diff --git a/externals/llvm-external-projects/torch-mlir-dialects/lib/Dialect/TMTensor/IR/TMTensorOps.cpp b/externals/llvm-external-projects/torch-mlir-dialects/lib/Dialect/TMTensor/IR/TMTensorOps.cpp
index f9339301f..89726a21e 100644
--- a/externals/llvm-external-projects/torch-mlir-dialects/lib/Dialect/TMTensor/IR/TMTensorOps.cpp
+++ b/externals/llvm-external-projects/torch-mlir-dialects/lib/Dialect/TMTensor/IR/TMTensorOps.cpp
@@ -120,8 +120,8 @@ LogicalResult ScanOp::verify() {
   }
   if (llvm::any_of(llvm::zip(expectedAccumulatorShape, accumulatorShape),
                    [](std::tuple<int64_t, int64_t> s) {
-                     return std::get<0>(s) != ShapedType::kDynamicSize &&
-                            std::get<1>(s) != ShapedType::kDynamicSize &&
+                     return std::get<0>(s) != ShapedType::kDynamic &&
+                            std::get<1>(s) != ShapedType::kDynamic &&
                             std::get<0>(s) != std::get<1>(s);
                    })) {
     return emitOpError("incompatible input/accumulator shapes");
@@ -134,8 +134,8 @@ LogicalResult ScanOp::verify() {
   }
   if (llvm::any_of(llvm::zip(inputShapes, outputShapes),
                    [](std::tuple<int64_t, int64_t> s) {
-                     return std::get<0>(s) != ShapedType::kDynamicSize &&
-                            std::get<1>(s) != ShapedType::kDynamicSize &&
+                     return std::get<0>(s) != ShapedType::kDynamic &&
+                            std::get<1>(s) != ShapedType::kDynamic &&
                             std::get<0>(s) != std::get<1>(s);
                    })) {
     return emitOpError("incompatible input/output shapes");
@@ -291,7 +291,7 @@ LogicalResult ScatterOp::verify() {
     return emitOpError("expected indices to be of rank 2 of i32 element type");
   }
   auto indexDepth = getIndexDepth();
-  if (indexDepth == ShapedType::kDynamicSize) {
+  if (indexDepth == ShapedType::kDynamic) {
     return emitOpError("expected index depth is static");
   }
 
diff --git a/externals/llvm-project b/externals/llvm-project
index 147fe9de2..2c1fa7345 160000
--- a/externals/llvm-project
+++ b/externals/llvm-project
@@ -1 +1 @@
-Subproject commit 147fe9de29dc13c14835127b35280c4d95c8e8ba
+Subproject commit 2c1fa734598c9470139720565fbf624a5156ec03
diff --git a/externals/mlir-hlo b/externals/mlir-hlo
index 1944b5fa6..dabc58a95 160000
--- a/externals/mlir-hlo
+++ b/externals/mlir-hlo
@@ -1 +1 @@
-Subproject commit 1944b5fa6062ec4c065d726c9c5d64f1487ee8c5
+Subproject commit dabc58a95b8bcd1140ffb0062730b81745d41724
diff --git a/include/torch-mlir/Conversion/Utils/Utils.h b/include/torch-mlir/Conversion/Utils/Utils.h
index 5a41311e2..ca967c05e 100644
--- a/include/torch-mlir/Conversion/Utils/Utils.h
+++ b/include/torch-mlir/Conversion/Utils/Utils.h
@@ -84,7 +84,7 @@ SmallVector<Value> getTypeConvertedValues(OpBuilder &b, Location loc,
 // should be converted builtin types.
 Value convertScalarToDtype(
     OpBuilder &b, Location loc, Value scalar, Type dtype,
-    llvm::Optional<Type> srcOriginalDtype = llvm::NoneType());
+    llvm::Optional<Type> srcOriginalDtype = llvm::None);
 
 // Return the number of elements of a tensor if the shape is static; otherwise,
 // return -1.
diff --git a/lib/Conversion/TorchToMhlo/Linear.cpp b/lib/Conversion/TorchToMhlo/Linear.cpp
index a62d5edbc..d124e13ea 100644
--- a/lib/Conversion/TorchToMhlo/Linear.cpp
+++ b/lib/Conversion/TorchToMhlo/Linear.cpp
@@ -88,12 +88,12 @@ RankedTensorType castContractingDim(PatternRewriter &rewriter, Operation *op,
   auto lhsContractingDimSize = lhsShape[lhsContractingDim];
   auto rhsContractingDimSize = rhsShape[rhsContractingDim];
   if (lhsContractingDimSize != rhsContractingDimSize) {
-    if (lhsContractingDimSize == ShapedType::kDynamicSize &&
+    if (lhsContractingDimSize == ShapedType::kDynamic &&
         rhsContractingDimSize >= 0) {
       lhsShape[lhsContractingDim] = rhsContractingDimSize;
       auto newRankTy = RankedTensorType::get(lhsShape, lhsTy.getElementType());
       lhs = rewriter.create<tensor::CastOp>(op->getLoc(), newRankTy, lhs);
-    } else if (rhsContractingDimSize == ShapedType::kDynamicSize &&
+    } else if (rhsContractingDimSize == ShapedType::kDynamic &&
                lhsContractingDimSize >= 0) {
       rhsShape[rhsContractingDim] = lhsContractingDimSize;
       auto newRankTy = RankedTensorType::get(rhsShape, rhsTy.getElementType());
@@ -112,7 +112,7 @@ RankedTensorType castContractingDim(PatternRewriter &rewriter, Operation *op,
       break;
     if (k == rhsResultDim || k == rhsContractingDim)
       continue;
-    if (outShape[b] == ShapedType::kDynamicSize && rhsShape[k] >= 0) {
+    if (outShape[b] == ShapedType::kDynamic && rhsShape[k] >= 0) {
       outShape[b] = rhsShape[k];
     }
     b++;
@@ -477,7 +477,7 @@ public:
     weightShapeVec[0] = ICDivGValue;
     weightShapeVec.insert(weightShapeVec.begin(), GValue);
 
-    if (weightShapeInt[0] == ShapedType::kDynamicSize) {
+    if (weightShapeInt[0] == ShapedType::kDynamic) {
       weightShapeInt.insert(weightShapeInt.begin(), groups);
     } else {
       weightShapeInt[0] /= groups;
@@ -499,7 +499,7 @@ public:
 
     // 3. [IC//G, G, OC, H, W, ...] => [IC//G, G*OC, H, W, ...]
     weightShapeInt.erase(weightShapeInt.begin());
-    if (weightShapeInt[1] != ShapedType::kDynamicSize) {
+    if (weightShapeInt[1] != ShapedType::kDynamic) {
       weightShapeInt[1] *= groups;
     }
     weightShapeVec.erase(weightShapeVec.begin());
@@ -533,7 +533,7 @@ public:
       auto finalOutShape = outType.getShape();
       std::copy(finalOutShape.begin(), finalOutShape.end(), outShape.begin());
       for (int i = 2; i < nDims; ++i) {
-        if (finalOutShape[i] == ShapedType::kDynamicSize)
+        if (finalOutShape[i] == ShapedType::kDynamic)
           continue;
         outShape[i] = finalOutShape[i] - outputPadding[i - 2];
       }
@@ -741,11 +741,11 @@ public:
 
     auto nSpatialDims = padding.size();
     auto nDims = inputTy.getRank();
-    
+
     // Kernel size must be constant.
     auto weightShape = weightTy.getShape();
     for (int i = 2; i < nDims; ++i) {
-      if (weightShape[i] == ShapedType::kDynamicSize) {
+      if (weightShape[i] == ShapedType::kDynamic) {
         return rewriter.notifyMatchFailure(
             op, "only constant kernel size is supported");
       }
diff --git a/lib/Conversion/TorchToMhlo/Pooling.cpp b/lib/Conversion/TorchToMhlo/Pooling.cpp
index b32aeea9a..b17f3c61f 100644
--- a/lib/Conversion/TorchToMhlo/Pooling.cpp
+++ b/lib/Conversion/TorchToMhlo/Pooling.cpp
@@ -279,9 +279,9 @@ LogicalResult ConvertAtenOp<AtenMaxPool2dWithIndicesOp>::matchAndRewrite(
 
   SmallVector<int64_t> initIndexShapeForType(inputShape.begin(),
                                              inputShape.end() - 2);
-  if (inputShape[inputRank - 1] == ShapedType::kDynamicSize ||
-      inputShape[inputRank - 2] == ShapedType::kDynamicSize) {
-    initIndexShapeForType.push_back(ShapedType::kDynamicSize);
+  if (inputShape[inputRank - 1] == ShapedType::kDynamic ||
+      inputShape[inputRank - 2] == ShapedType::kDynamic) {
+    initIndexShapeForType.push_back(ShapedType::kDynamic);
   } else {
     initIndexShapeForType.push_back(inputShape[inputRank - 1] *
                                     inputShape[inputRank - 2]);
@@ -501,7 +501,7 @@ LogicalResult ConvertAtenOp<AtenAvgPool2dOp>::matchAndRewrite(
       op->getLoc(),
       RankedTensorType::get(inputTy.getShape(), outTy.getElementType()),
       windowSizeConst, inputShapeTensor, rewriter.getI64TensorAttr({}));
-  
+
   Value zero = createInitialValueForAtenPoolingOp(op, inputElemTy, rewriter);
   auto reduceWindowSize = rewriter.create<mhlo::ReduceWindowOp>(
       op->getLoc(), RankedTensorType::get(outShape, inputElemTy),
diff --git a/lib/Conversion/TorchToMhlo/ViewLike.cpp b/lib/Conversion/TorchToMhlo/ViewLike.cpp
index a28667cd1..e8102dcec 100644
--- a/lib/Conversion/TorchToMhlo/ViewLike.cpp
+++ b/lib/Conversion/TorchToMhlo/ViewLike.cpp
@@ -308,7 +308,7 @@ LogicalResult ConvertAtenOp<AtenSqueezeOp>::matchAndRewrite(
   dims.reserve(rank);
   for (int r = 0; r < rank; ++r) {
     auto dSize = selfTy.getShape()[r];
-    if (dSize == ShapedType::kDynamicSize)
+    if (dSize == ShapedType::kDynamic)
       return rewriter.notifyMatchFailure(
           op, "the size of the dimension being squeezed can't be unknown");
     if (dSize != 1)
@@ -353,7 +353,7 @@ LogicalResult ConvertAtenOp<AtenSqueezeDimOp>::matchAndRewrite(
 
   dim = toPositiveDim(dim, rank);
   if (selfTy.getShape()[dim] != 1) {
-    if (selfTy.getShape()[dim] == ShapedType::kDynamicSize)
+    if (selfTy.getShape()[dim] == ShapedType::kDynamic)
       return rewriter.notifyMatchFailure(
           op, "the size of the dimension being squeezed is can't be unknown");
 
diff --git a/lib/Conversion/Utils/Utils.cpp b/lib/Conversion/Utils/Utils.cpp
index cc2b164c2..8def43845 100644
--- a/lib/Conversion/Utils/Utils.cpp
+++ b/lib/Conversion/Utils/Utils.cpp
@@ -331,21 +331,21 @@ int64_t getNumberOfElements(RankedTensorType inputType) {
 
 SmallVector<int64_t> makeShapeLLVMCompatible(ArrayRef<int64_t> shape) {
   SmallVector<int64_t> updatedShape(shape);
-  int64_t kDynamicSize = ShapedType::kDynamicSize;
+  int64_t kDynamic = ShapedType::kDynamic;
   for (unsigned i = 0; i < shape.size(); i++) {
     assert(shape[i] >= 0 || shape[i] == kUnknownSize);
     if (shape[i] == kUnknownSize)
-      updatedShape[i] = kDynamicSize;
+      updatedShape[i] = kDynamic;
   }
   return updatedShape;
 }
 
 SmallVector<int64_t> makeShapeTorchCompatible(ArrayRef<int64_t> shape) {
   SmallVector<int64_t> updatedShape(shape);
-  int64_t kDynamicSize = ShapedType::kDynamicSize;
+  int64_t kDynamic = ShapedType::kDynamic;
   for (unsigned i = 0; i < shape.size(); i++) {
-    assert(shape[i] >= 0 || shape[i] == kDynamicSize);
-    if (shape[i] == kDynamicSize)
+    assert(shape[i] >= 0 || shape[i] == kDynamic);
+    if (shape[i] == kDynamic)
       updatedShape[i] = kUnknownSize;
   }
   return updatedShape;