Linalg strategy passes.
 //===----------------------------------------------------------------------===//
 /// Create a LinalgStrategyTileAndFusePass.
 std::unique_ptr<OperationPass<FuncOp>> createLinalgStrategyTileAndFusePass(
-    StringRef opName = "", linalg::LinalgTilingAndFusionOptions opt = {},
-    linalg::LinalgTransformationFilter filter =
+    StringRef opName = "", const linalg::LinalgTilingAndFusionOptions &opt = {},
+    const linalg::LinalgTransformationFilter &filter =
         linalg::LinalgTransformationFilter());
 
 /// Create a LinalgStrategyTilePass.
 std::unique_ptr<OperationPass<FuncOp>> createLinalgStrategyTilePass(
     StringRef opName = "",
-    linalg::LinalgTilingOptions opt = linalg::LinalgTilingOptions(),
-    linalg::LinalgTransformationFilter filter =
+    const linalg::LinalgTilingOptions &opt = linalg::LinalgTilingOptions(),
+    const linalg::LinalgTransformationFilter &filter =
         linalg::LinalgTransformationFilter());
 
 /// Create a LinalgStrategyPadPass.
 std::unique_ptr<OperationPass<FuncOp>> createLinalgStrategyPadPass(
     StringRef opName = "",
-    linalg::LinalgPaddingOptions opt = linalg::LinalgPaddingOptions(),
-    linalg::LinalgTransformationFilter filter =
+    const linalg::LinalgPaddingOptions &opt = linalg::LinalgPaddingOptions(),
+    const linalg::LinalgTransformationFilter &filter =
         linalg::LinalgTransformationFilter());
 
 /// Create a LinalgStrategyPromotePass.
 std::unique_ptr<OperationPass<FuncOp>> createLinalgStrategyPromotePass(
     StringRef opName = "",
-    linalg::LinalgPromotionOptions opt = linalg::LinalgPromotionOptions(),
-    linalg::LinalgTransformationFilter filter =
+    const linalg::LinalgPromotionOptions &opt =
+        linalg::LinalgPromotionOptions(),
+    const linalg::LinalgTransformationFilter &filter =
         linalg::LinalgTransformationFilter());
 
 /// Create a LinalgStrategyGeneralizePass.
-std::unique_ptr<OperationPass<FuncOp>>
-createLinalgStrategyGeneralizePass(StringRef opName = "",
-                                   linalg::LinalgTransformationFilter filter =
-                                       linalg::LinalgTransformationFilter());
+std::unique_ptr<OperationPass<FuncOp>> createLinalgStrategyGeneralizePass(
+    StringRef opName = "", const linalg::LinalgTransformationFilter &filter =
 
 /// Create a LinalgStrategyDecomposePass.
 // TODO: if/when we need finer control add an `opName` parameter.
-std::unique_ptr<OperationPass<FuncOp>>
-createLinalgStrategyDecomposePass(linalg::LinalgTransformationFilter filter =
-                                      linalg::LinalgTransformationFilter());
+std::unique_ptr<OperationPass<FuncOp>> createLinalgStrategyDecomposePass(
+    const linalg::LinalgTransformationFilter &filter =
 
 /// Create a LinalgStrategyInterchangePass.
-std::unique_ptr<OperationPass<FuncOp>>
-createLinalgStrategyInterchangePass(ArrayRef<int64_t> iteratorInterchange = {},
-                                    linalg::LinalgTransformationFilter filter =
-                                        linalg::LinalgTransformationFilter());
+std::unique_ptr<OperationPass<FuncOp>> createLinalgStrategyInterchangePass(
+    ArrayRef<int64_t> iteratorInterchange = {},
+    const linalg::LinalgTransformationFilter &filter =
 
 /// Create a LinalgStrategyVectorizePass.
-std::unique_ptr<OperationPass<FuncOp>>
-createLinalgStrategyVectorizePass(StringRef opName = "",
-                                  linalg::LinalgVectorizationOptions opt =
-                                      linalg::LinalgVectorizationOptions(),
-                                  linalg::LinalgTransformationFilter filter =
-                                      linalg::LinalgTransformationFilter(),
-                                  bool padVectorize = false);
+std::unique_ptr<OperationPass<FuncOp>> createLinalgStrategyVectorizePass(
+    StringRef opName = "",
+    const linalg::LinalgTransformationFilter &filter =
 
 /// Create a LinalgStrategyEnablePass.
 std::unique_ptr<OperationPass<FuncOp>> createLinalgStrategyEnablePass(
     linalg::LinalgEnablingOptions opt = linalg::LinalgEnablingOptions(),
-    linalg::LinalgTransformationFilter filter =
+    const linalg::LinalgTransformationFilter &filter =
         linalg::LinalgTransformationFilter());
 
 /// Create a LinalgStrategyLowerVectorsPass.
-std::unique_ptr<OperationPass<FuncOp>>
-createLinalgStrategyLowerVectorsPass(linalg::LinalgVectorLoweringOptions opt =
-                                         linalg::LinalgVectorLoweringOptions(),
-                                     linalg::LinalgTransformationFilter filter =
-                                         linalg::LinalgTransformationFilter());
+std::unique_ptr<OperationPass<FuncOp>> createLinalgStrategyLowerVectorsPass(
+    linalg::LinalgVectorLoweringOptions opt =
+    const linalg::LinalgTransformationFilter &filter =
 
 /// Create a LinalgStrategyRemoveMarkersPass.
 std::unique_ptr<OperationPass<FuncOp>> createLinalgStrategyRemoveMarkersPass();
 
 //===----------------------------------------------------------------------===//
 // Registration
 //===----------------------------------------------------------------------===//
 
 /// Generate the code for registering passes.
 #define GEN_PASS_REGISTRATION
 #include "mlir/Dialect/Linalg/Passes.h.inc"
 
 } // namespace mlir
 
 #endif // MLIR_DIALECT_LINALG_PASSES_H_

mlir/include/mlir/Dialect/Linalg/Transforms/Transforms.h

 using ControlElementwiseOpsFusionFn =
     std::function<bool(const OpResult &producer, OpOperand &consumer)>;
 
 /// Patterns to fold an expanding (collapsing) tensor_reshape operation with its
 /// producer (consumer) generic operation by expanding the dimensionality of the
 /// loop in the generic op.
 void populateFoldReshapeOpsByExpansionPatterns(
     RewritePatternSet &patterns,
-    ControlElementwiseOpsFusionFn controlFoldingReshapes = skipUnitDimReshape);
+    const ControlElementwiseOpsFusionFn &controlFoldingReshapes =
+        skipUnitDimReshape);
 
 /// Patterns to fold a collapsing (expanding) tensor_reshape operation with its
 /// producer (consumer) generic operation by linearizing the indexing map used
 /// to access the source (target) of the reshape operation in the generic
 /// operation.
 void populateFoldReshapeOpsByLinearizationPatterns(RewritePatternSet &patterns);
 
 /// Patterns to fold a collapsing (expanding) tensor_reshape operation with its

 /// buffer as `fullLocalView` and the view that matches the size of the result
 /// of subview operation as `partialLocalView`.
 struct PromotionInfo {
   Value fullLocalView;
   Value partialLocalView;
 };
 FailureOr<PromotionInfo>
 promoteSubviewAsNewBuffer(OpBuilder &b, Location loc, memref::SubViewOp subView,
-                          AllocBufferCallbackFn allocationFn,
+                          const AllocBufferCallbackFn &allocationFn,
                           DataLayout &layout);
 
 /// Promotes the `subViews` into a new buffer allocated at the insertion point
 /// `b`. Promotion occurs in 3 steps:
 ///   1. Create a new buffer for a full tile (i.e. not clipped at the boundary).
 ///   2. Take a full view on the buffer.
 ///   3. Take a partial slice of the full view in step 2. and copy into it.
 /// Infers statically sized buffers from subViews unless `dynamicBuffers` is
 /// true.
 ///
 /// Returns the modified linalg op (the modification happens in place) as well
 /// as all the copy ops created.
 FailureOr<LinalgOp> promoteSubViews(OpBuilder &b, LinalgOp op,
-                                    LinalgPromotionOptions options);
+                                    const LinalgPromotionOptions &options);
 
 /// Emit a suitable vector form for a Linalg op with fully static shape.
 LogicalResult vectorizeLinalgOp(OpBuilder &builder, Operation *op,
                                 SmallVectorImpl<Value> &newResults);
 
 /// Emits a loop nest of `scf.for` with the proper body for `linalgOp`.
 FailureOr<LinalgLoops> linalgOpToLoops(PatternRewriter &rewriter,
                                        LinalgOp linalgOp);

 struct LinalgTransformationFilter {
   using FilterFunction = std::function<LogicalResult(Operation *)>;
 
   explicit LinalgTransformationFilter(
       ArrayRef<StringAttr> matchDisjunction = {},
       Optional<StringAttr> replacement = None);
 
   explicit LinalgTransformationFilter(
-      FilterFunction f, ArrayRef<StringAttr> matchDisjunction = {},
+      const FilterFunction &f, ArrayRef<StringAttr> matchDisjunction = {},
       Optional<StringAttr> replacement = None);
 
   LinalgTransformationFilter(LinalgTransformationFilter &&) = default;
   LinalgTransformationFilter(const LinalgTransformationFilter &) = default;
   LogicalResult checkAndNotify(PatternRewriter &rewriter, Operation *op) const;
   void replaceLinalgTransformationFilter(PatternRewriter &rewriter,
                                          Operation *op) const;
   bool hasReplacementFilter(Operation *op) const;

 };
 
 /// Linalg generalization patterns
 
 /// Populates `patterns` with patterns to convert spec-generated named ops to
 /// linalg.generic ops.
 void populateLinalgNamedOpsGeneralizationPatterns(
     RewritePatternSet &patterns,
-    LinalgTransformationFilter filter = LinalgTransformationFilter());
+    const LinalgTransformationFilter &filter = LinalgTransformationFilter());
 
 /// Linalg decompose convolutions patterns
 
 /// Populates patterns to decompose high-D convolution ops into low-D ones. This
 /// is a step in progressive lowering for convolution ops, afterwards we can
 /// vectorize the low-D convolution ops.
 void populateDecomposeConvolutionPatterns(
     RewritePatternSet &patterns,
-    LinalgTransformationFilter filter = LinalgTransformationFilter(),
+    const LinalgTransformationFilter &filter = LinalgTransformationFilter(),
     PatternBenefit benefit = 1);
 
 /// Linalg distribution patterns
 //
 /// Populates `patterns` with patterns to distribute linalg.tiled_loop.
 void populateLinalgDistributeTiledLoopPattern(
     RewritePatternSet &patterns, const LinalgLoopDistributionOptions &opts,
     const LinalgTransformationFilter &marker);

mlir/include/mlir/IR/AffineMap.h

public:
   bool isFunctionOfSymbol(unsigned position) const {
     return llvm::any_of(getResults(), [&](AffineExpr e) {
       return e.isFunctionOfSymbol(position);
     });
   }
 
   /// Walk all of the AffineExpr's in this mapping. Each node in an expression
   /// tree is visited in postorder.
-  void walkExprs(std::function<void(AffineExpr)> callback) const;
+  void walkExprs(llvm::function_ref<void(AffineExpr)> callback) const;
 
   /// This method substitutes any uses of dimensions and symbols (e.g.
   /// dim#0 with dimReplacements[0]) in subexpressions and returns the modified
   /// expression mapping.  Because this can be used to eliminate dims and
   /// symbols, the client needs to specify the number of dims and symbols in
   /// the result.  The returned map always has the same number of results.
   AffineMap replaceDimsAndSymbols(ArrayRef<AffineExpr> dimReplacements,
                                   ArrayRef<AffineExpr> symReplacements,

mlir/include/mlir/IR/Dialect.h

public:
   void insert() {
     insert<ConcreteDialect>();
     insert<OtherDialect, MoreDialects...>();
   }
 
   /// Add a new dialect constructor to the registry. The constructor must be
   /// calling MLIRContext::getOrLoadDialect in order for the context to take
   /// ownership of the dialect and for delayed interface registration to happen.
-  void insert(TypeID typeID, StringRef name, DialectAllocatorFunction ctor);
+  void insert(TypeID typeID, StringRef name,
+              const DialectAllocatorFunction &ctor);
 
   /// Return an allocation function for constructing the dialect identified by
   /// its namespace, or nullptr if the namespace is not in this registry.
   DialectAllocatorFunctionRef getDialectAllocator(StringRef name) const;
 
   // Register all dialects available in the current registry with the registry
   // in the provided context.
   void appendTo(DialectRegistry &destination) const {
public:
   /// Register any interfaces required for the given dialect (based on its
   /// TypeID). Users are not expected to call this directly.
   void registerDelayedInterfaces(Dialect *dialect) const;
 
 private:
   /// Add an interface constructed with the given allocation function to the
   /// dialect identified by its namespace.
   void addDialectInterface(StringRef dialectName, TypeID interfaceTypeID,
-                           DialectInterfaceAllocatorFunction allocator);
+                           const DialectInterfaceAllocatorFunction &allocator);
 
   /// Add an attribute/operation/type interface constructible with the given
   /// allocation function to the dialect identified by its namespace.
   void addObjectInterface(StringRef dialectName, TypeID objectID,
                           TypeID interfaceTypeID,
-                          ObjectInterfaceAllocatorFunction allocator);
+                          const ObjectInterfaceAllocatorFunction &allocator);
 
   /// Add an external model for an attribute/type interface to the dialect
   /// identified by its namespace.
   template <typename ObjectTy, typename ModelTy>
   void addStorageUserInterface(StringRef dialectName) {
     addObjectInterface(dialectName, TypeID::get<ObjectTy>(),
                        ModelTy::Interface::getInterfaceID(),
                        [](MLIRContext *context) {

mlir/include/mlir/Reducer/ReductionNode.h

 /// path to reduce the certain module.
 class ReductionNode {
 public:
   template <TraversalMode mode>
   class iterator;
 
   using Range = std::pair<int, int>;
 
-  ReductionNode(ReductionNode *parent, std::vector<Range> range,
+  ReductionNode(ReductionNode *parent, const std::vector<Range> &range,
                 llvm::SpecificBumpPtrAllocator<ReductionNode> &allocator);
 
   ReductionNode *getParent() const { return parent; }
 
   /// If the ReductionNode hasn't been tested the interestingness, it'll be the
   /// same module as the one in the parent node. Otherwise, the returned module
   /// will have been applied certain reduction strategies. Note that it's not
   /// necessary to be an interesting case or a reduced module (has smaller size

mlir/include/mlir/TableGen/GenInfo.h

 /// GenRegistration provides a global initializer that registers a generator
 /// function.
 ///
 /// Usage:
 ///
 ///   // At namespace scope.
 ///   static GenRegistration Print("print", "Print records", [](...){...});
 struct GenRegistration {
-  GenRegistration(StringRef arg, StringRef description, GenFunction function);
+  GenRegistration(StringRef arg, StringRef description,
+                  const GenFunction &function);
 };
 
 } // namespace mlir
 
 #endif // MLIR_TABLEGEN_GENINFO_H_

mlir/include/mlir/TableGen/Pattern.h

public:
 
   // Returns an iterator to the information of the given symbol named as `key`.
   const_iterator find(StringRef key) const;
 
   // Returns an iterator to the information of the given symbol named as `key`,
   // with index `argIndex` for operator `op`.
   const_iterator findBoundSymbol(StringRef key, DagNode node,
                                  const Operator &op, int argIndex) const;
-  const_iterator findBoundSymbol(StringRef key, SymbolInfo symbolInfo) const;
+  const_iterator findBoundSymbol(StringRef key,
+                                 const SymbolInfo &symbolInfo) const;
 
   // Returns the bounds of a range that includes all the elements which
   // bind to the `key`.
   std::pair<iterator, iterator> getRangeOfEqualElements(StringRef key);
 
   // Returns number of times symbol named as `key` was used.
   int count(StringRef key) const;
 

mlir/include/mlir/Translation.h

TranslateToMLIRRegistration(llvm::StringRef name,
                               const TranslateSourceMgrToMLIRFunction &function);
   TranslateToMLIRRegistration(llvm::StringRef name,
                               const TranslateStringRefToMLIRFunction &function);
 };
 
 struct TranslateFromMLIRRegistration {
   TranslateFromMLIRRegistration(
       llvm::StringRef name, const TranslateFromMLIRFunction &function,
-      std::function<void(DialectRegistry &)> dialectRegistration =
+      const std::function<void(DialectRegistry &)> &dialectRegistration =
           [](DialectRegistry &) {});
 };
 struct TranslateRegistration {
   TranslateRegistration(llvm::StringRef name,
                         const TranslateFunction &function);
 };
 /// \}
 

mlir/lib/Analysis/LoopAnalysis.cpp

static bool isVectorElement(LoadOrStoreOp memoryOp) {
   auto memRefType = memoryOp.getMemRefType();
   return memRefType.getElementType().template isa<VectorType>();
 }
 
 using VectorizableOpFun = std::function<bool(AffineForOp, Operation &)>;
 
 static bool
 isVectorizableLoopBodyWithOpCond(AffineForOp loop,
-                                 VectorizableOpFun isVectorizableOp,
+                                 const VectorizableOpFun &isVectorizableOp,
                                  NestedPattern &vectorTransferMatcher) {
   auto *forOp = loop.getOperation();
 
   // No vectorization across conditionals for now.
   auto conditionals = matcher::If();
   SmallVector<NestedMatch, 8> conditionalsMatched;
   conditionals.match(forOp, &conditionalsMatched);
   if (!conditionalsMatched.empty()) {

mlir/lib/Analysis/NestedMatcher.cpp

 //===- NestedMatcher.cpp - NestedMatcher Impl  ----------------------------===//
 //
 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
 // See https://llvm.org/LICENSE.txt for license information.
 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 //
 //===----------------------------------------------------------------------===//
 
+#include <utility>
+
 #include "mlir/Analysis/NestedMatcher.h"
 #include "mlir/Dialect/Affine/IR/AffineOps.h"
 #include "mlir/Dialect/StandardOps/IR/Ops.h"
 
 #include "llvm/ADT/ArrayRef.h"
 #include "llvm/ADT/STLExtras.h"
 #include "llvm/Support/Allocator.h"
 #include "llvm/Support/raw_ostream.h"

 
 void NestedPattern::freeNested() {
   for (const auto &p : nestedPatterns)
     p.~NestedPattern();
 }
 
 NestedPattern::NestedPattern(ArrayRef<NestedPattern> nested,
                              FilterFunctionType filter)
-    : nestedPatterns(), filter(filter), skip(nullptr) {
+    : nestedPatterns(), filter(std::move(filter)), skip(nullptr) {
   copyNestedToThis(nested);
 }
 
 NestedPattern::NestedPattern(const NestedPattern &other)
     : nestedPatterns(), filter(other.filter), skip(other.skip) {
   copyNestedToThis(other.nestedPatterns);
 }
 

 static bool isAffineForOp(Operation &op) { return isa<AffineForOp>(op); }
 
 static bool isAffineIfOp(Operation &op) { return isa<AffineIfOp>(op); }
 
 namespace mlir {
 namespace matcher {
 
 NestedPattern Op(FilterFunctionType filter) {
-  return NestedPattern({}, filter);
+  return NestedPattern({}, std::move(filter));
 }
 
-NestedPattern If(NestedPattern child) {
+NestedPattern If(const NestedPattern &child) {
   return NestedPattern(child, isAffineIfOp);
 }
-NestedPattern If(FilterFunctionType filter, NestedPattern child) {
+NestedPattern If(const FilterFunctionType &filter, const NestedPattern &child) {
   return NestedPattern(child, [filter](Operation &op) {
     return isAffineIfOp(op) && filter(op);
   });
 }
 NestedPattern If(ArrayRef<NestedPattern> nested) {
   return NestedPattern(nested, isAffineIfOp);
 }
-NestedPattern If(FilterFunctionType filter, ArrayRef<NestedPattern> nested) {
+NestedPattern If(const FilterFunctionType &filter,
+                 ArrayRef<NestedPattern> nested) {
   return NestedPattern(nested, [filter](Operation &op) {
     return isAffineIfOp(op) && filter(op);
   });
 }
 
-NestedPattern For(NestedPattern child) {
+NestedPattern For(const NestedPattern &child) {
   return NestedPattern(child, isAffineForOp);
 }
-NestedPattern For(FilterFunctionType filter, NestedPattern child) {
+NestedPattern For(const FilterFunctionType &filter,
+                  const NestedPattern &child) {
   return NestedPattern(
       child, [=](Operation &op) { return isAffineForOp(op) && filter(op); });
 }
 NestedPattern For(ArrayRef<NestedPattern> nested) {
   return NestedPattern(nested, isAffineForOp);
 }
-NestedPattern For(FilterFunctionType filter, ArrayRef<NestedPattern> nested) {
+NestedPattern For(const FilterFunctionType &filter,
+                  ArrayRef<NestedPattern> nested) {
   return NestedPattern(
       nested, [=](Operation &op) { return isAffineForOp(op) && filter(op); });
 }
 
 bool isLoadOrStore(Operation &op) {
   return isa<AffineLoadOp, AffineStoreOp>(op);
 }
 
 } // namespace matcher
 } // namespace mlir

mlir/lib/Bindings/Python/DialectSparseTensor.cpp

 #include "mlir/Bindings/Python/PybindAdaptors.h"
 
 namespace py = pybind11;
 using namespace llvm;
 using namespace mlir;
 using namespace mlir::python::adaptors;
 
 void mlir::python::populateDialectSparseTensorSubmodule(
-    py::module m, const py::module &irModule) {
+    const py::module &m, const py::module &irModule) {
   auto attributeClass = irModule.attr("Attribute");
 
   py::enum_<MlirSparseTensorDimLevelType>(m, "DimLevelType", py::module_local())
       .value("dense", MLIR_SPARSE_TENSOR_DIM_LEVEL_DENSE)
       .value("compressed", MLIR_SPARSE_TENSOR_DIM_LEVEL_COMPRESSED)
       .value("singleton", MLIR_SPARSE_TENSOR_DIM_LEVEL_SINGLETON);
 
   mlir_attribute_subclass(m, "EncodingAttr",

mlir/lib/Bindings/Python/Dialects.h

 #define MLIR_BINDINGS_PYTHON_DIALECTS_H
 
 #include <pybind11/pybind11.h>
 
 namespace mlir {
 namespace python {
 
 void populateDialectLinalgSubmodule(pybind11::module m);
-void populateDialectSparseTensorSubmodule(pybind11::module m,
+void populateDialectSparseTensorSubmodule(const pybind11::module &m,
                                           const pybind11::module &irModule);
 
 } // namespace python
 } // namespace mlir
 
 #endif // MLIR_BINDINGS_PYTHON_DIALECTS_H

mlir/lib/Bindings/Python/ExecutionEngineModule.cpp

public:
   }
   pybind11::object getCapsule() {
     return py::reinterpret_steal<py::object>(
         mlirPythonExecutionEngineToCapsule(get()));
   }
 
   // Add an object to the list of referenced objects whose lifetime must exceed
   // those of the ExecutionEngine.
-  void addReferencedObject(pybind11::object obj) {
+  void addReferencedObject(const pybind11::object &obj) {
     referencedObjects.push_back(obj);
   }
 
   static pybind11::object createFromCapsule(pybind11::object capsule) {
     MlirExecutionEngine rawPm =
         mlirPythonCapsuleToExecutionEngine(capsule.ptr());
     if (mlirExecutionEngineIsNull(rawPm))
       throw py::error_already_set();

mlir/lib/Bindings/Python/IRAffine.cpp

 //===- IRAffine.cpp - Exports 'ir' module affine related bindings ---------===//
 //
 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
 // See https://llvm.org/LICENSE.txt for license information.
 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 //
 //===----------------------------------------------------------------------===//
 
+#include <utility>
+
 #include "IRModule.h"
 
 #include "PybindUtils.h"
 
 #include "mlir-c/AffineMap.h"
 #include "mlir-c/Bindings/Python/Interop.h"
 #include "mlir-c/IntegerSet.h"
 
 namespace py = pybind11;
 using namespace mlir;
 using namespace mlir::python;
 
 using llvm::SmallVector;
 using llvm::StringRef;
 using llvm::Twine;
 
 static const char kDumpDocstring[] =
     R"(Dumps a debug representation of the object to stderr.)";
 
 /// Attempts to populate `result` with the content of `list` casted to the
 /// appropriate type (Python and C types are provided as template arguments).
 /// Throws errors in case of failure, using "action" to describe what the caller
 /// was attempting to do.
 template <typename PyType, typename CType>
-static void pyListToVector(py::list list, llvm::SmallVectorImpl<CType> &result,
+static void pyListToVector(const py::list &list,
+                           llvm::SmallVectorImpl<CType> &result,
                            StringRef action) {
   result.reserve(py::len(list));
   for (py::handle item : list) {
     try {
       result.push_back(item.cast<PyType>());
     } catch (py::cast_error &err) {
       std::string msg = (llvm::Twine("Invalid expression when ") + action +
                          " (" + err.what() + ")")

 
 class PyAffineAddExpr
     : public PyConcreteAffineExpr<PyAffineAddExpr, PyAffineBinaryExpr> {
 public:
   static constexpr IsAFunctionTy isaFunction = mlirAffineExprIsAAdd;
   static constexpr const char *pyClassName = "AffineAddExpr";
   using PyConcreteAffineExpr::PyConcreteAffineExpr;
 
-  static PyAffineAddExpr get(PyAffineExpr lhs, PyAffineExpr rhs) {
+  static PyAffineAddExpr get(PyAffineExpr lhs, const PyAffineExpr &rhs) {
     MlirAffineExpr expr = mlirAffineAddExprGet(lhs, rhs);
     return PyAffineAddExpr(lhs.getContext(), expr);
   }
 
   static PyAffineAddExpr getRHSConstant(PyAffineExpr lhs, intptr_t rhs) {
     MlirAffineExpr expr = mlirAffineAddExprGet(
         lhs, mlirAffineConstantExprGet(mlirAffineExprGetContext(lhs), rhs));
     return PyAffineAddExpr(lhs.getContext(), expr);

 
 class PyAffineMulExpr
     : public PyConcreteAffineExpr<PyAffineMulExpr, PyAffineBinaryExpr> {
 public:
   static constexpr IsAFunctionTy isaFunction = mlirAffineExprIsAMul;
   static constexpr const char *pyClassName = "AffineMulExpr";
   using PyConcreteAffineExpr::PyConcreteAffineExpr;
 
-  static PyAffineMulExpr get(PyAffineExpr lhs, PyAffineExpr rhs) {
+  static PyAffineMulExpr get(PyAffineExpr lhs, const PyAffineExpr &rhs) {
     MlirAffineExpr expr = mlirAffineMulExprGet(lhs, rhs);
     return PyAffineMulExpr(lhs.getContext(), expr);
   }
 
   static PyAffineMulExpr getRHSConstant(PyAffineExpr lhs, intptr_t rhs) {
     MlirAffineExpr expr = mlirAffineMulExprGet(
         lhs, mlirAffineConstantExprGet(mlirAffineExprGetContext(lhs), rhs));
     return PyAffineMulExpr(lhs.getContext(), expr);

 
 class PyAffineModExpr
     : public PyConcreteAffineExpr<PyAffineModExpr, PyAffineBinaryExpr> {
 public:
   static constexpr IsAFunctionTy isaFunction = mlirAffineExprIsAMod;
   static constexpr const char *pyClassName = "AffineModExpr";
   using PyConcreteAffineExpr::PyConcreteAffineExpr;
 
-  static PyAffineModExpr get(PyAffineExpr lhs, PyAffineExpr rhs) {
+  static PyAffineModExpr get(PyAffineExpr lhs, const PyAffineExpr &rhs) {
     MlirAffineExpr expr = mlirAffineModExprGet(lhs, rhs);
     return PyAffineModExpr(lhs.getContext(), expr);
   }
 
   static PyAffineModExpr getRHSConstant(PyAffineExpr lhs, intptr_t rhs) {
     MlirAffineExpr expr = mlirAffineModExprGet(
         lhs, mlirAffineConstantExprGet(mlirAffineExprGetContext(lhs), rhs));
     return PyAffineModExpr(lhs.getContext(), expr);

 
 class PyAffineFloorDivExpr
     : public PyConcreteAffineExpr<PyAffineFloorDivExpr, PyAffineBinaryExpr> {
 public:
   static constexpr IsAFunctionTy isaFunction = mlirAffineExprIsAFloorDiv;
   static constexpr const char *pyClassName = "AffineFloorDivExpr";
   using PyConcreteAffineExpr::PyConcreteAffineExpr;
 
-  static PyAffineFloorDivExpr get(PyAffineExpr lhs, PyAffineExpr rhs) {
+  static PyAffineFloorDivExpr get(PyAffineExpr lhs, const PyAffineExpr &rhs) {
     MlirAffineExpr expr = mlirAffineFloorDivExprGet(lhs, rhs);
     return PyAffineFloorDivExpr(lhs.getContext(), expr);
   }
 
   static PyAffineFloorDivExpr getRHSConstant(PyAffineExpr lhs, intptr_t rhs) {
     MlirAffineExpr expr = mlirAffineFloorDivExprGet(
         lhs, mlirAffineConstantExprGet(mlirAffineExprGetContext(lhs), rhs));
     return PyAffineFloorDivExpr(lhs.getContext(), expr);

 
 class PyAffineCeilDivExpr
     : public PyConcreteAffineExpr<PyAffineCeilDivExpr, PyAffineBinaryExpr> {
 public:
   static constexpr IsAFunctionTy isaFunction = mlirAffineExprIsACeilDiv;
   static constexpr const char *pyClassName = "AffineCeilDivExpr";
   using PyConcreteAffineExpr::PyConcreteAffineExpr;
 
-  static PyAffineCeilDivExpr get(PyAffineExpr lhs, PyAffineExpr rhs) {
+  static PyAffineCeilDivExpr get(PyAffineExpr lhs, const PyAffineExpr &rhs) {
     MlirAffineExpr expr = mlirAffineCeilDivExprGet(lhs, rhs);
     return PyAffineCeilDivExpr(lhs.getContext(), expr);
   }
 
   static PyAffineCeilDivExpr getRHSConstant(PyAffineExpr lhs, intptr_t rhs) {
     MlirAffineExpr expr = mlirAffineCeilDivExprGet(
         lhs, mlirAffineConstantExprGet(mlirAffineExprGetContext(lhs), rhs));
     return PyAffineCeilDivExpr(lhs.getContext(), expr);

 /// stored as a consecutive array leading to inexpensive random access. Both
 /// the map and the expression are owned by the context so we need not bother
 /// with lifetime extension.
 class PyAffineMapExprList
     : public Sliceable<PyAffineMapExprList, PyAffineExpr> {
 public:
   static constexpr const char *pyClassName = "AffineExprList";
 
-  PyAffineMapExprList(PyAffineMap map, intptr_t startIndex = 0,
+  PyAffineMapExprList(const PyAffineMap &map, intptr_t startIndex = 0,
                       intptr_t length = -1, intptr_t step = 1)
       : Sliceable(startIndex,
                   length == -1 ? mlirAffineMapGetNumResults(map) : length,
                   step),
         affineMap(map) {}
 
   intptr_t getNumElements() { return mlirAffineMapGetNumResults(affineMap); }
 

 
 //------------------------------------------------------------------------------
 // PyIntegerSet and utilities.
 //------------------------------------------------------------------------------
 namespace {
 
 class PyIntegerSetConstraint {
 public:
-  PyIntegerSetConstraint(PyIntegerSet set, intptr_t pos) : set(set), pos(pos) {}
+  PyIntegerSetConstraint(PyIntegerSet set, intptr_t pos)
+      : set(std::move(set)), pos(pos) {}
 
   PyAffineExpr getExpr() {
     return PyAffineExpr(set.getContext(),
                         mlirIntegerSetGetConstraint(set, pos));
   }
 
   bool isEq() { return mlirIntegerSetIsConstraintEq(set, pos); }
 
private:
   intptr_t pos;
 };
 
 class PyIntegerSetConstraintList
     : public Sliceable<PyIntegerSetConstraintList, PyIntegerSetConstraint> {
 public:
   static constexpr const char *pyClassName = "IntegerSetConstraintList";
 
-  PyIntegerSetConstraintList(PyIntegerSet set, intptr_t startIndex = 0,
+  PyIntegerSetConstraintList(const PyIntegerSet &set, intptr_t startIndex = 0,
                              intptr_t length = -1, intptr_t step = 1)
       : Sliceable(startIndex,
                   length == -1 ? mlirIntegerSetGetNumConstraints(set) : length,
                   step),
         set(set) {}
 
   intptr_t getNumElements() { return mlirIntegerSetGetNumConstraints(set); }
 
py::class_<PyAffineMap>(m, "AffineMap", py::module_local())
           "dump", [](PyAffineMap &self) { mlirAffineMapDump(self); },
           kDumpDocstring)
       .def_static(
           "get",
           [](intptr_t dimCount, intptr_t symbolCount, py::list exprs,
              DefaultingPyMlirContext context) {
             SmallVector<MlirAffineExpr> affineExprs;
             pyListToVector<PyAffineExpr, MlirAffineExpr>(
-                exprs, affineExprs, "attempting to create an AffineMap");
+                std::move(exprs), affineExprs,
+                "attempting to create an AffineMap");
             MlirAffineMap map =
                 mlirAffineMapGet(context->get(), dimCount, symbolCount,
                                  affineExprs.size(), affineExprs.data());
             return PyAffineMap(context->getRef(), map);
           },
           py::arg("dim_count"), py::arg("symbol_count"), py::arg("exprs"),
           py::arg("context") = py::none(),
           "Gets a map with the given expressions as results.")

mlir/lib/Bindings/Python/IRAttributes.cpp

 //===- IRAttributes.cpp - Exports builtin and standard attributes ---------===//
 //
 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
 // See https://llvm.org/LICENSE.txt for license information.
 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 //
 //===----------------------------------------------------------------------===//
 
+#include <utility>
+
 #include "IRModule.h"
 
 #include "PybindUtils.h"
 
 #include "mlir-c/BuiltinAttributes.h"
 #include "mlir-c/BuiltinTypes.h"
 
 namespace py = pybind11;

 class PyArrayAttribute : public PyConcreteAttribute<PyArrayAttribute> {
 public:
   static constexpr IsAFunctionTy isaFunction = mlirAttributeIsAArray;
   static constexpr const char *pyClassName = "ArrayAttr";
   using PyConcreteAttribute::PyConcreteAttribute;
 
   class PyArrayAttributeIterator {
   public:
-    PyArrayAttributeIterator(PyAttribute attr) : attr(attr) {}
+    PyArrayAttributeIterator(PyAttribute attr) : attr(std::move(attr)) {}
 
     PyArrayAttributeIterator &dunderIter() { return *this; }
 
     PyAttribute dunderNext() {
       if (nextIndex >= mlirArrayAttrGetNumElements(attr.get())) {
         throw py::stop_iteration();
       }
       return PyAttribute(attr.getContext(),
if (bulkLoadElementType) {
       return PyDenseElementsAttribute(contextWrapper->getRef(), attr);
     }
 
     throw std::invalid_argument(
         std::string("unimplemented array format conversion from format: ") +
         arrayInfo.format);
   }
 
-  static PyDenseElementsAttribute getSplat(PyType shapedType,
+  static PyDenseElementsAttribute getSplat(const PyType &shapedType,
                                            PyAttribute &elementAttr) {
     auto contextWrapper =
         PyMlirContext::forContext(mlirTypeGetContext(shapedType));
     if (!mlirAttributeIsAInteger(elementAttr) &&
         !mlirAttributeIsAFloat(elementAttr)) {
       std::string message = "Illegal element type for DenseElementsAttr: ";
       message.append(py::repr(py::cast(elementAttr)));
       throw SetPyError(PyExc_ValueError, message);

mlir/lib/Bindings/Python/IRCore.cpp

 #include "mlir-c/BuiltinTypes.h"
 #include "mlir-c/Debug.h"
 #include "mlir-c/IR.h"
 #include "mlir-c/Registration.h"
 #include "llvm/ADT/ArrayRef.h"
 #include "llvm/ADT/SmallVector.h"
 #include <pybind11/stl.h>
 
+#include <utility>
+
 namespace py = pybind11;
 using namespace mlir;
 using namespace mlir::python;
 
 using llvm::SmallVector;
 using llvm::StringRef;
 using llvm::Twine;
 

 static MlirStringRef toMlirStringRef(const std::string &s) {
   return mlirStringRefCreate(s.data(), s.size());
 }
 
 /// Wrapper for the global LLVM debugging flag.
 struct PyGlobalDebugFlag {
   static void set(py::object &o, bool enable) { mlirEnableGlobalDebug(enable); }
 
-  static bool get(py::object) { return mlirIsGlobalDebugEnabled(); }
+  static bool get(const py::object &) { return mlirIsGlobalDebugEnabled(); }
 
   static void bind(py::module &m) {
     // Debug flags.
     py::class_<PyGlobalDebugFlag>(m, "_GlobalDebug", py::module_local())
         .def_property_static("flag", &PyGlobalDebugFlag::get,
                              &PyGlobalDebugFlag::set, "LLVM-wide debug flag");
   }
 };
while (!mlirBlockIsNull(block)) {
         return PyBlock(operation, block);
       }
       block = mlirBlockGetNextInRegion(block);
       index -= 1;
     }
     throw SetPyError(PyExc_IndexError, "attempt to access out of bounds block");
   }
 
-  PyBlock appendBlock(py::args pyArgTypes) {
+  PyBlock appendBlock(const py::args &pyArgTypes) {
     operation->checkValid();
     llvm::SmallVector<MlirType, 4> argTypes;
     argTypes.reserve(pyArgTypes.size());
     for (auto &pyArg : pyArgTypes) {
       argTypes.push_back(pyArg.cast<PyType &>());
     }
 
     MlirBlock block = mlirBlockCreate(argTypes.size(), argTypes.data());

 size_t PyMlirContext::getLiveOperationCount() { return liveOperations.size(); }
 
 size_t PyMlirContext::getLiveModuleCount() { return liveModules.size(); }
 
 pybind11::object PyMlirContext::contextEnter() {
   return PyThreadContextEntry::pushContext(*this);
 }
 
-void PyMlirContext::contextExit(pybind11::object excType,
-                                pybind11::object excVal,
-                                pybind11::object excTb) {
+void PyMlirContext::contextExit(const pybind11::object &excType,
+                                const pybind11::object &excVal,
+                                const pybind11::object &excTb) {
   PyThreadContextEntry::popContext(*this);
 }
 
 PyMlirContext &DefaultingPyMlirContext::resolve() {
   PyMlirContext *context = PyThreadContextEntry::getDefaultContext();
   if (!context) {
     throw SetPyError(
         PyExc_RuntimeError,
PyLocation PyLocation::createFromCapsule(py::object capsule) {
   return PyLocation(PyMlirContext::forContext(mlirLocationGetContext(rawLoc)),
                     rawLoc);
 }
 
 py::object PyLocation::contextEnter() {
   return PyThreadContextEntry::pushLocation(*this);
 }
 
-void PyLocation::contextExit(py::object excType, py::object excVal,
-                             py::object excTb) {
+void PyLocation::contextExit(const pybind11::object &excType,
+                             const pybind11::object &excVal,
+                             const pybind11::object &excTb) {
   PyThreadContextEntry::popLocation(*this);
 }
 
 PyLocation &DefaultingPyLocation::resolve() {
   auto *location = PyThreadContextEntry::getDefaultLocation();
   if (!location) {
     throw SetPyError(
         PyExc_RuntimeError,
py::object PyOperation::createFromCapsule(py::object capsule) {
   if (mlirOperationIsNull(rawOperation))
     throw py::error_already_set();
   MlirContext rawCtxt = mlirOperationGetContext(rawOperation);
   return forOperation(PyMlirContext::forContext(rawCtxt), rawOperation)
       .releaseObject();
 }
 
 py::object PyOperation::create(
-    std::string name, llvm::Optional<std::vector<PyType *>> results,
+    const std::string &name, llvm::Optional<std::vector<PyType *>> results,
     llvm::Optional<std::vector<PyValue *>> operands,
     llvm::Optional<py::dict> attributes,
     llvm::Optional<std::vector<PyBlock *>> successors, int regions,
-    DefaultingPyLocation location, py::object maybeIp) {
+    DefaultingPyLocation location, const py::object &maybeIp) {
   llvm::SmallVector<MlirValue, 4> mlirOperands;
   llvm::SmallVector<MlirType, 4> mlirResults;
   llvm::SmallVector<MlirBlock, 4> mlirSuccessors;
   llvm::SmallVector<std::pair<std::string, MlirAttribute>, 4> mlirAttributes;
 
   // General parameter validation.
   if (regions < 0)
     throw SetPyError(PyExc_ValueError, "number of regions must be >= 0");
void PyOperation::erase() {
   valid = false;
 }
 
 //------------------------------------------------------------------------------
 // PyOpView
 //------------------------------------------------------------------------------
 
 py::object
-PyOpView::buildGeneric(py::object cls, py::list resultTypeList,
+PyOpView::buildGeneric(const py::object &cls, py::list resultTypeList,
                        py::list operandList,
                        llvm::Optional<py::dict> attributes,
                        llvm::Optional<std::vector<PyBlock *>> successors,
                        llvm::Optional<int> regions,
                        DefaultingPyLocation location, py::object maybeIp) {
   PyMlirContextRef context = location->getContext();
   // Class level operation construction metadata.
   std::string name = py::cast<std::string>(cls.attr("OPERATION_NAME"));
PyOpView::buildGeneric(const py::object &cls, py::list resultTypeList,
   }
 
   // Delegate to create.
   return PyOperation::create(std::move(name),
                              /*results=*/std::move(resultTypes),
                              /*operands=*/std::move(operands),
                              /*attributes=*/std::move(attributes),
                              /*successors=*/std::move(successors),
-                             /*regions=*/*regions, location, maybeIp);
+                             /*regions=*/*regions, location,
+                             std::move(maybeIp));
 }
 
-PyOpView::PyOpView(py::object operationObject)
+PyOpView::PyOpView(const py::object &operationObject)
     // Casting through the PyOperationBase base-class and then back to the
     // Operation lets us accept any PyOperationBase subclass.
     : operation(py::cast<PyOperationBase &>(operationObject).getOperation()),
       operationObject(operation.getRef().getObject()) {}
 
-py::object PyOpView::createRawSubclass(py::object userClass) {
+py::object PyOpView::createRawSubclass(const py::object &userClass) {
   // This is... a little gross. The typical pattern is to have a pure python
   // class that extends OpView like:
   //   class AddFOp(_cext.ir.OpView):
   //     def __init__(self, loc, lhs, rhs):
   //       operation = loc.context.create_operation(
   //           "addf", lhs, rhs, results=[lhs.type])
   //       super().__init__(operation)
   //
PyOperationRef terminatorOpRef = PyOperation::forOperation(
       block.getParentOperation()->getContext(), terminator);
   return PyInsertionPoint{block, std::move(terminatorOpRef)};
 }
 
 py::object PyInsertionPoint::contextEnter() {
   return PyThreadContextEntry::pushInsertionPoint(*this);
 }
 
-void PyInsertionPoint::contextExit(pybind11::object excType,
-                                   pybind11::object excVal,
-                                   pybind11::object excTb) {
+void PyInsertionPoint::contextExit(const pybind11::object &excType,
+                                   const pybind11::object &excVal,
+                                   const pybind11::object &excTb) {
   PyThreadContextEntry::popInsertionPoint(*this);
 }
 
 //------------------------------------------------------------------------------
 // PyAttribute.
 //------------------------------------------------------------------------------
 
 bool PyAttribute::operator==(const PyAttribute &other) {

 private:
   PyOperationRef operation;
 };
 
 /// A list of operation attributes. Can be indexed by name, producing
 /// attributes, or by index, producing named attributes.
 class PyOpAttributeMap {
 public:
-  PyOpAttributeMap(PyOperationRef operation) : operation(operation) {}
+  PyOpAttributeMap(PyOperationRef operation)
+      : operation(std::move(operation)) {}
 
   PyAttribute dunderGetItemNamed(const std::string &name) {
     MlirAttribute attr = mlirOperationGetAttributeByName(operation->get(),
                                                          toMlirStringRef(name));
     if (mlirAttributeIsNull(attr)) {
       throw SetPyError(PyExc_KeyError,
                        "attempt to access a non-existent attribute");
     }
     return PyAttribute(operation->getContext(), attr);
   }
 
   PyNamedAttribute dunderGetItemIndexed(intptr_t index) {
     if (index < 0 || index >= dunderLen()) {
       throw SetPyError(PyExc_IndexError,
                        "attempt to access out of bounds attribute");
     }
     MlirNamedAttribute namedAttr =
         mlirOperationGetAttribute(operation->get(), index);
     return PyNamedAttribute(
         namedAttr.attribute,
         std::string(mlirIdentifierStr(namedAttr.name).data,
                     mlirIdentifierStr(namedAttr.name).length));
   }
 
-  void dunderSetItem(const std::string &name, PyAttribute attr) {
+  void dunderSetItem(const std::string &name, const PyAttribute &attr) {
     mlirOperationSetAttributeByName(operation->get(), toMlirStringRef(name),
                                     attr);
   }
 
   void dunderDelItem(const std::string &name) {
     int removed = mlirOperationRemoveAttributeByName(operation->get(),
                                                      toMlirStringRef(name));
     if (!removed)

mlir/lib/Bindings/Python/IRInterfaces.cpp

 //===- IRInterfaces.cpp - MLIR IR interfaces pybind -----------------------===//
 //
 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
 // See https://llvm.org/LICENSE.txt for license information.
 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 //
 //===----------------------------------------------------------------------===//
 
+#include <utility>
+
 #include "IRModule.h"
 #include "mlir-c/BuiltinAttributes.h"
 #include "mlir-c/Interfaces.h"
 
 namespace py = pybind11;
 
 namespace mlir {
 namespace python {
protected:
   using ClassTy = py::class_<ConcreteIface>;
   using GetTypeIDFunctionTy = MlirTypeID (*)();
 
 public:
   /// Constructs an interface instance from an object that is either an
   /// operation or a subclass of OpView. In the latter case, only the static
   /// methods of the interface are accessible to the caller.
   PyConcreteOpInterface(py::object object, DefaultingPyMlirContext context)
-      : obj(object) {
+      : obj(std::move(object)) {
     try {
       operation = &py::cast<PyOperation &>(obj);
     } catch (py::cast_error &err) {
       // Do nothing.
     }
 
     try {
       operation = &py::cast<PyOpView &>(obj).getOperation();

mlir/lib/Bindings/Python/IRModule.h

public:
   size_t getLiveOperationCount();
 
   /// Gets the count of live modules associated with this context.
   /// Used for testing.
   size_t getLiveModuleCount();
 
   /// Enter and exit the context manager.
   pybind11::object contextEnter();
-  void contextExit(pybind11::object excType, pybind11::object excVal,
-                   pybind11::object excTb);
+  void contextExit(const pybind11::object &excType,
+                   const pybind11::object &excVal,
+                   const pybind11::object &excTb);
 
 private:
   PyMlirContext(MlirContext context);
   // Interns the mapping of live MlirContext::ptr to PyMlirContext instances,
   // preserving the relationship that an MlirContext maps to a single
   // PyMlirContext wrapper. This could be replaced in the future with an
   // extension mechanism on the MlirContext for stashing user pointers.
   // Note that this holds a handle, which does not imply ownership.
public:
   PyLocation(PyMlirContextRef contextRef, MlirLocation loc)
       : BaseContextObject(std::move(contextRef)), loc(loc) {}
 
   operator MlirLocation() const { return loc; }
   MlirLocation get() const { return loc; }
 
   /// Enter and exit the context manager.
   pybind11::object contextEnter();
-  void contextExit(pybind11::object excType, pybind11::object excVal,
-                   pybind11::object excTb);
+  void contextExit(const pybind11::object &excType,
+                   const pybind11::object &excVal,
+                   const pybind11::object &excTb);
 
   /// Gets a capsule wrapping the void* within the MlirLocation.
   pybind11::object getCapsule();
 
   /// Creates a PyLocation from the MlirLocation wrapped by a capsule.
   /// Note that PyLocation instances are uniqued, so the returned object
   /// may be a pre-existing object. Ownership of the underlying MlirLocation
   /// is taken by calling this function.
public:
 
   /// Creates a PyOperation from the MlirOperation wrapped by a capsule.
   /// Ownership of the underlying MlirOperation is taken by calling this
   /// function.
   static pybind11::object createFromCapsule(pybind11::object capsule);
 
   /// Creates an operation. See corresponding python docstring.
   static pybind11::object
-  create(std::string name, llvm::Optional<std::vector<PyType *>> results,
+  create(const std::string &name, llvm::Optional<std::vector<PyType *>> results,
          llvm::Optional<std::vector<PyValue *>> operands,
          llvm::Optional<pybind11::dict> attributes,
          llvm::Optional<std::vector<PyBlock *>> successors, int regions,
-         DefaultingPyLocation location, pybind11::object ip);
+         DefaultingPyLocation location, const pybind11::object &ip);
 
   /// Creates an OpView suitable for this operation.
   pybind11::object createOpView();
 
   /// Erases the underlying MlirOperation, removes its pointer from the
   /// parent context's live operations map, and sets the valid bit false.
   void erase();
 

 
 /// A PyOpView is equivalent to the C++ "Op" wrappers: these are the basis for
 /// providing more instance-specific accessors and serve as the base class for
 /// custom ODS-style operation classes. Since this class is subclass on the
 /// python side, it must present an __init__ method that operates in pure
 /// python types.
 class PyOpView : public PyOperationBase {
 public:
-  PyOpView(pybind11::object operationObject);
+  PyOpView(const pybind11::object &operationObject);
   PyOperation &getOperation() override { return operation; }
 
-  static pybind11::object createRawSubclass(pybind11::object userClass);
+  static pybind11::object createRawSubclass(const pybind11::object &userClass);
 
   pybind11::object getOperationObject() { return operationObject; }
 
   static pybind11::object
-  buildGeneric(pybind11::object cls, pybind11::list resultTypeList,
+  buildGeneric(const pybind11::object &cls, pybind11::list resultTypeList,
                pybind11::list operandList,
                llvm::Optional<pybind11::dict> attributes,
                llvm::Optional<std::vector<PyBlock *>> successors,
                llvm::Optional<int> regions, DefaultingPyLocation location,
                pybind11::object maybeIp);
 
 private:
   PyOperation &operation;           // For efficient, cast-free access from C++
public:
   /// Shortcut to create an insertion point before the block terminator.
   static PyInsertionPoint atBlockTerminator(PyBlock &block);
 
   /// Inserts an operation.
   void insert(PyOperationBase &operationBase);
 
   /// Enter and exit the context manager.
   pybind11::object contextEnter();
-  void contextExit(pybind11::object excType, pybind11::object excVal,
-                   pybind11::object excTb);
+  void contextExit(const pybind11::object &excType,
+                   const pybind11::object &excVal,
+                   const pybind11::object &excTb);
 
   PyBlock &getBlock() { return block; }
 
 private:
   // Trampoline constructor that avoids null initializing members while
   // looking up parents.
   PyInsertionPoint(PyBlock block, llvm::Optional<PyOperationRef> refOperation)
       : refOperation(std::move(refOperation)), block(std::move(block)) {}

mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp

 using namespace mlir;
 
 static SmallVector<StringRef> getNParallelLoopsAttrs(unsigned nParallelLoops) {
   return SmallVector<StringRef>(nParallelLoops, getParallelIteratorTypeName());
 }
 
 template <typename T>
 static arith::ConstantOp
-createConstFromIntAttribute(Operation *op, std::string attrName,
+createConstFromIntAttribute(Operation *op, const std::string &attrName,
                             Type requiredAttrType, OpBuilder &rewriter) {
   auto castedN = static_cast<T>(
       op->getAttr(attrName).cast<IntegerAttr>().getValue().getSExtValue());
   return rewriter.create<arith::ConstantOp>(
       op->getLoc(), IntegerAttr::get(requiredAttrType, castedN));
 }
 
 template <typename T>
static mlir::SelectOp clampHelper(Location loc, Value arg,
                                   P pred, OpBuilder &rewriter) {
   auto smallerThanMin = rewriter.create<T>(loc, pred, arg, min);
   auto minOrArg =
       rewriter.create<mlir::SelectOp>(loc, smallerThanMin, min, arg);
   auto largerThanMax = rewriter.create<T>(loc, pred, max, arg);
   return rewriter.create<mlir::SelectOp>(loc, largerThanMax, max, minOrArg);
 }
 
-static SmallVector<Value> filterDynamicDims(SmallVector<Value> dynDims) {
+static mlir::Value applyPad(Location loc, Value input, ArrayRef<int64_t> pad,
+                            Attribute padAttr, OpBuilder &rewriter) {
+  // Input should be padded if necessary.
+  if (llvm::all_of(pad, [](int64_t p) { return p == 0; }))
+    return input;
+
+  ShapedType inputTy = input.getType().cast<ShapedType>();
+  Type inputETy = inputTy.getElementType();
+  auto inputShape = inputTy.getShape();
+
+  assert((inputShape.size() * 2) == pad.size());
+
+  SmallVector<int64_t, 4> paddedShape;
+  SmallVector<OpFoldResult, 8> lowIndices;
+  SmallVector<OpFoldResult, 8> highIndices;
+  for (int i = 0, s = inputShape.size(); i < s; i++) {
+    auto lowPad = pad[i * 2];
+    auto highPad = pad[i * 2 + 1];
+    paddedShape.push_back(inputShape[i] + highPad + lowPad);
+    lowIndices.push_back(rewriter.getIndexAttr(lowPad));
+    highIndices.push_back(rewriter.getIndexAttr(highPad));
+  }
+
+  Value padValue = rewriter.create<arith::ConstantOp>(loc, padAttr);
+
+  return linalg::PadTensorOp::createPadScalarOp(
+             RankedTensorType::get(paddedShape, inputETy), input, padValue,
+             lowIndices, highIndices, /*nofold=*/false, loc, rewriter)
+      .result();
+}
+
+static SmallVector<Value> filterDynamicDims(const SmallVector<Value> &dynDims) {
   SmallVector<Value> filteredDims;
   for (auto dim : dynDims)
     if (dim)
       filteredDims.push_back(dim);
   return filteredDims;
 }
 
 static Value

mlir/lib/Dialect/Arithmetic/IR/ArithmeticOps.cpp

 //===- ArithmeticOps.cpp - MLIR Arithmetic dialect ops implementation -----===//
 //
 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
 // See https://llvm.org/LICENSE.txt for license information.
 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 //
 //===----------------------------------------------------------------------===//
 
+#include <utility>
+
 #include "mlir/Dialect/Arithmetic/IR/Arithmetic.h"
 #include "mlir/Dialect/CommonFolders.h"
 #include "mlir/IR/Builders.h"
 #include "mlir/IR/Matchers.h"
 #include "mlir/IR/OpImplementation.h"
 #include "mlir/IR/PatternMatch.h"
 #include "mlir/IR/TypeUtilities.h"
 

 // AddIOp
 //===----------------------------------------------------------------------===//
 
 OpFoldResult arith::AddIOp::fold(ArrayRef<Attribute> operands) {
   // addi(x, 0) -> x
   if (matchPattern(getRhs(), m_Zero()))
     return getLhs();
 
-  return constFoldBinaryOp<IntegerAttr>(operands,
-                                        [](APInt a, APInt b) { return a + b; });
+  return constFoldBinaryOp<IntegerAttr>(
+      operands, [](APInt a, const APInt &b) { return std::move(a) + b; });
 }
 
 void arith::AddIOp::getCanonicalizationPatterns(
     OwningRewritePatternList &patterns, MLIRContext *context) {
   patterns.insert<AddIAddConstant, AddISubConstantRHS, AddISubConstantLHS>(
       context);
 }
 
 //===----------------------------------------------------------------------===//
 // SubIOp
 //===----------------------------------------------------------------------===//
 
 OpFoldResult arith::SubIOp::fold(ArrayRef<Attribute> operands) {
   // subi(x,x) -> 0
   if (getOperand(0) == getOperand(1))
     return Builder(getContext()).getZeroAttr(getType());
   // subi(x,0) -> x
   if (matchPattern(getRhs(), m_Zero()))
     return getLhs();
 
-  return constFoldBinaryOp<IntegerAttr>(operands,
-                                        [](APInt a, APInt b) { return a - b; });
+  return constFoldBinaryOp<IntegerAttr>(
+      operands, [](APInt a, const APInt &b) { return std::move(a) - b; });
 }
 
 void arith::SubIOp::getCanonicalizationPatterns(
     OwningRewritePatternList &patterns, MLIRContext *context) {
   patterns.insert<SubIRHSAddConstant, SubILHSAddConstant, SubIRHSSubConstantRHS,
                   SubIRHSSubConstantLHS, SubILHSSubConstantRHS,
                   SubILHSSubConstantLHS>(context);
 }
 
 //===----------------------------------------------------------------------===//
 // MulIOp
 //===----------------------------------------------------------------------===//
 
 OpFoldResult arith::MulIOp::fold(ArrayRef<Attribute> operands) {
   // muli(x, 0) -> 0
   if (matchPattern(getRhs(), m_Zero()))
     return getRhs();
   // muli(x, 1) -> x
   if (matchPattern(getRhs(), m_One()))
     return getOperand(0);
   // TODO: Handle the overflow case.
 
   // default folder
-  return constFoldBinaryOp<IntegerAttr>(operands,
-                                        [](APInt a, APInt b) { return a * b; });
+  return constFoldBinaryOp<IntegerAttr>(
+      operands, [](const APInt &a, const APInt &b) { return a * b; });
 }
 
 //===----------------------------------------------------------------------===//
 // DivUIOp
 //===----------------------------------------------------------------------===//
 
 OpFoldResult arith::DivUIOp::fold(ArrayRef<Attribute> operands) {
   // Don't fold if it would require a division by zero.
   bool div0 = false;
-  auto result = constFoldBinaryOp<IntegerAttr>(operands, [&](APInt a, APInt b) {
+  auto result =
+      constFoldBinaryOp<IntegerAttr>(operands, [&](APInt a, const APInt &b) {
         if (div0 || !b) {
           div0 = true;
           return a;
         }
         return a.udiv(b);
       });
 
   // Fold out division by one. Assumes all tensors of all ones are splats.
   if (auto rhs = operands[1].dyn_cast_or_null<IntegerAttr>()) {
     if (rhs.getValue() == 1)
       return getLhs();
   } else if (auto rhs = operands[1].dyn_cast_or_null<SplatElementsAttr>()) {
     if (rhs.getSplatValue<IntegerAttr>().getValue() == 1)
       return getLhs();
   }
 
   return div0 ? Attribute() : result;
 }
 
 //===----------------------------------------------------------------------===//
 // DivSIOp
 //===----------------------------------------------------------------------===//
 
 OpFoldResult arith::DivSIOp::fold(ArrayRef<Attribute> operands) {
   // Don't fold if it would overflow or if it requires a division by zero.
   bool overflowOrDiv0 = false;
-  auto result = constFoldBinaryOp<IntegerAttr>(operands, [&](APInt a, APInt b) {
+  auto result =
+      constFoldBinaryOp<IntegerAttr>(operands, [&](APInt a, const APInt &b) {
         if (overflowOrDiv0 || !b) {
           overflowOrDiv0 = true;
           return a;
         }
         return a.sdiv_ov(b, overflowOrDiv0);
       });
 
   // Fold out division by one. Assumes all tensors of all ones are splats.
   if (auto rhs = operands[1].dyn_cast_or_null<IntegerAttr>()) {
     if (rhs.getValue() == 1)
       return getLhs();
   } else if (auto rhs = operands[1].dyn_cast_or_null<SplatElementsAttr>()) {
     if (rhs.getSplatValue<IntegerAttr>().getValue() == 1)
       return getLhs();
   }
 
   return overflowOrDiv0 ? Attribute() : result;
 }
 
 //===----------------------------------------------------------------------===//
 // Ceil and floor division folding helpers
 //===----------------------------------------------------------------------===//
 
-static APInt signedCeilNonnegInputs(APInt a, APInt b, bool &overflow) {
+static APInt signedCeilNonnegInputs(const APInt &a, const APInt &b,
+                                    bool &overflow) {
   // Returns (a-1)/b + 1
   APInt one(a.getBitWidth(), 1, true); // Signed value 1.
   APInt val = a.ssub_ov(one, overflow).sdiv_ov(b, overflow);
   return val.sadd_ov(one, overflow);
 }
 
 //===----------------------------------------------------------------------===//
 // CeilDivUIOp
 //===----------------------------------------------------------------------===//
 
 OpFoldResult arith::CeilDivUIOp::fold(ArrayRef<Attribute> operands) {
   bool overflowOrDiv0 = false;
-  auto result = constFoldBinaryOp<IntegerAttr>(operands, [&](APInt a, APInt b) {
+  auto result =
+      constFoldBinaryOp<IntegerAttr>(operands, [&](APInt a, const APInt &b) {
         if (overflowOrDiv0 || !b) {
           overflowOrDiv0 = true;
           return a;
         }
         APInt quotient = a.udiv(b);
         if (!a.urem(b))
           return quotient;
         APInt one(a.getBitWidth(), 1, true);
         return quotient.uadd_ov(one, overflowOrDiv0);
       });
   // Fold out ceil division by one. Assumes all tensors of all ones are
   // splats.
   if (auto rhs = operands[1].dyn_cast_or_null<IntegerAttr>()) {
     if (rhs.getValue() == 1)
       return getLhs();
   } else if (auto rhs = operands[1].dyn_cast_or_null<SplatElementsAttr>()) {
     if (rhs.getSplatValue<IntegerAttr>().getValue() == 1)
       return getLhs();
   }
 
   return overflowOrDiv0 ? Attribute() : result;
 }
 
 //===----------------------------------------------------------------------===//
 // CeilDivSIOp
 //===----------------------------------------------------------------------===//
 
 OpFoldResult arith::CeilDivSIOp::fold(ArrayRef<Attribute> operands) {
   // Don't fold if it would overflow or if it requires a division by zero.
   bool overflowOrDiv0 = false;
-  auto result = constFoldBinaryOp<IntegerAttr>(operands, [&](APInt a, APInt b) {
+  auto result =
+      constFoldBinaryOp<IntegerAttr>(operands, [&](APInt a, const APInt &b) {
         if (overflowOrDiv0 || !b) {
           overflowOrDiv0 = true;
           return a;
         }
         unsigned bits = a.getBitWidth();
         APInt zero = APInt::getZero(bits);
         if (a.sgt(zero) && b.sgt(zero)) {
           // Both positive, return ceil(a, b).
           return signedCeilNonnegInputs(a, b, overflowOrDiv0);
         }
         if (a.slt(zero) && b.slt(zero)) {
           // Both negative, return ceil(-a, -b).
           APInt posA = zero.ssub_ov(a, overflowOrDiv0);
           APInt posB = zero.ssub_ov(b, overflowOrDiv0);
           return signedCeilNonnegInputs(posA, posB, overflowOrDiv0);
         }
         if (a.slt(zero) && b.sgt(zero)) {
           // A is negative, b is positive, return - ( -a / b).
           APInt posA = zero.ssub_ov(a, overflowOrDiv0);
           APInt div = posA.sdiv_ov(b, overflowOrDiv0);
           return zero.ssub_ov(div, overflowOrDiv0);
         }
         // A is positive (or zero), b is negative, return - (a / -b).
         APInt posB = zero.ssub_ov(b, overflowOrDiv0);
         APInt div = a.sdiv_ov(posB, overflowOrDiv0);
         return zero.ssub_ov(div, overflowOrDiv0);
       });
 
   // Fold out ceil division by one. Assumes all tensors of all ones are
   // splats.
   if (auto rhs = operands[1].dyn_cast_or_null<IntegerAttr>()) {
     if (rhs.getValue() == 1)
       return getLhs();
   } else if (auto rhs = operands[1].dyn_cast_or_null<SplatElementsAttr>()) {
     if (rhs.getSplatValue<IntegerAttr>().getValue() == 1)
       return getLhs();
   }
 
   return overflowOrDiv0 ? Attribute() : result;
 }
 
 //===----------------------------------------------------------------------===//
 // FloorDivSIOp
 //===----------------------------------------------------------------------===//
 
 OpFoldResult arith::FloorDivSIOp::fold(ArrayRef<Attribute> operands) {
   // Don't fold if it would overflow or if it requires a division by zero.
   bool overflowOrDiv0 = false;
-  auto result = constFoldBinaryOp<IntegerAttr>(operands, [&](APInt a, APInt b) {
+  auto result =
+      constFoldBinaryOp<IntegerAttr>(operands, [&](APInt a, const APInt &b) {
         if (overflowOrDiv0 || !b) {
           overflowOrDiv0 = true;
           return a;
         }
         unsigned bits = a.getBitWidth();
         APInt zero = APInt::getZero(bits);
         if (a.sge(zero) && b.sgt(zero)) {
           // Both positive (or a is zero), return a / b.
           return a.sdiv_ov(b, overflowOrDiv0);
         }
         if (a.sle(zero) && b.slt(zero)) {
           // Both negative (or a is zero), return -a / -b.
           APInt posA = zero.ssub_ov(a, overflowOrDiv0);
           APInt posB = zero.ssub_ov(b, overflowOrDiv0);
           return posA.sdiv_ov(posB, overflowOrDiv0);
         }
         if (a.slt(zero) && b.sgt(zero)) {
           // A is negative, b is positive, return - ceil(-a, b).
           APInt posA = zero.ssub_ov(a, overflowOrDiv0);
           APInt ceil = signedCeilNonnegInputs(posA, b, overflowOrDiv0);
           return zero.ssub_ov(ceil, overflowOrDiv0);
         }
         // A is positive, b is negative, return - ceil(a, -b).
         APInt posB = zero.ssub_ov(b, overflowOrDiv0);
         APInt ceil = signedCeilNonnegInputs(a, posB, overflowOrDiv0);
         return zero.ssub_ov(ceil, overflowOrDiv0);
       });
 
   // Fold out floor division by one. Assumes all tensors of all ones are
   // splats.
   if (auto rhs = operands[1].dyn_cast_or_null<IntegerAttr>()) {
     if (rhs.getValue() == 1)
       return getLhs();
   } else if (auto rhs = operands[1].dyn_cast_or_null<SplatElementsAttr>()) {
     if (rhs.getSplatValue<IntegerAttr>().getValue() == 1)
OpFoldResult arith::AndIOp::fold(ArrayRef<Attribute> operands) {
   /// and(x, 0) -> 0
   if (matchPattern(getRhs(), m_Zero()))
     return getRhs();
   /// and(x, allOnes) -> x
   APInt intValue;
   if (matchPattern(getRhs(), m_ConstantInt(&intValue)) && intValue.isAllOnes())
     return getLhs();
 
-  return constFoldBinaryOp<IntegerAttr>(operands,
-                                        [](APInt a, APInt b) { return a & b; });
+  return constFoldBinaryOp<IntegerAttr>(
+      operands, [](APInt a, const APInt &b) { return std::move(a) & b; });
 }
 
 //===----------------------------------------------------------------------===//
 // OrIOp
 //===----------------------------------------------------------------------===//
 
 OpFoldResult arith::OrIOp::fold(ArrayRef<Attribute> operands) {
   /// or(x, 0) -> x
   if (matchPattern(getRhs(), m_Zero()))
     return getLhs();
   /// or(x, <all ones>) -> <all ones>
   if (auto rhsAttr = operands[1].dyn_cast_or_null<IntegerAttr>())
     if (rhsAttr.getValue().isAllOnes())
       return rhsAttr;
 
-  return constFoldBinaryOp<IntegerAttr>(operands,
-                                        [](APInt a, APInt b) { return a | b; });
+  return constFoldBinaryOp<IntegerAttr>(
+      operands, [](APInt a, const APInt &b) { return std::move(a) | b; });
 }
 
 //===----------------------------------------------------------------------===//
 // XOrIOp
 //===----------------------------------------------------------------------===//
 
 OpFoldResult arith::XOrIOp::fold(ArrayRef<Attribute> operands) {
   /// xor(x, 0) -> x
   if (matchPattern(getRhs(), m_Zero()))
     return getLhs();
   /// xor(x, x) -> 0
   if (getLhs() == getRhs())
     return Builder(getContext()).getZeroAttr(getType());
 
-  return constFoldBinaryOp<IntegerAttr>(operands,
-                                        [](APInt a, APInt b) { return a ^ b; });
+  return constFoldBinaryOp<IntegerAttr>(
+      operands, [](APInt a, const APInt &b) { return std::move(a) ^ b; });
 }
 
 void arith::XOrIOp::getCanonicalizationPatterns(
     OwningRewritePatternList &patterns, MLIRContext *context) {
   patterns.insert<XOrINotCmpI>(context);
 }
 
 //===----------------------------------------------------------------------===//
 // AddFOp
 //===----------------------------------------------------------------------===//
 
 OpFoldResult arith::AddFOp::fold(ArrayRef<Attribute> operands) {
   return constFoldBinaryOp<FloatAttr>(
-      operands, [](APFloat a, APFloat b) { return a + b; });
+      operands, [](const APFloat &a, const APFloat &b) { return a + b; });
 }
 
 //===----------------------------------------------------------------------===//
 // SubFOp
 //===----------------------------------------------------------------------===//
 
 OpFoldResult arith::SubFOp::fold(ArrayRef<Attribute> operands) {
   return constFoldBinaryOp<FloatAttr>(
-      operands, [](APFloat a, APFloat b) { return a - b; });
+      operands, [](const APFloat &a, const APFloat &b) { return a - b; });
 }
 
 //===----------------------------------------------------------------------===//
 // MaxSIOp
 //===----------------------------------------------------------------------===//
 
 OpFoldResult MaxSIOp::fold(ArrayRef<Attribute> operands) {
   assert(operands.size() == 2 && "binary operation takes two operands");
 
   // maxsi(x,x) -> x
   if (getLhs() == getRhs())
     return getRhs();
 
   APInt intValue;
   // maxsi(x,MAX_INT) -> MAX_INT
   if (matchPattern(getRhs(), m_ConstantInt(&intValue)) &&
       intValue.isMaxSignedValue())
     return getRhs();
 
   // maxsi(x, MIN_INT) -> x
   if (matchPattern(getRhs(), m_ConstantInt(&intValue)) &&
       intValue.isMinSignedValue())
     return getLhs();
 
-  return constFoldBinaryOp<IntegerAttr>(
-      operands, [](APInt a, APInt b) { return llvm::APIntOps::smax(a, b); });
+  return constFoldBinaryOp<IntegerAttr>(operands,
+                                        [](const APInt &a, const APInt &b) {
+                                          return llvm::APIntOps::smax(a, b);
+                                        });
 }
 
 //===----------------------------------------------------------------------===//
 // MaxUIOp
 //===----------------------------------------------------------------------===//
 
 OpFoldResult MaxUIOp::fold(ArrayRef<Attribute> operands) {
   assert(operands.size() == 2 && "binary operation takes two operands");
 
   // maxui(x,x) -> x
   if (getLhs() == getRhs())
     return getRhs();
 
   APInt intValue;
   // maxui(x,MAX_INT) -> MAX_INT
   if (matchPattern(getRhs(), m_ConstantInt(&intValue)) && intValue.isMaxValue())
     return getRhs();
 
   // maxui(x, MIN_INT) -> x
   if (matchPattern(getRhs(), m_ConstantInt(&intValue)) && intValue.isMinValue())
     return getLhs();
 
-  return constFoldBinaryOp<IntegerAttr>(
-      operands, [](APInt a, APInt b) { return llvm::APIntOps::umax(a, b); });
+  return constFoldBinaryOp<IntegerAttr>(operands,
+                                        [](const APInt &a, const APInt &b) {
+                                          return llvm::APIntOps::umax(a, b);
+                                        });
 }
 
 //===----------------------------------------------------------------------===//
 // MinSIOp
 //===----------------------------------------------------------------------===//
 
 OpFoldResult MinSIOp::fold(ArrayRef<Attribute> operands) {
   assert(operands.size() == 2 && "binary operation takes two operands");
 
   // minsi(x,x) -> x
   if (getLhs() == getRhs())
     return getRhs();
 
   APInt intValue;
   // minsi(x,MIN_INT) -> MIN_INT
   if (matchPattern(getRhs(), m_ConstantInt(&intValue)) &&
       intValue.isMinSignedValue())
     return getRhs();
 
   // minsi(x, MAX_INT) -> x
   if (matchPattern(getRhs(), m_ConstantInt(&intValue)) &&
       intValue.isMaxSignedValue())
     return getLhs();
 
-  return constFoldBinaryOp<IntegerAttr>(
-      operands, [](APInt a, APInt b) { return llvm::APIntOps::smin(a, b); });
+  return constFoldBinaryOp<IntegerAttr>(operands,
+                                        [](const APInt &a, const APInt &b) {
+                                          return llvm::APIntOps::smin(a, b);
+                                        });
 }
 
 //===----------------------------------------------------------------------===//
 // MinUIOp
 //===----------------------------------------------------------------------===//
 
 OpFoldResult MinUIOp::fold(ArrayRef<Attribute> operands) {
   assert(operands.size() == 2 && "binary operation takes two operands");
 
   // minui(x,x) -> x
   if (getLhs() == getRhs())
     return getRhs();
 
   APInt intValue;
   // minui(x,MIN_INT) -> MIN_INT
   if (matchPattern(getRhs(), m_ConstantInt(&intValue)) && intValue.isMinValue())
     return getRhs();
 
   // minui(x, MAX_INT) -> x
   if (matchPattern(getRhs(), m_ConstantInt(&intValue)) && intValue.isMaxValue())
     return getLhs();
 
-  return constFoldBinaryOp<IntegerAttr>(
-      operands, [](APInt a, APInt b) { return llvm::APIntOps::umin(a, b); });
+  return constFoldBinaryOp<IntegerAttr>(operands,
+                                        [](const APInt &a, const APInt &b) {
+                                          return llvm::APIntOps::umin(a, b);
+                                        });
 }
 
 //===----------------------------------------------------------------------===//
 // MulFOp
 //===----------------------------------------------------------------------===//
 
 OpFoldResult arith::MulFOp::fold(ArrayRef<Attribute> operands) {
   return constFoldBinaryOp<FloatAttr>(
-      operands, [](APFloat a, APFloat b) { return a * b; });
+      operands, [](const APFloat &a, const APFloat &b) { return a * b; });
 }
 
 //===----------------------------------------------------------------------===//
 // DivFOp
 //===----------------------------------------------------------------------===//
 
 OpFoldResult arith::DivFOp::fold(ArrayRef<Attribute> operands) {
   return constFoldBinaryOp<FloatAttr>(
-      operands, [](APFloat a, APFloat b) { return a / b; });
+      operands, [](const APFloat &a, const APFloat &b) { return a / b; });
 }
 
 //===----------------------------------------------------------------------===//
 // Utility functions for verifying cast ops
 //===----------------------------------------------------------------------===//
 
 template <typename... Types>
 using type_list = std::tuple<Types...> *;

mlir/lib/Dialect/Async/Transforms/AsyncParallelFor.cpp

 //===- AsyncParallelFor.cpp - Implementation of Async Parallel For --------===//
 //
 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
 // See https://llvm.org/LICENSE.txt for license information.
 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 //
 //===----------------------------------------------------------------------===//
 //
 // This file implements scf.parallel to scf.for + async.execute conversion pass.
 //
 //===----------------------------------------------------------------------===//
 
+#include <utility>
+
 #include "PassDetail.h"
 #include "mlir/Dialect/Arithmetic/IR/Arithmetic.h"
 #include "mlir/Dialect/Async/IR/Async.h"
 #include "mlir/Dialect/Async/Passes.h"
 #include "mlir/Dialect/Async/Transforms.h"
 #include "mlir/Dialect/SCF/SCF.h"
 #include "mlir/Dialect/StandardOps/IR/Ops.h"
 #include "mlir/IR/BlockAndValueMapping.h"

 
 struct AsyncParallelForRewrite : public OpRewritePattern<scf::ParallelOp> {
 public:
   AsyncParallelForRewrite(
       MLIRContext *ctx, bool asyncDispatch, int32_t numWorkerThreads,
       AsyncMinTaskSizeComputationFunction computeMinTaskSize)
       : OpRewritePattern(ctx), asyncDispatch(asyncDispatch),
         numWorkerThreads(numWorkerThreads),
-        computeMinTaskSize(computeMinTaskSize) {}
+        computeMinTaskSize(std::move(computeMinTaskSize)) {}
 
   LogicalResult matchAndRewrite(scf::ParallelOp op,
                                 PatternRewriter &rewriter) const override;
 
 private:
   bool asyncDispatch;
   int32_t numWorkerThreads;
   AsyncMinTaskSizeComputationFunction computeMinTaskSize;
getParallelComputeFunctionType(scf::ParallelOp op, PatternRewriter &rewriter) {
 
   // Convert captures to vector for later convenience.
   SmallVector<Value> capturesVector(captures.begin(), captures.end());
   return {rewriter.getFunctionType(inputs, TypeRange()), capturesVector};
 }
 
 // Create a parallel compute fuction from the parallel operation.
 static ParallelComputeFunction createParallelComputeFunction(
-    scf::ParallelOp op, ParallelComputeFunctionBounds bounds,
+    scf::ParallelOp op, const ParallelComputeFunctionBounds &bounds,
     unsigned numBlockAlignedInnerLoops, PatternRewriter &rewriter) {
   OpBuilder::InsertionGuard guard(rewriter);
   ImplicitLocOpBuilder b(op.getLoc(), rewriter);
 
   ModuleOp module = op->getParentOfType<ModuleOp>();
 
   ParallelComputeFunctionType computeFuncType =
       getParallelComputeFunctionType(op, rewriter);
std::unique_ptr<Pass> mlir::createAsyncParallelForPass(bool asyncDispatch,
                                                        int32_t numWorkerThreads,
                                                        int32_t minTaskSize) {
   return std::make_unique<AsyncParallelForPass>(asyncDispatch, numWorkerThreads,
                                                 minTaskSize);
 }
 
 void mlir::async::populateAsyncParallelForPatterns(
     RewritePatternSet &patterns, bool asyncDispatch, int32_t numWorkerThreads,
-    AsyncMinTaskSizeComputationFunction computeMinTaskSize) {
+    const AsyncMinTaskSizeComputationFunction &computeMinTaskSize) {
   MLIRContext *ctx = patterns.getContext();
   patterns.add<AsyncParallelForRewrite>(ctx, asyncDispatch, numWorkerThreads,
                                         computeMinTaskSize);
 }

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

 #include "mlir/Transforms/BufferUtils.h"
 #include "llvm/ADT/SetOperations.h"
 
 using namespace mlir;
 
 /// Walks over all immediate return-like terminators in the given region.
 static LogicalResult
 walkReturnOperations(Region *region,
-                     std::function<LogicalResult(Operation *)> func) {
+                     llvm::function_ref<LogicalResult(Operation *)> func) {
   for (Block &block : *region) {
     Operation *terminator = block.getTerminator();
     // Skip non region-return-like terminators.
     if (isRegionReturnLike(terminator)) {
       if (failed(func(terminator)))
         return failure();
     }
   }

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

 /// querying `existingDependencies` and `availableValues`.
 /// If an operand is not yet available, we recursively check whether it can be
 /// made available by siking its defining op.
 /// Operations that are indentified for sinking are added to `beneficiaryOps` in
 /// the order they should appear in the kernel. Furthermore, `availableValues`
 /// is updated with results that will be available after sinking the identified
 /// ops.
 static bool
-extractBeneficiaryOps(Operation *op, SetVector<Value> existingDependencies,
+extractBeneficiaryOps(Operation *op,
+                      const SetVector<Value> &existingDependencies,
                       SetVector<Operation *> &beneficiaryOps,
                       llvm::SmallPtrSetImpl<Value> &availableValues) {
   if (beneficiaryOps.count(op))
     return true;
 
   if (!isSinkingBeneficiary(op))
     return false;
 

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

static ParseResult parseSwitchOpCases(
   caseValues = DenseIntElementsAttr::get(caseValueType, values);
   return success();
 }
 
 static void printSwitchOpCases(OpAsmPrinter &p, SwitchOp op, Type flagType,
                                ElementsAttr caseValues,
                                SuccessorRange caseDestinations,
                                OperandRangeRange caseOperands,
-                               TypeRangeRange caseOperandTypes) {
+                               const TypeRangeRange &caseOperandTypes) {
   if (!caseValues)
     return;
 
   size_t index = 0;
   llvm::interleave(
       llvm::zip(caseValues.cast<DenseIntElementsAttr>(), caseDestinations),
       [&](auto i) {
         p << "  ";

 }
 
 //===----------------------------------------------------------------------===//
 // Builder, printer and parser for for LLVM::LoadOp.
 //===----------------------------------------------------------------------===//
 
 LogicalResult verifySymbolAttribute(
     Operation *op, StringRef attributeName,
-    std::function<LogicalResult(Operation *, SymbolRefAttr)> verifySymbolType) {
+    llvm::function_ref<LogicalResult(Operation *, SymbolRefAttr)>
+        verifySymbolType) {
   if (Attribute attribute = op->getAttr(attributeName)) {
     // The attribute is already verified to be a symbol ref array attribute via
     // a constraint in the operation definition.
     for (SymbolRefAttr symbolRef :
          attribute.cast<ArrayAttr>().getAsRange<SymbolRefAttr>()) {
       StringAttr metadataName = symbolRef.getRootReference();
       StringAttr symbolName = symbolRef.getLeafReference();
       // We want @metadata::@symbol, not just @symbol

mlir/lib/Dialect/Linalg/ComprehensiveBufferize/BufferizableOpInterface.cpp

 
 // Starting from `value`, follow the use-def chain in reverse, always selecting
 // the aliasing OpOperands. Find and return Values for which `condition`
 // evaluates to true. OpOperands of such matching Values are not traversed any
 // further.
 llvm::SetVector<Value>
 mlir::linalg::comprehensive_bufferize::BufferizationState::
     findValueInReverseUseDefChain(Value value,
-                                  std::function<bool(Value)> condition) {
+                                  llvm::function_ref<bool(Value)> condition) {
   llvm::SetVector<Value> result, workingSet;
   workingSet.insert(value);
 
   while (!workingSet.empty()) {
     Value value = workingSet.pop_back_val();
     if (condition(value) || value.isa<BlockArgument>()) {
       result.insert(value);
       continue;

mlir/lib/Dialect/Linalg/ComprehensiveBufferize/SCFInterfaceImpl.cpp

LogicalResult bufferize(Operation *op, OpBuilder & /*b*/,
     DenseSet<int64_t> indices;
     for (const auto &it : llvm::enumerate(forOp.getInitArgs()))
       if (it.value().getType().isa<TensorType>())
         indices.insert(it.index());
 
     // Given a range of values, apply `func` to those marked in `indices`.
     // Otherwise, store the unmodified value in the result vector.
     auto convert = [&](ValueRange values,
-                       std::function<Value(Value, int64_t)> func) {
+                       llvm::function_ref<Value(Value, int64_t)> func) {
       SmallVector<Value> result;
       for (const auto &it : llvm::enumerate(values)) {
         size_t idx = it.index();
         Value val = it.value();
         result.push_back(indices.contains(idx) ? func(val, idx) : val);
       }
       return result;
     };

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

 /// This is used by both builders and parsers.
 /// This function creates the block in the region with arguments corresponding
 /// to the elemental types of `inputTypes` and `outputTypes`. The latter are
 /// asserted to be of ShapedType.
 template <typename NamedStructuredOpType>
 static void fillStructuredOpRegion(
     OpBuilder &opBuilder, Region &region, TypeRange inputTypes,
     TypeRange outputTypes,
-    std::function<void(unsigned, unsigned)> errorHandler = nullptr);
+    llvm::function_ref<void(unsigned, unsigned)> errorHandler = nullptr);
 
 /// Generic entry point to create both the region and the block of a LinalgOp.
 template <typename NamedStructuredOpType>
 static void
 createAndFillStructuredOpRegion(OpBuilder &opBuilder, OperationState &result,
                                 TypeRange inputTypes, TypeRange outputTypes);
 
 /// Common parsing and printing used for both named structured ops created by
void yieldOutputs(ValueRange values) {
     assert(!values.empty() && "linalg ops must yield outputs");
     if (values.empty())
       return;
     Value first = values.front();
     OpBuilder builder = getBuilder();
     builder.create<YieldOp>(first.getLoc(), values);
   }
 
-  Value constant(std::string value) {
+  Value constant(const std::string &value) {
     OpBuilder builder = getBuilder();
     Location loc = builder.getUnknownLoc();
     Attribute valueAttr = parseAttribute(value, builder.getContext());
     return builder.create<arith::ConstantOp>(loc, valueAttr.getType(),
                                              valueAttr);
   }
 
   Value index(int64_t dim) {

 /// Generic entry point to create the block for the region of a LinalgOp.
 /// This is used by both named structured ops created by ods-gen and by manually
 /// defined C++ ops.
 /// This is used by both builders and parsers.
 /// This function creates the block in the region with arguments corresponding
 /// to the elemental types of `inputTypes` and `outputTypes`, which are asserted
 /// to be ShapedType.
 template <typename NamedStructuredOpType>
-static void
-fillStructuredOpRegion(OpBuilder &opBuilder, Region &region,
-                       TypeRange inputTypes, TypeRange outputTypes,
-                       std::function<void(unsigned, unsigned)> errorHandler) {
+static void fillStructuredOpRegion(
+    OpBuilder &opBuilder, Region &region, TypeRange inputTypes,
+    TypeRange outputTypes,
+    llvm::function_ref<void(unsigned, unsigned)> errorHandler) {
   assert(llvm::all_of(outputTypes, [](Type t) { return t.isa<ShapedType>(); }));
 
   // TODO: atm all operands go through getElementTypeOrSelf,
   // reconsider when we have evidence we need to.
   SmallVector<Type, 8> argTypes;
   for (auto containers : {inputTypes, outputTypes})
     for (auto t : containers)
       argTypes.push_back(getElementTypeOrSelf(t));

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

 #include "mlir/Dialect/Linalg/Passes.h"
 #include "mlir/Dialect/StandardOps/Transforms/FuncConversions.h"
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/IR/OpDefinition.h"
 #include "mlir/Transforms/DialectConversion.h"
 #include "mlir/Transforms/GreedyPatternRewriteDriver.h"
 #include <iterator>
 #include <memory>
+#include <utility>
 
 using namespace mlir;
 using namespace mlir::linalg;
 
 static Value sourceMaterializationCallback(OpBuilder &builder, Type type,
                                            ValueRange inputs, Location loc) {
   assert(inputs.size() == 1);
   if (inputs[0].getType().isa<TensorType>())

 /// A conversion pattern for detensoring internal (non-entry) blocks within a
 /// function.
 struct FunctionNonEntryBlockConversion : public ConversionPattern {
   FunctionNonEntryBlockConversion(MLIRContext *ctx, TypeConverter &converter,
                                   DenseSet<BlockArgument> blockArgsToDetensor)
       : ConversionPattern(converter, MatchTraitOpTypeTag(),
                           TypeID::get<OpTrait::FunctionLike>(), /*benefit=*/1,
                           ctx),
-        blockArgsToDetensor(blockArgsToDetensor) {}
+        blockArgsToDetensor(std::move(blockArgsToDetensor)) {}
 
   LogicalResult
   matchAndRewrite(Operation *op, ArrayRef<Value> operands,
                   ConversionPatternRewriter &rewriter) const override {
     rewriter.startRootUpdate(op);
     Region &region = function_like_impl::getFunctionBody(op);
     SmallVector<TypeConverter::SignatureConversion, 2> conversions;
 

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

 //===- Distibution.cpp - linalg named ops to generic ops  --------------===//
 //
 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
 // See https://llvm.org/LICENSE.txt for license information.
 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 //
 //===----------------------------------------------------------------------===//
 //
 // This file implements the Linalg distibution pass. It updates `tiled_loop`
 // control variables depending on the distribution type.
 //
 //===----------------------------------------------------------------------===//
 //
+#include <utility>
+
 #include "mlir/Dialect/Linalg/Transforms/Transforms.h"
 #include "mlir/Dialect/Linalg/Utils/Utils.h"
 #include "mlir/IR/MLIRContext.h"
 #include "mlir/Pass/Pass.h"
 #include "mlir/Transforms/DialectConversion.h"
 
 #define DEBUG_TYPE "linalg-distribution"
 
 #define DBGS() (llvm::dbgs() << '[' << DEBUG_TYPE << "] ")
 
 using namespace mlir;
 using namespace mlir::linalg;
 
 namespace {
 
 struct DistributeTiledLoopPattern
     : public OpRewritePattern<linalg::TiledLoopOp> {
   DistributeTiledLoopPattern(MLIRContext *context,
                              LinalgLoopDistributionOptions options,
                              LinalgTransformationFilter marker)
-      : OpRewritePattern<linalg::TiledLoopOp>(context), options(options),
-        marker(marker) {}
+      : OpRewritePattern<linalg::TiledLoopOp>(context),
+        options(std::move(options)), marker(std::move(marker)) {}
   LogicalResult matchAndRewrite(linalg::TiledLoopOp op,
                                 PatternRewriter &rewriter) const override {
     if (failed(marker.checkAndNotify(rewriter, op)))
       return failure();
     if (!op.distribution_types().hasValue())
       return failure();
 
     Location loc = op.getLoc();

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

 //===- ElementwiseOpFusion.cpp - Implementation of linalg Fusion ---------===///
 //
 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
 // See https://llvm.org/LICENSE.txt for license information.
 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 //
 //===----------------------------------------------------------------------===//
 //
 // This file implements the linalg dialect Fusion on tensors operations pass.
 //
 //===----------------------------------------------------------------------===//
+#include <utility>
+
 #include "PassDetail.h"
 #include "mlir/Dialect/Affine/IR/AffineOps.h"
 #include "mlir/Dialect/Linalg/IR/Linalg.h"
 #include "mlir/Dialect/Linalg/Passes.h"
 #include "mlir/Dialect/Linalg/Transforms/Transforms.h"
 #include "mlir/Dialect/Linalg/Utils/Utils.h"
 #include "mlir/IR/AffineExpr.h"
 #include "mlir/IR/AffineMap.h"

 /// in the consumer is expanded.
 class FoldWithProducerReshapeOpByExpansion
     : public OpRewritePattern<GenericOp> {
 public:
   FoldWithProducerReshapeOpByExpansion(
       MLIRContext *context, ControlElementwiseOpsFusionFn foldReshapes,
       PatternBenefit benefit = 1)
       : OpRewritePattern<GenericOp>(context, benefit),
-        controlFoldingReshapes(foldReshapes) {}
+        controlFoldingReshapes(std::move(foldReshapes)) {}
 
   LogicalResult matchAndRewrite(GenericOp genericOp,
                                 PatternRewriter &rewriter) const override {
     for (OpOperand *opOperand : genericOp.getInputTensorOperands()) {
       tensor::CollapseShapeOp reshapeOp =
           opOperand->get().getDefiningOp<tensor::CollapseShapeOp>();
       if (!reshapeOp)
         continue;

 /// by expanding the dimensionality of the loop in the producer op.
 struct FoldReshapeWithGenericOpByExpansion
     : public OpRewritePattern<tensor::ExpandShapeOp> {
 
   FoldReshapeWithGenericOpByExpansion(
       MLIRContext *context, ControlElementwiseOpsFusionFn foldReshapes,
       PatternBenefit benefit = 1)
       : OpRewritePattern<tensor::ExpandShapeOp>(context, benefit),
-        controlFoldingReshapes(foldReshapes) {}
+        controlFoldingReshapes(std::move(foldReshapes)) {}
 
   LogicalResult matchAndRewrite(tensor::ExpandShapeOp reshapeOp,
                                 PatternRewriter &rewriter) const override {
     // Fold only if all constraints of fusing with reshape by expansion are met.
     GenericOp producer = reshapeOp.src().getDefiningOp<GenericOp>();
     if (!producer || producer.getNumOutputs() != 1 ||
         !isFusableWithReshapeByDimExpansion(producer,
                                             producer.getOutputOperand(0)) ||
patterns
            FoldProducerReshapeOpByLinearization<true, tensor::ExpandShapeOp>,
            FoldConsumerReshapeOpByLinearization<true, tensor::CollapseShapeOp>,
            FoldConsumerReshapeOpByLinearization<true, tensor::ExpandShapeOp>>(
           patterns.getContext());
 }
 
 void mlir::linalg::populateFoldReshapeOpsByExpansionPatterns(
     RewritePatternSet &patterns,
-    ControlElementwiseOpsFusionFn controlFoldingReshapes) {
+    const ControlElementwiseOpsFusionFn &controlFoldingReshapes) {
   patterns.add<FoldReshapeWithGenericOpByExpansion>(patterns.getContext(),
                                                     controlFoldingReshapes);
   patterns.add<FoldWithProducerReshapeOpByExpansion>(patterns.getContext(),
                                                      controlFoldingReshapes);
 }
 
 void mlir::linalg::populateElementwiseOpsFusionPatterns(
     RewritePatternSet &patterns, LinalgElementwiseFusionOptions options) {

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

 void LinalgGeneralizationPass::runOnFunction() {
   FuncOp func = getFunction();
   RewritePatternSet patterns(&getContext());
   populateLinalgNamedOpsGeneralizationPatterns(patterns);
   (void)applyPatternsAndFoldGreedily(func.getBody(), std::move(patterns));
 }
 
 void mlir::linalg::populateLinalgNamedOpsGeneralizationPatterns(
-    RewritePatternSet &patterns, LinalgTransformationFilter marker) {
+    RewritePatternSet &patterns, const LinalgTransformationFilter &marker) {
   patterns.add<LinalgGeneralizationPattern>(patterns.getContext(), marker);
 }
 
 std::unique_ptr<OperationPass<FuncOp>> mlir::createLinalgGeneralizationPass() {
   return std::make_unique<LinalgGeneralizationPass>();
 }

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

 //===- DynamicPass.cpp - Implementation of a dynamic configurable pass ----===//
 //
 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
 // See https://llvm.org/LICENSE.txt for license information.
 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 //
 //===----------------------------------------------------------------------===//
 //
 // This file implements a configurable pass that can apply patterns liberally
 // and be plugged in a pass pipeline.
 //
 //===----------------------------------------------------------------------===//
 
+#include <utility>
+
 #include "PassDetail.h"
 #include "mlir/Analysis/SliceAnalysis.h"
 #include "mlir/Dialect/Affine/IR/AffineOps.h"
 #include "mlir/Dialect/Linalg/IR/Linalg.h"
 #include "mlir/Dialect/Linalg/Passes.h"
 #include "mlir/Dialect/Linalg/Transforms/Hoisting.h"
 #include "mlir/Dialect/Linalg/Transforms/Transforms.h"
 #include "mlir/Dialect/Linalg/Utils/Utils.h"

 struct LinalgStrategyTileAndFusePass
     : public LinalgStrategyTileAndFusePassBase<LinalgStrategyTileAndFusePass> {
 
   LinalgStrategyTileAndFusePass() = default;
 
   LinalgStrategyTileAndFusePass(StringRef opName,
                                 LinalgTilingAndFusionOptions opt,
                                 LinalgTransformationFilter filt)
-      : options(opt), filter(filt) {
+      : options(std::move(opt)), filter(std::move(filt)) {
     this->anchorOpName.setValue(opName.str());
   }
 
   void runOnFunction() override {
     auto funcOp = getFunction();
     if (!anchorFuncName.empty() && funcOp.getName() != anchorFuncName)
       return;
 

 /// Configurable pass to apply pattern-based linalg tiling.
 struct LinalgStrategyTilePass
     : public LinalgStrategyTilePassBase<LinalgStrategyTilePass> {
 
   LinalgStrategyTilePass() = default;
 
   LinalgStrategyTilePass(StringRef opName, LinalgTilingOptions opt,
                          LinalgTransformationFilter filt)
-      : options(opt), filter(filt) {
+      : options(std::move(opt)), filter(std::move(filt)) {
     this->anchorOpName.setValue(opName.str());
   }
 
   void runOnFunction() override {
     auto funcOp = getFunction();
     if (!anchorFuncName.empty() && funcOp.getName() != anchorFuncName)
       return;
 

 /// Configurable pass to apply hoisting and padding.
 struct LinalgStrategyPadPass
     : public LinalgStrategyPadPassBase<LinalgStrategyPadPass> {
 
   LinalgStrategyPadPass() = default;
 
   LinalgStrategyPadPass(StringRef opName, LinalgPaddingOptions opt,
                         LinalgTransformationFilter filt)
-      : options(opt), filter(filt) {
+      : options(std::move(opt)), filter(std::move(filt)) {
     this->anchorOpName.setValue(opName.str());
   }
 
   void runOnFunction() override {
     auto funcOp = getFunction();
     if (!anchorFuncName.empty() && funcOp.getName() != anchorFuncName)
       return;
 

 /// Configurable pass to apply pattern-based linalg generalization.
 struct LinalgStrategyGeneralizePass
     : public LinalgStrategyGeneralizePassBase<LinalgStrategyGeneralizePass> {
 
   LinalgStrategyGeneralizePass() = default;
 
   LinalgStrategyGeneralizePass(StringRef opName,
                                LinalgTransformationFilter filter)
-      : filter(filter) {
+      : filter(std::move(filter)) {
     this->anchorOpName.setValue(opName.str());
   }
 
   void runOnFunction() override {
     auto funcOp = getFunction();
     if (!anchorFuncName.empty() && funcOp.getName() != anchorFuncName)
       return;
 

 /// Configurable pass to apply lowering of coarser-grained named linalg ops into
 /// finer-grained named versions.
 struct LinalgStrategyDecomposePass
     : public LinalgStrategyDecomposePassBase<LinalgStrategyDecomposePass> {
 
   LinalgStrategyDecomposePass() = default;
 
   LinalgStrategyDecomposePass(LinalgTransformationFilter filter)
-      : filter(filter) {}
+      : filter(std::move(filter)) {}
 
   void runOnFunction() override {
     auto funcOp = getFunction();
     if (!anchorFuncName.empty() && funcOp.getName() != anchorFuncName)
       return;
     RewritePatternSet decompositionPattern(funcOp.getContext());
     populateDecomposeConvolutionPatterns(decompositionPattern, filter);
     if (failed(applyPatternsAndFoldGreedily(funcOp,
struct LinalgStrategyInterchangePass
     : public LinalgStrategyInterchangePassBase<LinalgStrategyInterchangePass> {
 
   LinalgStrategyInterchangePass() = default;
 
   LinalgStrategyInterchangePass(ArrayRef<int64_t> iteratorInterchange,
                                 LinalgTransformationFilter filter)
       : iteratorInterchange(iteratorInterchange.begin(),
                             iteratorInterchange.end()),
-        filter(filter) {}
+        filter(std::move(filter)) {}
 
   void runOnFunction() override {
     auto funcOp = getFunction();
     if (!anchorFuncName.empty() && funcOp.getName() != anchorFuncName)
       return;
 
     SmallVector<unsigned> interchangeVector(iteratorInterchange.begin(),
                                             iteratorInterchange.end());

 /// Configurable pass to apply pattern-based linalg promotion.
 struct LinalgStrategyPromotePass
     : public LinalgStrategyPromotePassBase<LinalgStrategyPromotePass> {
 
   LinalgStrategyPromotePass() = default;
 
   LinalgStrategyPromotePass(StringRef opName, LinalgPromotionOptions opt,
                             LinalgTransformationFilter filt)
-      : options(opt), filter(filt) {
+      : options(std::move(opt)), filter(std::move(filt)) {
     this->anchorOpName.setValue(opName.str());
   }
 
   void runOnFunction() override {
     auto funcOp = getFunction();
     if (!anchorFuncName.empty() && funcOp.getName() != anchorFuncName)
       return;
 

 struct LinalgStrategyVectorizePass
     : public LinalgStrategyVectorizePassBase<LinalgStrategyVectorizePass> {
 
   LinalgStrategyVectorizePass() = default;
 
   LinalgStrategyVectorizePass(StringRef opName, LinalgVectorizationOptions opt,
                               LinalgTransformationFilter filt,
                               bool padVectorize = false)
-      : options(opt), filter(filt) {
+      : options(opt), filter(std::move(filt)) {
     this->anchorOpName.setValue(opName.str());
     this->vectorizePadding.setValue(padVectorize);
   }
 
   void runOnFunction() override {
     auto funcOp = getFunction();
     if (!anchorFuncName.empty() && funcOp.getName() != anchorFuncName)
       return;

 
 /// Configurable pass to enable the application of other pattern-based linalg
 /// passes.
 struct LinalgStrategyEnablePass
     : public LinalgStrategyEnablePassBase<LinalgStrategyEnablePass> {
 
   LinalgStrategyEnablePass(LinalgEnablingOptions opt,
                            LinalgTransformationFilter filt)
-      : options(opt), filter(filt) {}
+      : options(opt), filter(std::move(filt)) {}
 
   void runOnFunction() override {
     auto funcOp = getFunction();
     if (!anchorFuncName.empty() && funcOp.getName() != anchorFuncName)
       return;
 
     MLIRContext *context = funcOp.getContext();
     RewritePatternSet patterns =

 
 /// Configurable pass to lower vector operations.
 struct LinalgStrategyLowerVectorsPass
     : public LinalgStrategyLowerVectorsPassBase<
           LinalgStrategyLowerVectorsPass> {
 
   LinalgStrategyLowerVectorsPass(LinalgVectorLoweringOptions opt,
                                  LinalgTransformationFilter filt)
-      : options(opt), filter(filt) {}
+      : options(opt), filter(std::move(filt)) {}
 
   void runOnFunction() override {
     auto funcOp = getFunction();
     if (!anchorFuncName.empty() && funcOp.getName() != anchorFuncName)
       return;
 
     MLIRContext *context = funcOp.getContext();
     RewritePatternSet patterns(context);
funcOp.walk([](LinalgOp op) {
       op->removeAttr(LinalgTransforms::kLinalgTransformMarker);
     });
   }
 };
 } // namespace
 
 /// Create a LinalgStrategyTileAndFusePass.
 std::unique_ptr<OperationPass<FuncOp>>
-mlir::createLinalgStrategyTileAndFusePass(StringRef opName,
-                                          LinalgTilingAndFusionOptions options,
-                                          LinalgTransformationFilter filter) {
+mlir::createLinalgStrategyTileAndFusePass(
+    StringRef opName, const LinalgTilingAndFusionOptions &options,
+    const LinalgTransformationFilter &filter) {
   return std::make_unique<LinalgStrategyTileAndFusePass>(opName, options,
                                                          filter);
 }
 
 /// Create a LinalgStrategyTilePass.
 std::unique_ptr<OperationPass<FuncOp>>
-mlir::createLinalgStrategyTilePass(StringRef opName, LinalgTilingOptions opt,
-                                   LinalgTransformationFilter filter) {
+mlir::createLinalgStrategyTilePass(StringRef opName,
+                                   const LinalgTilingOptions &opt,
+                                   const LinalgTransformationFilter &filter) {
   return std::make_unique<LinalgStrategyTilePass>(opName, opt, filter);
 }
 
 /// Create a LinalgStrategyPadPass.
 std::unique_ptr<OperationPass<FuncOp>>
-mlir::createLinalgStrategyPadPass(StringRef opName, LinalgPaddingOptions opt,
-                                  LinalgTransformationFilter filter) {
+mlir::createLinalgStrategyPadPass(StringRef opName,
+                                  const LinalgPaddingOptions &opt,
+                                  const LinalgTransformationFilter &filter) {
   return std::make_unique<LinalgStrategyPadPass>(opName, opt, filter);
 }
 
 /// Create a LinalgStrategyPromotePass.
-std::unique_ptr<OperationPass<FuncOp>>
-mlir::createLinalgStrategyPromotePass(StringRef opName,
-                                      LinalgPromotionOptions opt,
-                                      LinalgTransformationFilter filter) {
+std::unique_ptr<OperationPass<FuncOp>> mlir::createLinalgStrategyPromotePass(
+    StringRef opName, const LinalgPromotionOptions &opt,
+    const LinalgTransformationFilter &filter) {
   return std::make_unique<LinalgStrategyPromotePass>(opName, opt, filter);
 }
 
 /// Create a LinalgStrategyGeneralizePass.
-std::unique_ptr<OperationPass<FuncOp>>
-mlir::createLinalgStrategyGeneralizePass(StringRef opName,
-                                         LinalgTransformationFilter filter) {
+std::unique_ptr<OperationPass<FuncOp>> mlir::createLinalgStrategyGeneralizePass(
+    StringRef opName, const LinalgTransformationFilter &filter) {
   return std::make_unique<LinalgStrategyGeneralizePass>(opName, filter);
 }
 
 /// Create a LinalgStrategyDecomposePass.
 // TODO: if/when we need finer control add an `opName` parameter.
-std::unique_ptr<OperationPass<FuncOp>>
-mlir::createLinalgStrategyDecomposePass(LinalgTransformationFilter filter) {
+std::unique_ptr<OperationPass<FuncOp>> mlir::createLinalgStrategyDecomposePass(
+    const LinalgTransformationFilter &filter) {
   return std::make_unique<LinalgStrategyDecomposePass>(filter);
 }
 
 /// Create a LinalgStrategyInterchangePass.
 std::unique_ptr<OperationPass<FuncOp>>
-mlir::createLinalgStrategyInterchangePass(ArrayRef<int64_t> iteratorInterchange,
-                                          LinalgTransformationFilter filter) {
+mlir::createLinalgStrategyInterchangePass(
+    ArrayRef<int64_t> iteratorInterchange,
+    const LinalgTransformationFilter &filter) {
   return std::make_unique<LinalgStrategyInterchangePass>(iteratorInterchange,
                                                          filter);
 }
 
 /// Create a LinalgStrategyVectorizePass.
 std::unique_ptr<OperationPass<FuncOp>> mlir::createLinalgStrategyVectorizePass(
     StringRef opName, LinalgVectorizationOptions opt,
-    LinalgTransformationFilter filter, bool padVectorize) {
+    const LinalgTransformationFilter &filter, bool padVectorize) {
   return std::make_unique<LinalgStrategyVectorizePass>(opName, opt, filter,
                                                        padVectorize);
 }
 
 /// Create a LinalgStrategyEnablePass.
 std::unique_ptr<OperationPass<FuncOp>>
 mlir::createLinalgStrategyEnablePass(LinalgEnablingOptions opt,
-                                     LinalgTransformationFilter filter) {
+                                     const LinalgTransformationFilter &filter) {
   return std::make_unique<LinalgStrategyEnablePass>(opt, filter);
 }
 
 /// Create a LinalgStrategyLowerVectorsPass.
 std::unique_ptr<OperationPass<FuncOp>>
-mlir::createLinalgStrategyLowerVectorsPass(LinalgVectorLoweringOptions opt,
-                                           LinalgTransformationFilter filter) {
+mlir::createLinalgStrategyLowerVectorsPass(
+    LinalgVectorLoweringOptions opt, const LinalgTransformationFilter &filter) {
   return std::make_unique<LinalgStrategyLowerVectorsPass>(opt, filter);
 }
 
 /// Create a LinalgStrategyRemoveMarkersPass.
 std::unique_ptr<OperationPass<FuncOp>>
 mlir::createLinalgStrategyRemoveMarkersPass() {
   return std::make_unique<LinalgStrategyRemoveMarkersPass>();
 }

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

 // The point of the full tile buffer is that constant static tile sizes are
 // folded and result in a buffer type with statically known size and alignment
 // properties.
 // To account for general boundary effects, padding must be performed on the
 // boundary tiles. For now this is done with an unconditional `fill` op followed
 // by a partial `copy` op.
 FailureOr<PromotionInfo> mlir::linalg::promoteSubviewAsNewBuffer(
     OpBuilder &b, Location loc, memref::SubViewOp subView,
-    AllocBufferCallbackFn allocationFn, DataLayout &layout) {
+    const AllocBufferCallbackFn &allocationFn, DataLayout &layout) {
   auto viewType = subView.getType();
   auto rank = viewType.getRank();
   SmallVector<Value, 4> fullSizes;
   SmallVector<OpFoldResult> partialSizes;
   fullSizes.reserve(rank);
   partialSizes.reserve(rank);
   for (auto en : llvm::enumerate(subView.getOrCreateRanges(b, loc))) {
     auto rangeValue = en.value();
mlir::linalg::promoteSubviewsPrecondition(Operation *op,
   }
   // TODO: Check all subviews requested are bound by a static constant.
   // TODO: Check that the total footprint fits within a given size.
   return failure();
 }
 
 FailureOr<LinalgOp>
 mlir::linalg::promoteSubViews(OpBuilder &builder, LinalgOp linalgOp,
-                              LinalgPromotionOptions options) {
+                              const LinalgPromotionOptions &options) {
   LinalgOpInstancePromotionOptions linalgOptions(linalgOp, options);
   auto layout = DataLayout::closest(linalgOp);
   ImplicitLocOpBuilder b(linalgOp.getLoc(), builder);
   auto res = ::promoteSubViews(b, linalgOp, linalgOptions, layout);
   if (failed(res))
     return failure();
   return res;
 }

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

 //===- Tiling.cpp - Implementation of linalg Tiling -----------------------===//
 //
 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
 // See https://llvm.org/LICENSE.txt for license information.
 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 //
 //===----------------------------------------------------------------------===//
 //
 // This file implements the linalg dialect Tiling pass.
 //
 //===----------------------------------------------------------------------===//
 
+#include <utility>
+
 #include "PassDetail.h"
 #include "mlir/Dialect/Linalg/IR/Linalg.h"
 #include "mlir/Dialect/Linalg/Passes.h"
 #include "mlir/Dialect/Linalg/Transforms/Transforms.h"
 #include "mlir/Dialect/Linalg/Utils/Utils.h"
 #include "mlir/Dialect/MemRef/IR/MemRef.h"
 #include "mlir/Dialect/SCF/Transforms.h"
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
loopNest = mlir::scf::buildLoopNest(
         return scf::ValueVector({yieldValue});
       });
   return success();
 }
 
 namespace {
 struct PadTensorOpTilingPattern : public OpRewritePattern<PadTensorOp> {
   PadTensorOpTilingPattern(MLIRContext *ctx, LinalgTilingOptions opt)
-      : OpRewritePattern<PadTensorOp>(ctx), options(opt) {}
+      : OpRewritePattern<PadTensorOp>(ctx), options(std::move(opt)) {}
 
   LogicalResult matchAndRewrite(PadTensorOp op,
                                 PatternRewriter &rewriter) const override {
     if (op->hasAttr(LinalgTransforms::kLinalgTransformMarker))
       return failure();
     PadTensorOp newPadOp;
     LoopNest loopNest;
     if (failed(tilePadTensorOp(rewriter, op, newPadOp, loopNest, options)))

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

 #include "mlir/Pass/Pass.h"
 #include "mlir/Support/LLVM.h"
 #include "mlir/Transforms/GreedyPatternRewriteDriver.h"
 #include "llvm/ADT/ScopeExit.h"
 #include "llvm/ADT/TypeSwitch.h"
 #include "llvm/Support/Debug.h"
 #include "llvm/Support/raw_ostream.h"
 #include <type_traits>
+#include <utility>
 
 #define DEBUG_TYPE "linalg-transforms"
 
 using namespace mlir;
 using namespace mlir::linalg;
 
 #define DBGS() (llvm::dbgs() << "[" DEBUG_TYPE << "]: ")
 
 //===----------------------------------------------------------------------===//
 // Transformations exposed as rewrite patterns.
 //===----------------------------------------------------------------------===//
 // Marker used as attribute name in generated Linalg rewriting transformations.
 const StringLiteral mlir::linalg::LinalgTransforms::kLinalgTransformMarker =
     "__internal_linalg_transform__";
 
 mlir::linalg::LinalgTransformationFilter::LinalgTransformationFilter(
     ArrayRef<StringAttr> matchDisjunction, Optional<StringAttr> replacement)
     : matchDisjunction(matchDisjunction.begin(), matchDisjunction.end()),
       replacement(replacement), matchByDefault(false) {}
 
 mlir::linalg::LinalgTransformationFilter::LinalgTransformationFilter(
-    FilterFunction f, ArrayRef<StringAttr> matchDisjunction,
+    const FilterFunction &f, ArrayRef<StringAttr> matchDisjunction,
     Optional<StringAttr> replacement)
     : filters(),
       matchDisjunction(matchDisjunction.begin(), matchDisjunction.end()),
       replacement(replacement), matchByDefault(false) {
   if (f)
     filters.push_back(f);
 }
 
linalg::rewriteAsPaddedOp(OpBuilder &b, LinalgOp opToPad,
   }
   return paddedSubviewResults;
 }
 
 /// Linalg base tiling pattern.
 mlir::linalg::LinalgBaseTilingPattern::LinalgBaseTilingPattern(
     StringRef opName, MLIRContext *context, LinalgTilingOptions options,
     LinalgTransformationFilter filter, PatternBenefit benefit)
-    : RewritePattern(opName, benefit, context), filter(filter),
-      options(options) {}
+    : RewritePattern(opName, benefit, context), filter(std::move(filter)),
+      options(std::move(options)) {}
 
 mlir::linalg::LinalgBaseTilingPattern::LinalgBaseTilingPattern(
     MLIRContext *context, LinalgTilingOptions options,
     LinalgTransformationFilter filter, PatternBenefit benefit)
-    : RewritePattern(MatchAnyOpTypeTag(), benefit, context), filter(filter),
-      options(options) {}
+    : RewritePattern(MatchAnyOpTypeTag(), benefit, context),
+      filter(std::move(filter)), options(std::move(options)) {}
 
 /// Try to peel a loop `op` and return the new result.
 // TODO: Add support for scf.parallel and affine.for loops.
 static SmallVector<Value, 4> peelLoop(RewriterBase &rewriter, Operation *op) {
   return llvm::TypeSwitch<Operation *, SmallVector<Value, 4>>(op)
       .Case<scf::ForOp>([&](scf::ForOp forOp) {
         scf::ForOp partialIteration;
         if (succeeded(scf::peelAndCanonicalizeForLoop(rewriter, forOp,

 
 mlir::linalg::LinalgBaseTileAndFusePattern::LinalgBaseTileAndFusePattern(
     StringRef opName, MLIRContext *context,
     const LinalgDependenceGraph &dependenceGraph,
     LinalgTilingOptions tilingOptions, LinalgFusionOptions fusionOptions,
     LinalgTransformationFilter filter, LinalgTransformationFilter fusedOpMarker,
     LinalgTransformationFilter originalOpMarker, PatternBenefit benefit)
     : RewritePattern(opName, benefit, context, {}),
-      dependenceGraph(dependenceGraph), tilingOptions(tilingOptions),
-      fusionOptions(fusionOptions), filter(filter),
-      fusedOpMarker(fusedOpMarker), originalOpMarker(originalOpMarker) {}
+      dependenceGraph(dependenceGraph), tilingOptions(std::move(tilingOptions)),
+      fusionOptions(std::move(fusionOptions)), filter(std::move(filter)),
+      fusedOpMarker(std::move(fusedOpMarker)),
+      originalOpMarker(std::move(originalOpMarker)) {}
 
 LogicalResult mlir::linalg::LinalgBaseTileAndFusePattern::matchAndRewrite(
     Operation *op, PatternRewriter &rewriter) const {
   LinalgOp linalgOp = dyn_cast<LinalgOp>(op);
   // TODO: remove hasIndexSemantics check once index ops are supported.
   if (!linalgOp || linalgOp.hasIndexSemantics())
     return failure();
   if (failed(filter.checkAndNotify(rewriter, linalgOp)))
LogicalResult mlir::linalg::LinalgBaseTileAndFusePattern::matchAndRewrite(
   });
   return success();
 }
 
 /// Linalg padding pattern.
 mlir::linalg::LinalgPaddingPattern::LinalgPaddingPattern(
     MLIRContext *context, LinalgPaddingOptions options,
     LinalgTransformationFilter filter, PatternBenefit benefit)
-    : RewritePattern(MatchAnyOpTypeTag(), benefit, context), filter(filter),
-      options(options) {}
+    : RewritePattern(MatchAnyOpTypeTag(), benefit, context),
+      filter(std::move(filter)), options(std::move(options)) {}
 
 mlir::linalg::LinalgPaddingPattern::LinalgPaddingPattern(
     StringRef opName, MLIRContext *context, LinalgPaddingOptions options,
     LinalgTransformationFilter filter, PatternBenefit benefit)
-    : RewritePattern(opName, benefit, context, {}), filter(filter),
-      options(options) {}
+    : RewritePattern(opName, benefit, context, {}), filter(std::move(filter)),
+      options(std::move(options)) {}
 
 LogicalResult mlir::linalg::LinalgPaddingPattern::matchAndRewrite(
     Operation *op, PatternRewriter &rewriter) const {
   LinalgOp linalgOp = dyn_cast<LinalgOp>(op);
   if (!linalgOp)
     return failure();
   if (!linalgOp.hasTensorSemantics())
     return failure();

 }
 
 /// Linalg tile and fuse tensor ops pattern.
 mlir::linalg::LinalgTileAndFuseTensorOpsPattern::
     LinalgTileAndFuseTensorOpsPattern(MLIRContext *context,
                                       LinalgTilingAndFusionOptions options,
                                       LinalgTransformationFilter filter,
                                       PatternBenefit benefit)
-    : RewritePattern(MatchAnyOpTypeTag(), benefit, context), filter(filter),
-      options(options) {}
+    : RewritePattern(MatchAnyOpTypeTag(), benefit, context),
+      filter(std::move(filter)), options(std::move(options)) {}
 
 mlir::linalg::LinalgTileAndFuseTensorOpsPattern::
     LinalgTileAndFuseTensorOpsPattern(StringRef opName, MLIRContext *context,
                                       LinalgTilingAndFusionOptions options,
                                       LinalgTransformationFilter filter,
                                       PatternBenefit benefit)
-    : RewritePattern(opName, benefit, context), filter(filter),
-      options(options) {}
+    : RewritePattern(opName, benefit, context), filter(std::move(filter)),
+      options(std::move(options)) {}
 
 LogicalResult mlir::linalg::LinalgTileAndFuseTensorOpsPattern::matchAndRewrite(
     Operation *op, PatternRewriter &rewriter) const {
   LinalgOp rootOp = dyn_cast<LinalgOp>(op);
   if (!rootOp)
     return failure();
   if (failed(filter.checkAndNotify(rewriter, op)))
     return failure();
for (LinalgOp linalgOp : tileLoopNest->getAllTiledAndFusedOps())
     filter.replaceLinalgTransformationFilter(rewriter, linalgOp);
   return failure();
 }
 
 /// Linalg generic interchange pattern.
 mlir::linalg::GenericOpInterchangePattern::GenericOpInterchangePattern(
     MLIRContext *context, ArrayRef<unsigned> interchangeVector,
     LinalgTransformationFilter filter, PatternBenefit benefit)
-    : OpRewritePattern(context, benefit), filter(filter),
+    : OpRewritePattern(context, benefit), filter(std::move(filter)),
       interchangeVector(interchangeVector.begin(), interchangeVector.end()) {}
 
 LogicalResult mlir::linalg::GenericOpInterchangePattern::matchAndRewrite(
     GenericOp genericOp, PatternRewriter &rewriter) const {
   if (failed(filter.checkAndNotify(rewriter, genericOp)))
     return failure();
   if (failed(interchangeGenericOpPrecondition(genericOp, interchangeVector)))
     return failure();
 
   // TODO: figure out how this interplays with named ops. In particular this
   // should break the named op property.
   rewriter.updateRootInPlace(genericOp, [&]() {
     interchangeGenericOp(rewriter, genericOp, interchangeVector);
     // New filter if specified.
     filter.replaceLinalgTransformationFilter(rewriter, genericOp);
   });
   return success();
 }
 
 /// Linalg generalization pattern.
 mlir::linalg::LinalgGeneralizationPattern::LinalgGeneralizationPattern(
     MLIRContext *context, LinalgTransformationFilter filter,
     PatternBenefit benefit)
-    : RewritePattern(MatchAnyOpTypeTag(), benefit, context), filter(filter) {}
+    : RewritePattern(MatchAnyOpTypeTag(), benefit, context),
+      filter(std::move(filter)) {}
 
 mlir::linalg::LinalgGeneralizationPattern::LinalgGeneralizationPattern(
     StringRef opName, MLIRContext *context, LinalgTransformationFilter filter,
     PatternBenefit benefit)
-    : RewritePattern(opName, benefit, context, {}), filter(filter) {}
+    : RewritePattern(opName, benefit, context, {}), filter(std::move(filter)) {}
 
 LogicalResult mlir::linalg::LinalgGeneralizationPattern::matchAndRewrite(
     Operation *op, PatternRewriter &rewriter) const {
   if (failed(filter.checkAndNotify(rewriter, op)))
     return failure();
   if (failed(generalizeNamedOpPrecondition(op)))
     return failure();
 
   GenericOp genericOp = generalizeNamedOp(rewriter, op);
   rewriter.replaceOp(op, genericOp.getResults());
   filter.replaceLinalgTransformationFilter(rewriter, genericOp);
   return success();
 }
 
 mlir::linalg::LinalgBasePromotionPattern::LinalgBasePromotionPattern(
     MLIRContext *context, LinalgTransformationFilter filter,
     LinalgPromotionOptions options, PatternBenefit benefit)
-    : RewritePattern(MatchAnyOpTypeTag(), benefit, context), filter(filter),
-      options(options) {}
+    : RewritePattern(MatchAnyOpTypeTag(), benefit, context),
+      filter(std::move(filter)), options(std::move(options)) {}
 
 mlir::linalg::LinalgBasePromotionPattern::LinalgBasePromotionPattern(
     StringRef opName, MLIRContext *context, LinalgPromotionOptions options,
     LinalgTransformationFilter filter, PatternBenefit benefit)
-    : RewritePattern(opName, benefit, context, {}), filter(filter),
-      options(options) {}
+    : RewritePattern(opName, benefit, context, {}), filter(std::move(filter)),
+      options(std::move(options)) {}
 
 LogicalResult mlir::linalg::LinalgBasePromotionPattern::matchAndRewrite(
     Operation *op, PatternRewriter &rewriter) const {
   if (failed(filter.checkAndNotify(rewriter, op)))
     return failure();
   if (failed(promoteSubviewsPrecondition(op, options)))
     return failure();
 
LogicalResult mlir::linalg::LinalgBasePromotionPattern::matchAndRewrite(
   rewriter.finalizeRootUpdate(op);
   filter.replaceLinalgTransformationFilter(rewriter, op);
   return success();
 }
 
 mlir::linalg::LinalgBaseVectorizationPattern::LinalgBaseVectorizationPattern(
     MLIRContext *context, LinalgTransformationFilter filter,
     PatternBenefit benefit)
-    : RewritePattern(MatchAnyOpTypeTag(), benefit, context), filter(filter) {}
+    : RewritePattern(MatchAnyOpTypeTag(), benefit, context),
+      filter(std::move(filter)) {}
 
 mlir::linalg::LinalgBaseVectorizationPattern::LinalgBaseVectorizationPattern(
     StringRef opName, MLIRContext *context, LinalgTransformationFilter filter,
     PatternBenefit benefit)
-    : RewritePattern(opName, benefit, context, {}), filter(filter) {}
+    : RewritePattern(opName, benefit, context, {}), filter(std::move(filter)) {}
 
 LogicalResult mlir::linalg::LinalgBaseVectorizationPattern::matchAndRewrite(
     Operation *op, PatternRewriter &rewriter) const {
   LinalgOp linalgOp = dyn_cast<LinalgOp>(op);
   if (!linalgOp)
     return failure();
   if (failed(filter.checkAndNotify(rewriter, linalgOp)))
     return failure();

 /// Rewrites 2-D convolution ops with size-1 window dimensions into 1-D
 /// convolution ops.
 struct DownscaleSizeOneWindowed2DConvolution final
     : public OpRewritePattern<Conv2DNhwcHwcfOp> {
   DownscaleSizeOneWindowed2DConvolution(
       MLIRContext *context,
       LinalgTransformationFilter filter = LinalgTransformationFilter(),
       PatternBenefit benefit = 1)
-      : OpRewritePattern<Conv2DNhwcHwcfOp>(context, benefit), filter(filter) {}
+      : OpRewritePattern<Conv2DNhwcHwcfOp>(context, benefit),
+        filter(std::move(filter)) {}
 
   LogicalResult matchAndRewrite(linalg::Conv2DNhwcHwcfOp convOp,
                                 PatternRewriter &rewriter) const override {
     if (failed(filter.checkAndNotify(rewriter, convOp)))
       return failure();
     if (convOp.hasBufferSemantics())
       return failure(); // To be implemented
 

 /// dimensions into 1-D depthwise convolution ops.
 struct DownscaleDepthwiseConv2DNhwcHwcOp final
     : public OpRewritePattern<DepthwiseConv2DNhwcHwcOp> {
   DownscaleDepthwiseConv2DNhwcHwcOp(
       MLIRContext *context,
       LinalgTransformationFilter filter = LinalgTransformationFilter(),
       PatternBenefit benefit = 1)
       : OpRewritePattern<DepthwiseConv2DNhwcHwcOp>(context, benefit),
-        filter(filter) {}
+        filter(std::move(filter)) {}
 
   LogicalResult matchAndRewrite(DepthwiseConv2DNhwcHwcOp convOp,
                                 PatternRewriter &rewriter) const override {
     if (failed(filter.checkAndNotify(rewriter, convOp)))
       return failure();
     if (convOp.hasBufferSemantics())
       return failure(); // To be implemented
 

 private:
   /// LinalgTransformMarker handles special attribute manipulations.
   LinalgTransformationFilter filter;
 };
 
 } // namespace
 
 void linalg::populateDecomposeConvolutionPatterns(
-    RewritePatternSet &patterns, LinalgTransformationFilter filter,
+    RewritePatternSet &patterns, const LinalgTransformationFilter &filter,
     PatternBenefit benefit) {
   patterns.add<DownscaleSizeOneWindowed2DConvolution,
                DownscaleDepthwiseConv2DNhwcHwcOp>(patterns.getContext(), filter,
                                                   benefit);
 }

mlir/lib/Dialect/Math/Transforms/PolynomialApproximation.cpp

 // fixed size 1-D vectors (e.g. AVX expects vectors of width 8).
 //
 // It is the caller's responsibility to verify that the inner dimension is
 // divisible by the vectorWidth, and that all operands have the same vector
 // shape.
 static Value
 handleMultidimensionalVectors(ImplicitLocOpBuilder &builder,
                               ValueRange operands, int64_t vectorWidth,
-                              std::function<Value(ValueRange)> compute) {
+                              llvm::function_ref<Value(ValueRange)> compute) {
   assert(!operands.empty() && "operands must be not empty");
   assert(vectorWidth > 0 && "vector width must be larger than 0");
 
   VectorType inputType = operands[0].getType().cast<VectorType>();
   ArrayRef<int64_t> inputShape = inputType.getShape();
 
   // If input shape matches target vector width, we can just call the
   // user-provided compute function with the operands.

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

 //===- SPIRVCanonicalization.cpp - MLIR SPIR-V canonicalization patterns --===//
 //
 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
 // See https://llvm.org/LICENSE.txt for license information.
 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 //
 //===----------------------------------------------------------------------===//
 //
 // This file defines the folders and canonicalization patterns for SPIR-V ops.
 //
 //===----------------------------------------------------------------------===//
 
+#include <utility>
+
 #include "mlir/Dialect/SPIRV/IR/SPIRVOps.h"
 
 #include "mlir/Dialect/CommonFolders.h"
 #include "mlir/Dialect/SPIRV/IR/SPIRVDialect.h"
 #include "mlir/Dialect/SPIRV/IR/SPIRVTypes.h"
 #include "mlir/IR/Matchers.h"
 #include "mlir/IR/PatternMatch.h"
 
OpFoldResult spirv::IAddOp::fold(ArrayRef<Attribute> operands) {
   if (matchPattern(operand2(), m_Zero()))
     return operand1();
 
   // According to the SPIR-V spec:
   //
   // The resulting value will equal the low-order N bits of the correct result
   // R, where N is the component width and R is computed with enough precision
   // to avoid overflow and underflow.
-  return constFoldBinaryOp<IntegerAttr>(operands,
-                                        [](APInt a, APInt b) { return a + b; });
+  return constFoldBinaryOp<IntegerAttr>(
+      operands, [](APInt a, const APInt &b) { return std::move(a) + b; });
 }
 
 //===----------------------------------------------------------------------===//
 // spv.IMul
 //===----------------------------------------------------------------------===//
 
 OpFoldResult spirv::IMulOp::fold(ArrayRef<Attribute> operands) {
   assert(operands.size() == 2 && "spv.IMul expects two operands");
   // x * 0 == 0
   if (matchPattern(operand2(), m_Zero()))
     return operand2();
   // x * 1 = x
   if (matchPattern(operand2(), m_One()))
     return operand1();
 
   // According to the SPIR-V spec:
   //
   // The resulting value will equal the low-order N bits of the correct result
   // R, where N is the component width and R is computed with enough precision
   // to avoid overflow and underflow.
-  return constFoldBinaryOp<IntegerAttr>(operands,
-                                        [](APInt a, APInt b) { return a * b; });
+  return constFoldBinaryOp<IntegerAttr>(
+      operands, [](const APInt &a, const APInt &b) { return a * b; });
 }
 
 //===----------------------------------------------------------------------===//
 // spv.ISub
 //===----------------------------------------------------------------------===//
 
 OpFoldResult spirv::ISubOp::fold(ArrayRef<Attribute> operands) {
   // x - x = 0
   if (operand1() == operand2())
     return Builder(getContext()).getIntegerAttr(getType(), 0);
 
   // According to the SPIR-V spec:
   //
   // The resulting value will equal the low-order N bits of the correct result
   // R, where N is the component width and R is computed with enough precision
   // to avoid overflow and underflow.
-  return constFoldBinaryOp<IntegerAttr>(operands,
-                                        [](APInt a, APInt b) { return a - b; });
+  return constFoldBinaryOp<IntegerAttr>(
+      operands, [](APInt a, const APInt &b) { return std::move(a) - b; });
 }
 
 //===----------------------------------------------------------------------===//
 // spv.LogicalAnd
 //===----------------------------------------------------------------------===//
 
 OpFoldResult spirv::LogicalAndOp::fold(ArrayRef<Attribute> operands) {
   assert(operands.size() == 2 && "spv.LogicalAnd should take two operands");

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

 //===- Shape.cpp - MLIR Shape Operations ----------------------------------===//
 //
 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
 // See https://llvm.org/LICENSE.txt for license information.
 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 //
 //===----------------------------------------------------------------------===//
 
+#include <utility>
+
 #include "mlir/Dialect/Shape/IR/Shape.h"
 
 #include "mlir/Dialect/Arithmetic/IR/Arithmetic.h"
 #include "mlir/Dialect/CommonFolders.h"
 #include "mlir/Dialect/StandardOps/IR/Ops.h"
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/Dialect/Traits.h"
 #include "mlir/IR/Builders.h"
bool mlir::shape::AddOp::isCompatibleReturnTypes(TypeRange l, TypeRange r) {
   return eachHasOnlyOneOfTypes<SizeType, IndexType>(l, r);
 }
 
 OpFoldResult mlir::shape::AddOp::fold(ArrayRef<Attribute> operands) {
   // add(x, 0) -> x
   if (matchPattern(getRhs(), m_Zero()))
     return getLhs();
 
-  return constFoldBinaryOp<IntegerAttr>(operands,
-                                        [](APInt a, APInt b) { return a + b; });
+  return constFoldBinaryOp<IntegerAttr>(
+      operands, [](APInt a, const APInt &b) { return std::move(a) + b; });
 }
 
 //===----------------------------------------------------------------------===//
 // AssumingAllOp
 //===----------------------------------------------------------------------===//
 
 namespace {
 struct AssumingAllToCstrEqCanonicalization

mlir/lib/Dialect/StandardOps/IR/Ops.cpp

static ParseResult parseSwitchOpCases(
   }
   return success();
 }
 
 static void printSwitchOpCases(
     OpAsmPrinter &p, SwitchOp op, Type flagType, Block *defaultDestination,
     OperandRange defaultOperands, TypeRange defaultOperandTypes,
     DenseIntElementsAttr caseValues, SuccessorRange caseDestinations,
-    OperandRangeRange caseOperands, TypeRangeRange caseOperandTypes) {
+    OperandRangeRange caseOperands, const TypeRangeRange &caseOperandTypes) {
   p << "  default: ";
   p.printSuccessorAndUseList(defaultDestination, defaultOperands);
 
   if (!caseValues)
     return;
 
   for (const auto &it : llvm::enumerate(caseValues.getValues<APInt>())) {
     p << ',';

 /// Helper for folding a switch with a constant value.
 /// switch %c_42 : i32, [
 ///   default: ^bb1 ,
 ///   42: ^bb2,
 ///   43: ^bb3
 /// ]
 /// -> br ^bb2
 static void foldSwitch(SwitchOp op, PatternRewriter &rewriter,
-                       APInt caseValue) {
+                       const APInt &caseValue) {
   auto caseValues = op.getCaseValues();
   for (const auto &it : llvm::enumerate(caseValues->getValues<APInt>())) {
     if (it.value() == caseValue) {
       rewriter.replaceOpWithNewOp<BranchOp>(
           op, op.getCaseDestinations()[it.index()],
           op.getCaseOperands(it.index()));
       return;
     }

mlir/lib/Dialect/Utils/ReshapeOpsUtils.cpp

unsigned getMaxPosOfType(ArrayRef<ReassociationExprs> exprArrays) {
   }
   return pos;
 }
 
 ArrayAttr mlir::getReassociationIndicesAttribute(
     OpBuilder &b, ArrayRef<ReassociationIndices> reassociation) {
   SmallVector<Attribute, 4> reassociationAttr =
       llvm::to_vector<4>(llvm::map_range(
-          reassociation, [&](ReassociationIndices indices) -> Attribute {
+          reassociation, [&](const ReassociationIndices &indices) -> Attribute {
             return b.getI64ArrayAttr(indices).cast<Attribute>();
           }));
   return b.getArrayAttr(reassociationAttr);
 }
 
 SmallVector<ReassociationIndices, 2> mlir::convertReassociationMapsToIndices(
     OpBuilder &b, ArrayRef<ReassociationExprs> reassociationExprs) {
   SmallVector<ReassociationIndices, 2> reassociationIndices;

mlir/lib/Dialect/Vector/VectorMultiDimReductionTransforms.cpp

LogicalResult matchAndRewrite(vector::MultiDimReductionOp multiReductionOp,
     auto src = multiReductionOp.source();
     auto loc = multiReductionOp.getLoc();
     auto srcRank = multiReductionOp.getSourceVectorType().getRank();
 
     // Separate reduction and parallel dims
     auto reductionDimsRange =
         multiReductionOp.reduction_dims().getAsValueRange<IntegerAttr>();
     auto reductionDims = llvm::to_vector<4>(llvm::map_range(
-        reductionDimsRange, [](APInt a) { return a.getZExtValue(); }));
+        reductionDimsRange, [](const APInt &a) { return a.getZExtValue(); }));
     llvm::SmallDenseSet<int64_t> reductionDimsSet(reductionDims.begin(),
                                                   reductionDims.end());
     int64_t reductionSize = reductionDims.size();
     SmallVector<int64_t, 4> parallelDims;
     for (int64_t i = 0; i < srcRank; ++i)
       if (!reductionDimsSet.contains(i))
         parallelDims.push_back(i);
 

mlir/lib/IR/AffineExpr.cpp

 //===- AffineExpr.cpp - MLIR Affine Expr Classes --------------------------===//
 //
 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
 // See https://llvm.org/LICENSE.txt for license information.
 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 //
 //===----------------------------------------------------------------------===//
 
+#include <utility>
+
 #include "mlir/IR/AffineExpr.h"
 #include "AffineExprDetail.h"
 #include "mlir/IR/AffineExprVisitor.h"
 #include "mlir/IR/AffineMap.h"
 #include "mlir/IR/IntegerSet.h"
 #include "mlir/Support/MathExtras.h"
 #include "mlir/Support/TypeID.h"
 #include "llvm/ADT/STLExtras.h"
 
 using namespace mlir;
 using namespace mlir::detail;
 
 MLIRContext *AffineExpr::getContext() const { return expr->context; }
 
 AffineExprKind AffineExpr::getKind() const { return expr->kind; }
 
 /// Walk all of the AffineExprs in this subgraph in postorder.
 void AffineExpr::walk(std::function<void(AffineExpr)> callback) const {
   struct AffineExprWalker : public AffineExprVisitor<AffineExprWalker> {
     std::function<void(AffineExpr)> callback;
 
     AffineExprWalker(std::function<void(AffineExpr)> callback)
-        : callback(callback) {}
+        : callback(std::move(callback)) {}
 
     void visitAffineBinaryOpExpr(AffineBinaryOpExpr expr) { callback(expr); }
     void visitConstantExpr(AffineConstantExpr expr) { callback(expr); }
     void visitDimExpr(AffineDimExpr expr) { callback(expr); }
     void visitSymbolExpr(AffineSymbolExpr expr) { callback(expr); }
   };
 
-  AffineExprWalker(callback).walkPostOrder(*this);
+  AffineExprWalker(std::move(callback)).walkPostOrder(*this);
 }
 
 // Dispatch affine expression construction based on kind.
 AffineExpr mlir::getAffineBinaryOpExpr(AffineExprKind kind, AffineExpr lhs,
                                        AffineExpr rhs) {
   if (kind == AffineExprKind::Add)
     return lhs + rhs;
   if (kind == AffineExprKind::Mul)

mlir/lib/IR/AffineMap.cpp

for (auto expr : getResults()) {
     }
   }
 
   return get(getNumDims(), getNumSymbols(), exprs, getContext());
 }
 
 /// Walk all of the AffineExpr's in this mapping. Each node in an expression
 /// tree is visited in postorder.
-void AffineMap::walkExprs(std::function<void(AffineExpr)> callback) const {
+void AffineMap::walkExprs(llvm::function_ref<void(AffineExpr)> callback) const {
   for (auto expr : getResults())
     expr.walk(callback);
 }
 
 /// This method substitutes any uses of dimensions and symbols (e.g.
 /// dim#0 with dimReplacements[0]) in subexpressions and returns the modified
 /// expression mapping.  Because this can be used to eliminate dims and
 /// symbols, the client needs to specify the number of dims and symbols in

mlir/lib/IR/Diagnostics.cpp

~ParallelDiagnosticHandlerImpl() override {
 
     // Emit the diagnostics back to the context.
     emitDiagnostics([&](Diagnostic diag) {
       return context->getDiagEngine().emit(std::move(diag));
     });
   }
 
   /// Utility method to emit any held diagnostics.
-  void emitDiagnostics(std::function<void(Diagnostic)> emitFn) const {
+  void emitDiagnostics(llvm::function_ref<void(Diagnostic)> emitFn) const {
     // Stable sort all of the diagnostics that were emitted. This creates a
     // deterministic ordering for the diagnostics based upon which order id they
     // were emitted for.
     std::stable_sort(diagnostics.begin(), diagnostics.end());
 
     // Emit each diagnostic to the context again.
     for (ThreadDiagnostic &diag : diagnostics)
       emitFn(std::move(diag.diag));

mlir/lib/IR/Dialect.cpp

 //===----------------------------------------------------------------------===//
 // DialectRegistry
 //===----------------------------------------------------------------------===//
 
 DialectRegistry::DialectRegistry() { insert<BuiltinDialect>(); }
 
 void DialectRegistry::addDialectInterface(
     StringRef dialectName, TypeID interfaceTypeID,
-    DialectInterfaceAllocatorFunction allocator) {
+    const DialectInterfaceAllocatorFunction &allocator) {
   assert(allocator && "unexpected null interface allocation function");
   auto it = registry.find(dialectName.str());
   assert(it != registry.end() &&
          "adding an interface for an unregistered dialect");
 
   // Bail out if the interface with the given ID is already in the registry for
   // the given dialect. We expect a small number (dozens) of interfaces so a
   // linear search is fine here.
   auto &ifaces = interfaces[it->second.first];
   for (const auto &kvp : ifaces.dialectInterfaces) {
     if (kvp.first == interfaceTypeID) {
       LLVM_DEBUG(llvm::dbgs()
                  << "[" DEBUG_TYPE
                     "] repeated interface registration for dialect "
                  << dialectName);
       return;
     }
   }
 
   ifaces.dialectInterfaces.emplace_back(interfaceTypeID, allocator);
 }
 
 void DialectRegistry::addObjectInterface(
     StringRef dialectName, TypeID objectID, TypeID interfaceTypeID,
-    ObjectInterfaceAllocatorFunction allocator) {
+    const ObjectInterfaceAllocatorFunction &allocator) {
   assert(allocator && "unexpected null interface allocation function");
 
   auto it = registry.find(dialectName.str());
   assert(it != registry.end() &&
          "adding an interface for an op from an unregistered dialect");
 
   auto dialectID = it->second.first;
   auto &ifaces = interfaces[dialectID];

 DialectRegistry::getDialectAllocator(StringRef name) const {
   auto it = registry.find(name.str());
   if (it == registry.end())
     return nullptr;
   return it->second.second;
 }
 
 void DialectRegistry::insert(TypeID typeID, StringRef name,
-                             DialectAllocatorFunction ctor) {
+                             const DialectAllocatorFunction &ctor) {
   auto inserted = registry.insert(
       std::make_pair(std::string(name), std::make_pair(typeID, ctor)));
   if (!inserted.second && inserted.first->second.first != typeID) {
     llvm::report_fatal_error(
         "Trying to register different dialects for the same namespace: " +
         name);
   }
 }

mlir/lib/IR/Operation.cpp

LogicalResult OpTrait::impl::verifyValueSizeAttr(Operation *op,
   if (llvm::any_of(sizeAttr.getValues<APInt>(), [](const APInt &element) {
         return !element.isNonNegative();
       }))
     return op->emitOpError("'")
            << attrName << "' attribute cannot have negative elements";
 
   size_t totalCount = std::accumulate(
       sizeAttr.begin(), sizeAttr.end(), 0,
-      [](unsigned all, APInt one) { return all + one.getZExtValue(); });
+      [](unsigned all, const APInt &one) { return all + one.getZExtValue(); });
 
   if (totalCount != expectedCount)
     return op->emitOpError()
            << valueGroupName << " count (" << expectedCount
            << ") does not match with the total size (" << totalCount
            << ") specified in attribute '" << attrName << "'";
   return success();
 }

mlir/lib/Pass/IRPrinting.cpp

struct BasicIRPrinterConfig : public PassManager::IRPrinterConfig {
   BasicIRPrinterConfig(
       std::function<bool(Pass *, Operation *)> shouldPrintBeforePass,
       std::function<bool(Pass *, Operation *)> shouldPrintAfterPass,
       bool printModuleScope, bool printAfterOnlyOnChange,
       bool printAfterOnlyOnFailure, OpPrintingFlags opPrintingFlags,
       raw_ostream &out)
       : IRPrinterConfig(printModuleScope, printAfterOnlyOnChange,
                         printAfterOnlyOnFailure, opPrintingFlags),
-        shouldPrintBeforePass(shouldPrintBeforePass),
-        shouldPrintAfterPass(shouldPrintAfterPass), out(out) {
-    assert((shouldPrintBeforePass || shouldPrintAfterPass) &&
+        shouldPrintBeforePass(std::move(shouldPrintBeforePass)),
+        shouldPrintAfterPass(std::move(shouldPrintAfterPass)), out(out) {
+    assert((this->shouldPrintBeforePass || this->shouldPrintAfterPass) &&
            "expected at least one valid filter function");
   }
 
   void printBeforeIfEnabled(Pass *pass, Operation *operation,
                             PrintCallbackFn printCallback) final {
     if (shouldPrintBeforePass && shouldPrintBeforePass(pass, operation))
       printCallback(out);
   }

mlir/lib/Pass/PassRegistry.cpp

 //===- PassRegistry.cpp - Pass Registration Utilities ---------------------===//
 //
 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
 // See https://llvm.org/LICENSE.txt for license information.
 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 //
 //===----------------------------------------------------------------------===//
 
+#include <utility>
+
 #include "mlir/Pass/PassRegistry.h"
 #include "mlir/Pass/Pass.h"
 #include "mlir/Pass/PassManager.h"
 #include "llvm/ADT/DenseMap.h"
 #include "llvm/Support/Format.h"
 #include "llvm/Support/ManagedStatic.h"
 #include "llvm/Support/MemoryBuffer.h"
 #include "llvm/Support/SourceMgr.h"

 
 //===----------------------------------------------------------------------===//
 // PassPipelineInfo
 //===----------------------------------------------------------------------===//
 
 void mlir::registerPassPipeline(
     StringRef arg, StringRef description, const PassRegistryFunction &function,
     std::function<void(function_ref<void(const PassOptions &)>)> optHandler) {
-  PassPipelineInfo pipelineInfo(arg, description, function, optHandler);
+  PassPipelineInfo pipelineInfo(arg, description, function,
+                                std::move(optHandler));
   bool inserted = passPipelineRegistry->try_emplace(arg, pipelineInfo).second;
   assert(inserted && "Pass pipeline registered multiple times");
   (void)inserted;
 }
 
 //===----------------------------------------------------------------------===//
 // PassInfo
 //===----------------------------------------------------------------------===//

mlir/lib/Reducer/ReductionNode.cpp

 #include "llvm/ADT/STLExtras.h"
 
 #include <algorithm>
 #include <limits>
 
 using namespace mlir;
 
 ReductionNode::ReductionNode(
-    ReductionNode *parentNode, std::vector<Range> ranges,
+    ReductionNode *parentNode, const std::vector<Range> &ranges,
     llvm::SpecificBumpPtrAllocator<ReductionNode> &allocator)
     /// Root node will have the parent pointer point to themselves.
     : parent(parentNode == nullptr ? this : parentNode),
       size(std::numeric_limits<size_t>::max()),
       interesting(Tester::Interestingness::Untested), ranges(ranges),
       startRanges(ranges), allocator(allocator) {
   if (parent != this)
     if (failed(initialize(parent->getModule(), parent->getRegion())))

mlir/lib/TableGen/Pattern.cpp

 //===- Pattern.cpp - Pattern wrapper class --------------------------------===//
 //
 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
 // See https://llvm.org/LICENSE.txt for license information.
 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 //
 //===----------------------------------------------------------------------===//
 //
 // Pattern wrapper class to simplify using TableGen Record defining a MLIR
 // Pattern.
 //
 //===----------------------------------------------------------------------===//
 
+#include <utility>
+
 #include "mlir/TableGen/Pattern.h"
 #include "llvm/ADT/StringExtras.h"
 #include "llvm/ADT/Twine.h"
 #include "llvm/Support/Debug.h"
 #include "llvm/Support/FormatVariadic.h"
 #include "llvm/TableGen/Error.h"
 #include "llvm/TableGen/Record.h"
 
StringRef SymbolInfoMap::getValuePackName(StringRef symbol, int *index) {
   if (index) {
     *index = idx;
   }
   return name;
 }
 
 SymbolInfoMap::SymbolInfo::SymbolInfo(const Operator *op, SymbolInfo::Kind kind,
                                       Optional<DagAndConstant> dagAndConstant)
-    : op(op), kind(kind), dagAndConstant(dagAndConstant) {}
+    : op(op), kind(kind), dagAndConstant(std::move(dagAndConstant)) {}
 
 int SymbolInfoMap::SymbolInfo::getStaticValueCount() const {
   switch (kind) {
   case Kind::Attr:
   case Kind::Operand:
   case Kind::Value:
     return 1;
   case Kind::Result:

 
 SymbolInfoMap::const_iterator
 SymbolInfoMap::findBoundSymbol(StringRef key, DagNode node, const Operator &op,
                                int argIndex) const {
   return findBoundSymbol(key, SymbolInfo::getOperand(node, &op, argIndex));
 }
 
 SymbolInfoMap::const_iterator
-SymbolInfoMap::findBoundSymbol(StringRef key, SymbolInfo symbolInfo) const {
+SymbolInfoMap::findBoundSymbol(StringRef key,
+                               const SymbolInfo &symbolInfo) const {
   std::string name = getValuePackName(key).str();
   auto range = symbolInfoMap.equal_range(name);
 
   for (auto it = range.first; it != range.second; ++it)
     if (it->second.dagAndConstant == symbolInfo.dagAndConstant)
       return it;
 
   return symbolInfoMap.end();

mlir/lib/TableGen/Predicate.cpp

for (auto &child : children) {
     node->children.push_back(simplifiedChild);
   }
   return node;
 }
 
 // Combine a list of predicate expressions using a binary combiner.  If a list
 // is empty, return "init".
 static std::string combineBinary(ArrayRef<std::string> children,
-                                 std::string combiner, std::string init) {
+                                 const std::string &combiner,
+                                 std::string init) {
   if (children.empty())
     return init;
 
   auto size = children.size();
   if (size == 1)
     return children.front();
 
   std::string str;

mlir/lib/Target/Cpp/TranslateToCpp.cpp

 //===- TranslateToCpp.cpp - Translating to C++ calls ----------------------===//
 //
 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
 // See https://llvm.org/LICENSE.txt for license information.
 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 //
 //===----------------------------------------------------------------------===//
 
+#include <utility>
+
 #include "mlir/Dialect/EmitC/IR/EmitC.h"
 #include "mlir/Dialect/SCF/SCF.h"
 #include "mlir/Dialect/StandardOps/IR/Ops.h"
 #include "mlir/IR/BuiltinOps.h"
 #include "mlir/IR/BuiltinTypes.h"
 #include "mlir/IR/Dialect.h"
 #include "mlir/IR/Operation.h"
 #include "mlir/Support/IndentedOstream.h"

 
 bool CppEmitter::hasValueInScope(Value val) { return valueMapper.count(val); }
 
 bool CppEmitter::hasBlockLabel(Block &block) {
   return blockMapper.count(&block);
 }
 
 LogicalResult CppEmitter::emitAttribute(Location loc, Attribute attr) {
-  auto printInt = [&](APInt val, bool isUnsigned) {
+  auto printInt = [&](const APInt &val, bool isUnsigned) {
     if (val.getBitWidth() == 1) {
       if (val.getBoolValue())
         os << "true";
       else
         os << "false";
     } else {
       SmallString<128> strValue;
       val.toString(strValue, 10, !isUnsigned, false);
       os << strValue;
     }
   };
 
-  auto printFloat = [&](APFloat val) {
+  auto printFloat = [&](const APFloat &val) {
     if (val.isFinite()) {
       SmallString<128> strValue;
       // Use default values of toString except don't truncate zeros.
       val.toString(strValue, 0, 0, false);
       switch (llvm::APFloatBase::SemanticsToEnum(val.getSemantics())) {
       case llvm::APFloatBase::S_IEEEsingle:
         os << "(float)";
         break;
LogicalResult CppEmitter::emitAttribute(Location loc, Attribute attr) {
 
   // Print floating point attributes.
   if (auto fAttr = attr.dyn_cast<FloatAttr>()) {
     printFloat(fAttr.getValue());
     return success();
   }
   if (auto dense = attr.dyn_cast<DenseFPElementsAttr>()) {
     os << '{';
-    interleaveComma(dense, os, [&](APFloat val) { printFloat(val); });
+    interleaveComma(dense, os,
+                    [&](APFloat val) { printFloat(std::move(val)); });
     os << '}';
     return success();
   }
 
   // Print integer attributes.
   if (auto iAttr = attr.dyn_cast<IntegerAttr>()) {
     if (auto iType = iAttr.getType().dyn_cast<IntegerType>()) {
       printInt(iAttr.getValue(), shouldMapToUnsigned(iType.getSignedness()));
       return success();
     }
     if (auto iType = iAttr.getType().dyn_cast<IndexType>()) {
       printInt(iAttr.getValue(), false);
       return success();
     }
   }
   if (auto dense = attr.dyn_cast<DenseIntElementsAttr>()) {
     if (auto iType = dense.getType()
                          .cast<TensorType>()
                          .getElementType()
                          .dyn_cast<IntegerType>()) {
       os << '{';
       interleaveComma(dense, os, [&](APInt val) {
-        printInt(val, shouldMapToUnsigned(iType.getSignedness()));
+        printInt(std::move(val), shouldMapToUnsigned(iType.getSignedness()));
       });
       os << '}';
       return success();
     }
     if (auto iType = dense.getType()
                          .cast<TensorType>()
                          .getElementType()
                          .dyn_cast<IndexType>()) {
       os << '{';
-      interleaveComma(dense, os, [&](APInt val) { printInt(val, false); });
+      interleaveComma(dense, os,
+                      [&](APInt val) { printInt(std::move(val), false); });
       os << '}';
       return success();
     }
   }
 
   // Print opaque attributes.
   if (auto oAttr = attr.dyn_cast<emitc::OpaqueAttr>()) {
     os << oAttr.getValue();

mlir/lib/Tools/mlir-lsp-server/lsp/Transport.cpp

 //===--- JSONTransport.cpp - sending and receiving LSP messages over JSON -===//
 //
 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
 // See https://llvm.org/LICENSE.txt for license information.
 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 //
 //===----------------------------------------------------------------------===//
 
 #include "Transport.h"
 #include "Logging.h"
 #include "Protocol.h"
 #include "llvm/ADT/SmallString.h"
 #include "llvm/Support/Errno.h"
 #include "llvm/Support/Error.h"
 #include <system_error>
+#include <utility>
 
 using namespace mlir;
 using namespace mlir::lsp;
 
 //===----------------------------------------------------------------------===//
 // Reply
 //===----------------------------------------------------------------------===//
 
bool MessageHandler::onNotify(llvm::StringRef method, llvm::json::Value value) {
 
   if (method == "exit")
     return false;
   if (method == "$cancel") {
     // TODO: Add support for cancelling requests.
   } else {
     auto it = notificationHandlers.find(method);
     if (it != notificationHandlers.end())
-      it->second(value);
+      it->second(std::move(value));
   }
   return true;
 }
 
 bool MessageHandler::onCall(llvm::StringRef method, llvm::json::Value params,
                             llvm::json::Value id) {
   Logger::info("--> {0}({1})", method, id);
 
   Reply reply(id, method, transport);
 
   auto it = methodHandlers.find(method);
   if (it != methodHandlers.end()) {
-    it->second(params, std::move(reply));
+    it->second(std::move(params), std::move(reply));
   } else {
     reply(llvm::make_error<LSPError>("method not found: " + method.str(),
                                      ErrorCode::MethodNotFound));
   }
   return true;
 }
 
 bool MessageHandler::onReply(llvm::json::Value id,

mlir/lib/Transforms/Inliner.cpp

private:
   /// instead of the default. The vector size is the number of threads used
   /// during optimization.
   SmallVector<llvm::StringMap<OpPassManager>, 8> opPipelines;
 };
 } // namespace
 
 InlinerPass::InlinerPass() : InlinerPass(defaultInlinerOptPipeline) {}
 InlinerPass::InlinerPass(std::function<void(OpPassManager &)> defaultPipeline)
-    : defaultPipeline(defaultPipeline) {
+    : defaultPipeline(std::move(defaultPipeline)) {
   opPipelines.push_back({});
 
   // Initialize the pass options with the provided arguments.
   if (defaultPipeline) {
     OpPassManager fakePM("__mlir_fake_pm_op");
     defaultPipeline(fakePM);
     llvm::raw_string_ostream strStream(defaultPipelineStr);
     fakePM.printAsTextualPipeline(strStream);

mlir/lib/Transforms/ViewOpGraph.cpp

 //===- ViewOpGraph.cpp - View/write op graphviz graphs --------------------===//
 //
 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
 // See https://llvm.org/LICENSE.txt for license information.
 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 //
 //===----------------------------------------------------------------------===//
 
+#include <utility>
+
 #include "mlir/Transforms/ViewOpGraph.h"
 #include "PassDetail.h"
 #include "mlir/IR/Block.h"
 #include "mlir/IR/Operation.h"
 #include "mlir/Support/IndentedOstream.h"
 #include "llvm/Support/Format.h"
 #include "llvm/Support/GraphWriter.h"
 

 }
 
 /// Escape special characters such as '\n' and quotation marks.
 static std::string escapeString(std::string str) {
   return strFromOs([&](raw_ostream &os) { os.write_escaped(str); });
 }
 
 /// Put quotation marks around a given string.
-static std::string quoteString(std::string str) { return "\"" + str + "\""; }
+static std::string quoteString(const std::string &str) {
+  return "\"" + str + "\"";
+}
 
 using AttributeMap = llvm::StringMap<std::string>;
 
 namespace {
 
 /// This struct represents a node in the DOT language. Each node has an
 /// identifier and an optional identifier for the cluster (subgraph) that
 /// contains the node.
private:
   /// Emit a cluster (subgraph). The specified builder generates the body of the
   /// cluster. Return the anchor node of the cluster.
   Node emitClusterStmt(function_ref<void()> builder, std::string label = "") {
     int clusterId = ++counter;
     os << "subgraph cluster_" << clusterId << " {\n";
     os.indent();
     // Emit invisible anchor node from/to which arrows can be drawn.
     Node anchorNode = emitNodeStmt(" ", kShapeNone);
-    os << attrStmt("label", quoteString(escapeString(label))) << ";\n";
+    os << attrStmt("label", quoteString(escapeString(std::move(label))))
+       << ";\n";
     builder();
     os.unindent();
     os << "}\n";
     return Node(anchorNode.id, clusterId);
   }
 
   /// Generate an attribute statement.
   std::string attrStmt(const Twine &key, const Twine &value) {
private:
   /// Note: Edges are written to the output stream via `emitAllEdgeStmts`.
   void emitEdgeStmt(Node n1, Node n2, std::string label, StringRef style) {
     AttributeMap attrs;
     attrs["style"] = style.str();
     // Do not label edges that start/end at a cluster boundary. Such edges are
     // clipped at the boundary, but labels are not. This can lead to labels
     // floating around without any edge next to them.
     if (!n1.clusterId && !n2.clusterId)
-      attrs["label"] = quoteString(escapeString(label));
+      attrs["label"] = quoteString(escapeString(std::move(label)));
     // Use `ltail` and `lhead` to draw edges between clusters.
     if (n1.clusterId)
       attrs["ltail"] = "cluster_" + std::to_string(*n1.clusterId);
     if (n2.clusterId)
       attrs["lhead"] = "cluster_" + std::to_string(*n2.clusterId);
 
     edges.push_back(strFromOs([&](raw_ostream &os) {
       os << llvm::format("v%i -> v%i ", n1.id, n2.id);
void emitGraph(function_ref<void()> builder) {
     os.unindent();
     os << "}\n";
   }
 
   /// Emit a node statement.
   Node emitNodeStmt(std::string label, StringRef shape = kShapeNode) {
     int nodeId = ++counter;
     AttributeMap attrs;
-    attrs["label"] = quoteString(escapeString(label));
+    attrs["label"] = quoteString(escapeString(std::move(label)));
     attrs["shape"] = shape.str();
     os << llvm::format("v%i ", nodeId);
     emitAttrList(os, attrs);
     os << ";\n";
     return Node(nodeId);
   }
 
   /// Generate a label for an operation.

mlir/lib/Translation/Translation.cpp

 }
 
 //===----------------------------------------------------------------------===//
 // Translation from MLIR
 //===----------------------------------------------------------------------===//
 
 TranslateFromMLIRRegistration::TranslateFromMLIRRegistration(
     StringRef name, const TranslateFromMLIRFunction &function,
-    std::function<void(DialectRegistry &)> dialectRegistration) {
+    const std::function<void(DialectRegistry &)> &dialectRegistration) {
   registerTranslation(name, [function, dialectRegistration](
                                 llvm::SourceMgr &sourceMgr, raw_ostream &output,
                                 MLIRContext *context) {
     DialectRegistry registry;
     dialectRegistration(registry);
     context->appendDialectRegistry(registry);
     auto module = OwningModuleRef(parseSourceFile(sourceMgr, context));
     if (!module || failed(verify(*module)))

mlir/test/lib/Dialect/Linalg/TestLinalgCodegenStrategy.cpp

 //===- TestLinalgCodegenStrategy.cpp - Test Linalg codegen strategy -------===//
 //
 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
 // See https://llvm.org/LICENSE.txt for license information.
 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 //
 //===----------------------------------------------------------------------===//
 //
 // This file implements logic for testing the Linalg codegen strategy.
 //
 //===----------------------------------------------------------------------===//
 
+#include <utility>
+
 #include "mlir/Dialect/Affine/IR/AffineOps.h"
 #include "mlir/Dialect/GPU/GPUDialect.h"
 #include "mlir/Dialect/Linalg/IR/Linalg.h"
 #include "mlir/Dialect/Linalg/Transforms/CodegenStrategy.h"
 #include "mlir/Dialect/Linalg/Utils/Utils.h"
 #include "mlir/Dialect/StandardOps/IR/Ops.h"
 #include "mlir/Dialect/Vector/VectorOps.h"
 #include "mlir/IR/PatternMatch.h"
void TestLinalgCodegenStrategy::runStrategy(
     LinalgTilingOptions tilingOptions,
     LinalgTilingOptions registerTilingOptions,
     LinalgPaddingOptions paddingOptions,
     vector::VectorContractLowering vectorContractLowering,
     vector::VectorTransferSplit vectorTransferSplit) {
   CodegenStrategy strategy;
   strategy
       .tileAndFuseIf(fuse && !tileSizes.empty(), anchorOpName,
-                     tilingAndFusionOptions)
-      .tileIf(!fuse && !tileSizes.empty(), anchorOpName, tilingOptions)
+                     std::move(tilingAndFusionOptions))
+      .tileIf(!fuse && !tileSizes.empty(), anchorOpName,
+              std::move(tilingOptions))
       .promoteIf(!fuse && promote, anchorOpName,
                  LinalgPromotionOptions()
                      .setAlignment(16)
                      .setUseFullTileBuffersByDefault(promoteFullTile))
       .tileIf(!fuse && !registerTileSizes.empty(), anchorOpName,
-              registerTilingOptions)
+              std::move(registerTilingOptions))
       .promoteIf(!fuse && registerPromote, anchorOpName,
                  LinalgPromotionOptions()
                      .setAlignment(16)
                      .setUseFullTileBuffersByDefault(registerPromoteFullTile))
-      .padIf(pad, "", paddingOptions)
+      .padIf(pad, "", std::move(paddingOptions))
       .decomposeIf(decompose)
       .generalizeIf(generalize, "")
       .interchangeIf(!iteratorInterchange.empty(), iteratorInterchange)
       .vectorizeIf(vectorize, "", nullptr, vectorizePadding)
       .vectorLowering(
           LinalgVectorLoweringOptions()
               .setVectorTransformsOptions(
                   vector::VectorTransformsOptions()

mlir/test/lib/Dialect/Linalg/TestLinalgTransforms.cpp

 }
 
 static void applyExtractSliceOfPadTensorSwapPattern(FuncOp funcOp) {
   RewritePatternSet patterns(funcOp.getContext());
   patterns.add<ExtractSliceOfPadTensorSwapPattern>(funcOp.getContext());
   (void)applyPatternsAndFoldGreedily(funcOp, std::move(patterns));
 }
 
-static void applyTilePattern(FuncOp funcOp, std::string loopType,
+static void applyTilePattern(FuncOp funcOp, const std::string &loopType,
                              ArrayRef<int64_t> tileSizes,
                              ArrayRef<int64_t> peeledLoops,
                              bool scalarizeDynamicDims) {
   MLIRContext *context = funcOp.getContext();
   RewritePatternSet tilingPattern(context);
   LinalgTilingLoopType type =
       llvm::StringSwitch<LinalgTilingLoopType>(loopType)
           .Case("for", LinalgTilingLoopType::Loops)

mlir/tools/mlir-tblgen/DialectGen.cpp

 /// The code block for the op interface fallback hook.
 static const char *const operationInterfaceFallbackDecl = R"(
     /// Provides a hook for op interface.
     void *getRegisteredInterfaceForOp(mlir::TypeID interfaceID,
                                       mlir::OperationName opName) override;
 )";
 
 /// Generate the declaration for the given dialect class.
-static void emitDialectDecl(Dialect &dialect,
-                            iterator_range<DialectFilterIterator> dialectAttrs,
-                            iterator_range<DialectFilterIterator> dialectTypes,
-                            raw_ostream &os) {
+static void
+emitDialectDecl(Dialect &dialect,
+                const iterator_range<DialectFilterIterator> &dialectAttrs,
+                const iterator_range<DialectFilterIterator> &dialectTypes,
   /// Build the list of dependent dialects
   std::string dependentDialectRegistrations;
   {
     llvm::raw_string_ostream dialectsOs(dependentDialectRegistrations);
     for (StringRef dependentDialect : dialect.getDependentDialects())
       dialectsOs << llvm::formatv(dialectRegistrationTemplate,
                                   dependentDialect);
   }

mlir/tools/mlir-tblgen/OpDocGen.cpp

do {
     split = split.second.split('\n');
     StringRef formatChunk = split.first.trim();
     if (!formatChunk.empty())
       os.indent(indent) << formatChunk << "\n";
   } while (!split.second.empty());
   os << "```\n\n";
 }
 
-static void emitOpTraitsDoc(Operator op, raw_ostream &os) {
+static void emitOpTraitsDoc(const Operator &op, raw_ostream &os) {
   // TODO: We should link to the trait/documentation of it. That also means we
   // should add descriptions to traits that can be queried.
   // Collect using set to sort effects, interfaces & traits.
   std::set<std::string> effects, interfaces, traits;
   for (auto &trait : op.getTraits()) {
     if (isa<PredTrait>(&trait))
       continue;
 

mlir/tools/mlir-tblgen/OpFormatGen.cpp

private:
   /// same type as all of the operands.
   void handleSameTypesConstraint(
       llvm::StringMap<TypeResolutionInstance> &variableTyResolver,
       bool includeResults);
   /// Check for inferable type resolution based on another operand, result, or
   /// attribute.
   void handleTypesMatchConstraint(
       llvm::StringMap<TypeResolutionInstance> &variableTyResolver,
-      llvm::Record def);
+      const llvm::Record &def);
 
   /// Returns an argument or attribute with the given name that has been seen
   /// within the format.
   ConstArgument findSeenArg(StringRef name);
 
   /// Parse a specific element.
   LogicalResult parseElement(std::unique_ptr<Element> &element,
                              ParserContext context);
if (includeResults) {
     for (unsigned i = 0, e = op.getNumResults(); i != e; ++i)
       if (!seenResultTypes.test(i) && !op.getResultName(i).empty())
         variableTyResolver[op.getResultName(i)] = {resolver, llvm::None};
   }
 }
 
 void FormatParser::handleTypesMatchConstraint(
     llvm::StringMap<TypeResolutionInstance> &variableTyResolver,
-    llvm::Record def) {
+    const llvm::Record &def) {
   StringRef lhsName = def.getValueAsString("lhs");
   StringRef rhsName = def.getValueAsString("rhs");
   StringRef transformer = def.getValueAsString("transformer");
   if (ConstArgument arg = findSeenArg(lhsName))
     variableTyResolver[rhsName] = {arg, transformer};
 }
 
 ConstArgument FormatParser::findSeenArg(StringRef name) {

mlir/tools/mlir-tblgen/OpInterfacesGen.cpp

struct TypeInterfaceGenerator : public InterfaceGenerator {
   }
 };
 } // namespace
 
 //===----------------------------------------------------------------------===//
 // GEN: Interface definitions
 //===----------------------------------------------------------------------===//
 
-static void emitInterfaceDef(Interface interface, StringRef valueType,
+static void emitInterfaceDef(const Interface &interface, StringRef valueType,
                              raw_ostream &os) {
   StringRef interfaceName = interface.getName();
   StringRef cppNamespace = interface.getCppNamespace();
   cppNamespace.consume_front("::");
 
   // Insert the method definitions.
   bool isOpInterface = isa<OpInterface>(interface);
   for (auto &method : interface.getMethods()) {

mlir/tools/mlir-tblgen/mlir-tblgen.cpp

 #include "llvm/TableGen/TableGenBackend.h"
 
 using namespace llvm;
 using namespace mlir;
 
 static llvm::ManagedStatic<std::vector<GenInfo>> generatorRegistry;
 
 mlir::GenRegistration::GenRegistration(StringRef arg, StringRef description,
-                                       GenFunction function) {
+                                       const GenFunction &function) {
   generatorRegistry->emplace_back(arg, description, function);
 }
 
 GenNameParser::GenNameParser(llvm::cl::Option &opt)
     : llvm::cl::parser<const GenInfo *>(opt) {
   for (const auto &kv : *generatorRegistry) {
     addLiteralOption(kv.getGenArgument(), &kv, kv.getGenDescription());
   }

mlir/unittests/Analysis/AffineStructuresParserTest.cpp

TEST(ParseFACTest, InvalidInputTest) {
   fac = parseIntegerSetToFAC("(x) : (2 * x + 9223372036854775808 >= 0)",
                              &context, false);
   EXPECT_TRUE(failed(fac)) << "should not accept strings with integer literals "
                               "that do not fit into int64_t";
 }
 
 /// Parses and compares the `str` to the `ex`. The equality check is performed
 /// by using PresburgerSet::isEqual
-static bool parseAndCompare(StringRef str, FlatAffineConstraints ex,
+static bool parseAndCompare(StringRef str, const FlatAffineConstraints &ex,
                             MLIRContext *context) {
   FailureOr<FlatAffineConstraints> fac = parseIntegerSetToFAC(str, context);
 
   EXPECT_TRUE(succeeded(fac));
 
   return PresburgerSet(*fac).isEqual(PresburgerSet(ex));
 }
 

mlir/unittests/Analysis/Presburger/SimplexTest.cpp

for (int run = 0; run < 4; run++) {
     EXPECT_EQ(simplex.getNumConstraints(), 0u);
 
     expectInequalityMakesSetEmpty(simplex, checkCoeffs[0], false);
     expectInequalityMakesSetEmpty(simplex, checkCoeffs[1], false);
   }
 }
 
 Simplex simplexFromConstraints(unsigned nDim,
-                               SmallVector<SmallVector<int64_t, 8>, 8> ineqs,
-                               SmallVector<SmallVector<int64_t, 8>, 8> eqs) {
+                               ArrayRef<SmallVector<int64_t, 8>> ineqs,
+                               ArrayRef<SmallVector<int64_t, 8>> eqs) {
   Simplex simplex(nDim);
   for (const auto &ineq : ineqs)
     simplex.addInequality(ineq);
   for (const auto &eq : eqs)
     simplex.addEquality(eq);
   return simplex;
 }
 
 TEST(SimplexTest, isUnbounded) {

mlir/unittests/Analysis/PresburgerSetTest.cpp

static PresburgerSet parsePresburgerSetFromFACStrings(unsigned numDims,
   PresburgerSet set = PresburgerSet::getEmptySet(numDims);
   for (StringRef str : strs)
     set.unionFACInPlace(parseFAC(str, context));
   return set;
 }
 
 /// Compute the union of s and t, and check that each of the given points
 /// belongs to the union iff it belongs to at least one of s and t.
-static void testUnionAtPoints(PresburgerSet s, PresburgerSet t,
+static void testUnionAtPoints(const PresburgerSet &s, const PresburgerSet &t,
                               ArrayRef<SmallVector<int64_t, 4>> points) {
   PresburgerSet unionSet = s.unionSet(t);
   for (const SmallVector<int64_t, 4> &point : points) {
     bool inS = s.containsPoint(point);
     bool inT = t.containsPoint(point);
     bool inUnion = unionSet.containsPoint(point);
     EXPECT_EQ(inUnion, inS || inT);
   }
 }
 
 /// Compute the intersection of s and t, and check that each of the given points
 /// belongs to the intersection iff it belongs to both s and t.
-static void testIntersectAtPoints(PresburgerSet s, PresburgerSet t,
+static void testIntersectAtPoints(const PresburgerSet &s,
+                                  const PresburgerSet &t,
                                   ArrayRef<SmallVector<int64_t, 4>> points) {
   PresburgerSet intersection = s.intersect(t);
   for (const SmallVector<int64_t, 4> &point : points) {
     bool inS = s.containsPoint(point);
     bool inT = t.containsPoint(point);
     bool inIntersection = intersection.containsPoint(point);
     EXPECT_EQ(inIntersection, inS && inT);
   }
 }
 
 /// Compute the set difference s \ t, and check that each of the given points
 /// belongs to the difference iff it belongs to s and does not belong to t.
-static void testSubtractAtPoints(PresburgerSet s, PresburgerSet t,
+static void testSubtractAtPoints(const PresburgerSet &s, const PresburgerSet &t,
                                  ArrayRef<SmallVector<int64_t, 4>> points) {
   PresburgerSet diff = s.subtract(t);
   for (const SmallVector<int64_t, 4> &point : points) {
     bool inS = s.containsPoint(point);
     bool inT = t.containsPoint(point);
     bool inDiff = diff.containsPoint(point);
     if (inT)
       EXPECT_FALSE(inDiff);
     else
       EXPECT_EQ(inDiff, inS);
   }
 }
 
 /// Compute the complement of s, and check that each of the given points
 /// belongs to the complement iff it does not belong to s.
-static void testComplementAtPoints(PresburgerSet s,
+static void testComplementAtPoints(const PresburgerSet &s,
                                    ArrayRef<SmallVector<int64_t, 4>> points) {
   PresburgerSet complement = s.complement();
   complement.complement();
   for (const SmallVector<int64_t, 4> &point : points) {
     bool inS = s.containsPoint(point);
     bool inComplement = complement.containsPoint(point);
     if (inS)
       EXPECT_FALSE(inComplement);
TEST(SetTest, isEqual) {
   EXPECT_FALSE(universeSquare.isEqual(square));
   EXPECT_FALSE(rect.complement().isEqual(square.complement()));
 }
 
 void expectEqual(const PresburgerSet &s, const PresburgerSet &t) {
   EXPECT_TRUE(s.isEqual(t));
 }
 
-void expectEmpty(PresburgerSet s) { EXPECT_TRUE(s.isIntegerEmpty()); }
+void expectEmpty(const PresburgerSet &s) { EXPECT_TRUE(s.isIntegerEmpty()); }
 
 TEST(SetTest, divisions) {
   MLIRContext context;
 
   // evens = {x : exists q, x = 2q}.
   PresburgerSet evens{
       parseFAC("(x) : (x - 2 * (x floordiv 2) == 0)", &context)};
 
TEST(SetTest, divisions) {
   PresburgerSet setA{parseFAC("(x) : (-x >= 0)", &context)};
   PresburgerSet setB{parseFAC("(x) : (x floordiv 2 - 4 >= 0)", &context)};
   EXPECT_TRUE(setA.subtract(setB).isEqual(setA));
 }
 
 /// Coalesce `set` and check that the `newSet` is equal to `set and that
 /// `expectedNumFACs` matches the number of FACs in the coalesced set.
 /// If one of the two
-void expectCoalesce(size_t expectedNumFACs, const PresburgerSet set) {
+void expectCoalesce(size_t expectedNumFACs, const PresburgerSet &set) {
   PresburgerSet newSet = set.coalesce();
   EXPECT_TRUE(set.isEqual(newSet));
   EXPECT_TRUE(expectedNumFACs == newSet.getNumFACs());
 }
 
 TEST(SetTest, coalesceNoFAC) {
   PresburgerSet set = makeSetFromFACs(0, {});
   expectCoalesce(0, set);

mlir/unittests/Conversion/PDLToPDLInterp/RootOrderingTest.cpp

void createValues() {
     builder.setInsertionPointToStart(&block);
     for (int i = 0; i < 4; ++i)
       // Ops will be deleted when `block` is destroyed.
       v[i] = builder.create<ConstantIntOp>(builder.getUnknownLoc(), i, 32);
   }
 
   /// Checks that optimal branching on graph has the given cost and
   /// its preorder traversal results in the specified edges.
-  void check(unsigned cost, OptimalBranching::EdgeList edges) {
+  void check(unsigned cost, const OptimalBranching::EdgeList &edges) {
     OptimalBranching opt(graph, v[0]);
     EXPECT_EQ(opt.solve(), cost);
     EXPECT_EQ(opt.preOrderTraversal({v, v + edges.size()}), edges);
     for (std::pair<Value, Value> edge : edges)
       EXPECT_EQ(opt.getRootOrderingParents().lookup(edge.first), edge.second);
   }
 
 protected:

mlir/unittests/Dialect/SparseTensor/MergerTest.cpp

struct Pattern {
   /// Tensor operations point to their children.
   std::shared_ptr<Pattern> e0;
   std::shared_ptr<Pattern> e1;
 
   /// Constructors.
   /// Rather than using these, please use the readable helper constructor
   /// functions below to make tests more readable.
   Pattern(unsigned tensorNum) : kind(Kind::kTensor), tensorNum(tensorNum) {}
-  Pattern(Kind kind, std::shared_ptr<Pattern> e0, std::shared_ptr<Pattern> e1)
+  Pattern(Kind kind, const std::shared_ptr<Pattern> &e0,
+          const std::shared_ptr<Pattern> &e1)
       : kind(kind), e0(e0), e1(e1) {
     assert(kind >= Kind::kMulF);
     assert(e0 && e1);
   }
 };
 
 ///
 /// Readable Pattern builder functions.
 /// These should be preferred over the actual constructors.
 ///
 
 static std::shared_ptr<Pattern> tensorPattern(unsigned tensorNum) {
   return std::make_shared<Pattern>(tensorNum);
 }
 
-static std::shared_ptr<Pattern> addfPattern(std::shared_ptr<Pattern> e0,
-                                            std::shared_ptr<Pattern> e1) {
+static std::shared_ptr<Pattern>
+addfPattern(const std::shared_ptr<Pattern> &e0,
+            const std::shared_ptr<Pattern> &e1) {
   return std::make_shared<Pattern>(Kind::kAddF, e0, e1);
 }
 
-static std::shared_ptr<Pattern> mulfPattern(std::shared_ptr<Pattern> e0,
-                                            std::shared_ptr<Pattern> e1) {
+static std::shared_ptr<Pattern>
+mulfPattern(const std::shared_ptr<Pattern> &e0,
+            const std::shared_ptr<Pattern> &e1) {
   return std::make_shared<Pattern>(Kind::kMulF, e0, e1);
 }
 
 class MergerTestBase : public ::testing::Test {
 protected:
   MergerTestBase(unsigned numTensors, unsigned numLoops)
       : numTensors(numTensors), numLoops(numLoops),
         merger(numTensors, numLoops) {}
protected:
 
   /// Returns true if a lattice point with an expression matching the given
   /// pattern and bits matching the given bits is present in lattice points
   /// [p, p+n) of lattice set s. This is useful for testing partial ordering
   /// constraints between lattice points. We generally know how contiguous
   /// groups of lattice points should be ordered with respect to other groups,
   /// but there is no required ordering within groups.
   bool latPointWithinRange(unsigned s, unsigned p, unsigned n,
-                           std::shared_ptr<Pattern> pattern,
-                           llvm::BitVector bits) {
+                           const std::shared_ptr<Pattern> &pattern,
+                           const llvm::BitVector &bits) {
     for (unsigned i = p; i < p + n; ++i) {
       if (compareExpression(merger.lat(merger.set(s)[i]).exp, pattern) &&
           compareBits(s, i, bits))
         return true;
     }
     return false;
   }
 
   /// Wrapper over latPointWithinRange for readability of tests.
   void expectLatPointWithinRange(unsigned s, unsigned p, unsigned n,
                                  std::shared_ptr<Pattern> pattern,
                                  llvm::BitVector bits) {
     EXPECT_TRUE(latPointWithinRange(s, p, n, pattern, bits));
   }
 
   /// Wrapper over expectLatPointWithinRange for a single lat point.
   void expectLatPoint(unsigned s, unsigned p, std::shared_ptr<Pattern> pattern,
                       llvm::BitVector bits) {
     EXPECT_TRUE(latPointWithinRange(s, p, 1, pattern, bits));
   }
 
   /// Converts a vector of (loop, tensor) pairs to a bitvector with the
   /// corresponding bits set.
   llvm::BitVector
-  loopsToBits(std::vector<std::pair<unsigned, unsigned>> loops) {
+  loopsToBits(const std::vector<std::pair<unsigned, unsigned>> &loops) {
     llvm::BitVector testBits = llvm::BitVector(numTensors + 1, false);
     for (auto l : loops) {
       auto loop = std::get<0>(l);
       auto tensor = std::get<1>(l);
       testBits.set(numTensors * loop + tensor);
     }
     return testBits;
   }
 
   /// Returns true if the bits of lattice point p in set s match the given bits.
-  bool compareBits(unsigned s, unsigned p, llvm::BitVector bits) {
+  bool compareBits(unsigned s, unsigned p, const llvm::BitVector &bits) {
     return merger.lat(merger.set(s)[p]).bits == bits;
   }
 
   /// Check that there are n lattice points in set s.
   void expectNumLatPoints(unsigned s, unsigned n) {
     EXPECT_THAT(merger.set(s).size(), n);
   }
 
   /// Compares expressions for equality. Equality is defined recursively as:
   /// - Two expressions can only be equal if they have the same Kind.
   /// - Two binary expressions are equal if they have the same Kind and their
   ///     children are equal.
   /// - Expressions with Kind invariant or tensor are equal if they have the
   ///     same expression id.
-  bool compareExpression(unsigned e, std::shared_ptr<Pattern> pattern) {
+  bool compareExpression(unsigned e, const std::shared_ptr<Pattern> &pattern) {
     auto tensorExp = merger.exp(e);
     if (tensorExp.kind != pattern->kind)
       return false;
     assert(tensorExp.kind != Kind::kInvariant &&
            "Invariant comparison not yet supported");
     switch (tensorExp.kind) {
     case Kind::kTensor:
       return tensorExp.tensor == pattern->tensorNum;