diff --git a/lib/Conversion/TorchOnnxToTorch/DefaultDomainAtoF.cpp b/lib/Conversion/TorchOnnxToTorch/DefaultDomainAtoF.cpp
index 54cfb3e2a..bed22b084 100644
--- a/lib/Conversion/TorchOnnxToTorch/DefaultDomainAtoF.cpp
+++ b/lib/Conversion/TorchOnnxToTorch/DefaultDomainAtoF.cpp
@@ -1472,4 +1472,85 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
                       binder.op, resultType, operand);
                   return success();
                 });
+  patterns.onOp(
+      "ConstantOfShape", 20,
+      [](OpBinder binder, ConversionPatternRewriter &rewriter) {
+        Torch::ValueTensorType resultType;
+        Value shape;
+        if (binder.tensorOperand(shape) || binder.tensorResultType(resultType))
+          return failure();
+
+        // convert shape tensor to list of ints
+        auto shapeSizes =
+            dyn_cast<Torch::ValueTensorType>(shape.getType()).getSizes();
+        SmallVector<Value> dimList;
+        SmallVector<int64_t> selectSizes;
+        selectSizes.push_back(1);
+        Torch::BaseTensorType shapeType =
+            shape.getType().cast<Torch::BaseTensorType>();
+        Type selectResultType = shapeType.getWithSizesAndDtype(
+            llvm::ArrayRef(selectSizes), shapeType.getOptionalDtype());
+        Value zero = rewriter.create<Torch::ConstantIntOp>(
+            binder.getLoc(), rewriter.getType<Torch::IntType>(),
+            rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));
+        for (int i = 0; i < shapeSizes[0]; i++) {
+          Value selectIndex = rewriter.create<Torch::ConstantIntOp>(
+              binder.getLoc(), rewriter.getType<Torch::IntType>(),
+              rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
+          Value extract = rewriter.create<Torch::AtenSelectIntOp>(
+              binder.getLoc(), selectResultType, shape, zero, selectIndex);
+          Value dim = rewriter.create<Torch::AtenItemOp>(
+              binder.getLoc(), rewriter.getType<Torch::IntType>(), extract);
+          dimList.push_back(dim);
+        }
+        Value dimValueList = rewriter.create<Torch::PrimListConstructOp>(
+            binder.getLoc(),
+            Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
+            dimList);
+        Value noneVal = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
+
+        // Get fill_value if it is present.
+        // Assumption : resultDType and value attr type match.
+        Value value_const;
+        auto attr = binder.op->getAttr("torch.onnx.value");
+        auto resultDType = resultType.getDtype();
+
+        // Extract the fill value and dtype
+        // ONNX requires value attr to be a tensor
+        if (!attr) {
+          attr = DenseElementsAttr::get(
+              resultType.toBuiltinTensor().clone(resultDType),
+              rewriter.getFloatAttr(resultDType, 0.0));
+        }
+        if (!isa<DenseElementsAttr>(attr)) {
+          return rewriter.notifyMatchFailure(
+              binder.op, "`value` attr needs to be a tensor.");
+        }
+
+        auto denseAttr = attr.cast<DenseElementsAttr>();
+        auto denseAttrEleType = denseAttr.getElementType();
+        if (!isa<FloatType, IntegerType>(denseAttrEleType)) {
+          return rewriter.notifyMatchFailure(
+              binder.op,
+              "`value` attr tensor only supports types int and float for now.");
+        }
+
+        // Create constant op for value
+        if (denseAttrEleType.isa<IntegerType>()) {
+          int64_t intVal = denseAttr.getSplatValue<IntegerAttr>().getSInt();
+          value_const = rewriter.create<Torch::ConstantIntOp>(
+              binder.getLoc(), rewriter.getI64IntegerAttr(intVal));
+        }
+        if (denseAttrEleType.isa<FloatType>()) {
+          float floatVal =
+              denseAttr.getSplatValue<FloatAttr>().getValue().convertToFloat();
+          value_const = rewriter.create<Torch::ConstantFloatOp>(
+              binder.getLoc(), rewriter.getF64FloatAttr(floatVal));
+        }
+
+        rewriter.replaceOpWithNewOp<Torch::AtenFullOp>(
+            binder.op, resultType, dimValueList, value_const, /*dtype=*/noneVal,
+            /*layout=*/noneVal, /*device=*/noneVal, /*pin_memory=*/noneVal);
+        return success();
+      });
 }
diff --git a/test/Conversion/TorchOnnxToTorch/simple_ops_a_to_f.mlir b/test/Conversion/TorchOnnxToTorch/simple_ops_a_to_f.mlir
index 493cdc983..2c06567bd 100644
--- a/test/Conversion/TorchOnnxToTorch/simple_ops_a_to_f.mlir
+++ b/test/Conversion/TorchOnnxToTorch/simple_ops_a_to_f.mlir
@@ -1413,3 +1413,75 @@ func.func @test_flatten_1d_axis_1(%arg0: !torch.vtensor<[2],f32>) -> !torch.vten
   %0 = torch.operator "onnx.Flatten"(%arg0) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[2],f32>) -> !torch.vtensor<[2,1],f32>
   return %0 : !torch.vtensor<[2,1],f32>
 }
+
+// -----
+
+// CHECK-LABEL: func.func @test_constant_of_shape_dense_float_default
+func.func @test_constant_of_shape_dense_float_default() -> !torch.vtensor<[2,3,4], f32> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 20 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[SHAPE_CST:.*]] = torch.vtensor.literal(dense<[2, 3, 4]> : tensor<3xsi64>) : !torch.vtensor<[3],si64>
+  // CHECK: %[[INT0:.*]] = torch.constant.int 0
+  // CHECK: %[[INT0_0:.*]] = torch.constant.int 0
+  // CHECK: %[[EXTRACT_0:.*]] = torch.aten.select.int %[[SHAPE_CST]], %[[INT0]], %[[INT0_0]] : !torch.vtensor<[3],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
+  // CHECK: %[[ELE_0:.*]] = torch.aten.item %[[EXTRACT_0]] : !torch.vtensor<[1],si64> -> !torch.int
+  // CHECK: %[[INT1:.*]] = torch.constant.int 1
+  // CHECK: %[[EXTRACT_1:.*]] = torch.aten.select.int %[[SHAPE_CST]], %[[INT0]], %[[INT1]] : !torch.vtensor<[3],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
+  // CHECK: %[[ELE_1:.*]] = torch.aten.item %[[EXTRACT_1]] : !torch.vtensor<[1],si64> -> !torch.int
+  // CHECK: %[[INT2:.*]]  = torch.constant.int 2
+  // CHECK: %[[EXTRACT_2:.*]] = torch.aten.select.int %[[SHAPE_CST]], %[[INT0]], %[[INT2]] : !torch.vtensor<[3],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
+  // CHECK: %[[ELE_2:.*]] = torch.aten.item %[[EXTRACT_2]] : !torch.vtensor<[1],si64> -> !torch.int
+  // CHECK: %[[DIM_LIST:.*]] = torch.prim.ListConstruct %[[ELE_0]], %[[ELE_1]], %[[ELE_2]] : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[NONE:.*]] = torch.constant.none
+  // CHECK: %[[FILL_VAL:.*]] = torch.constant.float 0.000000e+00
+  // CHECK: %[[ATEN_FULL:.*]] = torch.aten.full %[[DIM_LIST]], %[[FILL_VAL]], %[[NONE]], %[[NONE]], %[[NONE]], %[[NONE]] : !torch.list<int>, !torch.float, !torch.none, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[2,3,4],f32>
+  %cst = torch.vtensor.literal(dense<[2,3,4]> : tensor<3xsi64>) : !torch.vtensor<[3], si64>
+  %0 = "torch.operator"(%cst) <{name = "onnx.ConstantOfShape"}> : (!torch.vtensor<[3], si64>) -> !torch.vtensor<[2,3,4], f32>
+  return %0 : !torch.vtensor<[2,3,4], f32>
+}
+
+// -----
+
+// CHECK-LABEL: func.func @test_constant_of_shape_dense_float_cst
+func.func @test_constant_of_shape_dense_float_cst() -> !torch.vtensor<[2,3,4], f32> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 20 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[SHAPE_CST:.*]] = torch.vtensor.literal(dense<[2, 3, 4]> : tensor<3xsi64>) : !torch.vtensor<[3],si64>
+  // CHECK: %[[INT0:.*]] = torch.constant.int 0
+  // CHECK: %[[INT0_0:.*]] = torch.constant.int 0
+  // CHECK: %[[EXTRACT_0:.*]] = torch.aten.select.int %[[SHAPE_CST]], %[[INT0]], %[[INT0_0]] : !torch.vtensor<[3],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
+  // CHECK: %[[ELE_0:.*]] = torch.aten.item %[[EXTRACT_0]] : !torch.vtensor<[1],si64> -> !torch.int
+  // CHECK: %[[INT1:.*]] = torch.constant.int 1
+  // CHECK: %[[EXTRACT_1:.*]] = torch.aten.select.int %[[SHAPE_CST]], %[[INT0]], %[[INT1]] : !torch.vtensor<[3],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
+  // CHECK: %[[ELE_1:.*]] = torch.aten.item %[[EXTRACT_1]] : !torch.vtensor<[1],si64> -> !torch.int
+  // CHECK: %[[INT2:.*]]  = torch.constant.int 2
+  // CHECK: %[[EXTRACT_2:.*]] = torch.aten.select.int %[[SHAPE_CST]], %[[INT0]], %[[INT2]] : !torch.vtensor<[3],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
+  // CHECK: %[[ELE_2:.*]] = torch.aten.item %[[EXTRACT_2]] : !torch.vtensor<[1],si64> -> !torch.int
+  // CHECK: %[[DIM_LIST:.*]] = torch.prim.ListConstruct %[[ELE_0]], %[[ELE_1]], %[[ELE_2]] : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[NONE:.*]] = torch.constant.none
+  // CHECK: %[[FILL_VAL:.*]] = torch.constant.float 3.4000000953674316
+  // CHECK: %[[ATEN_FULL:.*]] = torch.aten.full %[[DIM_LIST]], %[[FILL_VAL]], %[[NONE]], %[[NONE]], %[[NONE]], %[[NONE]] : !torch.list<int>, !torch.float, !torch.none, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[2,3,4],f32>
+  %cst = torch.vtensor.literal(dense<[2,3,4]> : tensor<3xsi64>) : !torch.vtensor<[3], si64>
+  %0 = "torch.operator"(%cst) <{name = "onnx.ConstantOfShape"}> {torch.onnx.value = dense<3.4> : tensor<1xf32>}: (!torch.vtensor<[3], si64>) -> !torch.vtensor<[2,3,4], f32>
+  return %0 : !torch.vtensor<[2,3,4], f32>
+}
+
+// -----
+
+// CHECK-LABEL: func.func @test_constant_of_shape_dense_int_cst
+func.func @test_constant_of_shape_dense_int_cst() -> !torch.vtensor<[2,3,4], si64> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 20 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[SHAPE_CST:.*]] = torch.vtensor.literal(dense<[2, 3, 4]> : tensor<3xsi64>) : !torch.vtensor<[3],si64>
+  // CHECK: %[[INT0:.*]] = torch.constant.int 0
+  // CHECK: %[[INT0_0:.*]] = torch.constant.int 0
+  // CHECK: %[[EXTRACT_0:.*]] = torch.aten.select.int %[[SHAPE_CST]], %[[INT0]], %[[INT0_0]] : !torch.vtensor<[3],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
+  // CHECK: %[[ELE_0:.*]] = torch.aten.item %[[EXTRACT_0]] : !torch.vtensor<[1],si64> -> !torch.int
+  // CHECK: %[[INT1:.*]] = torch.constant.int 1
+  // CHECK: %[[EXTRACT_1:.*]] = torch.aten.select.int %[[SHAPE_CST]], %[[INT0]], %[[INT1]] : !torch.vtensor<[3],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
+  // CHECK: %[[ELE_1:.*]] = torch.aten.item %[[EXTRACT_1]] : !torch.vtensor<[1],si64> -> !torch.int
+  // CHECK: %[[INT2:.*]]  = torch.constant.int 2
+  // CHECK: %[[EXTRACT_2:.*]] = torch.aten.select.int %[[SHAPE_CST]], %[[INT0]], %[[INT2]] : !torch.vtensor<[3],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
+  // CHECK: %[[ELE_2:.*]] = torch.aten.item %[[EXTRACT_2]] : !torch.vtensor<[1],si64> -> !torch.int
+  // CHECK: %[[DIM_LIST:.*]] = torch.prim.ListConstruct %[[ELE_0]], %[[ELE_1]], %[[ELE_2]] : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[NONE:.*]] = torch.constant.none
+  // CHECK: %[[FILL_VAL:.*]] = torch.constant.int 3
+  // CHECK: %[[ATEN_FULL:.*]] = torch.aten.full %[[DIM_LIST]], %[[FILL_VAL]], %[[NONE]], %[[NONE]], %[[NONE]], %[[NONE]] : !torch.list<int>, !torch.int, !torch.none, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[2,3,4],si64>
+  %cst = torch.vtensor.literal(dense<[2,3,4]> : tensor<3xsi64>) : !torch.vtensor<[3], si64>
+  %0 = "torch.operator"(%cst) <{name = "onnx.ConstantOfShape"}> {torch.onnx.value = dense<3> : tensor<1xsi64>}: (!torch.vtensor<[3], si64>) -> !torch.vtensor<[2,3,4], si64>
+  return %0 : !torch.vtensor<[2,3,4], si64>
+}