diff --git a/mlir/examples/toy/Ch1/parser/AST.cpp b/mlir/examples/toy/Ch1/parser/AST.cpp
--- a/mlir/examples/toy/Ch1/parser/AST.cpp
+++ b/mlir/examples/toy/Ch1/parser/AST.cpp
@@ -118,7 +118,7 @@
 ///    <2,2>[<2>[ 1, 2 ], <2>[ 3, 4 ] ]
 void printLitHelper(ExprAST *litOrNum) {
   // Inside a literal expression we can have either a number or another literal
-  if (auto num = llvm::dyn_cast<NumberExprAST>(litOrNum)) {
+  if (auto *num = llvm::dyn_cast<NumberExprAST>(litOrNum)) {
     llvm::errs() << num->getValue();
     return;
   }
diff --git a/mlir/examples/toy/Ch1/toyc.cpp b/mlir/examples/toy/Ch1/toyc.cpp
--- a/mlir/examples/toy/Ch1/toyc.cpp
+++ b/mlir/examples/toy/Ch1/toyc.cpp
@@ -27,7 +27,7 @@
                                           cl::value_desc("filename"));
 namespace {
 enum Action { None, DumpAST };
-}
+} // namespace
 
 static cl::opt<enum Action>
     emitAction("emit", cl::desc("Select the kind of output desired"),
diff --git a/mlir/examples/toy/Ch2/mlir/MLIRGen.cpp b/mlir/examples/toy/Ch2/mlir/MLIRGen.cpp
--- a/mlir/examples/toy/Ch2/mlir/MLIRGen.cpp
+++ b/mlir/examples/toy/Ch2/mlir/MLIRGen.cpp
@@ -58,8 +58,8 @@
     // add them to the module.
     theModule = mlir::ModuleOp::create(builder.getUnknownLoc());
 
-    for (FunctionAST &F : moduleAST) {
-      auto func = mlirGen(F);
+    for (FunctionAST &f : moduleAST) {
+      auto func = mlirGen(f);
       if (!func)
         return nullptr;
       theModule.push_back(func);
@@ -113,16 +113,16 @@
 
     // This is a generic function, the return type will be inferred later.
     // Arguments type are uniformly unranked tensors.
-    llvm::SmallVector<mlir::Type, 4> arg_types(proto.getArgs().size(),
-                                               getType(VarType{}));
-    auto func_type = builder.getFunctionType(arg_types, llvm::None);
-    return mlir::FuncOp::create(location, proto.getName(), func_type);
+    llvm::SmallVector<mlir::Type, 4> argTypes(proto.getArgs().size(),
+                                              getType(VarType{}));
+    auto funcType = builder.getFunctionType(argTypes, llvm::None);
+    return mlir::FuncOp::create(location, proto.getName(), funcType);
   }
 
   /// Emit a new function and add it to the MLIR module.
   mlir::FuncOp mlirGen(FunctionAST &funcAST) {
     // Create a scope in the symbol table to hold variable declarations.
-    ScopedHashTableScope<llvm::StringRef, mlir::Value> var_scope(symbolTable);
+    ScopedHashTableScope<llvm::StringRef, mlir::Value> varScope(symbolTable);
 
     // Create an MLIR function for the given prototype.
     mlir::FuncOp function(mlirGen(*funcAST.getProto()));
@@ -371,7 +371,7 @@
   /// Future expressions will be able to reference this variable through symbol
   /// table lookup.
   mlir::Value mlirGen(VarDeclExprAST &vardecl) {
-    auto init = vardecl.getInitVal();
+    auto *init = vardecl.getInitVal();
     if (!init) {
       emitError(loc(vardecl.loc()),
                 "missing initializer in variable declaration");
@@ -398,7 +398,7 @@
 
   /// Codegen a list of expression, return failure if one of them hit an error.
   mlir::LogicalResult mlirGen(ExprASTList &blockAST) {
-    ScopedHashTableScope<StringRef, mlir::Value> var_scope(symbolTable);
+    ScopedHashTableScope<StringRef, mlir::Value> varScope(symbolTable);
     for (auto &expr : blockAST) {
       // Specific handling for variable declarations, return statement, and
       // print. These can only appear in block list and not in nested
diff --git a/mlir/examples/toy/Ch2/parser/AST.cpp b/mlir/examples/toy/Ch2/parser/AST.cpp
--- a/mlir/examples/toy/Ch2/parser/AST.cpp
+++ b/mlir/examples/toy/Ch2/parser/AST.cpp
@@ -118,7 +118,7 @@
 ///    <2,2>[<2>[ 1, 2 ], <2>[ 3, 4 ] ]
 void printLitHelper(ExprAST *litOrNum) {
   // Inside a literal expression we can have either a number or another literal
-  if (auto num = llvm::dyn_cast<NumberExprAST>(litOrNum)) {
+  if (auto *num = llvm::dyn_cast<NumberExprAST>(litOrNum)) {
     llvm::errs() << num->getValue();
     return;
   }
diff --git a/mlir/examples/toy/Ch2/toyc.cpp b/mlir/examples/toy/Ch2/toyc.cpp
--- a/mlir/examples/toy/Ch2/toyc.cpp
+++ b/mlir/examples/toy/Ch2/toyc.cpp
@@ -38,7 +38,7 @@
 
 namespace {
 enum InputType { Toy, MLIR };
-}
+} // namespace
 static cl::opt<enum InputType> inputType(
     "x", cl::init(Toy), cl::desc("Decided the kind of output desired"),
     cl::values(clEnumValN(Toy, "toy", "load the input file as a Toy source.")),
@@ -47,7 +47,7 @@
 
 namespace {
 enum Action { None, DumpAST, DumpMLIR };
-}
+} // namespace
 static cl::opt<enum Action> emitAction(
     "emit", cl::desc("Select the kind of output desired"),
     cl::values(clEnumValN(DumpAST, "ast", "output the AST dump")),
@@ -89,8 +89,8 @@
   // Otherwise, the input is '.mlir'.
   llvm::ErrorOr<std::unique_ptr<llvm::MemoryBuffer>> fileOrErr =
       llvm::MemoryBuffer::getFileOrSTDIN(inputFilename);
-  if (std::error_code EC = fileOrErr.getError()) {
-    llvm::errs() << "Could not open input file: " << EC.message() << "\n";
+  if (std::error_code ec = fileOrErr.getError()) {
+    llvm::errs() << "Could not open input file: " << ec.message() << "\n";
     return -1;
   }
 
diff --git a/mlir/examples/toy/Ch3/mlir/MLIRGen.cpp b/mlir/examples/toy/Ch3/mlir/MLIRGen.cpp
--- a/mlir/examples/toy/Ch3/mlir/MLIRGen.cpp
+++ b/mlir/examples/toy/Ch3/mlir/MLIRGen.cpp
@@ -58,8 +58,8 @@
     // add them to the module.
     theModule = mlir::ModuleOp::create(builder.getUnknownLoc());
 
-    for (FunctionAST &F : moduleAST) {
-      auto func = mlirGen(F);
+    for (FunctionAST &f : moduleAST) {
+      auto func = mlirGen(f);
       if (!func)
         return nullptr;
       theModule.push_back(func);
@@ -113,16 +113,16 @@
 
     // This is a generic function, the return type will be inferred later.
     // Arguments type are uniformly unranked tensors.
-    llvm::SmallVector<mlir::Type, 4> arg_types(proto.getArgs().size(),
-                                               getType(VarType{}));
-    auto func_type = builder.getFunctionType(arg_types, llvm::None);
-    return mlir::FuncOp::create(location, proto.getName(), func_type);
+    llvm::SmallVector<mlir::Type, 4> argTypes(proto.getArgs().size(),
+                                              getType(VarType{}));
+    auto funcType = builder.getFunctionType(argTypes, llvm::None);
+    return mlir::FuncOp::create(location, proto.getName(), funcType);
   }
 
   /// Emit a new function and add it to the MLIR module.
   mlir::FuncOp mlirGen(FunctionAST &funcAST) {
     // Create a scope in the symbol table to hold variable declarations.
-    ScopedHashTableScope<llvm::StringRef, mlir::Value> var_scope(symbolTable);
+    ScopedHashTableScope<llvm::StringRef, mlir::Value> varScope(symbolTable);
 
     // Create an MLIR function for the given prototype.
     mlir::FuncOp function(mlirGen(*funcAST.getProto()));
@@ -371,7 +371,7 @@
   /// Future expressions will be able to reference this variable through symbol
   /// table lookup.
   mlir::Value mlirGen(VarDeclExprAST &vardecl) {
-    auto init = vardecl.getInitVal();
+    auto *init = vardecl.getInitVal();
     if (!init) {
       emitError(loc(vardecl.loc()),
                 "missing initializer in variable declaration");
@@ -398,7 +398,7 @@
 
   /// Codegen a list of expression, return failure if one of them hit an error.
   mlir::LogicalResult mlirGen(ExprASTList &blockAST) {
-    ScopedHashTableScope<StringRef, mlir::Value> var_scope(symbolTable);
+    ScopedHashTableScope<StringRef, mlir::Value> varScope(symbolTable);
     for (auto &expr : blockAST) {
       // Specific handling for variable declarations, return statement, and
       // print. These can only appear in block list and not in nested
diff --git a/mlir/examples/toy/Ch3/parser/AST.cpp b/mlir/examples/toy/Ch3/parser/AST.cpp
--- a/mlir/examples/toy/Ch3/parser/AST.cpp
+++ b/mlir/examples/toy/Ch3/parser/AST.cpp
@@ -118,7 +118,7 @@
 ///    <2,2>[<2>[ 1, 2 ], <2>[ 3, 4 ] ]
 void printLitHelper(ExprAST *litOrNum) {
   // Inside a literal expression we can have either a number or another literal
-  if (auto num = llvm::dyn_cast<NumberExprAST>(litOrNum)) {
+  if (auto *num = llvm::dyn_cast<NumberExprAST>(litOrNum)) {
     llvm::errs() << num->getValue();
     return;
   }
diff --git a/mlir/examples/toy/Ch3/toyc.cpp b/mlir/examples/toy/Ch3/toyc.cpp
--- a/mlir/examples/toy/Ch3/toyc.cpp
+++ b/mlir/examples/toy/Ch3/toyc.cpp
@@ -40,7 +40,7 @@
 
 namespace {
 enum InputType { Toy, MLIR };
-}
+} // namespace
 static cl::opt<enum InputType> inputType(
     "x", cl::init(Toy), cl::desc("Decided the kind of output desired"),
     cl::values(clEnumValN(Toy, "toy", "load the input file as a Toy source.")),
@@ -49,7 +49,7 @@
 
 namespace {
 enum Action { None, DumpAST, DumpMLIR };
-}
+} // namespace
 static cl::opt<enum Action> emitAction(
     "emit", cl::desc("Select the kind of output desired"),
     cl::values(clEnumValN(DumpAST, "ast", "output the AST dump")),
@@ -86,8 +86,8 @@
   // Otherwise, the input is '.mlir'.
   llvm::ErrorOr<std::unique_ptr<llvm::MemoryBuffer>> fileOrErr =
       llvm::MemoryBuffer::getFileOrSTDIN(inputFilename);
-  if (std::error_code EC = fileOrErr.getError()) {
-    llvm::errs() << "Could not open input file: " << EC.message() << "\n";
+  if (std::error_code ec = fileOrErr.getError()) {
+    llvm::errs() << "Could not open input file: " << ec.message() << "\n";
     return -1;
   }
 
diff --git a/mlir/examples/toy/Ch4/mlir/MLIRGen.cpp b/mlir/examples/toy/Ch4/mlir/MLIRGen.cpp
--- a/mlir/examples/toy/Ch4/mlir/MLIRGen.cpp
+++ b/mlir/examples/toy/Ch4/mlir/MLIRGen.cpp
@@ -58,8 +58,8 @@
     // add them to the module.
     theModule = mlir::ModuleOp::create(builder.getUnknownLoc());
 
-    for (FunctionAST &F : moduleAST) {
-      auto func = mlirGen(F);
+    for (FunctionAST &f : moduleAST) {
+      auto func = mlirGen(f);
       if (!func)
         return nullptr;
       theModule.push_back(func);
@@ -113,16 +113,16 @@
 
     // This is a generic function, the return type will be inferred later.
     // Arguments type are uniformly unranked tensors.
-    llvm::SmallVector<mlir::Type, 4> arg_types(proto.getArgs().size(),
-                                               getType(VarType{}));
-    auto func_type = builder.getFunctionType(arg_types, llvm::None);
-    return mlir::FuncOp::create(location, proto.getName(), func_type);
+    llvm::SmallVector<mlir::Type, 4> argTypes(proto.getArgs().size(),
+                                              getType(VarType{}));
+    auto funcType = builder.getFunctionType(argTypes, llvm::None);
+    return mlir::FuncOp::create(location, proto.getName(), funcType);
   }
 
   /// Emit a new function and add it to the MLIR module.
   mlir::FuncOp mlirGen(FunctionAST &funcAST) {
     // Create a scope in the symbol table to hold variable declarations.
-    ScopedHashTableScope<llvm::StringRef, mlir::Value> var_scope(symbolTable);
+    ScopedHashTableScope<llvm::StringRef, mlir::Value> varScope(symbolTable);
 
     // Create an MLIR function for the given prototype.
     mlir::FuncOp function(mlirGen(*funcAST.getProto()));
@@ -375,7 +375,7 @@
   /// Future expressions will be able to reference this variable through symbol
   /// table lookup.
   mlir::Value mlirGen(VarDeclExprAST &vardecl) {
-    auto init = vardecl.getInitVal();
+    auto *init = vardecl.getInitVal();
     if (!init) {
       emitError(loc(vardecl.loc()),
                 "missing initializer in variable declaration");
@@ -402,7 +402,7 @@
 
   /// Codegen a list of expression, return failure if one of them hit an error.
   mlir::LogicalResult mlirGen(ExprASTList &blockAST) {
-    ScopedHashTableScope<StringRef, mlir::Value> var_scope(symbolTable);
+    ScopedHashTableScope<StringRef, mlir::Value> varScope(symbolTable);
     for (auto &expr : blockAST) {
       // Specific handling for variable declarations, return statement, and
       // print. These can only appear in block list and not in nested
diff --git a/mlir/examples/toy/Ch4/parser/AST.cpp b/mlir/examples/toy/Ch4/parser/AST.cpp
--- a/mlir/examples/toy/Ch4/parser/AST.cpp
+++ b/mlir/examples/toy/Ch4/parser/AST.cpp
@@ -118,7 +118,7 @@
 ///    <2,2>[<2>[ 1, 2 ], <2>[ 3, 4 ] ]
 void printLitHelper(ExprAST *litOrNum) {
   // Inside a literal expression we can have either a number or another literal
-  if (auto num = llvm::dyn_cast<NumberExprAST>(litOrNum)) {
+  if (auto *num = llvm::dyn_cast<NumberExprAST>(litOrNum)) {
     llvm::errs() << num->getValue();
     return;
   }
diff --git a/mlir/examples/toy/Ch4/toyc.cpp b/mlir/examples/toy/Ch4/toyc.cpp
--- a/mlir/examples/toy/Ch4/toyc.cpp
+++ b/mlir/examples/toy/Ch4/toyc.cpp
@@ -41,7 +41,7 @@
 
 namespace {
 enum InputType { Toy, MLIR };
-}
+} // namespace
 static cl::opt<enum InputType> inputType(
     "x", cl::init(Toy), cl::desc("Decided the kind of output desired"),
     cl::values(clEnumValN(Toy, "toy", "load the input file as a Toy source.")),
@@ -50,7 +50,7 @@
 
 namespace {
 enum Action { None, DumpAST, DumpMLIR };
-}
+} // namespace
 static cl::opt<enum Action> emitAction(
     "emit", cl::desc("Select the kind of output desired"),
     cl::values(clEnumValN(DumpAST, "ast", "output the AST dump")),
@@ -87,8 +87,8 @@
   // Otherwise, the input is '.mlir'.
   llvm::ErrorOr<std::unique_ptr<llvm::MemoryBuffer>> fileOrErr =
       llvm::MemoryBuffer::getFileOrSTDIN(inputFilename);
-  if (std::error_code EC = fileOrErr.getError()) {
-    llvm::errs() << "Could not open input file: " << EC.message() << "\n";
+  if (std::error_code ec = fileOrErr.getError()) {
+    llvm::errs() << "Could not open input file: " << ec.message() << "\n";
     return -1;
   }
 
diff --git a/mlir/examples/toy/Ch5/mlir/MLIRGen.cpp b/mlir/examples/toy/Ch5/mlir/MLIRGen.cpp
--- a/mlir/examples/toy/Ch5/mlir/MLIRGen.cpp
+++ b/mlir/examples/toy/Ch5/mlir/MLIRGen.cpp
@@ -58,8 +58,8 @@
     // add them to the module.
     theModule = mlir::ModuleOp::create(builder.getUnknownLoc());
 
-    for (FunctionAST &F : moduleAST) {
-      auto func = mlirGen(F);
+    for (FunctionAST &f : moduleAST) {
+      auto func = mlirGen(f);
       if (!func)
         return nullptr;
       theModule.push_back(func);
@@ -113,16 +113,16 @@
 
     // This is a generic function, the return type will be inferred later.
     // Arguments type are uniformly unranked tensors.
-    llvm::SmallVector<mlir::Type, 4> arg_types(proto.getArgs().size(),
-                                               getType(VarType{}));
-    auto func_type = builder.getFunctionType(arg_types, llvm::None);
-    return mlir::FuncOp::create(location, proto.getName(), func_type);
+    llvm::SmallVector<mlir::Type, 4> argTypes(proto.getArgs().size(),
+                                              getType(VarType{}));
+    auto funcType = builder.getFunctionType(argTypes, llvm::None);
+    return mlir::FuncOp::create(location, proto.getName(), funcType);
   }
 
   /// Emit a new function and add it to the MLIR module.
   mlir::FuncOp mlirGen(FunctionAST &funcAST) {
     // Create a scope in the symbol table to hold variable declarations.
-    ScopedHashTableScope<llvm::StringRef, mlir::Value> var_scope(symbolTable);
+    ScopedHashTableScope<llvm::StringRef, mlir::Value> varScope(symbolTable);
 
     // Create an MLIR function for the given prototype.
     mlir::FuncOp function(mlirGen(*funcAST.getProto()));
@@ -375,7 +375,7 @@
   /// Future expressions will be able to reference this variable through symbol
   /// table lookup.
   mlir::Value mlirGen(VarDeclExprAST &vardecl) {
-    auto init = vardecl.getInitVal();
+    auto *init = vardecl.getInitVal();
     if (!init) {
       emitError(loc(vardecl.loc()),
                 "missing initializer in variable declaration");
@@ -402,7 +402,7 @@
 
   /// Codegen a list of expression, return failure if one of them hit an error.
   mlir::LogicalResult mlirGen(ExprASTList &blockAST) {
-    ScopedHashTableScope<StringRef, mlir::Value> var_scope(symbolTable);
+    ScopedHashTableScope<StringRef, mlir::Value> varScope(symbolTable);
     for (auto &expr : blockAST) {
       // Specific handling for variable declarations, return statement, and
       // print. These can only appear in block list and not in nested
diff --git a/mlir/examples/toy/Ch5/parser/AST.cpp b/mlir/examples/toy/Ch5/parser/AST.cpp
--- a/mlir/examples/toy/Ch5/parser/AST.cpp
+++ b/mlir/examples/toy/Ch5/parser/AST.cpp
@@ -118,7 +118,7 @@
 ///    <2,2>[<2>[ 1, 2 ], <2>[ 3, 4 ] ]
 void printLitHelper(ExprAST *litOrNum) {
   // Inside a literal expression we can have either a number or another literal
-  if (auto num = llvm::dyn_cast<NumberExprAST>(litOrNum)) {
+  if (auto *num = llvm::dyn_cast<NumberExprAST>(litOrNum)) {
     llvm::errs() << num->getValue();
     return;
   }
diff --git a/mlir/examples/toy/Ch5/toyc.cpp b/mlir/examples/toy/Ch5/toyc.cpp
--- a/mlir/examples/toy/Ch5/toyc.cpp
+++ b/mlir/examples/toy/Ch5/toyc.cpp
@@ -43,7 +43,7 @@
 
 namespace {
 enum InputType { Toy, MLIR };
-}
+} // namespace
 static cl::opt<enum InputType> inputType(
     "x", cl::init(Toy), cl::desc("Decided the kind of output desired"),
     cl::values(clEnumValN(Toy, "toy", "load the input file as a Toy source.")),
@@ -52,7 +52,7 @@
 
 namespace {
 enum Action { None, DumpAST, DumpMLIR, DumpMLIRAffine };
-}
+} // namespace
 static cl::opt<enum Action> emitAction(
     "emit", cl::desc("Select the kind of output desired"),
     cl::values(clEnumValN(DumpAST, "ast", "output the AST dump")),
@@ -91,8 +91,8 @@
   // Otherwise, the input is '.mlir'.
   llvm::ErrorOr<std::unique_ptr<llvm::MemoryBuffer>> fileOrErr =
       llvm::MemoryBuffer::getFileOrSTDIN(inputFilename);
-  if (std::error_code EC = fileOrErr.getError()) {
-    llvm::errs() << "Could not open input file: " << EC.message() << "\n";
+  if (std::error_code ec = fileOrErr.getError()) {
+    llvm::errs() << "Could not open input file: " << ec.message() << "\n";
     return -1;
   }
 
diff --git a/mlir/examples/toy/Ch6/mlir/MLIRGen.cpp b/mlir/examples/toy/Ch6/mlir/MLIRGen.cpp
--- a/mlir/examples/toy/Ch6/mlir/MLIRGen.cpp
+++ b/mlir/examples/toy/Ch6/mlir/MLIRGen.cpp
@@ -58,8 +58,8 @@
     // add them to the module.
     theModule = mlir::ModuleOp::create(builder.getUnknownLoc());
 
-    for (FunctionAST &F : moduleAST) {
-      auto func = mlirGen(F);
+    for (FunctionAST &f : moduleAST) {
+      auto func = mlirGen(f);
       if (!func)
         return nullptr;
       theModule.push_back(func);
@@ -113,16 +113,16 @@
 
     // This is a generic function, the return type will be inferred later.
     // Arguments type are uniformly unranked tensors.
-    llvm::SmallVector<mlir::Type, 4> arg_types(proto.getArgs().size(),
-                                               getType(VarType{}));
-    auto func_type = builder.getFunctionType(arg_types, llvm::None);
-    return mlir::FuncOp::create(location, proto.getName(), func_type);
+    llvm::SmallVector<mlir::Type, 4> argTypes(proto.getArgs().size(),
+                                              getType(VarType{}));
+    auto funcType = builder.getFunctionType(argTypes, llvm::None);
+    return mlir::FuncOp::create(location, proto.getName(), funcType);
   }
 
   /// Emit a new function and add it to the MLIR module.
   mlir::FuncOp mlirGen(FunctionAST &funcAST) {
     // Create a scope in the symbol table to hold variable declarations.
-    ScopedHashTableScope<llvm::StringRef, mlir::Value> var_scope(symbolTable);
+    ScopedHashTableScope<llvm::StringRef, mlir::Value> varScope(symbolTable);
 
     // Create an MLIR function for the given prototype.
     mlir::FuncOp function(mlirGen(*funcAST.getProto()));
@@ -375,7 +375,7 @@
   /// Future expressions will be able to reference this variable through symbol
   /// table lookup.
   mlir::Value mlirGen(VarDeclExprAST &vardecl) {
-    auto init = vardecl.getInitVal();
+    auto *init = vardecl.getInitVal();
     if (!init) {
       emitError(loc(vardecl.loc()),
                 "missing initializer in variable declaration");
@@ -402,7 +402,7 @@
 
   /// Codegen a list of expression, return failure if one of them hit an error.
   mlir::LogicalResult mlirGen(ExprASTList &blockAST) {
-    ScopedHashTableScope<StringRef, mlir::Value> var_scope(symbolTable);
+    ScopedHashTableScope<StringRef, mlir::Value> varScope(symbolTable);
     for (auto &expr : blockAST) {
       // Specific handling for variable declarations, return statement, and
       // print. These can only appear in block list and not in nested
diff --git a/mlir/examples/toy/Ch6/parser/AST.cpp b/mlir/examples/toy/Ch6/parser/AST.cpp
--- a/mlir/examples/toy/Ch6/parser/AST.cpp
+++ b/mlir/examples/toy/Ch6/parser/AST.cpp
@@ -118,7 +118,7 @@
 ///    <2,2>[<2>[ 1, 2 ], <2>[ 3, 4 ] ]
 void printLitHelper(ExprAST *litOrNum) {
   // Inside a literal expression we can have either a number or another literal
-  if (auto num = llvm::dyn_cast<NumberExprAST>(litOrNum)) {
+  if (auto *num = llvm::dyn_cast<NumberExprAST>(litOrNum)) {
     llvm::errs() << num->getValue();
     return;
   }
diff --git a/mlir/examples/toy/Ch6/toyc.cpp b/mlir/examples/toy/Ch6/toyc.cpp
--- a/mlir/examples/toy/Ch6/toyc.cpp
+++ b/mlir/examples/toy/Ch6/toyc.cpp
@@ -49,7 +49,7 @@
 
 namespace {
 enum InputType { Toy, MLIR };
-}
+} // namespace
 static cl::opt<enum InputType> inputType(
     "x", cl::init(Toy), cl::desc("Decided the kind of output desired"),
     cl::values(clEnumValN(Toy, "toy", "load the input file as a Toy source.")),
@@ -66,7 +66,7 @@
   DumpLLVMIR,
   RunJIT
 };
-}
+} // namespace
 static cl::opt<enum Action> emitAction(
     "emit", cl::desc("Select the kind of output desired"),
     cl::values(clEnumValN(DumpAST, "ast", "output the AST dump")),
@@ -110,8 +110,8 @@
   // Otherwise, the input is '.mlir'.
   llvm::ErrorOr<std::unique_ptr<llvm::MemoryBuffer>> fileOrErr =
       llvm::MemoryBuffer::getFileOrSTDIN(inputFilename);
-  if (std::error_code EC = fileOrErr.getError()) {
-    llvm::errs() << "Could not open input file: " << EC.message() << "\n";
+  if (std::error_code ec = fileOrErr.getError()) {
+    llvm::errs() << "Could not open input file: " << ec.message() << "\n";
     return -1;
   }
 
diff --git a/mlir/examples/toy/Ch7/mlir/MLIRGen.cpp b/mlir/examples/toy/Ch7/mlir/MLIRGen.cpp
--- a/mlir/examples/toy/Ch7/mlir/MLIRGen.cpp
+++ b/mlir/examples/toy/Ch7/mlir/MLIRGen.cpp
@@ -169,14 +169,14 @@
         return nullptr;
       argTypes.push_back(type);
     }
-    auto func_type = builder.getFunctionType(argTypes, llvm::None);
-    return mlir::FuncOp::create(location, proto.getName(), func_type);
+    auto funcType = builder.getFunctionType(argTypes, llvm::None);
+    return mlir::FuncOp::create(location, proto.getName(), funcType);
   }
 
   /// Emit a new function and add it to the MLIR module.
   mlir::FuncOp mlirGen(FunctionAST &funcAST) {
     // Create a scope in the symbol table to hold variable declarations.
-    SymbolTableScopeT var_scope(symbolTable);
+    SymbolTableScopeT varScope(symbolTable);
 
     // Create an MLIR function for the given prototype.
     mlir::FuncOp function(mlirGen(*funcAST.getProto()));
@@ -286,7 +286,7 @@
       return llvm::None;
 
     auto structVars = structAST->getVariables();
-    auto it = llvm::find_if(structVars, [&](auto &var) {
+    const auto *it = llvm::find_if(structVars, [&](auto &var) {
       return var->getName() == name->getName();
     });
     if (it == structVars.end())
@@ -569,7 +569,7 @@
   /// Future expressions will be able to reference this variable through symbol
   /// table lookup.
   mlir::Value mlirGen(VarDeclExprAST &vardecl) {
-    auto init = vardecl.getInitVal();
+    auto *init = vardecl.getInitVal();
     if (!init) {
       emitError(loc(vardecl.loc()),
                 "missing initializer in variable declaration");
@@ -612,7 +612,7 @@
 
   /// Codegen a list of expression, return failure if one of them hit an error.
   mlir::LogicalResult mlirGen(ExprASTList &blockAST) {
-    SymbolTableScopeT var_scope(symbolTable);
+    SymbolTableScopeT varScope(symbolTable);
     for (auto &expr : blockAST) {
       // Specific handling for variable declarations, return statement, and
       // print. These can only appear in block list and not in nested
diff --git a/mlir/examples/toy/Ch7/parser/AST.cpp b/mlir/examples/toy/Ch7/parser/AST.cpp
--- a/mlir/examples/toy/Ch7/parser/AST.cpp
+++ b/mlir/examples/toy/Ch7/parser/AST.cpp
@@ -121,7 +121,7 @@
 ///    <2,2>[<2>[ 1, 2 ], <2>[ 3, 4 ] ]
 void printLitHelper(ExprAST *litOrNum) {
   // Inside a literal expression we can have either a number or another literal
-  if (auto num = llvm::dyn_cast<NumberExprAST>(litOrNum)) {
+  if (auto *num = llvm::dyn_cast<NumberExprAST>(litOrNum)) {
     llvm::errs() << num->getValue();
     return;
   }
diff --git a/mlir/examples/toy/Ch7/toyc.cpp b/mlir/examples/toy/Ch7/toyc.cpp
--- a/mlir/examples/toy/Ch7/toyc.cpp
+++ b/mlir/examples/toy/Ch7/toyc.cpp
@@ -49,7 +49,7 @@
 
 namespace {
 enum InputType { Toy, MLIR };
-}
+} // namespace
 static cl::opt<enum InputType> inputType(
     "x", cl::init(Toy), cl::desc("Decided the kind of output desired"),
     cl::values(clEnumValN(Toy, "toy", "load the input file as a Toy source.")),
@@ -66,7 +66,7 @@
   DumpLLVMIR,
   RunJIT
 };
-}
+} // namespace
 static cl::opt<enum Action> emitAction(
     "emit", cl::desc("Select the kind of output desired"),
     cl::values(clEnumValN(DumpAST, "ast", "output the AST dump")),
@@ -110,8 +110,8 @@
   // Otherwise, the input is '.mlir'.
   llvm::ErrorOr<std::unique_ptr<llvm::MemoryBuffer>> fileOrErr =
       llvm::MemoryBuffer::getFileOrSTDIN(inputFilename);
-  if (std::error_code EC = fileOrErr.getError()) {
-    llvm::errs() << "Could not open input file: " << EC.message() << "\n";
+  if (std::error_code ec = fileOrErr.getError()) {
+    llvm::errs() << "Could not open input file: " << ec.message() << "\n";
     return -1;
   }
 
diff --git a/mlir/lib/Dialect/LLVMIR/IR/LLVMTypes.cpp b/mlir/lib/Dialect/LLVMIR/IR/LLVMTypes.cpp
--- a/mlir/lib/Dialect/LLVMIR/IR/LLVMTypes.cpp
+++ b/mlir/lib/Dialect/LLVMIR/IR/LLVMTypes.cpp
@@ -266,13 +266,14 @@
     unsigned size = kDefaultPointerSizeBits;
     unsigned abi = kDefaultPointerAlignment;
     auto newType = newEntry.getKey().get<Type>().cast<LLVMPointerType>();
-    auto it = llvm::find_if(oldLayout, [&](DataLayoutEntryInterface entry) {
-      if (auto type = entry.getKey().dyn_cast<Type>()) {
-        return type.cast<LLVMPointerType>().getAddressSpace() ==
-               newType.getAddressSpace();
-      }
-      return false;
-    });
+    const auto *it =
+        llvm::find_if(oldLayout, [&](DataLayoutEntryInterface entry) {
+          if (auto type = entry.getKey().dyn_cast<Type>()) {
+            return type.cast<LLVMPointerType>().getAddressSpace() ==
+                   newType.getAddressSpace();
+          }
+          return false;
+        });
     if (it == oldLayout.end()) {
       llvm::find_if(oldLayout, [&](DataLayoutEntryInterface entry) {
         if (auto type = entry.getKey().dyn_cast<Type>()) {
@@ -440,14 +441,15 @@
 
 namespace {
 enum class StructDLEntryPos { Abi = 0, Preferred = 1 };
-}
+} // namespace
 
 static Optional<unsigned>
 getStructDataLayoutEntry(DataLayoutEntryListRef params, LLVMStructType type,
                          StructDLEntryPos pos) {
-  auto currentEntry = llvm::find_if(params, [](DataLayoutEntryInterface entry) {
-    return entry.isTypeEntry();
-  });
+  const auto *currentEntry =
+      llvm::find_if(params, [](DataLayoutEntryInterface entry) {
+        return entry.isTypeEntry();
+      });
   if (currentEntry == params.end())
     return llvm::None;
 
@@ -509,7 +511,7 @@
     if (!newEntry.isTypeEntry())
       continue;
 
-    auto previousEntry =
+    const auto *previousEntry =
         llvm::find_if(oldLayout, [](DataLayoutEntryInterface entry) {
           return entry.isTypeEntry();
         });
diff --git a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
--- a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
+++ b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
@@ -1829,12 +1829,12 @@
     return failure();
 
   // Parse input tensors.
-  SmallVector<OpAsmParser::OperandType, 4> inputs, input_region_args;
+  SmallVector<OpAsmParser::OperandType, 4> inputs, inputRegionArgs;
   SmallVector<Type, 4> inputTypes;
   if (succeeded(parser.parseOptionalKeyword("ins"))) {
     llvm::SMLoc inputsOperandsLoc = parser.getCurrentLocation();
 
-    if (parser.parseAssignmentListWithTypes(input_region_args, inputs,
+    if (parser.parseAssignmentListWithTypes(inputRegionArgs, inputs,
                                             inputTypes))
       return failure();
 
@@ -1844,12 +1844,12 @@
   }
 
   // Parse output tensors.
-  SmallVector<OpAsmParser::OperandType, 4> outputs, output_region_args;
+  SmallVector<OpAsmParser::OperandType, 4> outputs, outputRegionArgs;
   SmallVector<Type, 4> outputTypes;
   if (succeeded(parser.parseOptionalKeyword("outs"))) {
     llvm::SMLoc outputsOperandsLoc = parser.getCurrentLocation();
 
-    if (parser.parseAssignmentListWithTypes(output_region_args, outputs,
+    if (parser.parseAssignmentListWithTypes(outputRegionArgs, outputs,
                                             outputTypes))
       return failure();
 
@@ -1905,15 +1905,15 @@
   // Parse the body.
   Region *body = result.addRegion();
 
-  SmallVector<Type, 4> region_types(ivs.size(), builder.getIndexType());
-  region_types.append(inputTypes);
-  region_types.append(outputTypes);
+  SmallVector<Type, 4> regionTypes(ivs.size(), builder.getIndexType());
+  regionTypes.append(inputTypes);
+  regionTypes.append(outputTypes);
 
-  SmallVector<OpAsmParser::OperandType, 4> region_args(ivs);
-  region_args.append(input_region_args);
-  region_args.append(output_region_args);
+  SmallVector<OpAsmParser::OperandType, 4> regionArgs(ivs);
+  regionArgs.append(inputRegionArgs);
+  regionArgs.append(outputRegionArgs);
 
-  if (parser.parseRegion(*body, region_args, region_types))
+  if (parser.parseRegion(*body, regionArgs, regionTypes))
     return failure();
 
   // Parse optional attributes.
diff --git a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
--- a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
@@ -1426,9 +1426,9 @@
 ///   Layout: {{n, strideW * w + dilationW * kw, c}, {kw, c}, {n, w, c}}
 /// ```
 /// kw is unrolled, w is unrolled iff dilationW > 1.
-struct Conv1D_NWC_Generator : public StructuredGenerator<LinalgOp> {
-  Conv1D_NWC_Generator(OpBuilder &builder, LinalgOp linalgOp, int strideW,
-                       int dilationW)
+struct Conv1DNwcGenerator : public StructuredGenerator<LinalgOp> {
+  Conv1DNwcGenerator(OpBuilder &builder, LinalgOp linalgOp, int strideW,
+                     int dilationW)
       : StructuredGenerator<LinalgOp>(builder, linalgOp), valid(false),
         strideW(strideW), dilationW(dilationW) {
     // Determine whether `linalgOp` can be generated with this generator
@@ -1594,7 +1594,7 @@
   /// ```
   /// kw is always unrolled.
   /// TODO: w (resp. kw) is unrolled when the strideW ( resp. dilationW) is > 1.
-  FailureOr<Operation *> dilated_conv() {
+  FailureOr<Operation *> dilatedConv() {
     if (!valid)
       return failure();
 
@@ -1730,7 +1730,7 @@
     if (layout({/*lhsIndex*/ {n, strideW * w + dilationW * kw, c},
                 /*rhsIndex*/ {kw, c},
                 /*resIndex*/ {n, w, c}}))
-      return dilated_conv();
+      return dilatedConv();
     return failure();
   }
 
@@ -1752,7 +1752,7 @@
   auto stride = strides ? *strides.getValues<uint64_t>().begin() : 1;
   auto dilation = dilations ? *dilations.getValues<uint64_t>().begin() : 1;
   LinalgOp linalgOp = cast<LinalgOp>(convOp.getOperation());
-  Conv1D_NWC_Generator e(b, linalgOp, stride, dilation);
+  Conv1DNwcGenerator e(b, linalgOp, stride, dilation);
   auto res = e.generateConv();
   if (succeeded(res))
     return res;
diff --git a/mlir/lib/Dialect/Math/Transforms/PolynomialApproximation.cpp b/mlir/lib/Dialect/Math/Transforms/PolynomialApproximation.cpp
--- a/mlir/lib/Dialect/Math/Transforms/PolynomialApproximation.cpp
+++ b/mlir/lib/Dialect/Math/Transforms/PolynomialApproximation.cpp
@@ -195,7 +195,7 @@
 // Decomposes given floating point value `arg` into a normalized fraction and
 // an integral power of two (see std::frexp). Returned values have float type.
 static std::pair<Value, Value> frexp(ImplicitLocOpBuilder &builder, Value arg,
-                                     bool is_positive = false) {
+                                     bool isPositive = false) {
   assert(getElementTypeOrSelf(arg).isF32() && "arg must be f32 type");
   ArrayRef<int64_t> shape = vectorShape(arg);
 
@@ -222,7 +222,7 @@
   Value normalizedFraction = builder.create<arith::BitcastOp>(f32Vec, tmp1);
 
   // Compute exponent.
-  Value arg0 = is_positive ? arg : builder.create<math::AbsOp>(arg);
+  Value arg0 = isPositive ? arg : builder.create<math::AbsOp>(arg);
   Value biasedExponentBits = builder.create<arith::ShRUIOp>(
       builder.create<arith::BitcastOp>(i32Vec, arg0),
       bcast(i32Cst(builder, 23)));
diff --git a/mlir/lib/Dialect/PDL/IR/PDL.cpp b/mlir/lib/Dialect/PDL/IR/PDL.cpp
--- a/mlir/lib/Dialect/PDL/IR/PDL.cpp
+++ b/mlir/lib/Dialect/PDL/IR/PDL.cpp
@@ -271,8 +271,8 @@
 static LogicalResult verify(PatternOp pattern) {
   Region &body = pattern.body();
   Operation *term = body.front().getTerminator();
-  auto rewrite_op = dyn_cast<RewriteOp>(term);
-  if (!rewrite_op) {
+  auto rewriteOp = dyn_cast<RewriteOp>(term);
+  if (!rewriteOp) {
     return pattern.emitOpError("expected body to terminate with `pdl.rewrite`")
         .attachNote(term->getLoc())
         .append("see terminator defined here");
diff --git a/mlir/lib/Dialect/Quant/IR/QuantTypes.cpp b/mlir/lib/Dialect/Quant/IR/QuantTypes.cpp
--- a/mlir/lib/Dialect/Quant/IR/QuantTypes.cpp
+++ b/mlir/lib/Dialect/Quant/IR/QuantTypes.cpp
@@ -104,7 +104,8 @@
   if (candidateType == getStorageType()) {
     // i.e. i32 -> quant<"uniform[i8:f32]{1.0}">
     return *this;
-  } else if (candidateType.isa<RankedTensorType>()) {
+  }
+  if (candidateType.isa<RankedTensorType>()) {
     // i.e. tensor<4xi8> -> tensor<4x!quant<"uniform[i8:f32]{1.0}">>
     return RankedTensorType::get(
         candidateType.cast<RankedTensorType>().getShape(), getStorageType());
@@ -124,7 +125,8 @@
   if (quantizedType.isa<QuantizedType>()) {
     // i.e. quant<"uniform[i8:f32]{1.0}"> -> i8
     return quantizedType.cast<QuantizedType>().getStorageType();
-  } else if (quantizedType.isa<ShapedType>()) {
+  }
+  if (quantizedType.isa<ShapedType>()) {
     // i.e. tensor<4xi8> -> tensor<4x!quant<"uniform[i8:f32]{1.0}">>
     ShapedType sType = quantizedType.cast<ShapedType>();
     if (!sType.getElementType().isa<QuantizedType>()) {
@@ -148,7 +150,8 @@
   if (candidateType == getExpressedType()) {
     // i.e. f32 -> quant<"uniform[i8:f32]{1.0}">
     return *this;
-  } else if (candidateType.isa<ShapedType>()) {
+  }
+  if (candidateType.isa<ShapedType>()) {
     ShapedType candidateShapedType = candidateType.cast<ShapedType>();
     if (candidateShapedType.getElementType() != getExpressedType()) {
       return nullptr;
@@ -173,7 +176,8 @@
   if (quantizedType.isa<QuantizedType>()) {
     // i.e. quant<"uniform[i8:f32]{1.0}"> -> f32
     return quantizedType.cast<QuantizedType>().getExpressedType();
-  } else if (quantizedType.isa<ShapedType>()) {
+  }
+  if (quantizedType.isa<ShapedType>()) {
     // i.e. tensor<4xi8> -> tensor<4x!quant<"uniform[i8:f32]{1.0}">>
     ShapedType sType = quantizedType.cast<ShapedType>();
     if (!sType.getElementType().isa<QuantizedType>()) {
diff --git a/mlir/lib/Dialect/Quant/Transforms/ConvertSimQuant.cpp b/mlir/lib/Dialect/Quant/Transforms/ConvertSimQuant.cpp
--- a/mlir/lib/Dialect/Quant/Transforms/ConvertSimQuant.cpp
+++ b/mlir/lib/Dialect/Quant/Transforms/ConvertSimQuant.cpp
@@ -126,7 +126,7 @@
   bool hadFailure = false;
   auto func = getFunction();
   RewritePatternSet patterns(func.getContext());
-  auto ctx = func.getContext();
+  auto *ctx = func.getContext();
   patterns.add<ConstFakeQuantRewrite, ConstFakeQuantPerAxisRewrite>(
       ctx, &hadFailure);
   (void)applyPatternsAndFoldGreedily(func, std::move(patterns));
diff --git a/mlir/lib/Dialect/Quant/Utils/FakeQuantSupport.cpp b/mlir/lib/Dialect/Quant/Utils/FakeQuantSupport.cpp
--- a/mlir/lib/Dialect/Quant/Utils/FakeQuantSupport.cpp
+++ b/mlir/lib/Dialect/Quant/Utils/FakeQuantSupport.cpp
@@ -140,10 +140,10 @@
     Location loc, unsigned numBits, int32_t quantizedDimension,
     ArrayRef<double> rmins, ArrayRef<double> rmaxs, bool narrowRange,
     Type expressedType, bool isSigned) {
-  size_t axis_size = rmins.size();
-  if (axis_size != rmaxs.size()) {
+  size_t axisSize = rmins.size();
+  if (axisSize != rmaxs.size()) {
     return (emitError(loc, "mismatched per-axis min and max size: ")
-                << axis_size << " vs. " << rmaxs.size(),
+                << axisSize << " vs. " << rmaxs.size(),
             nullptr);
   }
 
@@ -159,9 +159,9 @@
 
   SmallVector<double, 4> scales;
   SmallVector<int64_t, 4> zeroPoints;
-  scales.reserve(axis_size);
-  zeroPoints.reserve(axis_size);
-  for (size_t axis = 0; axis != axis_size; ++axis) {
+  scales.reserve(axisSize);
+  zeroPoints.reserve(axisSize);
+  for (size_t axis = 0; axis != axisSize; ++axis) {
     double rmin = rmins[axis];
     double rmax = rmaxs[axis];
     if (std::fabs(rmax - rmin) < std::numeric_limits<double>::epsilon()) {
diff --git a/mlir/lib/Dialect/Quant/Utils/QuantizeUtils.cpp b/mlir/lib/Dialect/Quant/Utils/QuantizeUtils.cpp
--- a/mlir/lib/Dialect/Quant/Utils/QuantizeUtils.cpp
+++ b/mlir/lib/Dialect/Quant/Utils/QuantizeUtils.cpp
@@ -106,7 +106,8 @@
         realValue.cast<DenseFPElementsAttr>(), quantizedElementType, converter);
     outConvertedType = converted.getType();
     return converted;
-  } else if (realValue.isa<SparseElementsAttr>()) {
+  }
+  if (realValue.isa<SparseElementsAttr>()) {
     // Sparse tensor or vector constant.
     auto converted = convertSparseElementsAttr(
         realValue.cast<SparseElementsAttr>(), quantizedElementType, converter);
@@ -132,9 +133,9 @@
     UniformQuantizedValueConverter converter(uniformQuantized);
     return quantizeAttrUniform(realValue, uniformQuantized, converter,
                                outConvertedType);
-
-  } else if (auto uniformQuantizedPerAxis =
-                 quantizedElementType.dyn_cast<UniformQuantizedPerAxisType>()) {
+  }
+  if (auto uniformQuantizedPerAxis =
+          quantizedElementType.dyn_cast<UniformQuantizedPerAxisType>()) {
     UniformQuantizedPerAxisValueConverter converter(uniformQuantizedPerAxis);
     auto converted = converter.convert(realValue);
     // TODO: why we need this outConvertedType? remove it?
diff --git a/mlir/lib/Dialect/SPIRV/IR/SPIRVCanonicalization.cpp b/mlir/lib/Dialect/SPIRV/IR/SPIRVCanonicalization.cpp
--- a/mlir/lib/Dialect/SPIRV/IR/SPIRVCanonicalization.cpp
+++ b/mlir/lib/Dialect/SPIRV/IR/SPIRVCanonicalization.cpp
@@ -74,7 +74,7 @@
 
 namespace {
 #include "SPIRVCanonicalization.inc"
-}
+} // namespace
 
 //===----------------------------------------------------------------------===//
 // spv.AccessChainOp
diff --git a/mlir/lib/Dialect/SPIRV/IR/SPIRVOps.cpp b/mlir/lib/Dialect/SPIRV/IR/SPIRVOps.cpp
--- a/mlir/lib/Dialect/SPIRV/IR/SPIRVOps.cpp
+++ b/mlir/lib/Dialect/SPIRV/IR/SPIRVOps.cpp
@@ -3250,13 +3250,13 @@
   return success();
 }
 
-static void print(spirv::CooperativeMatrixLoadNVOp M, OpAsmPrinter &printer) {
-  printer << " " << M.pointer() << ", " << M.stride() << ", "
-          << M.columnmajor();
+static void print(spirv::CooperativeMatrixLoadNVOp m, OpAsmPrinter &printer) {
+  printer << " " << m.pointer() << ", " << m.stride() << ", "
+          << m.columnmajor();
   // Print optional memory access attribute.
-  if (auto memAccess = M.memory_access())
+  if (auto memAccess = m.memory_access())
     printer << " [\"" << stringifyMemoryAccess(*memAccess) << "\"]";
-  printer << " : " << M.pointer().getType() << " as " << M.getType();
+  printer << " : " << m.pointer().getType() << " as " << m.getType();
 }
 
 static LogicalResult verifyPointerAndCoopMatrixType(Operation *op, Type pointer,
diff --git a/mlir/lib/Dialect/StandardOps/IR/Ops.cpp b/mlir/lib/Dialect/StandardOps/IR/Ops.cpp
--- a/mlir/lib/Dialect/StandardOps/IR/Ops.cpp
+++ b/mlir/lib/Dialect/StandardOps/IR/Ops.cpp
@@ -540,7 +540,8 @@
       rewriter.replaceOpWithNewOp<BranchOp>(condbr, condbr.getTrueDest(),
                                             condbr.getTrueOperands());
       return success();
-    } else if (matchPattern(condbr.getCondition(), m_Zero())) {
+    }
+    if (matchPattern(condbr.getCondition(), m_Zero())) {
       // False branch taken.
       rewriter.replaceOpWithNewOp<BranchOp>(condbr, condbr.getFalseDest(),
                                             condbr.getFalseOperands());
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
--- a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
@@ -771,8 +771,8 @@
                                                  OperationState &result,
                                                  Type outputType, Value input,
                                                  Value paddings,
-                                                 Value pad_const) {
-  result.addOperands({input, paddings, pad_const});
+                                                 Value padConst) {
+  result.addOperands({input, paddings, padConst});
   auto quantAttr = buildPadOpQuantizationAttr(builder, input);
   if (quantAttr)
     result.addAttribute("quantization_info", quantAttr);
diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp
--- a/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp
+++ b/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp
@@ -33,9 +33,9 @@
 }
 
 template <typename TosaOp, typename... Args>
-TosaOp CreateOpAndInfer(PatternRewriter &rewriter, Location loc, Type result_ty,
+TosaOp createOpAndInfer(PatternRewriter &rewriter, Location loc, Type resultTy,
                         Args &&...args) {
-  auto op = rewriter.create<TosaOp>(loc, result_ty, args...);
+  auto op = rewriter.create<TosaOp>(loc, resultTy, args...);
 
   InferShapedTypeOpInterface shapeInterface =
       dyn_cast<InferShapedTypeOpInterface>(op.getOperation());
@@ -57,12 +57,12 @@
   auto result = op->getResult(0);
   auto predictedShape = returnedShapes[0];
   auto currentKnowledge =
-      mlir::tosa::ValueKnowledge::getKnowledgeFromType(result_ty);
+      mlir::tosa::ValueKnowledge::getKnowledgeFromType(resultTy);
 
   // Compute the knowledge based on the inferred type.
   auto inferredKnowledge =
       mlir::tosa::ValueKnowledge::getPessimisticValueState();
-  inferredKnowledge.dtype = result_ty.cast<ShapedType>().getElementType();
+  inferredKnowledge.dtype = resultTy.cast<ShapedType>().getElementType();
   inferredKnowledge.hasRank = predictedShape.hasRank();
   if (predictedShape.hasRank()) {
     for (auto dim : predictedShape.getDims()) {
@@ -73,8 +73,8 @@
   // Compute the new type based on the joined version.
   auto newKnowledge =
       mlir::tosa::ValueKnowledge::join(currentKnowledge, inferredKnowledge);
-  auto new_ty = newKnowledge.getType();
-  result.setType(new_ty);
+  auto newTy = newKnowledge.getType();
+  result.setType(newTy);
   return op;
 }
 
@@ -205,19 +205,19 @@
         weightWidth % stride[1] ? stride[1] - weightWidth % stride[1] : 0;
     DenseElementsAttr weightPaddingAttr = DenseIntElementsAttr::get(
         RankedTensorType::get({4, 2}, rewriter.getI32Type()), weightPadding);
-    Value weightPaddingVal = CreateOpAndInfer<tosa::ConstOp>(
+    Value weightPaddingVal = createOpAndInfer<tosa::ConstOp>(
         rewriter, loc, weightPaddingAttr.getType(), weightPaddingAttr);
 
     if (op.quantization_info().hasValue()) {
       auto quantInfo = op.quantization_info().getValue();
-      weight = CreateOpAndInfer<tosa::PadOp>(
+      weight = createOpAndInfer<tosa::PadOp>(
           rewriter, loc, UnrankedTensorType::get(weightETy), weight,
           weightPaddingVal, nullptr,
           PadOpQuantizationAttr::get(quantInfo.weight_zp(),
                                      rewriter.getContext()));
 
     } else {
-      weight = CreateOpAndInfer<tosa::PadOp>(rewriter, loc,
+      weight = createOpAndInfer<tosa::PadOp>(rewriter, loc,
                                              UnrankedTensorType::get(weightETy),
                                              weight, weightPaddingVal);
     }
@@ -231,7 +231,7 @@
         outputChannels, weightHeight / stride[0],
         stride[0],      weightWidth / stride[1],
         stride[1],      inputChannels};
-    weight = CreateOpAndInfer<tosa::ReshapeOp>(
+    weight = createOpAndInfer<tosa::ReshapeOp>(
         rewriter, loc, UnrankedTensorType::get(weightETy), weight,
         rewriter.getI64ArrayAttr(weightReshapeDims0));
 
@@ -240,7 +240,7 @@
         loc, RankedTensorType::get({6}, rewriter.getI32Type()),
         rewriter.getI32TensorAttr({2, 4, 0, 1, 3, 5}));
 
-    weight = CreateOpAndInfer<tosa::TransposeOp>(
+    weight = createOpAndInfer<tosa::TransposeOp>(
         rewriter, loc, UnrankedTensorType::get(weightETy), weight,
         transposeWeightVal);
 
@@ -248,15 +248,15 @@
     llvm::SmallVector<int64_t, 6> weightReshapeDims1 = {
         outputChannels * stride[0] * stride[1], weightHeight / stride[0],
         weightWidth / stride[1], inputChannels};
-    weight = CreateOpAndInfer<tosa::ReshapeOp>(
+    weight = createOpAndInfer<tosa::ReshapeOp>(
         rewriter, loc, UnrankedTensorType::get(weightETy), weight,
         rewriter.getI64ArrayAttr(weightReshapeDims1));
     ShapedType restridedWeightTy = weight.getType().cast<ShapedType>();
 
-    weight = CreateOpAndInfer<tosa::ReverseOp>(
+    weight = createOpAndInfer<tosa::ReverseOp>(
         rewriter, loc, UnrankedTensorType::get(weightETy), weight,
         rewriter.getI64IntegerAttr(1));
-    weight = CreateOpAndInfer<tosa::ReverseOp>(
+    weight = createOpAndInfer<tosa::ReverseOp>(
         rewriter, loc, UnrankedTensorType::get(weightETy), weight,
         rewriter.getI64IntegerAttr(2));
 
@@ -270,18 +270,18 @@
     DenseElementsAttr inputPaddingAttr = DenseIntElementsAttr::get(
         RankedTensorType::get({4, 2}, rewriter.getI32Type()), inputPadding);
 
-    Value inputPaddingVal = CreateOpAndInfer<tosa::ConstOp>(
+    Value inputPaddingVal = createOpAndInfer<tosa::ConstOp>(
         rewriter, loc, inputPaddingAttr.getType(), inputPaddingAttr);
 
     if (op.quantization_info().hasValue()) {
       auto quantInfo = op.quantization_info().getValue();
-      input = CreateOpAndInfer<tosa::PadOp>(
+      input = createOpAndInfer<tosa::PadOp>(
           rewriter, loc, UnrankedTensorType::get(inputETy), input,
           inputPaddingVal, nullptr,
           PadOpQuantizationAttr::get(quantInfo.input_zp(),
                                      rewriter.getContext()));
     } else {
-      input = CreateOpAndInfer<tosa::PadOp>(rewriter, loc,
+      input = createOpAndInfer<tosa::PadOp>(rewriter, loc,
                                             UnrankedTensorType::get(inputETy),
                                             input, inputPaddingVal);
     }
@@ -299,7 +299,7 @@
     // Perform the convolution using the zero bias.
     Value conv2d;
     if (op.quantization_info().hasValue()) {
-      conv2d = CreateOpAndInfer<tosa::Conv2DOp>(
+      conv2d = createOpAndInfer<tosa::Conv2DOp>(
                    rewriter, loc, UnrankedTensorType::get(resultETy), input,
                    weight, zeroBias,
                    /*pad=*/rewriter.getI64ArrayAttr({0, 0, 0, 0}),
@@ -308,7 +308,7 @@
                    op.quantization_info().getValue())
                    .getResult();
     } else {
-      conv2d = CreateOpAndInfer<tosa::Conv2DOp>(
+      conv2d = createOpAndInfer<tosa::Conv2DOp>(
                    rewriter, loc, UnrankedTensorType::get(resultETy), input,
                    weight, zeroBias,
                    /*pad=*/rewriter.getI64ArrayAttr({0, 0, 0, 0}),
@@ -327,7 +327,7 @@
     // Factor striding out of the convolution result.
     llvm::SmallVector<int64_t, 6> convReshapeDims0 = {
         batch, convHeight, convWidth, stride[0], stride[1], outputChannels};
-    conv2d = CreateOpAndInfer<tosa::ReshapeOp>(
+    conv2d = createOpAndInfer<tosa::ReshapeOp>(
         rewriter, loc, UnrankedTensorType::get(resultETy), conv2d,
         rewriter.getI64ArrayAttr(convReshapeDims0));
 
@@ -336,14 +336,14 @@
         loc, RankedTensorType::get({6}, rewriter.getI32Type()),
         rewriter.getI32TensorAttr({0, 1, 3, 2, 4, 5}));
 
-    conv2d = CreateOpAndInfer<tosa::TransposeOp>(
+    conv2d = createOpAndInfer<tosa::TransposeOp>(
         rewriter, loc, UnrankedTensorType::get(convETy), conv2d,
         transposeConvVal);
 
     // Fuse striding behavior back into width / height.
     llvm::SmallVector<int64_t, 6> convReshapeDims1 = {
         batch, convHeight * stride[0], convWidth * stride[1], outputChannels};
-    conv2d = CreateOpAndInfer<tosa::ReshapeOp>(
+    conv2d = createOpAndInfer<tosa::ReshapeOp>(
         rewriter, loc, UnrankedTensorType::get(resultETy), conv2d,
         rewriter.getI64ArrayAttr(convReshapeDims1));
 
@@ -354,14 +354,14 @@
     sliceBegin[1] = pad[0];
     sliceBegin[2] = pad[1];
 
-    auto slice = CreateOpAndInfer<tosa::SliceOp>(
+    auto slice = createOpAndInfer<tosa::SliceOp>(
                      rewriter, loc, UnrankedTensorType::get(resultETy), conv2d,
                      rewriter.getI64ArrayAttr(sliceBegin),
                      rewriter.getI64ArrayAttr(resultTy.getShape()))
                      .getResult();
 
     auto addBias =
-        CreateOpAndInfer<tosa::AddOp>(rewriter, loc, op.getType(), slice, bias);
+        createOpAndInfer<tosa::AddOp>(rewriter, loc, op.getType(), slice, bias);
 
     rewriter.replaceOp(op, addBias.getResult());
 
diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaInferShapes.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaInferShapes.cpp
--- a/mlir/lib/Dialect/Tosa/Transforms/TosaInferShapes.cpp
+++ b/mlir/lib/Dialect/Tosa/Transforms/TosaInferShapes.cpp
@@ -223,7 +223,7 @@
           // Check whether this use case is replaceable. We define an op as
           // being replaceable if it is used by a ReturnOp or a TosaOp.
           bool replaceable = true;
-          for (auto user : result.getUsers()) {
+          for (auto *user : result.getUsers()) {
             if (isa<ReturnOp>(user))
               continue;
             if (user->getDialect()->getNamespace() ==
diff --git a/mlir/lib/Dialect/Vector/VectorTransforms.cpp b/mlir/lib/Dialect/Vector/VectorTransforms.cpp
--- a/mlir/lib/Dialect/Vector/VectorTransforms.cpp
+++ b/mlir/lib/Dialect/Vector/VectorTransforms.cpp
@@ -1179,7 +1179,7 @@
     return builder.create<vector::TransposeOp>(loc, v, perm);
   }
 
-  Value outer_prod(Value lhs, Value rhs, Value res, int reductionSize) {
+  Value outerProd(Value lhs, Value rhs, Value res, int reductionSize) {
     assert(reductionSize > 0);
     for (int64_t k = 0; k < reductionSize; ++k) {
       Value a = builder.create<vector::ExtractOp>(loc, lhs, k);
@@ -1199,31 +1199,31 @@
     bindDims(builder.getContext(), m, n, k);
     // Classical row-major matmul:  Just permute the lhs.
     if (layout({{m, k}, {k, n}, {m, n}}))
-      return outer_prod(t(lhs), rhs, res, lhsType.getDimSize(1));
+      return outerProd(t(lhs), rhs, res, lhsType.getDimSize(1));
     // TODO: may be better to fail and use some vector<k> -> scalar reduction.
     if (layout({{m, k}, {n, k}, {m, n}})) {
       Value tlhs = t(lhs);
-      return outer_prod(tlhs, t(rhs), res, lhsType.getDimSize(1));
+      return outerProd(tlhs, t(rhs), res, lhsType.getDimSize(1));
     }
     // No need to permute anything.
     if (layout({{k, m}, {k, n}, {m, n}}))
-      return outer_prod(lhs, rhs, res, lhsType.getDimSize(0));
+      return outerProd(lhs, rhs, res, lhsType.getDimSize(0));
     // Just permute the rhs.
     if (layout({{k, m}, {n, k}, {m, n}}))
-      return outer_prod(lhs, t(rhs), res, lhsType.getDimSize(0));
+      return outerProd(lhs, t(rhs), res, lhsType.getDimSize(0));
     // Transposed output: swap RHS and LHS.
     // Classical row-major matmul: permute the lhs.
     if (layout({{m, k}, {k, n}, {n, m}}))
-      return outer_prod(rhs, t(lhs), res, lhsType.getDimSize(1));
+      return outerProd(rhs, t(lhs), res, lhsType.getDimSize(1));
     // TODO: may be better to fail and use some vector<k> -> scalar reduction.
     if (layout({{m, k}, {n, k}, {n, m}})) {
       Value trhs = t(rhs);
-      return outer_prod(trhs, t(lhs), res, lhsType.getDimSize(1));
+      return outerProd(trhs, t(lhs), res, lhsType.getDimSize(1));
     }
     if (layout({{k, m}, {k, n}, {n, m}}))
-      return outer_prod(rhs, lhs, res, lhsType.getDimSize(0));
+      return outerProd(rhs, lhs, res, lhsType.getDimSize(0));
     if (layout({{k, m}, {n, k}, {n, m}}))
-      return outer_prod(t(rhs), lhs, res, lhsType.getDimSize(0));
+      return outerProd(t(rhs), lhs, res, lhsType.getDimSize(0));
     return failure();
   }
 
@@ -1236,16 +1236,16 @@
 
     // Case mat-vec: transpose.
     if (layout({{m, k}, {k}, {m}}))
-      return outer_prod(t(lhs), rhs, res, lhsType.getDimSize(1));
+      return outerProd(t(lhs), rhs, res, lhsType.getDimSize(1));
     // Case mat-trans-vec: ready to go.
     if (layout({{k, m}, {k}, {m}}))
-      return outer_prod(lhs, rhs, res, lhsType.getDimSize(0));
+      return outerProd(lhs, rhs, res, lhsType.getDimSize(0));
     // Case vec-mat: swap and transpose.
     if (layout({{k}, {m, k}, {m}}))
-      return outer_prod(t(rhs), lhs, res, lhsType.getDimSize(0));
+      return outerProd(t(rhs), lhs, res, lhsType.getDimSize(0));
     // Case vec-mat-trans: swap and ready to go.
     if (layout({{k}, {k, m}, {m}}))
-      return outer_prod(rhs, lhs, res, lhsType.getDimSize(0));
+      return outerProd(rhs, lhs, res, lhsType.getDimSize(0));
     return failure();
   }
 
@@ -1260,16 +1260,16 @@
 
     // Case mat-vec: transpose.
     if (layout({{m, k}, {k}, {m}}))
-      return outer_prod(t(lhs), rhs, res, lhsType.getDimSize(1));
+      return outerProd(t(lhs), rhs, res, lhsType.getDimSize(1));
     // Case mat-trans-vec: ready to go.
     if (layout({{k, m}, {k}, {m}}))
-      return outer_prod(lhs, rhs, res, lhsType.getDimSize(0));
+      return outerProd(lhs, rhs, res, lhsType.getDimSize(0));
     // Case vec-mat: swap and transpose.
     if (layout({{k}, {m, k}, {m}}))
-      return outer_prod(t(rhs), lhs, res, lhsType.getDimSize(0));
+      return outerProd(t(rhs), lhs, res, lhsType.getDimSize(0));
     // Case vec-mat-trans: swap and ready to go.
     if (layout({{k}, {k, m}, {m}}))
-      return outer_prod(rhs, lhs, res, lhsType.getDimSize(0));
+      return outerProd(rhs, lhs, res, lhsType.getDimSize(0));
     return failure();
   }
 
diff --git a/mlir/test/lib/Analysis/TestAliasAnalysis.cpp b/mlir/test/lib/Analysis/TestAliasAnalysis.cpp
--- a/mlir/test/lib/Analysis/TestAliasAnalysis.cpp
+++ b/mlir/test/lib/Analysis/TestAliasAnalysis.cpp
@@ -67,7 +67,7 @@
 
     // Check for aliasing behavior between each of the values.
     for (auto it = valsToCheck.begin(), e = valsToCheck.end(); it != e; ++it)
-      for (auto innerIt = valsToCheck.begin(); innerIt != it; ++innerIt)
+      for (auto *innerIt = valsToCheck.begin(); innerIt != it; ++innerIt)
         printAliasResult(aliasAnalysis.alias(*innerIt, *it), *innerIt, *it);
   }
 
diff --git a/mlir/test/lib/Dialect/Math/TestPolynomialApproximation.cpp b/mlir/test/lib/Dialect/Math/TestPolynomialApproximation.cpp
--- a/mlir/test/lib/Dialect/Math/TestPolynomialApproximation.cpp
+++ b/mlir/test/lib/Dialect/Math/TestPolynomialApproximation.cpp
@@ -52,9 +52,9 @@
 
 void TestMathPolynomialApproximationPass::runOnFunction() {
   RewritePatternSet patterns(&getContext());
-  MathPolynomialApproximationOptions approx_options;
-  approx_options.enableAvx2 = enableAvx2;
-  populateMathPolynomialApproximationPatterns(patterns, approx_options);
+  MathPolynomialApproximationOptions approxOptions;
+  approxOptions.enableAvx2 = enableAvx2;
+  populateMathPolynomialApproximationPatterns(patterns, approxOptions);
   (void)applyPatternsAndFoldGreedily(getOperation(), std::move(patterns));
 }
 
diff --git a/mlir/test/lib/Dialect/Test/TestDialect.cpp b/mlir/test/lib/Dialect/Test/TestDialect.cpp
--- a/mlir/test/lib/Dialect/Test/TestDialect.cpp
+++ b/mlir/test/lib/Dialect/Test/TestDialect.cpp
@@ -108,7 +108,7 @@
 
   void getAsmBlockArgumentNames(Block *block,
                                 OpAsmSetValueNameFn setNameFn) const final {
-    auto op = block->getParentOp();
+    auto *op = block->getParentOp();
     auto arrayAttr = op->getAttrOfType<ArrayAttr>("arg_names");
     if (!arrayAttr)
       return;
@@ -703,24 +703,24 @@
   Region &body = *result.addRegion();
   body.push_back(new Block);
   Block &block = body.back();
-  Operation *wrapped_op = parser.parseGenericOperation(&block, block.begin());
-  if (!wrapped_op)
+  Operation *wrappedOp = parser.parseGenericOperation(&block, block.begin());
+  if (!wrappedOp)
     return failure();
 
   // Create a return terminator in the inner region, pass as operand to the
   // terminator the returned values from the wrapped operation.
-  SmallVector<Value, 8> return_operands(wrapped_op->getResults());
+  SmallVector<Value, 8> returnOperands(wrappedOp->getResults());
   OpBuilder builder(parser.getContext());
   builder.setInsertionPointToEnd(&block);
-  builder.create<TestReturnOp>(wrapped_op->getLoc(), return_operands);
+  builder.create<TestReturnOp>(wrappedOp->getLoc(), returnOperands);
 
   // Get the results type for the wrapping op from the terminator operands.
-  Operation &return_op = body.back().back();
-  result.types.append(return_op.operand_type_begin(),
-                      return_op.operand_type_end());
+  Operation &returnOp = body.back().back();
+  result.types.append(returnOp.operand_type_begin(),
+                      returnOp.operand_type_end());
 
   // Use the location of the wrapped op for the "test.wrapping_region" op.
-  result.location = wrapped_op->getLoc();
+  result.location = wrappedOp->getLoc();
 
   return success();
 }
diff --git a/mlir/test/lib/Dialect/Tosa/TosaTestPasses.cpp b/mlir/test/lib/Dialect/Tosa/TosaTestPasses.cpp
--- a/mlir/test/lib/Dialect/Tosa/TosaTestPasses.cpp
+++ b/mlir/test/lib/Dialect/Tosa/TosaTestPasses.cpp
@@ -71,7 +71,7 @@
   double typeRangeMax = double(outputElementType.getStorageTypeMax() -
                                outputElementType.getZeroPoint()) *
                         outputElementType.getScale();
-  bool narrow_range = outputElementType.getStorageTypeMin() == 1 ? true : false;
+  bool narrowRange = outputElementType.getStorageTypeMin() == 1 ? true : false;
 
   auto dstQConstType = RankedTensorType::get(
       outputType.getShape(),
@@ -81,7 +81,7 @@
                            rewriter.getI32IntegerAttr(
                                outputElementType.getStorageTypeIntegralWidth()),
                            0, true /* signed */,
-                           rewriter.getBoolAttr(narrow_range)));
+                           rewriter.getBoolAttr(narrowRange)));
 
   ElementsAttr inputElems;
   if (!matchPattern(tosaNegateOp.input1(), m_Constant(&inputElems)))
diff --git a/mlir/test/lib/IR/TestMatchers.cpp b/mlir/test/lib/IR/TestMatchers.cpp
--- a/mlir/test/lib/IR/TestMatchers.cpp
+++ b/mlir/test/lib/IR/TestMatchers.cpp
@@ -76,19 +76,19 @@
   llvm::outs() << "Pattern mul(mul(*), mul(*)) matched " << countMatches(f, p7)
                << " times\n";
 
-  auto mul_of_mulmul =
+  auto mulOfMulmul =
       m_Op<arith::MulFOp>(m_Op<arith::MulFOp>(), m_Op<arith::MulFOp>());
-  auto p8 = m_Op<arith::MulFOp>(mul_of_mulmul, mul_of_mulmul);
+  auto p8 = m_Op<arith::MulFOp>(mulOfMulmul, mulOfMulmul);
   llvm::outs()
       << "Pattern mul(mul(mul(*), mul(*)), mul(mul(*), mul(*))) matched "
       << countMatches(f, p8) << " times\n";
 
   // clang-format off
-  auto mul_of_muladd = m_Op<arith::MulFOp>(m_Op<arith::MulFOp>(), m_Op<arith::AddFOp>());
-  auto mul_of_anyadd = m_Op<arith::MulFOp>(m_Any(), m_Op<arith::AddFOp>());
+  auto mulOfMuladd = m_Op<arith::MulFOp>(m_Op<arith::MulFOp>(), m_Op<arith::AddFOp>());
+  auto mulOfAnyadd = m_Op<arith::MulFOp>(m_Any(), m_Op<arith::AddFOp>());
   auto p9 = m_Op<arith::MulFOp>(m_Op<arith::MulFOp>(
-                     mul_of_muladd, m_Op<arith::MulFOp>()),
-                   m_Op<arith::MulFOp>(mul_of_anyadd, mul_of_anyadd));
+                     mulOfMuladd, m_Op<arith::MulFOp>()),
+                   m_Op<arith::MulFOp>(mulOfAnyadd, mulOfAnyadd));
   // clang-format on
   llvm::outs() << "Pattern mul(mul(mul(mul(*), add(*)), mul(*)), mul(mul(*, "
                   "add(*)), mul(*, add(*)))) matched "
@@ -118,12 +118,12 @@
   llvm::outs() << "Pattern mul(a, add(b, c)) matched " << countMatches(f, p15)
                << " times\n";
 
-  auto mul_of_aany = m_Op<arith::MulFOp>(a, m_Any());
-  auto p16 = m_Op<arith::MulFOp>(mul_of_aany, m_Op<arith::AddFOp>(a, c));
+  auto mulOfAany = m_Op<arith::MulFOp>(a, m_Any());
+  auto p16 = m_Op<arith::MulFOp>(mulOfAany, m_Op<arith::AddFOp>(a, c));
   llvm::outs() << "Pattern mul(mul(a, *), add(a, c)) matched "
                << countMatches(f, p16) << " times\n";
 
-  auto p17 = m_Op<arith::MulFOp>(mul_of_aany, m_Op<arith::AddFOp>(c, b));
+  auto p17 = m_Op<arith::MulFOp>(mulOfAany, m_Op<arith::AddFOp>(c, b));
   llvm::outs() << "Pattern mul(mul(a, *), add(c, b)) matched "
                << countMatches(f, p17) << " times\n";
 }
diff --git a/mlir/test/lib/IR/TestOpaqueLoc.cpp b/mlir/test/lib/IR/TestOpaqueLoc.cpp
--- a/mlir/test/lib/IR/TestOpaqueLoc.cpp
+++ b/mlir/test/lib/IR/TestOpaqueLoc.cpp
@@ -35,10 +35,10 @@
 
   void runOnOperation() override {
     std::vector<std::unique_ptr<MyLocation>> myLocs;
-    int last_it = 0;
+    int lastIt = 0;
 
     getOperation().getBody()->walk([&](Operation *op) {
-      myLocs.push_back(std::make_unique<MyLocation>(last_it++));
+      myLocs.push_back(std::make_unique<MyLocation>(lastIt++));
 
       Location loc = op->getLoc();
 
@@ -54,14 +54,13 @@
 
       /// Add the same operation but with fallback location to test the
       /// corresponding get method and serialization.
-      Operation *op_cloned_1 = builder.clone(*op);
-      op_cloned_1->setLoc(
-          OpaqueLoc::get<MyLocation *>(myLocs.back().get(), loc));
+      Operation *opCloned1 = builder.clone(*op);
+      opCloned1->setLoc(OpaqueLoc::get<MyLocation *>(myLocs.back().get(), loc));
 
       /// Add the same operation but with void* instead of MyLocation* to test
       /// getUnderlyingLocationOrNull method.
-      Operation *op_cloned_2 = builder.clone(*op);
-      op_cloned_2->setLoc(OpaqueLoc::get<void *>(nullptr, loc));
+      Operation *opCloned2 = builder.clone(*op);
+      opCloned2->setLoc(OpaqueLoc::get<void *>(nullptr, loc));
     });
 
     ScopedDiagnosticHandler diagHandler(&getContext(), [](Diagnostic &diag) {
diff --git a/mlir/test/lib/Transforms/TestLoopFusion.cpp b/mlir/test/lib/Transforms/TestLoopFusion.cpp
--- a/mlir/test/lib/Transforms/TestLoopFusion.cpp
+++ b/mlir/test/lib/Transforms/TestLoopFusion.cpp
@@ -156,7 +156,7 @@
 // If 'return_on_change' is true, returns on first invocation of 'fn' which
 // returns true.
 static bool iterateLoops(ArrayRef<SmallVector<AffineForOp, 2>> depthToLoops,
-                         LoopFunc fn, bool return_on_change = false) {
+                         LoopFunc fn, bool returnOnChange = false) {
   bool changed = false;
   for (unsigned loopDepth = 0, end = depthToLoops.size(); loopDepth < end;
        ++loopDepth) {
@@ -167,7 +167,7 @@
         if (j != k)
           changed |=
               fn(loops[j], loops[k], j, k, loopDepth, depthToLoops.size());
-        if (changed && return_on_change)
+        if (changed && returnOnChange)
           return true;
       }
     }
diff --git a/mlir/test/mlir-spirv-cpu-runner/mlir_test_spirv_cpu_runner_c_wrappers.cpp b/mlir/test/mlir-spirv-cpu-runner/mlir_test_spirv_cpu_runner_c_wrappers.cpp
--- a/mlir/test/mlir-spirv-cpu-runner/mlir_test_spirv_cpu_runner_c_wrappers.cpp
+++ b/mlir/test/mlir-spirv-cpu-runner/mlir_test_spirv_cpu_runner_c_wrappers.cpp
@@ -12,27 +12,23 @@
 
 #include "mlir/ExecutionEngine/RunnerUtils.h"
 
-extern "C" void
-_mlir_ciface_fillI32Buffer(StridedMemRefType<int32_t, 1> *mem_ref,
-                           int32_t value) {
-  std::fill_n(mem_ref->basePtr, mem_ref->sizes[0], value);
+extern "C" void mlirCifaceFillI32Buffer(StridedMemRefType<int32_t, 1> *memRef,
+                                        int32_t value) {
+  std::fill_n(memRef->basePtr, memRef->sizes[0], value);
 }
 
-extern "C" void
-_mlir_ciface_fillF32Buffer1D(StridedMemRefType<float, 1> *mem_ref,
-                             float value) {
-  std::fill_n(mem_ref->basePtr, mem_ref->sizes[0], value);
+extern "C" void mlirCifaceFillF32Buffer1D(StridedMemRefType<float, 1> *memRef,
+                                          float value) {
+  std::fill_n(memRef->basePtr, memRef->sizes[0], value);
 }
 
-extern "C" void
-_mlir_ciface_fillF32Buffer2D(StridedMemRefType<float, 2> *mem_ref,
-                             float value) {
-  std::fill_n(mem_ref->basePtr, mem_ref->sizes[0] * mem_ref->sizes[1], value);
+extern "C" void mlirCifaceFillF32Buffer2D(StridedMemRefType<float, 2> *memRef,
+                                          float value) {
+  std::fill_n(memRef->basePtr, memRef->sizes[0] * memRef->sizes[1], value);
 }
 
-extern "C" void
-_mlir_ciface_fillF32Buffer3D(StridedMemRefType<float, 3> *mem_ref,
-                             float value) {
-  std::fill_n(mem_ref->basePtr,
-              mem_ref->sizes[0] * mem_ref->sizes[1] * mem_ref->sizes[2], value);
+extern "C" void mlirCifaceFillF32Buffer3D(StridedMemRefType<float, 3> *memRef,
+                                          float value) {
+  std::fill_n(memRef->basePtr,
+              memRef->sizes[0] * memRef->sizes[1] * memRef->sizes[2], value);
 }
diff --git a/mlir/tools/mlir-linalg-ods-gen/mlir-linalg-ods-yaml-gen.cpp b/mlir/tools/mlir-linalg-ods-gen/mlir-linalg-ods-yaml-gen.cpp
--- a/mlir/tools/mlir-linalg-ods-gen/mlir-linalg-ods-yaml-gen.cpp
+++ b/mlir/tools/mlir-linalg-ods-gen/mlir-linalg-ods-yaml-gen.cpp
@@ -1009,9 +1009,8 @@
     return success(
         succeeded(generateNamedGenericOpOds(opConfig, genContext)) &&
         succeeded(generateNamedGenericOpDefns(opConfig, genContext)));
-  } else {
-    return emitError(genContext.getLoc()) << "unsupported operation type";
   }
+  return emitError(genContext.getLoc()) << "unsupported operation type";
 }
 
 //===----------------------------------------------------------------------===//
diff --git a/mlir/tools/mlir-tblgen/DialectGen.cpp b/mlir/tools/mlir-tblgen/DialectGen.cpp
--- a/mlir/tools/mlir-tblgen/DialectGen.cpp
+++ b/mlir/tools/mlir-tblgen/DialectGen.cpp
@@ -68,9 +68,10 @@
     return llvm::None;
   }
 
-  auto dialectIt = llvm::find_if(dialectDefs, [](const llvm::Record *def) {
-    return Dialect(def).getName() == selectedDialect;
-  });
+  const auto *dialectIt =
+      llvm::find_if(dialectDefs, [](const llvm::Record *def) {
+        return Dialect(def).getName() == selectedDialect;
+      });
   if (dialectIt == dialectDefs.end()) {
     llvm::errs() << "selected dialect with '-dialect' does not exist\n";
     return llvm::None;
diff --git a/mlir/tools/mlir-tblgen/LLVMIRIntrinsicGen.cpp b/mlir/tools/mlir-tblgen/LLVMIRIntrinsicGen.cpp
--- a/mlir/tools/mlir-tblgen/LLVMIRIntrinsicGen.cpp
+++ b/mlir/tools/mlir-tblgen/LLVMIRIntrinsicGen.cpp
@@ -24,31 +24,31 @@
 #include "llvm/TableGen/Record.h"
 #include "llvm/TableGen/TableGenBackend.h"
 
-static llvm::cl::OptionCategory IntrinsicGenCat("Intrinsics Generator Options");
+static llvm::cl::OptionCategory intrinsicGenCat("Intrinsics Generator Options");
 
 static llvm::cl::opt<std::string>
     nameFilter("llvmir-intrinsics-filter",
                llvm::cl::desc("Only keep the intrinsics with the specified "
                               "substring in their record name"),
-               llvm::cl::cat(IntrinsicGenCat));
+               llvm::cl::cat(intrinsicGenCat));
 
 static llvm::cl::opt<std::string>
     opBaseClass("dialect-opclass-base",
                 llvm::cl::desc("The base class for the ops in the dialect we "
                                "are planning to emit"),
-                llvm::cl::init("LLVM_IntrOp"), llvm::cl::cat(IntrinsicGenCat));
+                llvm::cl::init("LLVM_IntrOp"), llvm::cl::cat(intrinsicGenCat));
 
 static llvm::cl::opt<std::string> accessGroupRegexp(
     "llvmir-intrinsics-access-group-regexp",
     llvm::cl::desc("Mark intrinsics that match the specified "
                    "regexp as taking an access group metadata"),
-    llvm::cl::cat(IntrinsicGenCat));
+    llvm::cl::cat(intrinsicGenCat));
 
 static llvm::cl::opt<std::string> aliasScopesRegexp(
     "llvmir-intrinsics-alias-scopes-regexp",
     llvm::cl::desc("Mark intrinsics that match the specified "
                    "regexp as taking alias.scopes and noalias metadata"),
-    llvm::cl::cat(IntrinsicGenCat));
+    llvm::cl::cat(intrinsicGenCat));
 
 // Used to represent the indices of overloadable operands/results.
 using IndicesTy = llvm::SmallBitVector;
@@ -104,7 +104,7 @@
     llvm::SmallVector<llvm::StringRef, 8> chunks;
     llvm::StringRef targetPrefix = record.getValueAsString("TargetPrefix");
     name.split(chunks, '_');
-    auto chunksBegin = chunks.begin();
+    auto *chunksBegin = chunks.begin();
     // Remove the target prefix from target specific intrinsics.
     if (!targetPrefix.empty()) {
       assert(targetPrefix == *chunksBegin &&
diff --git a/mlir/tools/mlir-tblgen/OpDefinitionsGen.cpp b/mlir/tools/mlir-tblgen/OpDefinitionsGen.cpp
--- a/mlir/tools/mlir-tblgen/OpDefinitionsGen.cpp
+++ b/mlir/tools/mlir-tblgen/OpDefinitionsGen.cpp
@@ -527,7 +527,7 @@
                                emitHelper.isEmittingForOp());
 
     // Prefix with `tblgen_` to avoid hiding the attribute accessor.
-    Twine varName = tblgenNamePrefix + attrName;
+    std::string varName = (tblgenNamePrefix + attrName).str();
 
     // If the attribute is not required and we cannot emit the condition, then
     // there is nothing to be done.
diff --git a/mlir/tools/mlir-tblgen/SPIRVUtilsGen.cpp b/mlir/tools/mlir-tblgen/SPIRVUtilsGen.cpp
--- a/mlir/tools/mlir-tblgen/SPIRVUtilsGen.cpp
+++ b/mlir/tools/mlir-tblgen/SPIRVUtilsGen.cpp
@@ -891,7 +891,7 @@
   unsigned operandNum = 0;
   for (unsigned i = 0, e = op.getNumArgs(); i < e; ++i) {
     auto argument = op.getArg(i);
-    if (auto valueArg = argument.dyn_cast<NamedTypeConstraint *>()) {
+    if (auto *valueArg = argument.dyn_cast<NamedTypeConstraint *>()) {
       if (valueArg->isVariableLength()) {
         if (i != e - 1) {
           PrintFatalError(loc, "SPIR-V ops can have Variadic<..> or "
@@ -921,7 +921,7 @@
       os << tabs << "}\n";
     } else {
       os << tabs << formatv("if ({0} < {1}.size()) {{\n", wordIndex, words);
-      auto attr = argument.get<NamedAttribute *>();
+      auto *attr = argument.get<NamedAttribute *>();
       auto newtabs = tabs.str() + "  ";
       emitAttributeDeserialization(
           (attr->attr.isOptional() ? attr->attr.getBaseAttr() : attr->attr),
diff --git a/mlir/tools/mlir-tblgen/mlir-tblgen.cpp b/mlir/tools/mlir-tblgen/mlir-tblgen.cpp
--- a/mlir/tools/mlir-tblgen/mlir-tblgen.cpp
+++ b/mlir/tools/mlir-tblgen/mlir-tblgen.cpp
@@ -41,7 +41,7 @@
 }
 
 void GenNameParser::printOptionInfo(const llvm::cl::Option &O,
-                                    size_t GlobalWidth) const {
+                                    size_t globalWidth) const {
   GenNameParser *TP = const_cast<GenNameParser *>(this);
   llvm::array_pod_sort(TP->Values.begin(), TP->Values.end(),
                        [](const GenNameParser::OptionInfo *VT1,
@@ -49,7 +49,7 @@
                          return VT1->Name.compare(VT2->Name);
                        });
   using llvm::cl::parser;
-  parser<const GenInfo *>::printOptionInfo(O, GlobalWidth);
+  parser<const GenInfo *>::printOptionInfo(O, globalWidth);
 }
 
 // Generator that prints records.
@@ -64,7 +64,7 @@
 
 // TableGenMain requires a function pointer so this function is passed in which
 // simply wraps the call to the generator.
-static bool MlirTableGenMain(raw_ostream &os, RecordKeeper &records) {
+static bool mlirTableGenMain(raw_ostream &os, RecordKeeper &records) {
   if (!generator) {
     os << records;
     return false;
@@ -79,5 +79,5 @@
   cl::ParseCommandLineOptions(argc, argv);
   ::generator = generator.getValue();
 
-  return TableGenMain(argv[0], &MlirTableGenMain);
+  return TableGenMain(argv[0], &mlirTableGenMain);
 }