diff --git a/mindspore/ops/operations/control_ops.py b/mindspore/ops/operations/control_ops.py
index 736855a046..f413fac0a9 100644
--- a/mindspore/ops/operations/control_ops.py
+++ b/mindspore/ops/operations/control_ops.py
@@ -69,6 +69,8 @@ class ControlDepend(Primitive):
     @prim_attr_register
     def __init__(self, depend_mode=0):
         """init"""
+        validator.check_int_range(
+            "depend_mode", depend_mode, 0, 1, Rel.INC_BOTH, self.name)
 
     def __call__(self, src, dst):
         return src
@@ -128,8 +130,10 @@ class GeSwitch(PrimitiveWithInfer):
         return (data, data)
 
     def infer_dtype(self, data_type, pred_type):
-        validator.check_subclass("data", data_type, (mstype.tensor,) + mstype.number_type, self.name)
-        validator.check_tensor_type_same({"pred": pred_type}, [mstype.bool_], self.name)
+        validator.check_subclass(
+            "data", data_type, (mstype.tensor,) + mstype.number_type, self.name)
+        validator.check_tensor_type_same(
+            {"pred": pred_type}, [mstype.bool_], self.name)
         return (data_type, data_type)
 
 
@@ -161,5 +165,6 @@ class Merge(PrimitiveWithInfer):
         for i, item in enumerate(inputs):
             args['inputs[%d]' % i] = item
 
-        validator.check_tensor_type_same(args, (mstype.bool_,) + mstype.number_type, self.name)
+        validator.check_tensor_type_same(
+            args, (mstype.bool_,) + mstype.number_type, self.name)
         return (inputs[0], mstype.int32)
diff --git a/tests/ut/python/ops/test_control_ops.py b/tests/ut/python/ops/test_control_ops.py
index 8d81811d9b..e77f6822b0 100644
--- a/tests/ut/python/ops/test_control_ops.py
+++ b/tests/ut/python/ops/test_control_ops.py
@@ -13,6 +13,7 @@
 # limitations under the License.
 # ============================================================================
 """ test control ops """
+import pytest
 import numpy as np
 
 import mindspore as ms
@@ -434,3 +435,11 @@ def test_index_to_switch_layer():
     C.grad_by_list(net, ParameterTuple(net.trainable_params()))(index,
                                                                 Tensor(np.full([128, 96], 0.6, dtype=np.float32)))
     C.grad_all(net)(index, Tensor(np.full([128, 96], 0.6, dtype=np.float32)))
+
+def test_control_depend_check():
+    with pytest.raises(TypeError) as e:
+        depend = P.ControlDepend(0.0)
+    with pytest.raises(ValueError) as e:
+        depend = P.ControlDepend(2)
+    with pytest.raises(TypeError) as e:
+        depend = P.ControlDepend((2,))
\ No newline at end of file