diff --git a/mindspore/ccsrc/pipeline/jit/static_analysis/prim.cc b/mindspore/ccsrc/pipeline/jit/static_analysis/prim.cc
index 765bbc22bf..b9292f9db0 100644
--- a/mindspore/ccsrc/pipeline/jit/static_analysis/prim.cc
+++ b/mindspore/ccsrc/pipeline/jit/static_analysis/prim.cc
@@ -267,11 +267,87 @@ py::object BuildValue(const ValuePtr &value_ptr) {
     return ValuePtrToPyData(value_ptr);
   }
 }
+
+py::dict AbstractTupleToPython(const AbstractBasePtr &abs_base) {
+  auto arg_tuple = dyn_cast<AbstractTuple>(abs_base);
+  size_t len = arg_tuple->size();
+  py::tuple shape_tuple(len);
+  py::tuple dtype_tuple(len);
+  py::tuple min_shape_tuple(len);
+  py::tuple max_shape_tuple(len);
+  auto dic = py::dict();
+  bool dyn_shape = false;
+
+  for (size_t i = 0; i < len; i++) {
+    auto arg = arg_tuple->elements()[i];
+    py::dict out = ConvertAbstractToPython(arg);
+    shape_tuple[i] = out[ATTR_SHAPE];
+    dtype_tuple[i] = out[ATTR_DTYPE];
+
+    // Elements in tuple is tensor, which shape is dynamic.
+    if (out.contains(py::str(ATTR_MIN_SHAPE)) && out.contains(py::str(ATTR_MAX_SHAPE))) {
+      min_shape_tuple[i] = out[ATTR_MIN_SHAPE];
+      max_shape_tuple[i] = out[ATTR_MAX_SHAPE];
+      dyn_shape = true;
+    } else {
+      min_shape_tuple[i] = out[ATTR_SHAPE];
+      max_shape_tuple[i] = out[ATTR_SHAPE];
+    }
+  }
+  dic[ATTR_SHAPE] = shape_tuple;
+  dic[ATTR_DTYPE] = dtype_tuple;
+  dic[ATTR_VALUE] = BuildValue(arg_tuple->BuildValue());
+
+  if (dyn_shape) {
+    dic[ATTR_MIN_SHAPE] = min_shape_tuple;
+    dic[ATTR_MAX_SHAPE] = max_shape_tuple;
+  }
+
+  return dic;
+}
+
+py::dict AbstractListToPython(const AbstractBasePtr &abs_base) {
+  auto arg_list = dyn_cast<AbstractList>(abs_base);
+  size_t len = arg_list->size();
+  py::list shape_list(len);
+  py::list dtype_list(len);
+  py::list min_shape_list(len);
+  py::list max_shape_list(len);
+  auto dic = py::dict();
+  bool dyn_shape = false;
+
+  for (size_t i = 0; i < len; i++) {
+    py::dict out = ConvertAbstractToPython(arg_list->elements()[i]);
+    shape_list[i] = out[ATTR_SHAPE];
+    dtype_list[i] = out[ATTR_DTYPE];
+
+    // Elements in list is tensor, which shape is dynamic.
+    if (out.contains(py::str(ATTR_MIN_SHAPE)) && out.contains(py::str(ATTR_MAX_SHAPE))) {
+      min_shape_list[i] = out[ATTR_MIN_SHAPE];
+      max_shape_list[i] = out[ATTR_MAX_SHAPE];
+      dyn_shape = true;
+    } else {
+      min_shape_list[i] = out[ATTR_SHAPE];
+      max_shape_list[i] = out[ATTR_SHAPE];
+    }
+  }
+
+  if (dyn_shape) {
+    dic[ATTR_MIN_SHAPE] = min_shape_list;
+    dic[ATTR_MAX_SHAPE] = max_shape_list;
+  }
+
+  dic[ATTR_SHAPE] = shape_list;
+  dic[ATTR_DTYPE] = dtype_list;
+  dic[ATTR_VALUE] = BuildValue(arg_list->BuildValue());
+
+  return dic;
+}
 }  // end anonymous namespace
 
 py::dict ConvertAbstractToPython(const AbstractBasePtr &abs_base) {
   MS_EXCEPTION_IF_NULL(abs_base);
-  py::dict dic;
+  auto dic = py::dict();
   if (abs_base->isa<AbstractTensor>()) {
     auto arg_tensor = dyn_cast<AbstractTensor>(abs_base);
     dic[ATTR_SHAPE] = arg_tensor->shape()->shape();
@@ -311,33 +387,9 @@ py::dict ConvertAbstractToPython(const AbstractBasePtr &abs_base) {
     dic[ATTR_DTYPE] = py::ellipsis();
     dic[ATTR_VALUE] = py::ellipsis();
   } else if (abs_base->isa<AbstractTuple>()) {
-    auto arg_tuple = dyn_cast<AbstractTuple>(abs_base);
-    size_t len = arg_tuple->size();
-    py::tuple shape_tuple(len);
-    py::tuple dtype_tuple(len);
-
-    for (size_t i = 0; i < len; i++) {
-      py::dict out = ConvertAbstractToPython(arg_tuple->elements()[i]);
-      shape_tuple[i] = out[ATTR_SHAPE];
-      dtype_tuple[i] = out[ATTR_DTYPE];
-    }
-    dic[ATTR_SHAPE] = shape_tuple;
-    dic[ATTR_DTYPE] = dtype_tuple;
-    dic[ATTR_VALUE] = BuildValue(arg_tuple->BuildValue());
+    return AbstractTupleToPython(abs_base);
   } else if (abs_base->isa<AbstractList>()) {
-    auto arg_list = dyn_cast<AbstractList>(abs_base);
-    size_t len = arg_list->size();
-    py::list shape_list(len);
-    py::list dtype_list(len);
-
-    for (size_t i = 0; i < len; i++) {
-      py::dict out = ConvertAbstractToPython(arg_list->elements()[i]);
-      shape_list[i] = out[ATTR_SHAPE];
-      dtype_list[i] = out[ATTR_DTYPE];
-    }
-    dic[ATTR_SHAPE] = shape_list;
-    dic[ATTR_DTYPE] = dtype_list;
-    dic[ATTR_VALUE] = BuildValue(arg_list->BuildValue());
+    return AbstractListToPython(abs_base);
   } else if (abs_base->isa<AbstractNone>()) {
     dic[ATTR_SHAPE] = py::none();
     dic[ATTR_DTYPE] = py::none();
diff --git a/tests/ut/python/ops/test_dynamic_shape.py b/tests/ut/python/ops/test_dynamic_shape.py
index 9cc4673fdf..5813540fd8 100755
--- a/tests/ut/python/ops/test_dynamic_shape.py
+++ b/tests/ut/python/ops/test_dynamic_shape.py
@@ -108,3 +108,21 @@ def test_gatherv2():
     y = Tensor(np.ones([8], dtype=np.int32))
     net = Net()
     net(x, y)
+
+
+def test_addn():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+            self.unq = P.Unique()
+            self.addn = P.AddN()
+
+        def construct(self, x):
+            u, _ = self.unq(x)
+            u = self.addn((u, u, u))
+            z = self.addn([u, u])
+            return z
+
+    y = Tensor(np.ones([8], dtype=np.int32))
+    net = Net()
+    net(y)