diff --git a/src/relay/op/tensor/unary.cc b/src/relay/op/tensor/unary.cc
index b20cd8dfaaf47..fdfaef9259164 100644
--- a/src/relay/op/tensor/unary.cc
+++ b/src/relay/op/tensor/unary.cc
@@ -280,34 +280,34 @@ RELAY_REGISTER_OP("shape_of")
 .set_attr<FTVMCompute>("FTVMCompute", ShapeOfCompute);
 
 
-TVM_REGISTER_NODE_TYPE(NumElementsAttrs);
+TVM_REGISTER_NODE_TYPE(SizeAttrs);
 
-bool NumElementsRel(const Array<Type>& types,
+bool SizeRel(const Array<Type>& types,
              int num_inputs,
              const Attrs& attrs,
              const TypeReporter& reporter) {
   CHECK_EQ(num_inputs, 1);
   auto tt = types[0].as<TensorTypeNode>();
   CHECK(tt != nullptr);
-  const auto* param = attrs.as<NumElementsAttrs>();
+  const auto* param = attrs.as<SizeAttrs>();
   CHECK(param != nullptr);
   reporter->Assign(types[1], TensorTypeNode::make({1}, param->dtype));
   return true;
 }
 
-Array<Tensor> NumElementsCompute(const Attrs& attrs,
+Array<Tensor> SizeCompute(const Attrs& attrs,
                           const Array<Tensor>& inputs,
                           const Type& out_type,
                           const Target& target) {
   CHECK_EQ(inputs.size(), 1);
-  const auto* param = attrs.as<NumElementsAttrs>();
+  const auto* param = attrs.as<SizeAttrs>();
   CHECK(param != nullptr);
   return Array<Tensor>{topi::size(inputs[0], param->dtype)};
 }
 
 TVM_REGISTER_API("relay.op.contrib._make.size")
 .set_body_typed<Expr(Expr, DataType)>([](Expr data, DataType dtype) {
-  auto attrs = make_node<NumElementsAttrs>();
+  auto attrs = make_node<SizeAttrs>();
   attrs->dtype = dtype;
   static const Op& op = Op::Get("contrib.ndarray_size");
   return CallNode::make(op, {data}, Attrs(attrs), {});
@@ -318,15 +318,15 @@ RELAY_REGISTER_OP("contrib.ndarray_size")
 
 )code" TVM_ADD_FILELINE)
 .set_num_inputs(1)
-.set_attrs_type_key("relay.attrs.NumElementsAttrs")
+.set_attrs_type_key("relay.attrs.SizeAttrs")
 .add_argument("data", "Tensor", "The input tensor.")
-.add_type_rel("NumElements", NumElementsRel)
+.add_type_rel("Size", SizeRel)
 .set_attr<TOpIsStateful>("TOpIsStateful", false)
 .set_attr<TOpPattern>("TOpPattern", kInjective)
 .set_attr<FInferCorrectLayout>("FInferCorrectLayout",
 ElemwiseArbitraryLayout)
 .set_support_level(10)
-.set_attr<FTVMCompute>("FTVMCompute", NumElementsCompute);
+.set_attr<FTVMCompute>("FTVMCompute", SizeCompute);
 
 }  // namespace relay
 }  // namespace tvm
diff --git a/tests/python/relay/test_op_level10.py b/tests/python/relay/test_op_level10.py
index 52b6cc8023ce9..f3520f3650a39 100644
--- a/tests/python/relay/test_op_level10.py
+++ b/tests/python/relay/test_op_level10.py
@@ -218,8 +218,8 @@ def test_shape_of():
 def test_ndarray_size():
     def verify_ndarray_size(shape):
         x = relay.var("x", shape=shape)
-        func = relay.Function([x], relay.op.contrib.num_elements(x))
-        func = relay.ir_pass.infer_type(func)
+        func = relay.Function([x], relay.op.contrib.ndarray_size(x))
+        func = run_infer_type(func)
 
         x_data = np.random.uniform(size=shape).astype("float32")
         ref_res = np.size(x_data)