diff --git a/src/builder/frontend_dialect_transformer.cpp b/src/builder/frontend_dialect_transformer.cpp
index c091903..43f8aa9 100644
--- a/src/builder/frontend_dialect_transformer.cpp
+++ b/src/builder/frontend_dialect_transformer.cpp
@@ -680,7 +680,7 @@ private:
 
     auto tensor_val =
         frontend_symbols_.GetTensorByOnnxName(output_tensor_legalized_name);
-    ret_types.emplace_back(tensor_val->getType());
+    ret_types.emplace_back(tensor_val.getType());
     ret_vals.push_back(tensor_val);
   }
 
diff --git a/src/dialect/onnx/onnx_ops.cpp b/src/dialect/onnx/onnx_ops.cpp
index 6ff0fad..4b68fe7 100644
--- a/src/dialect/onnx/onnx_ops.cpp
+++ b/src/dialect/onnx/onnx_ops.cpp
@@ -65,14 +65,14 @@ ONNXEntryPointOp ONNXEntryPointOp::create(mlir::Location location,
 // Exp
 /// Infer the output shape of the ONNXExpOp. This method is required by the
 /// shape inference interface.
-void ONNXExpOp::inferShapes() { getResult()->setType(getOperand()->getType()); }
+void ONNXExpOp::inferShapes() { getResult().setType(getOperand().getType()); }
 
 //===----------------------------------------------------------------------===//
 // Tanh
 /// Infer the output shape of the ONNXTanhOp. This method is required by the
 /// shape inference interface.
 void ONNXTanhOp::inferShapes() {
-  getResult()->setType(getOperand()->getType());
+  getResult().setType(getOperand().getType());
 }
 
 //===----------------------------------------------------------------------===//
@@ -80,7 +80,7 @@ void ONNXTanhOp::inferShapes() {
 /// Infer the output shape of the ONNXSinhOp. This method is required by the
 /// shape inference interface.
 void ONNXSinhOp::inferShapes() {
-  getResult()->setType(getOperand()->getType());
+  getResult().setType(getOperand().getType());
 }
 
 //===----------------------------------------------------------------------===//
@@ -88,27 +88,27 @@ void ONNXSinhOp::inferShapes() {
 /// Infer the output shape of the ONNXCoshOp. This method is required by the
 /// shape inference interface.
 void ONNXCoshOp::inferShapes() {
-  getResult()->setType(getOperand()->getType());
+  getResult().setType(getOperand().getType());
 }
 
 //===----------------------------------------------------------------------===//
 // Cos
 /// Infer the output shape of the ONNXCosOp. This method is required by the
 /// shape inference interface.
-void ONNXCosOp::inferShapes() { getResult()->setType(getOperand()->getType()); }
+void ONNXCosOp::inferShapes() { getResult().setType(getOperand().getType()); }
 
 //===----------------------------------------------------------------------===//
 // Log
 /// Infer the output shape of the ONNXLogOp. This method is required by the
 /// shape inference interface.
-void ONNXLogOp::inferShapes() { getResult()->setType(getOperand()->getType()); }
+void ONNXLogOp::inferShapes() { getResult().setType(getOperand().getType()); }
 
 //===----------------------------------------------------------------------===//
 // HardSigmoid
 /// Infer the output shape of the ONNXHardSigmoidOp. This method is required by
 /// the shape inference interface.
 void ONNXHardSigmoidOp::inferShapes() {
-  getResult()->setType(getOperand()->getType());
+  getResult().setType(getOperand().getType());
 }
 
 //===----------------------------------------------------------------------===//
@@ -116,21 +116,21 @@ void ONNXHardSigmoidOp::inferShapes() {
 /// Infer the output shape of the ONNXSigmoidOp. This method is required by the
 /// shape inference interface.
 void ONNXSigmoidOp::inferShapes() {
-  getResult()->setType(getOperand()->getType());
+  getResult().setType(getOperand().getType());
 }
 
 //===----------------------------------------------------------------------===//
 // Elu
 /// Infer the output shape of the ONNXEluOp. This method is required by the
 /// shape inference interface.
-void ONNXEluOp::inferShapes() { getResult()->setType(getOperand()->getType()); }
+void ONNXEluOp::inferShapes() { getResult().setType(getOperand().getType()); }
 
 //===----------------------------------------------------------------------===//
 // Relu
 /// Infer the output shape of the ONNXReluOp. This method is required by the
 /// shape inference interface.
 void ONNXReluOp::inferShapes() {
-  getResult()->setType(getOperand()->getType());
+  getResult().setType(getOperand().getType());
 }
 
 //===----------------------------------------------------------------------===//
@@ -138,7 +138,7 @@ void ONNXReluOp::inferShapes() {
 /// Infer the output shape of the ONNXLeakyReluOp. This method is required by
 /// the shape inference interface.
 void ONNXLeakyReluOp::inferShapes() {
-  getResult()->setType(getOperand()->getType());
+  getResult().setType(getOperand().getType());
 }
 
 //===----------------------------------------------------------------------===//
@@ -146,7 +146,7 @@ void ONNXLeakyReluOp::inferShapes() {
 /// Infer the output shape of the ONNXSeluOp. This method is required by
 /// the shape inference interface.
 void ONNXSeluOp::inferShapes() {
-  getResult()->setType(getOperand()->getType());
+  getResult().setType(getOperand().getType());
 }
 
 //===----------------------------------------------------------------------===//
@@ -154,7 +154,7 @@ void ONNXSeluOp::inferShapes() {
 /// Infer the output shape of the ONNXReciprocalOp. This method is required by
 /// the shape inference interface.
 void ONNXReciprocalOp::inferShapes() {
-  getResult()->setType(getOperand()->getType());
+  getResult().setType(getOperand().getType());
 }
 
 //===----------------------------------------------------------------------===//
@@ -162,12 +162,12 @@ void ONNXReciprocalOp::inferShapes() {
 /// Infer the output shape of the ONNXAddOp. This method is required by the
 /// shape inference interface.
 void ONNXAddOp::inferShapes() {
-  if (!getOperand(0)->getType().isa<RankedTensorType>() ||
-      !getOperand(1)->getType().isa<RankedTensorType>())
+  if (!getOperand(0).getType().isa<RankedTensorType>() ||
+      !getOperand(1).getType().isa<RankedTensorType>())
     return;
-  auto lhsTy = getOperand(0)->getType().cast<RankedTensorType>();
-  auto rhsTy = getOperand(1)->getType().cast<RankedTensorType>();
-  getResult()->setType(getBroadcastedType(lhsTy, rhsTy));
+  auto lhsTy = getOperand(0).getType().cast<RankedTensorType>();
+  auto rhsTy = getOperand(1).getType().cast<RankedTensorType>();
+  getResult().setType(getBroadcastedType(lhsTy, rhsTy));
 }
 
 //===----------------------------------------------------------------------===//
@@ -175,12 +175,12 @@ void ONNXAddOp::inferShapes() {
 /// Infer the output shape of the ONNXMulOp. This method is required by the
 /// shape inference interface.
 void ONNXMulOp::inferShapes() {
-  if (!getOperand(0)->getType().isa<RankedTensorType>() ||
-      !getOperand(1)->getType().isa<RankedTensorType>())
+  if (!getOperand(0).getType().isa<RankedTensorType>() ||
+      !getOperand(1).getType().isa<RankedTensorType>())
     return;
-  auto lhsTy = getOperand(0)->getType().cast<RankedTensorType>();
-  auto rhsTy = getOperand(1)->getType().cast<RankedTensorType>();
-  getResult()->setType(getBroadcastedType(lhsTy, rhsTy));
+  auto lhsTy = getOperand(0).getType().cast<RankedTensorType>();
+  auto rhsTy = getOperand(1).getType().cast<RankedTensorType>();
+  getResult().setType(getBroadcastedType(lhsTy, rhsTy));
 }
 
 //===----------------------------------------------------------------------===//
@@ -188,12 +188,12 @@ void ONNXMulOp::inferShapes() {
 /// Infer the output shape of the ONNXDivOp. This method is required by the
 /// shape inference interface.
 void ONNXDivOp::inferShapes() {
-  if (!getOperand(0)->getType().isa<RankedTensorType>() ||
-      !getOperand(1)->getType().isa<RankedTensorType>())
+  if (!getOperand(0).getType().isa<RankedTensorType>() ||
+      !getOperand(1).getType().isa<RankedTensorType>())
     return;
-  auto lhsTy = getOperand(0)->getType().cast<RankedTensorType>();
-  auto rhsTy = getOperand(1)->getType().cast<RankedTensorType>();
-  getResult()->setType(getBroadcastedType(lhsTy, rhsTy));
+  auto lhsTy = getOperand(0).getType().cast<RankedTensorType>();
+  auto rhsTy = getOperand(1).getType().cast<RankedTensorType>();
+  getResult().setType(getBroadcastedType(lhsTy, rhsTy));
 }
 
 //===----------------------------------------------------------------------===//
@@ -201,12 +201,12 @@ void ONNXDivOp::inferShapes() {
 /// Infer the output shape of the ONNXSubOp. This method is required by the
 /// shape inference interface.
 void ONNXSubOp::inferShapes() {
-  if (!getOperand(0)->getType().isa<RankedTensorType>() ||
-      !getOperand(1)->getType().isa<RankedTensorType>())
+  if (!getOperand(0).getType().isa<RankedTensorType>() ||
+      !getOperand(1).getType().isa<RankedTensorType>())
     return;
-  auto lhsTy = getOperand(0)->getType().cast<RankedTensorType>();
-  auto rhsTy = getOperand(1)->getType().cast<RankedTensorType>();
-  getResult()->setType(getBroadcastedType(lhsTy, rhsTy));
+  auto lhsTy = getOperand(0).getType().cast<RankedTensorType>();
+  auto rhsTy = getOperand(1).getType().cast<RankedTensorType>();
+  getResult().setType(getBroadcastedType(lhsTy, rhsTy));
 }
 
 //===----------------------------------------------------------------------===//
@@ -214,12 +214,12 @@ void ONNXSubOp::inferShapes() {
 /// Infer the output shape of the ONNXAndOp. This method is required by the
 /// shape inference interface.
 void ONNXAndOp::inferShapes() {
-  if (!getOperand(0)->getType().isa<RankedTensorType>() ||
-      !getOperand(1)->getType().isa<RankedTensorType>())
+  if (!getOperand(0).getType().isa<RankedTensorType>() ||
+      !getOperand(1).getType().isa<RankedTensorType>())
     return;
-  auto lhsTy = getOperand(0)->getType().cast<RankedTensorType>();
-  auto rhsTy = getOperand(1)->getType().cast<RankedTensorType>();
-  getResult()->setType(getBroadcastedType(lhsTy, rhsTy));
+  auto lhsTy = getOperand(0).getType().cast<RankedTensorType>();
+  auto rhsTy = getOperand(1).getType().cast<RankedTensorType>();
+  getResult().setType(getBroadcastedType(lhsTy, rhsTy));
 }
 
 //===----------------------------------------------------------------------===//
@@ -227,12 +227,12 @@ void ONNXAndOp::inferShapes() {
 /// Infer the output shape of the ONNXOrOp. This method is required by the
 /// shape inference interface.
 void ONNXOrOp::inferShapes() {
-  if (!getOperand(0)->getType().isa<RankedTensorType>() ||
-      !getOperand(1)->getType().isa<RankedTensorType>())
+  if (!getOperand(0).getType().isa<RankedTensorType>() ||
+      !getOperand(1).getType().isa<RankedTensorType>())
     return;
-  auto lhsTy = getOperand(0)->getType().cast<RankedTensorType>();
-  auto rhsTy = getOperand(1)->getType().cast<RankedTensorType>();
-  getResult()->setType(getBroadcastedType(lhsTy, rhsTy));
+  auto lhsTy = getOperand(0).getType().cast<RankedTensorType>();
+  auto rhsTy = getOperand(1).getType().cast<RankedTensorType>();
+  getResult().setType(getBroadcastedType(lhsTy, rhsTy));
 }
 
 //===----------------------------------------------------------------------===//
@@ -240,12 +240,12 @@ void ONNXOrOp::inferShapes() {
 /// Infer the output shape of the ONNXXorOp. This method is required by the
 /// shape inference interface.
 void ONNXXorOp::inferShapes() {
-  if (!getOperand(0)->getType().isa<RankedTensorType>() ||
-      !getOperand(1)->getType().isa<RankedTensorType>())
+  if (!getOperand(0).getType().isa<RankedTensorType>() ||
+      !getOperand(1).getType().isa<RankedTensorType>())
     return;
-  auto lhsTy = getOperand(0)->getType().cast<RankedTensorType>();
-  auto rhsTy = getOperand(1)->getType().cast<RankedTensorType>();
-  getResult()->setType(getBroadcastedType(lhsTy, rhsTy));
+  auto lhsTy = getOperand(0).getType().cast<RankedTensorType>();
+  auto rhsTy = getOperand(1).getType().cast<RankedTensorType>();
+  getResult().setType(getBroadcastedType(lhsTy, rhsTy));
 }
 
 //===----------------------------------------------------------------------===//
@@ -256,15 +256,15 @@ void ONNXXorOp::inferShapes() {
 /// shape inference interface.
 void ONNXSumOp::inferShapes() {
   for (int i = 0; i < getNumOperands(); ++i) {
-    if (!getOperand(i)->getType().cast<RankedTensorType>())
+    if (!getOperand(i).getType().cast<RankedTensorType>())
       return;
   }
-  Type resultTy = getOperand(0)->getType().cast<RankedTensorType>();
+  Type resultTy = getOperand(0).getType().cast<RankedTensorType>();
   for (int i = 1; i < getNumOperands(); ++i) {
-    Type nextTy = getOperand(i)->getType().cast<RankedTensorType>();
+    Type nextTy = getOperand(i).getType().cast<RankedTensorType>();
     resultTy = getBroadcastedType(resultTy, nextTy);
   }
-  getResult()->setType(resultTy);
+  getResult().setType(resultTy);
 }
 
 //===----------------------------------------------------------------------===//
@@ -273,15 +273,15 @@ void ONNXSumOp::inferShapes() {
 /// shape inference interface.
 void ONNXMaxOp::inferShapes() {
   for (int i = 0; i < getNumOperands(); ++i) {
-    if (!getOperand(i)->getType().cast<RankedTensorType>())
+    if (!getOperand(i).getType().cast<RankedTensorType>())
       return;
   }
-  Type resultTy = getOperand(0)->getType().cast<RankedTensorType>();
+  Type resultTy = getOperand(0).getType().cast<RankedTensorType>();
   for (int i = 1; i < getNumOperands(); ++i) {
-    Type nextTy = getOperand(i)->getType().cast<RankedTensorType>();
+    Type nextTy = getOperand(i).getType().cast<RankedTensorType>();
     resultTy = getBroadcastedType(resultTy, nextTy);
   }
-  getResult()->setType(resultTy);
+  getResult().setType(resultTy);
 }
 
 //===----------------------------------------------------------------------===//
@@ -290,15 +290,15 @@ void ONNXMaxOp::inferShapes() {
 /// shape inference interface.
 void ONNXMinOp::inferShapes() {
   for (int i = 0; i < getNumOperands(); ++i) {
-    if (!getOperand(i)->getType().cast<RankedTensorType>())
+    if (!getOperand(i).getType().cast<RankedTensorType>())
       return;
   }
-  Type resultTy = getOperand(0)->getType().cast<RankedTensorType>();
+  Type resultTy = getOperand(0).getType().cast<RankedTensorType>();
   for (int i = 1; i < getNumOperands(); ++i) {
-    Type nextTy = getOperand(i)->getType().cast<RankedTensorType>();
+    Type nextTy = getOperand(i).getType().cast<RankedTensorType>();
     resultTy = getBroadcastedType(resultTy, nextTy);
   }
-  getResult()->setType(resultTy);
+  getResult().setType(resultTy);
 }
 
 //===----------------------------------------------------------------------===//
@@ -306,7 +306,7 @@ void ONNXMinOp::inferShapes() {
 /// Infer the output shape of the ONNXIdentityOp. This method is required by the
 /// shape inference interface.
 void ONNXIdentityOp::inferShapes() {
-  getResult()->setType(getOperand()->getType());
+  getResult().setType(getOperand().getType());
 }
 
 //===----------------------------------------------------------------------===//
@@ -315,15 +315,15 @@ void ONNXIdentityOp::inferShapes() {
 
 void ONNXMatMulOp::inferShapes() {
   // Cannot infer shape if no shape exists.
-  if (!getOperand(0)->getType().isa<RankedTensorType>() ||
-      !getOperand(1)->getType().isa<RankedTensorType>())
+  if (!getOperand(0).getType().isa<RankedTensorType>() ||
+      !getOperand(1).getType().isa<RankedTensorType>())
     return;
-  auto lhsTy = getOperand(0)->getType().cast<RankedTensorType>();
-  auto rhsTy = getOperand(1)->getType().cast<RankedTensorType>();
+  auto lhsTy = getOperand(0).getType().cast<RankedTensorType>();
+  auto rhsTy = getOperand(1).getType().cast<RankedTensorType>();
   SmallVector<int64_t, 2> dims;
   dims.emplace_back(lhsTy.getShape()[0]);
   dims.emplace_back(rhsTy.getShape()[1]);
-  getResult()->setType(RankedTensorType::get(dims, lhsTy.getElementType()));
+  getResult().setType(RankedTensorType::get(dims, lhsTy.getElementType()));
 }
 
 // TODO:
@@ -336,30 +336,30 @@ void ONNXMatMulOp::inferShapes() {
 
 void ONNXGemmOp::inferShapes() {
   // Cannot infer shape if no shape exists.
-  if (!getOperand(0)->getType().isa<RankedTensorType>() ||
-      !getOperand(1)->getType().isa<RankedTensorType>())
+  if (!getOperand(0).getType().isa<RankedTensorType>() ||
+      !getOperand(1).getType().isa<RankedTensorType>())
     return;
-  auto lhsTy = getOperand(0)->getType().cast<RankedTensorType>();
-  auto rhsTy = getOperand(1)->getType().cast<RankedTensorType>();
+  auto lhsTy = getOperand(0).getType().cast<RankedTensorType>();
+  auto rhsTy = getOperand(1).getType().cast<RankedTensorType>();
   SmallVector<int64_t, 2> dims;
   dims.emplace_back(lhsTy.getShape()[0]);
   dims.emplace_back(rhsTy.getShape()[1]);
-  getResult()->setType(RankedTensorType::get(dims, lhsTy.getElementType()));
+  getResult().setType(RankedTensorType::get(dims, lhsTy.getElementType()));
 }
 
 // FullGemm
 
 void ONNXFullGemmOp::inferShapes() {
   // Cannot infer shape if no shape exists.
-  if (!getOperand(0)->getType().isa<RankedTensorType>() ||
-      !getOperand(1)->getType().isa<RankedTensorType>())
+  if (!getOperand(0).getType().isa<RankedTensorType>() ||
+      !getOperand(1).getType().isa<RankedTensorType>())
     return;
-  auto lhsTy = getOperand(0)->getType().cast<RankedTensorType>();
-  auto rhsTy = getOperand(1)->getType().cast<RankedTensorType>();
+  auto lhsTy = getOperand(0).getType().cast<RankedTensorType>();
+  auto rhsTy = getOperand(1).getType().cast<RankedTensorType>();
   SmallVector<int64_t, 2> dims;
   dims.emplace_back(lhsTy.getShape()[0]);
   dims.emplace_back(rhsTy.getShape()[1]);
-  getResult()->setType(RankedTensorType::get(dims, lhsTy.getElementType()));
+  getResult().setType(RankedTensorType::get(dims, lhsTy.getElementType()));
 }
 
 // TODO:
@@ -372,11 +372,11 @@ void ONNXFullGemmOp::inferShapes() {
 
 void ONNXReshapeOp::inferShapes() {
   // Cannot infer shape if no shape tensor is specified.
-  if (!getOperand(1)->getType().isa<RankedTensorType>())
+  if (!getOperand(1).getType().isa<RankedTensorType>())
     emitError("Shape tensor not ranked.");
 
-  auto inputTensorTy = getOperand(0)->getType().cast<RankedTensorType>();
-  auto shapeTensorTy = getOperand(1)->getType().cast<RankedTensorType>();
+  auto inputTensorTy = getOperand(0).getType().cast<RankedTensorType>();
+  auto shapeTensorTy = getOperand(1).getType().cast<RankedTensorType>();
 
   // Only rank 1 shape tensors are supported.
   if (shapeTensorTy.getShape().size() != 1)
@@ -392,7 +392,7 @@ void ONNXReshapeOp::inferShapes() {
   for (int i = 0; i < outputRank; ++i)
     dims.emplace_back(-1);
 
-  getResult()->setType(
+  getResult().setType(
       RankedTensorType::get(dims, inputTensorTy.getElementType()));
 }
 
@@ -402,16 +402,16 @@ void ONNXReshapeOp::inferShapes() {
 
 void ONNXTransposeOp::inferShapes() {
   // Cannot infer shape if no shape exists.
-  if (!getOperand()->getType().isa<RankedTensorType>())
+  if (!getOperand().getType().isa<RankedTensorType>())
     emitError("Shape tensor not ranked.");
 
   // Naive transposition which handles the default case of
   // reversing the shape of the tensor (similar to numpy.transpose).
   // TODO: Once attributes are supported we can handle the case where the
   // transposition uses a permutation vector to interchange the axes.
-  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()));
 }
 
 //===----------------------------------------------------------------------===//
diff --git a/src/pass/lower_frontend_to_krnl.cpp b/src/pass/lower_frontend_to_krnl.cpp
index a17484f..a578479 100644
--- a/src/pass/lower_frontend_to_krnl.cpp
+++ b/src/pass/lower_frontend_to_krnl.cpp
@@ -61,7 +61,7 @@ static Value insertAllocAndDealloc(MemRefType type, Location loc,
         Value maxDim = nullptr;
         for (int i = 0; i < operands.size(); i++) {
           auto operandShape =
-              operands[i]->getType().cast<MemRefType>().getShape();
+              operands[i].getType().cast<MemRefType>().getShape();
           int operandDimIdx = operandShape.size() - 1 - reversedIdx;
 
           if (operandDimIdx < 0)
@@ -162,7 +162,7 @@ getBroadcastedDimInfo(Location loc, ConversionPatternRewriter &rewriter,
     int dimIdx = rank - 1 - reversedIdx;
     sharedDimCount[dimIdx] = 0;
     for (int i = 0; i < operands.size(); ++i) {
-      auto shape = operands[i]->getType().cast<MemRefType>().getShape();
+      auto shape = operands[i].getType().cast<MemRefType>().getShape();
       if (reversedIdx <= shape.size() - 1)
         sharedDimCount[dimIdx]++;
     }
@@ -174,7 +174,7 @@ getBroadcastedDimInfo(Location loc, ConversionPatternRewriter &rewriter,
   // more than one, since they are potentially broadcasted dimensions.
   for (int i = 0; i < operands.size(); ++i) {
     std::map<int, Value> broadcastedDims;
-    auto shape = operands[i]->getType().cast<MemRefType>().getShape();
+    auto shape = operands[i].getType().cast<MemRefType>().getShape();
     int size = shape.size();
     for (int j = 0; j < shape.size(); ++j) {
       if (shape[j] < 0 and sharedDimCount[rank - size + j] > 1) {
@@ -198,7 +198,7 @@ getLoopIVsForBroadcasting(Location loc, ConversionPatternRewriter &rewriter,
                           std::map<int, Value> broadcastedDims) {
   // `operand` must has a ranked type. This should have been checked by the
   // shape inference pass.
-  auto operandShape = operand->getType().cast<MemRefType>().getShape();
+  auto operandShape = operand.getType().cast<MemRefType>().getShape();
   auto rank = operandShape.size();
   auto loopCount = loopIVs.size();
 
@@ -319,7 +319,7 @@ Value mapToLowerScalarOp(Operation *op, ArrayRef<Type> result_types,
   /* Lower UnaryOp to Ops in the Standard dialect.
    */
   auto loc = op->getLoc();
-  Type element_type = operands.front()->getType();
+  Type element_type = operands.front().getType();
   if (element_type.isa<IntegerType>()) {
     return rewriter.create<ScalarIOp<UnaryOp>>(loc, result_types, operands,
                                                mlir::None);
diff --git a/src/pass/onnx_combine.td b/src/pass/onnx_combine.td
index 199e27a..8a40928 100644
--- a/src/pass/onnx_combine.td
+++ b/src/pass/onnx_combine.td
@@ -24,7 +24,7 @@ include "dialect/onnx/onnx.td"
 ///    dag benefitsAdded = (addBenefit 0)
 /// >;
 
-def HasOneUse : Constraint<CPred<"$0->hasOneUse()">>;
+def HasOneUse : Constraint<CPred<"$0.hasOneUse()">>;
 
 //===----------------------------------------------------------------------===//
 // Pattern-Match and Rewrite
diff --git a/src/transform/lower_krnl.cpp b/src/transform/lower_krnl.cpp
index 5578862..da16f81 100644
--- a/src/transform/lower_krnl.cpp
+++ b/src/transform/lower_krnl.cpp
@@ -55,7 +55,7 @@ struct KrnlIterateOpLowering : public OpRewritePattern<KrnlIterateOp> {
     for (size_t i = 0; i < nestedForOps.size() - 1; i++) {
       auto iterateIV = iterateOp.bodyRegion().front().getArgument(0);
       auto forIV = nestedForOps[i].getBody()->getArgument(0);
-      iterateIV->replaceAllUsesWith(forIV);
+      iterateIV.replaceAllUsesWith(forIV);
       iterateOp.bodyRegion().front().eraseArgument(0);
     }
 
diff --git a/src/transform/lower_to_llvm.cpp b/src/transform/lower_to_llvm.cpp
index b61b848..be6d291 100644
--- a/src/transform/lower_to_llvm.cpp
+++ b/src/transform/lower_to_llvm.cpp
@@ -65,7 +65,7 @@ public:
 
     // First operand.
     Type dstType =
-        operands[0]->getType().cast<LLVM::LLVMType>().getStructElementType(1);
+        operands[0].getType().cast<LLVM::LLVMType>().getStructElementType(1);
     Value alignedDstMemory = rewriter.create<LLVM::ExtractValueOp>(
         loc, dstType, operands[0], rewriter.getI64ArrayAttr(1));
     Value alignedInt8PtrDstMemory = rewriter.create<LLVM::BitcastOp>(
@@ -73,7 +73,7 @@ public:
 
     // Second operand.
     Type srcType =
-        operands[1]->getType().cast<LLVM::LLVMType>().getStructElementType(1);
+        operands[1].getType().cast<LLVM::LLVMType>().getStructElementType(1);
     Value alignedSrcMemory = rewriter.create<LLVM::ExtractValueOp>(
         loc, srcType, operands[1], rewriter.getI64ArrayAttr(1));
     Value alignedInt8PtrSrcMemory = rewriter.create<LLVM::BitcastOp>(
@@ -253,7 +253,7 @@ public:
     // Get the first memref returned, convert to a dynamic memref and store
     // it in the wrapped Output.
     auto outMemRef = outputMemRefs.getResult(0);
-    auto outMemRefTy = outMemRef->getType().dyn_cast<LLVMType>();
+    auto outMemRefTy = outMemRef.getType().dyn_cast<LLVMType>();
     auto outMemRefRank =
         outMemRefTy.getStructElementType(3).getArrayNumElements();
     auto outMemRefRankVal = rewriter.create<LLVM::ConstantOp>(