diff --git a/lib/Dialect/mhlo/IR/hlo_ops.cc b/lib/Dialect/mhlo/IR/hlo_ops.cc
index 2767dcd..2794727 100644
--- a/lib/Dialect/mhlo/IR/hlo_ops.cc
+++ b/lib/Dialect/mhlo/IR/hlo_ops.cc
@@ -642,11 +642,33 @@ void DynamicIotaOp::getCanonicalizationPatterns(
   results.insert<DynamicIotaBroadcast>(context);
 }
 
+static Value castToIndexTensor(OpBuilder& builder, Location loc,
+                               Value shape_op) {
+  ShapedType result_ty = shape::getExtentTensorType(
+      builder.getContext(),
+      shape_op.getType().cast<ShapedType>().getDimSize(0));
+  if (shape_op.getType() == result_ty) return shape_op;  // Nothing to do.
+  // index_cast is not defined on tensors, so emit a tensor.generate instead.
+  return builder.create<tensor::GenerateOp>(
+      loc, result_ty,
+      result_ty.hasStaticShape()
+          ? ValueRange{}
+          : ValueRange{builder.create<memref::DimOp>(loc, shape_op, 0)},
+      [&](OpBuilder& b, Location loc, ValueRange args) {
+        Value dim = args.front();
+        Value extent = b.create<tensor::ExtractOp>(loc, shape_op, dim);
+        Value casted =
+            b.create<IndexCastOp>(loc, extent, result_ty.getElementType());
+        b.create<tensor::YieldOp>(loc, casted);
+      });
+}
+
 LogicalResult DynamicIotaOp::reifyReturnTypeShapes(
-    OpBuilder&, ValueRange operands,
+    OpBuilder& builder, ValueRange operands,
     SmallVectorImpl<Value>& reifiedReturnShapes) {
   DynamicIotaOp::Adaptor adaptor(operands);
-  reifiedReturnShapes.push_back(adaptor.output_shape());
+  reifiedReturnShapes.push_back(
+      castToIndexTensor(builder, getLoc(), adaptor.output_shape()));
   return success();
 }
 
@@ -1192,10 +1214,11 @@ void DynamicBroadcastInDimOp::getCanonicalizationPatterns(
 }
 
 LogicalResult DynamicBroadcastInDimOp::reifyReturnTypeShapes(
-    OpBuilder&, ValueRange operands,
+    OpBuilder& builder, ValueRange operands,
     SmallVectorImpl<Value>& reifiedReturnShapes) {
   DynamicBroadcastInDimOp::Adaptor adaptor(operands);
-  reifiedReturnShapes.push_back(adaptor.output_dimensions());
+  reifiedReturnShapes.push_back(
+      castToIndexTensor(builder, getLoc(), adaptor.output_dimensions()));
   return success();
 }
 
@@ -1627,10 +1650,11 @@ static LogicalResult Verify(DynamicReshapeOp op) {
 }
 
 LogicalResult DynamicReshapeOp::reifyReturnTypeShapes(
-    OpBuilder&, ValueRange operands,
+    OpBuilder& builder, ValueRange operands,
     SmallVectorImpl<Value>& reifiedReturnShapes) {
   DynamicReshapeOp::Adaptor adaptor(operands);
-  reifiedReturnShapes.push_back(adaptor.output_shape());
+  reifiedReturnShapes.push_back(
+      castToIndexTensor(builder, getLoc(), adaptor.output_shape()));
   return success();
 }
 
diff --git a/tests/reify-result-types.mlir b/tests/reify-result-types.mlir
new file mode 100644
index 0000000..136c724
--- /dev/null
+++ b/tests/reify-result-types.mlir
@@ -0,0 +1,50 @@
+// RUN: mlir-hlo-opt -resolve-shaped-type-result-dims -canonicalize \
+// RUN: -split-input-file %s -o - | FileCheck %s
+
+// CHECK-LABEL: @dynamic_broadcast_i32_shape
+func @dynamic_broadcast_i32_shape(%arg0 : tensor<?xi32>, %arg1 : tensor<*xf32>)
+     -> index {
+  // CHECK: %[[C0:.*]] = constant 0 : index
+  // CHECK: %[[DIM:.*]] = tensor.extract %arg0[%[[C0]]] : tensor<?xi32>
+  // CHECK: %[[RESULT:.*]] = index_cast %[[DIM]] : i32 to index
+  // CHECK: return %[[RESULT]]
+  %c0 = constant 0 : index
+  %0 = "mhlo.dynamic_broadcast_in_dim"(%arg1, %arg0)
+       { broadcast_dimensions = dense<0> : tensor<1xi64> }
+     : (tensor<*xf32>, tensor<?xi32>) -> tensor<*xf32>
+  %1 = memref.dim %0, %c0 : tensor<*xf32>
+  return %1 : index
+}
+
+// -----
+
+// CHECK-LABEL: @dynamic_iota_i32_shape
+func @dynamic_iota_i32_shape(%arg0 : tensor<?xi32>) -> index {
+  // CHECK: %[[C0:.*]] = constant 0 : index
+  // CHECK: %[[DIM:.*]] = tensor.extract %arg0[%[[C0]]] : tensor<?xi32>
+  // CHECK: %[[RESULT:.*]] = index_cast %[[DIM]] : i32 to index
+  // CHECK: return %[[RESULT]]
+  %c0 = constant 0 : index
+  %0 = "mhlo.dynamic_iota"(%arg0)
+       {iota_dimension = 0 : i64}
+     : (tensor<?xi32>) -> tensor<?xi32>
+  %1 = memref.dim %0, %c0 : tensor<?xi32>
+  return %1 : index
+}
+
+// -----
+
+// CHECK-LABEL: @dynamic_reshape_i32_shape
+func @dynamic_reshape_i32_shape(%arg0 : tensor<?xi32>, %arg1 : tensor<*xf32>)
+     -> index {
+  // CHECK: %[[C0:.*]] = constant 0 : index
+  // CHECK: %[[DIM:.*]] = tensor.extract %arg0[%[[C0]]] : tensor<?xi32>
+  // CHECK: %[[RESULT:.*]] = index_cast %[[DIM]] : i32 to index
+  // CHECK: return %[[RESULT]]
+  %c0 = constant 0 : index
+  %0 = "mhlo.dynamic_reshape"(%arg1, %arg0)
+       { broadcast_dimensions = dense<0> : tensor<1xi64> }
+     : (tensor<*xf32>, tensor<?xi32>) -> tensor<*xf32>
+  %1 = memref.dim %0, %c0 : tensor<*xf32>
+  return %1 : index
+}