diff --git a/mindspore/ccsrc/pipeline/static_analysis/abstract_value.cc b/mindspore/ccsrc/pipeline/static_analysis/abstract_value.cc
index b59545e5ae..6c07f92274 100644
--- a/mindspore/ccsrc/pipeline/static_analysis/abstract_value.cc
+++ b/mindspore/ccsrc/pipeline/static_analysis/abstract_value.cc
@@ -838,7 +838,8 @@ bool AbstractRef::operator==(const AbstractBase &other) const {
 AbstractBasePtr AbstractRef::Join(const AbstractBasePtr &other) {
   auto other_ref = other->cast<AbstractRefPtr>();
   if (other_ref == nullptr) {
-    MS_LOG(EXCEPTION) << "Join failed as type mismatch, this: " << ToString() << ", other: " << other->ToString();
+    auto new_ref = ref_->Join(other);
+    return std::make_shared<AbstractRef>(ref_key_, new_ref, ref_origin_);
   }
   if (*this == *other) {
     return shared_from_base<AbstractBase>();
diff --git a/tests/ut/python/parameter_feature/test_var_grad.py b/tests/ut/python/parameter_feature/test_var_grad.py
index 7a332b1c3b..f0358394e7 100644
--- a/tests/ut/python/parameter_feature/test_var_grad.py
+++ b/tests/ut/python/parameter_feature/test_var_grad.py
@@ -22,7 +22,7 @@ from mindspore.common.parameter import ParameterTuple
 from mindspore.nn import Cell
 from mindspore.ops import operations as P
 
-context.set_context(mode=context.GRAPH_MODE)
+context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
 
 
 def test_net_vargs_expand():
@@ -184,6 +184,27 @@ def test_grad_var_args_with_sens():
     _ = grad_net(x, y, sens)
 
 
+def test_grad_with_param_sens():
+    """"test grad_with_sens parameter"""
+
+    class GradNet(Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.weights = ParameterTuple(net.trainable_params())
+            self.net = net
+            self.sens = Parameter(Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), name='sens', requires_grad=False)
+            self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
+
+        def construct(self, x, y):
+            return self.grad(self.net, self.weights)(x, y, self.sens)
+
+    x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
+    net = SecondNet()
+    grad_net = GradNet(net)
+    _ = grad_net(x, y)
+
+
 def test_var_args_grad():
     class VarNet(Cell):
         def __init__(self, net):