[mlir] NFC: Remove Value::operator* and Value::operator-> now that Value is properly value-typed.

Summary: These were temporary methods used to simplify the transition.

Reviewed By: antiagainst

Differential Revision: https://reviews.llvm.org/D72548

17 files changed, 58 insertions, 63 deletions

diff --git a/mlir/examples/toy/Ch2/mlir/Dialect.cpp b/mlir/examples/toy/Ch2/mlir/Dialect.cpp
index 6b4d669d18e..f9301605b46 100644
--- a/mlir/examples/toy/Ch2/mlir/Dialect.cpp
+++ b/mlir/examples/toy/Ch2/mlir/Dialect.cpp
@@ -54,8 +54,7 @@ void ConstantOp::build(mlir::Builder *builder, mlir::OperationState &state,
 static mlir::LogicalResult verify(ConstantOp op) {
   // If the return type of the constant is not an unranked tensor, the shape
   // must match the shape of the attribute holding the data.
-  auto resultType =
-      op.getResult()->getType().dyn_cast<mlir::RankedTensorType>();
+  auto resultType = op.getResult().getType().dyn_cast<mlir::RankedTensorType>();
   if (!resultType)
     return success();
 
@@ -158,7 +157,7 @@ void TransposeOp::build(mlir::Builder *builder, mlir::OperationState &state,
 }
 
 static mlir::LogicalResult verify(TransposeOp op) {
-  auto inputType = op.getOperand()->getType().dyn_cast<RankedTensorType>();
+  auto inputType = op.getOperand().getType().dyn_cast<RankedTensorType>();
   auto resultType = op.getType().dyn_cast<RankedTensorType>();
   if (!inputType || !resultType)
     return mlir::success();
diff --git a/mlir/examples/toy/Ch3/mlir/Dialect.cpp b/mlir/examples/toy/Ch3/mlir/Dialect.cpp
index 6b4d669d18e..f9301605b46 100644
--- a/mlir/examples/toy/Ch3/mlir/Dialect.cpp
+++ b/mlir/examples/toy/Ch3/mlir/Dialect.cpp
@@ -54,8 +54,7 @@ void ConstantOp::build(mlir::Builder *builder, mlir::OperationState &state,
 static mlir::LogicalResult verify(ConstantOp op) {
   // If the return type of the constant is not an unranked tensor, the shape
   // must match the shape of the attribute holding the data.
-  auto resultType =
-      op.getResult()->getType().dyn_cast<mlir::RankedTensorType>();
+  auto resultType = op.getResult().getType().dyn_cast<mlir::RankedTensorType>();
   if (!resultType)
     return success();
 
@@ -158,7 +157,7 @@ void TransposeOp::build(mlir::Builder *builder, mlir::OperationState &state,
 }
 
 static mlir::LogicalResult verify(TransposeOp op) {
-  auto inputType = op.getOperand()->getType().dyn_cast<RankedTensorType>();
+  auto inputType = op.getOperand().getType().dyn_cast<RankedTensorType>();
   auto resultType = op.getType().dyn_cast<RankedTensorType>();
   if (!inputType || !resultType)
     return mlir::success();
diff --git a/mlir/examples/toy/Ch3/mlir/ToyCombine.cpp b/mlir/examples/toy/Ch3/mlir/ToyCombine.cpp
index e3205402179..e261e77b02e 100644
--- a/mlir/examples/toy/Ch3/mlir/ToyCombine.cpp
+++ b/mlir/examples/toy/Ch3/mlir/ToyCombine.cpp
@@ -41,7 +41,7 @@ struct SimplifyRedundantTranspose : public mlir::OpRewritePattern<TransposeOp> {
     // Look through the input of the current transpose.
     mlir::Value transposeInput = op.getOperand();
     TransposeOp transposeInputOp =
-        llvm::dyn_cast_or_null<TransposeOp>(transposeInput->getDefiningOp());
+        llvm::dyn_cast_or_null<TransposeOp>(transposeInput.getDefiningOp());
 
     // If the input is defined by another Transpose, bingo!
     if (!transposeInputOp)
diff --git a/mlir/examples/toy/Ch3/mlir/ToyCombine.td b/mlir/examples/toy/Ch3/mlir/ToyCombine.td
index e6e33e84d7e..fc7ffafa430 100644
--- a/mlir/examples/toy/Ch3/mlir/ToyCombine.td
+++ b/mlir/examples/toy/Ch3/mlir/ToyCombine.td
@@ -41,7 +41,7 @@ def ReshapeReshapeOptPattern : Pat<(ReshapeOp(ReshapeOp $arg)),
 
 // Reshape(Constant(x)) = x'
 def ReshapeConstant :
-  NativeCodeCall<"$0.reshape(($1->getType()).cast<ShapedType>())">;
+  NativeCodeCall<"$0.reshape(($1.getType()).cast<ShapedType>())">;
 def FoldConstantReshapeOptPattern : Pat<
   (ReshapeOp:$res (ConstantOp $arg)),
   (ConstantOp (ReshapeConstant $arg, $res))>;
@@ -54,7 +54,7 @@ def FoldConstantReshapeOptPattern : Pat<
 // on operand properties.
 
 // Reshape(x) = x, where input and output shapes are identical
-def TypesAreIdentical : Constraint<CPred<"$0->getType() == $1->getType()">>;
+def TypesAreIdentical : Constraint<CPred<"$0.getType() == $1.getType()">>;
 def RedundantReshapeOptPattern : Pat<
   (ReshapeOp:$res $arg), (replaceWithValue $arg),
   [(TypesAreIdentical $res, $arg)]>;
diff --git a/mlir/examples/toy/Ch4/mlir/Dialect.cpp b/mlir/examples/toy/Ch4/mlir/Dialect.cpp
index 0a9ded0c3d3..c0bd6f79aa1 100644
--- a/mlir/examples/toy/Ch4/mlir/Dialect.cpp
+++ b/mlir/examples/toy/Ch4/mlir/Dialect.cpp
@@ -53,7 +53,7 @@ struct ToyInlinerInterface : public DialectInlinerInterface {
     // Replace the values directly with the return operands.
     assert(returnOp.getNumOperands() == valuesToRepl.size());
     for (const auto &it : llvm::enumerate(returnOp.getOperands()))
-      valuesToRepl[it.index()]->replaceAllUsesWith(it.value());
+      valuesToRepl[it.index()].replaceAllUsesWith(it.value());
   }
 
   /// Attempts to materialize a conversion for a type mismatch between a call
@@ -104,8 +104,7 @@ void ConstantOp::build(mlir::Builder *builder, mlir::OperationState &state,
 static mlir::LogicalResult verify(ConstantOp op) {
   // If the return type of the constant is not an unranked tensor, the shape
   // must match the shape of the attribute holding the data.
-  auto resultType =
-      op.getResult()->getType().dyn_cast<mlir::RankedTensorType>();
+  auto resultType = op.getResult().getType().dyn_cast<mlir::RankedTensorType>();
   if (!resultType)
     return success();
 
@@ -142,14 +141,14 @@ void AddOp::build(mlir::Builder *builder, mlir::OperationState &state,
 
 /// Infer the output shape of the AddOp, this is required by the shape inference
 /// interface.
-void AddOp::inferShapes() { getResult()->setType(getOperand(0)->getType()); }
+void AddOp::inferShapes() { getResult().setType(getOperand(0).getType()); }
 
 //===----------------------------------------------------------------------===//
 // CastOp
 
 /// Infer the output shape of the CastOp, this is required by the shape
 /// inference interface.
-void CastOp::inferShapes() { getResult()->setType(getOperand()->getType()); }
+void CastOp::inferShapes() { getResult().setType(getOperand().getType()); }
 
 //===----------------------------------------------------------------------===//
 // GenericCallOp
@@ -183,7 +182,7 @@ void MulOp::build(mlir::Builder *builder, mlir::OperationState &state,
 
 /// Infer the output shape of the MulOp, this is required by the shape inference
 /// interface.
-void MulOp::inferShapes() { getResult()->setType(getOperand(0)->getType()); }
+void MulOp::inferShapes() { getResult().setType(getOperand(0).getType()); }
 
 //===----------------------------------------------------------------------===//
 // ReturnOp
@@ -233,13 +232,13 @@ void TransposeOp::build(mlir::Builder *builder, mlir::OperationState &state,
 }
 
 void TransposeOp::inferShapes() {
-  auto arrayTy = getOperand()->getType().cast<RankedTensorType>();
+  auto arrayTy = getOperand().getType().cast<RankedTensorType>();
   SmallVector<int64_t, 2> dims(llvm::reverse(arrayTy.getShape()));
-  getResult()->setType(RankedTensorType::get(dims, arrayTy.getElementType()));
+  getResult().setType(RankedTensorType::get(dims, arrayTy.getElementType()));
 }
 
 static mlir::LogicalResult verify(TransposeOp op) {
-  auto inputType = op.getOperand()->getType().dyn_cast<RankedTensorType>();
+  auto inputType = op.getOperand().getType().dyn_cast<RankedTensorType>();
   auto resultType = op.getType().dyn_cast<RankedTensorType>();
   if (!inputType || !resultType)
     return mlir::success();
diff --git a/mlir/examples/toy/Ch4/mlir/ToyCombine.cpp b/mlir/examples/toy/Ch4/mlir/ToyCombine.cpp
index 82c247c1be2..3c41958ed31 100644
--- a/mlir/examples/toy/Ch4/mlir/ToyCombine.cpp
+++ b/mlir/examples/toy/Ch4/mlir/ToyCombine.cpp
@@ -46,7 +46,7 @@ struct SimplifyRedundantTranspose : public mlir::OpRewritePattern<TransposeOp> {
     // Look through the input of the current transpose.
     mlir::Value transposeInput = op.getOperand();
     TransposeOp transposeInputOp =
-        llvm::dyn_cast_or_null<TransposeOp>(transposeInput->getDefiningOp());
+        llvm::dyn_cast_or_null<TransposeOp>(transposeInput.getDefiningOp());
 
     // If the input is defined by another Transpose, bingo!
     if (!transposeInputOp)
diff --git a/mlir/examples/toy/Ch4/mlir/ToyCombine.td b/mlir/examples/toy/Ch4/mlir/ToyCombine.td
index e6e33e84d7e..fc7ffafa430 100644
--- a/mlir/examples/toy/Ch4/mlir/ToyCombine.td
+++ b/mlir/examples/toy/Ch4/mlir/ToyCombine.td
@@ -41,7 +41,7 @@ def ReshapeReshapeOptPattern : Pat<(ReshapeOp(ReshapeOp $arg)),
 
 // Reshape(Constant(x)) = x'
 def ReshapeConstant :
-  NativeCodeCall<"$0.reshape(($1->getType()).cast<ShapedType>())">;
+  NativeCodeCall<"$0.reshape(($1.getType()).cast<ShapedType>())">;
 def FoldConstantReshapeOptPattern : Pat<
   (ReshapeOp:$res (ConstantOp $arg)),
   (ConstantOp (ReshapeConstant $arg, $res))>;
@@ -54,7 +54,7 @@ def FoldConstantReshapeOptPattern : Pat<
 // on operand properties.
 
 // Reshape(x) = x, where input and output shapes are identical
-def TypesAreIdentical : Constraint<CPred<"$0->getType() == $1->getType()">>;
+def TypesAreIdentical : Constraint<CPred<"$0.getType() == $1.getType()">>;
 def RedundantReshapeOptPattern : Pat<
   (ReshapeOp:$res $arg), (replaceWithValue $arg),
   [(TypesAreIdentical $res, $arg)]>;
diff --git a/mlir/examples/toy/Ch5/mlir/Dialect.cpp b/mlir/examples/toy/Ch5/mlir/Dialect.cpp
index 0a9ded0c3d3..c0bd6f79aa1 100644
--- a/mlir/examples/toy/Ch5/mlir/Dialect.cpp
+++ b/mlir/examples/toy/Ch5/mlir/Dialect.cpp
@@ -53,7 +53,7 @@ struct ToyInlinerInterface : public DialectInlinerInterface {
     // Replace the values directly with the return operands.
     assert(returnOp.getNumOperands() == valuesToRepl.size());
     for (const auto &it : llvm::enumerate(returnOp.getOperands()))
-      valuesToRepl[it.index()]->replaceAllUsesWith(it.value());
+      valuesToRepl[it.index()].replaceAllUsesWith(it.value());
   }
 
   /// Attempts to materialize a conversion for a type mismatch between a call
@@ -104,8 +104,7 @@ void ConstantOp::build(mlir::Builder *builder, mlir::OperationState &state,
 static mlir::LogicalResult verify(ConstantOp op) {
   // If the return type of the constant is not an unranked tensor, the shape
   // must match the shape of the attribute holding the data.
-  auto resultType =
-      op.getResult()->getType().dyn_cast<mlir::RankedTensorType>();
+  auto resultType = op.getResult().getType().dyn_cast<mlir::RankedTensorType>();
   if (!resultType)
     return success();
 
@@ -142,14 +141,14 @@ void AddOp::build(mlir::Builder *builder, mlir::OperationState &state,
 
 /// Infer the output shape of the AddOp, this is required by the shape inference
 /// interface.
-void AddOp::inferShapes() { getResult()->setType(getOperand(0)->getType()); }
+void AddOp::inferShapes() { getResult().setType(getOperand(0).getType()); }
 
 //===----------------------------------------------------------------------===//
 // CastOp
 
 /// Infer the output shape of the CastOp, this is required by the shape
 /// inference interface.
-void CastOp::inferShapes() { getResult()->setType(getOperand()->getType()); }
+void CastOp::inferShapes() { getResult().setType(getOperand().getType()); }
 
 //===----------------------------------------------------------------------===//
 // GenericCallOp
@@ -183,7 +182,7 @@ void MulOp::build(mlir::Builder *builder, mlir::OperationState &state,
 
 /// Infer the output shape of the MulOp, this is required by the shape inference
 /// interface.
-void MulOp::inferShapes() { getResult()->setType(getOperand(0)->getType()); }
+void MulOp::inferShapes() { getResult().setType(getOperand(0).getType()); }
 
 //===----------------------------------------------------------------------===//
 // ReturnOp
@@ -233,13 +232,13 @@ void TransposeOp::build(mlir::Builder *builder, mlir::OperationState &state,
 }
 
 void TransposeOp::inferShapes() {
-  auto arrayTy = getOperand()->getType().cast<RankedTensorType>();
+  auto arrayTy = getOperand().getType().cast<RankedTensorType>();
   SmallVector<int64_t, 2> dims(llvm::reverse(arrayTy.getShape()));
-  getResult()->setType(RankedTensorType::get(dims, arrayTy.getElementType()));
+  getResult().setType(RankedTensorType::get(dims, arrayTy.getElementType()));
 }
 
 static mlir::LogicalResult verify(TransposeOp op) {
-  auto inputType = op.getOperand()->getType().dyn_cast<RankedTensorType>();
+  auto inputType = op.getOperand().getType().dyn_cast<RankedTensorType>();
   auto resultType = op.getType().dyn_cast<RankedTensorType>();
   if (!inputType || !resultType)
     return mlir::success();
diff --git a/mlir/examples/toy/Ch5/mlir/ToyCombine.cpp b/mlir/examples/toy/Ch5/mlir/ToyCombine.cpp
index 82c247c1be2..3c41958ed31 100644
--- a/mlir/examples/toy/Ch5/mlir/ToyCombine.cpp
+++ b/mlir/examples/toy/Ch5/mlir/ToyCombine.cpp
@@ -46,7 +46,7 @@ struct SimplifyRedundantTranspose : public mlir::OpRewritePattern<TransposeOp> {
     // Look through the input of the current transpose.
     mlir::Value transposeInput = op.getOperand();
     TransposeOp transposeInputOp =
-        llvm::dyn_cast_or_null<TransposeOp>(transposeInput->getDefiningOp());
+        llvm::dyn_cast_or_null<TransposeOp>(transposeInput.getDefiningOp());
 
     // If the input is defined by another Transpose, bingo!
     if (!transposeInputOp)
diff --git a/mlir/examples/toy/Ch5/mlir/ToyCombine.td b/mlir/examples/toy/Ch5/mlir/ToyCombine.td
index e6e33e84d7e..fc7ffafa430 100644
--- a/mlir/examples/toy/Ch5/mlir/ToyCombine.td
+++ b/mlir/examples/toy/Ch5/mlir/ToyCombine.td
@@ -41,7 +41,7 @@ def ReshapeReshapeOptPattern : Pat<(ReshapeOp(ReshapeOp $arg)),
 
 // Reshape(Constant(x)) = x'
 def ReshapeConstant :
-  NativeCodeCall<"$0.reshape(($1->getType()).cast<ShapedType>())">;
+  NativeCodeCall<"$0.reshape(($1.getType()).cast<ShapedType>())">;
 def FoldConstantReshapeOptPattern : Pat<
   (ReshapeOp:$res (ConstantOp $arg)),
   (ConstantOp (ReshapeConstant $arg, $res))>;
@@ -54,7 +54,7 @@ def FoldConstantReshapeOptPattern : Pat<
 // on operand properties.
 
 // Reshape(x) = x, where input and output shapes are identical
-def TypesAreIdentical : Constraint<CPred<"$0->getType() == $1->getType()">>;
+def TypesAreIdentical : Constraint<CPred<"$0.getType() == $1.getType()">>;
 def RedundantReshapeOptPattern : Pat<
   (ReshapeOp:$res $arg), (replaceWithValue $arg),
   [(TypesAreIdentical $res, $arg)]>;
diff --git a/mlir/examples/toy/Ch6/mlir/Dialect.cpp b/mlir/examples/toy/Ch6/mlir/Dialect.cpp
index 0a9ded0c3d3..c0bd6f79aa1 100644
--- a/mlir/examples/toy/Ch6/mlir/Dialect.cpp
+++ b/mlir/examples/toy/Ch6/mlir/Dialect.cpp
@@ -53,7 +53,7 @@ struct ToyInlinerInterface : public DialectInlinerInterface {
     // Replace the values directly with the return operands.
     assert(returnOp.getNumOperands() == valuesToRepl.size());
     for (const auto &it : llvm::enumerate(returnOp.getOperands()))
-      valuesToRepl[it.index()]->replaceAllUsesWith(it.value());
+      valuesToRepl[it.index()].replaceAllUsesWith(it.value());
   }
 
   /// Attempts to materialize a conversion for a type mismatch between a call
@@ -104,8 +104,7 @@ void ConstantOp::build(mlir::Builder *builder, mlir::OperationState &state,
 static mlir::LogicalResult verify(ConstantOp op) {
   // If the return type of the constant is not an unranked tensor, the shape
   // must match the shape of the attribute holding the data.
-  auto resultType =
-      op.getResult()->getType().dyn_cast<mlir::RankedTensorType>();
+  auto resultType = op.getResult().getType().dyn_cast<mlir::RankedTensorType>();
   if (!resultType)
     return success();
 
@@ -142,14 +141,14 @@ void AddOp::build(mlir::Builder *builder, mlir::OperationState &state,
 
 /// Infer the output shape of the AddOp, this is required by the shape inference
 /// interface.
-void AddOp::inferShapes() { getResult()->setType(getOperand(0)->getType()); }
+void AddOp::inferShapes() { getResult().setType(getOperand(0).getType()); }
 
 //===----------------------------------------------------------------------===//
 // CastOp
 
 /// Infer the output shape of the CastOp, this is required by the shape
 /// inference interface.
-void CastOp::inferShapes() { getResult()->setType(getOperand()->getType()); }
+void CastOp::inferShapes() { getResult().setType(getOperand().getType()); }
 
 //===----------------------------------------------------------------------===//
 // GenericCallOp
@@ -183,7 +182,7 @@ void MulOp::build(mlir::Builder *builder, mlir::OperationState &state,
 
 /// Infer the output shape of the MulOp, this is required by the shape inference
 /// interface.
-void MulOp::inferShapes() { getResult()->setType(getOperand(0)->getType()); }
+void MulOp::inferShapes() { getResult().setType(getOperand(0).getType()); }
 
 //===----------------------------------------------------------------------===//
 // ReturnOp
@@ -233,13 +232,13 @@ void TransposeOp::build(mlir::Builder *builder, mlir::OperationState &state,
 }
 
 void TransposeOp::inferShapes() {
-  auto arrayTy = getOperand()->getType().cast<RankedTensorType>();
+  auto arrayTy = getOperand().getType().cast<RankedTensorType>();
   SmallVector<int64_t, 2> dims(llvm::reverse(arrayTy.getShape()));
-  getResult()->setType(RankedTensorType::get(dims, arrayTy.getElementType()));
+  getResult().setType(RankedTensorType::get(dims, arrayTy.getElementType()));
 }
 
 static mlir::LogicalResult verify(TransposeOp op) {
-  auto inputType = op.getOperand()->getType().dyn_cast<RankedTensorType>();
+  auto inputType = op.getOperand().getType().dyn_cast<RankedTensorType>();
   auto resultType = op.getType().dyn_cast<RankedTensorType>();
   if (!inputType || !resultType)
     return mlir::success();
diff --git a/mlir/examples/toy/Ch6/mlir/ToyCombine.cpp b/mlir/examples/toy/Ch6/mlir/ToyCombine.cpp
index 82c247c1be2..3c41958ed31 100644
--- a/mlir/examples/toy/Ch6/mlir/ToyCombine.cpp
+++ b/mlir/examples/toy/Ch6/mlir/ToyCombine.cpp
@@ -46,7 +46,7 @@ struct SimplifyRedundantTranspose : public mlir::OpRewritePattern<TransposeOp> {
     // Look through the input of the current transpose.
     mlir::Value transposeInput = op.getOperand();
     TransposeOp transposeInputOp =
-        llvm::dyn_cast_or_null<TransposeOp>(transposeInput->getDefiningOp());
+        llvm::dyn_cast_or_null<TransposeOp>(transposeInput.getDefiningOp());
 
     // If the input is defined by another Transpose, bingo!
     if (!transposeInputOp)
diff --git a/mlir/examples/toy/Ch6/mlir/ToyCombine.td b/mlir/examples/toy/Ch6/mlir/ToyCombine.td
index e6e33e84d7e..fc7ffafa430 100644
--- a/mlir/examples/toy/Ch6/mlir/ToyCombine.td
+++ b/mlir/examples/toy/Ch6/mlir/ToyCombine.td
@@ -41,7 +41,7 @@ def ReshapeReshapeOptPattern : Pat<(ReshapeOp(ReshapeOp $arg)),
 
 // Reshape(Constant(x)) = x'
 def ReshapeConstant :
-  NativeCodeCall<"$0.reshape(($1->getType()).cast<ShapedType>())">;
+  NativeCodeCall<"$0.reshape(($1.getType()).cast<ShapedType>())">;
 def FoldConstantReshapeOptPattern : Pat<
   (ReshapeOp:$res (ConstantOp $arg)),
   (ConstantOp (ReshapeConstant $arg, $res))>;
@@ -54,7 +54,7 @@ def FoldConstantReshapeOptPattern : Pat<
 // on operand properties.
 
 // Reshape(x) = x, where input and output shapes are identical
-def TypesAreIdentical : Constraint<CPred<"$0->getType() == $1->getType()">>;
+def TypesAreIdentical : Constraint<CPred<"$0.getType() == $1.getType()">>;
 def RedundantReshapeOptPattern : Pat<
   (ReshapeOp:$res $arg), (replaceWithValue $arg),
   [(TypesAreIdentical $res, $arg)]>;
diff --git a/mlir/examples/toy/Ch7/mlir/Dialect.cpp b/mlir/examples/toy/Ch7/mlir/Dialect.cpp
index 7e37f61a473..619185d3bba 100644
--- a/mlir/examples/toy/Ch7/mlir/Dialect.cpp
+++ b/mlir/examples/toy/Ch7/mlir/Dialect.cpp
@@ -54,7 +54,7 @@ struct ToyInlinerInterface : public DialectInlinerInterface {
     // Replace the values directly with the return operands.
     assert(returnOp.getNumOperands() == valuesToRepl.size());
     for (const auto &it : llvm::enumerate(returnOp.getOperands()))
-      valuesToRepl[it.index()]->replaceAllUsesWith(it.value());
+      valuesToRepl[it.index()].replaceAllUsesWith(it.value());
   }
 
   /// Attempts to materialize a conversion for a type mismatch between a call
@@ -171,16 +171,16 @@ static mlir::LogicalResult verifyConstantForType(mlir::Type type,
 /// Verifier for the constant operation. This corresponds to the `::verify(...)`
 /// in the op definition.
 static mlir::LogicalResult verify(ConstantOp op) {
-  return verifyConstantForType(op.getResult()->getType(), op.value(), op);
+  return verifyConstantForType(op.getResult().getType(), op.value(), op);
 }
 
 static mlir::LogicalResult verify(StructConstantOp op) {
-  return verifyConstantForType(op.getResult()->getType(), op.value(), op);
+  return verifyConstantForType(op.getResult().getType(), op.value(), op);
 }
 
 /// Infer the output shape of the ConstantOp, this is required by the shape
 /// inference interface.
-void ConstantOp::inferShapes() { getResult()->setType(value().getType()); }
+void ConstantOp::inferShapes() { getResult().setType(value().getType()); }
 
 //===----------------------------------------------------------------------===//
 // AddOp
@@ -193,14 +193,14 @@ void AddOp::build(mlir::Builder *builder, mlir::OperationState &state,
 
 /// Infer the output shape of the AddOp, this is required by the shape inference
 /// interface.
-void AddOp::inferShapes() { getResult()->setType(getOperand(0)->getType()); }
+void AddOp::inferShapes() { getResult().setType(getOperand(0).getType()); }
 
 //===----------------------------------------------------------------------===//
 // CastOp
 
 /// Infer the output shape of the CastOp, this is required by the shape
 /// inference interface.
-void CastOp::inferShapes() { getResult()->setType(getOperand()->getType()); }
+void CastOp::inferShapes() { getResult().setType(getOperand().getType()); }
 
 //===----------------------------------------------------------------------===//
 // GenericCallOp
@@ -234,7 +234,7 @@ void MulOp::build(mlir::Builder *builder, mlir::OperationState &state,
 
 /// Infer the output shape of the MulOp, this is required by the shape inference
 /// interface.
-void MulOp::inferShapes() { getResult()->setType(getOperand(0)->getType()); }
+void MulOp::inferShapes() { getResult().setType(getOperand(0).getType()); }
 
 //===----------------------------------------------------------------------===//
 // ReturnOp
@@ -280,7 +280,7 @@ static mlir::LogicalResult verify(ReturnOp op) {
 void StructAccessOp::build(mlir::Builder *b, mlir::OperationState &state,
                            mlir::Value input, size_t index) {
   // Extract the result type from the input type.
-  StructType structTy = input->getType().cast<StructType>();
+  StructType structTy = input.getType().cast<StructType>();
   assert(index < structTy.getNumElementTypes());
   mlir::Type resultType = structTy.getElementTypes()[index];
 
@@ -289,12 +289,12 @@ void StructAccessOp::build(mlir::Builder *b, mlir::OperationState &state,
 }
 
 static mlir::LogicalResult verify(StructAccessOp op) {
-  StructType structTy = op.input()->getType().cast<StructType>();
+  StructType structTy = op.input().getType().cast<StructType>();
   size_t index = op.index().getZExtValue();
   if (index >= structTy.getNumElementTypes())
     return op.emitOpError()
            << "index should be within the range of the input struct type";
-  mlir::Type resultType = op.getResult()->getType();
+  mlir::Type resultType = op.getResult().getType();
   if (resultType != structTy.getElementTypes()[index])
     return op.emitOpError() << "must have the same result type as the struct "
                                "element referred to by the index";
@@ -311,13 +311,13 @@ void TransposeOp::build(mlir::Builder *builder, mlir::OperationState &state,
 }
 
 void TransposeOp::inferShapes() {
-  auto arrayTy = getOperand()->getType().cast<RankedTensorType>();
+  auto arrayTy = getOperand().getType().cast<RankedTensorType>();
   SmallVector<int64_t, 2> dims(llvm::reverse(arrayTy.getShape()));
-  getResult()->setType(RankedTensorType::get(dims, arrayTy.getElementType()));
+  getResult().setType(RankedTensorType::get(dims, arrayTy.getElementType()));
 }
 
 static mlir::LogicalResult verify(TransposeOp op) {
-  auto inputType = op.getOperand()->getType().dyn_cast<RankedTensorType>();
+  auto inputType = op.getOperand().getType().dyn_cast<RankedTensorType>();
   auto resultType = op.getType().dyn_cast<RankedTensorType>();
   if (!inputType || !resultType)
     return mlir::success();
diff --git a/mlir/examples/toy/Ch7/mlir/MLIRGen.cpp b/mlir/examples/toy/Ch7/mlir/MLIRGen.cpp
index 3d543f69bdc..c1bcee7e1b9 100644
--- a/mlir/examples/toy/Ch7/mlir/MLIRGen.cpp
+++ b/mlir/examples/toy/Ch7/mlir/MLIRGen.cpp
@@ -585,11 +585,11 @@ private:
       mlir::Type type = getType(varType, vardecl.loc());
       if (!type)
         return nullptr;
-      if (type != value->getType()) {
+      if (type != value.getType()) {
         emitError(loc(vardecl.loc()))
             << "struct type of initializer is different than the variable "
                "declaration. Got "
-            << value->getType() << ", but expected " << type;
+            << value.getType() << ", but expected " << type;
         return nullptr;
       }
 
diff --git a/mlir/examples/toy/Ch7/mlir/ToyCombine.cpp b/mlir/examples/toy/Ch7/mlir/ToyCombine.cpp
index c688a53d86f..088603bb8c6 100644
--- a/mlir/examples/toy/Ch7/mlir/ToyCombine.cpp
+++ b/mlir/examples/toy/Ch7/mlir/ToyCombine.cpp
@@ -64,7 +64,7 @@ struct SimplifyRedundantTranspose : public mlir::OpRewritePattern<TransposeOp> {
     // Look through the input of the current transpose.
     mlir::Value transposeInput = op.getOperand();
     TransposeOp transposeInputOp =
-        llvm::dyn_cast_or_null<TransposeOp>(transposeInput->getDefiningOp());
+        llvm::dyn_cast_or_null<TransposeOp>(transposeInput.getDefiningOp());
 
     // If the input is defined by another Transpose, bingo!
     if (!transposeInputOp)
diff --git a/mlir/examples/toy/Ch7/mlir/ToyCombine.td b/mlir/examples/toy/Ch7/mlir/ToyCombine.td
index e6e33e84d7e..fc7ffafa430 100644
--- a/mlir/examples/toy/Ch7/mlir/ToyCombine.td
+++ b/mlir/examples/toy/Ch7/mlir/ToyCombine.td
@@ -41,7 +41,7 @@ def ReshapeReshapeOptPattern : Pat<(ReshapeOp(ReshapeOp $arg)),
 
 // Reshape(Constant(x)) = x'
 def ReshapeConstant :
-  NativeCodeCall<"$0.reshape(($1->getType()).cast<ShapedType>())">;
+  NativeCodeCall<"$0.reshape(($1.getType()).cast<ShapedType>())">;
 def FoldConstantReshapeOptPattern : Pat<
   (ReshapeOp:$res (ConstantOp $arg)),
   (ConstantOp (ReshapeConstant $arg, $res))>;
@@ -54,7 +54,7 @@ def FoldConstantReshapeOptPattern : Pat<
 // on operand properties.
 
 // Reshape(x) = x, where input and output shapes are identical
-def TypesAreIdentical : Constraint<CPred<"$0->getType() == $1->getType()">>;
+def TypesAreIdentical : Constraint<CPred<"$0.getType() == $1.getType()">>;
 def RedundantReshapeOptPattern : Pat<
   (ReshapeOp:$res $arg), (replaceWithValue $arg),
   [(TypesAreIdentical $res, $arg)]>;