diff --git a/mindspore/nn/probability/distribution/_utils/utils.py b/mindspore/nn/probability/distribution/_utils/utils.py
index 8484fdeddc..604fa54494 100644
--- a/mindspore/nn/probability/distribution/_utils/utils.py
+++ b/mindspore/nn/probability/distribution/_utils/utils.py
@@ -218,6 +218,11 @@ def raise_not_impl_error(name):
     raise ValueError(
         f"{name} function should be implemented for non-linear transformation")
 
+@constexpr
+def raise_not_implemented_util(func_name, obj, *args, **kwargs):
+    raise NotImplementedError(
+        f"{func_name} is not implemented for {obj} distribution.")
+
 
 @constexpr
 def check_distribution_name(name, expected_name):
diff --git a/mindspore/nn/probability/distribution/distribution.py b/mindspore/nn/probability/distribution/distribution.py
index ca6cabea43..0547810c4e 100644
--- a/mindspore/nn/probability/distribution/distribution.py
+++ b/mindspore/nn/probability/distribution/distribution.py
@@ -19,7 +19,8 @@ from mindspore.nn.cell import Cell
 from mindspore._checkparam import Validator as validator
 from mindspore._checkparam import Rel
 from mindspore.common import get_seed
-from ._utils.utils import raise_none_error, cast_to_tensor, set_param_type, cast_type_for_device
+from ._utils.utils import raise_none_error, cast_to_tensor, set_param_type, cast_type_for_device,\
+                          raise_not_implemented_util
 from ._utils.utils import CheckTuple, CheckTensor
 from ._utils.custom_ops import broadcast_to, exp_generic, log_generic
 
@@ -245,6 +246,8 @@ class Distribution(Cell):
             self._call_prob = self._prob
         elif hasattr(self, '_log_prob'):
             self._call_prob = self._calc_prob_from_log_prob
+        else:
+            self._call_prob = self._raise_not_implemented_error('prob')
 
     def _set_sd(self):
         """
@@ -254,6 +257,8 @@ class Distribution(Cell):
             self._call_sd = self._sd
         elif hasattr(self, '_var'):
             self._call_sd = self._calc_sd_from_var
+        else:
+            self._call_sd = self._raise_not_implemented_error('sd')
 
     def _set_var(self):
         """
@@ -263,6 +268,8 @@ class Distribution(Cell):
             self._call_var = self._var
         elif hasattr(self, '_sd'):
             self._call_var = self._calc_var_from_sd
+        else:
+            self._call_var = self._raise_not_implemented_error('var')
 
     def _set_log_prob(self):
         """
@@ -272,6 +279,8 @@ class Distribution(Cell):
             self._call_log_prob = self._log_prob
         elif hasattr(self, '_prob'):
             self._call_log_prob = self._calc_log_prob_from_prob
+        else:
+            self._call_log_prob = self._raise_not_implemented_error('log_prob')
 
     def _set_cdf(self):
         """
@@ -286,13 +295,18 @@ class Distribution(Cell):
             self._call_cdf = self._calc_cdf_from_survival
         elif hasattr(self, '_log_survival'):
             self._call_cdf = self._calc_cdf_from_log_survival
+        else:
+            self._call_cdf = self._raise_not_implemented_error('cdf')
 
     def _set_survival(self):
         """
         Set survival function based on the availability of _survival function and `_log_survival`
         and `_call_cdf`.
         """
-        if hasattr(self, '_survival_function'):
+        if not (hasattr(self, '_survival_function') or hasattr(self, '_log_survival') or \
+                hasattr(self, '_cdf') or hasattr(self, '_log_cdf')):
+            self._call_survival = self._raise_not_implemented_error('survival_function')
+        elif hasattr(self, '_survival_function'):
             self._call_survival = self._survival_function
         elif hasattr(self, '_log_survival'):
             self._call_survival = self._calc_survival_from_log_survival
@@ -303,7 +317,10 @@ class Distribution(Cell):
         """
         Set log cdf based on the availability of `_log_cdf` and `_call_cdf`.
         """
-        if hasattr(self, '_log_cdf'):
+        if not (hasattr(self, '_log_cdf') or hasattr(self, '_cdf') or \
+                hasattr(self, '_survival_function') or hasattr(self, '_log_survival')):
+            self._call_log_cdf = self._raise_not_implemented_error('log_cdf')
+        elif hasattr(self, '_log_cdf'):
             self._call_log_cdf = self._log_cdf
         elif hasattr(self, '_call_cdf'):
             self._call_log_cdf = self._calc_log_cdf_from_call_cdf
@@ -312,7 +329,10 @@ class Distribution(Cell):
         """
         Set log survival based on the availability of `_log_survival` and `_call_survival`.
         """
-        if hasattr(self, '_log_survival'):
+        if not (hasattr(self, '_log_survival') or hasattr(self, '_survival_function') or \
+                hasattr(self, '_log_cdf') or hasattr(self, '_cdf')):
+            self._call_log_survival = self._raise_not_implemented_error('log_cdf')
+        elif hasattr(self, '_log_survival'):
             self._call_log_survival = self._log_survival
         elif hasattr(self, '_call_survival'):
             self._call_log_survival = self._calc_log_survival_from_call_survival
@@ -323,6 +343,14 @@ class Distribution(Cell):
         """
         if hasattr(self, '_cross_entropy'):
             self._call_cross_entropy = self._cross_entropy
+        else:
+            self._call_cross_entropy = self._raise_not_implemented_error('cross_entropy')
+
+    def _raise_not_implemented_error(self, func_name):
+        name = self.name
+        def raise_error(*args, **kwargs):
+            return raise_not_implemented_util(func_name, name, *args, **kwargs)
+        return raise_error
 
     def log_prob(self, value, *args, **kwargs):
         """
@@ -495,6 +523,9 @@ class Distribution(Cell):
         """
         return self.log_base(self._call_survival(value, *args, **kwargs))
 
+    def _kl_loss(self, *args, **kwargs):
+        return raise_not_implemented_util('kl_loss', self.name, *args, **kwargs)
+
     def kl_loss(self, dist, *args, **kwargs):
         """
         Evaluate the KL divergence, i.e. KL(a||b).
@@ -510,6 +541,9 @@ class Distribution(Cell):
         """
         return self._kl_loss(dist, *args, **kwargs)
 
+    def _mean(self, *args, **kwargs):
+        return raise_not_implemented_util('mean', self.name, *args, **kwargs)
+
     def mean(self, *args, **kwargs):
         """
         Evaluate the mean.
@@ -524,6 +558,9 @@ class Distribution(Cell):
         """
         return self._mean(*args, **kwargs)
 
+    def _mode(self, *args, **kwargs):
+        return raise_not_implemented_util('mode', self.name, *args, **kwargs)
+
     def mode(self, *args, **kwargs):
         """
         Evaluate the mode.
@@ -584,6 +621,9 @@ class Distribution(Cell):
         """
         return self.sq_base(self._sd(*args, **kwargs))
 
+    def _entropy(self, *args, **kwargs):
+        return raise_not_implemented_util('entropy', self.name, *args, **kwargs)
+
     def entropy(self, *args, **kwargs):
         """
         Evaluate the entropy.
@@ -622,6 +662,9 @@ class Distribution(Cell):
         """
         return self._entropy(*args, **kwargs) + self._kl_loss(dist, *args, **kwargs)
 
+    def _sample(self, *args, **kwargs):
+        return raise_not_implemented_util('sample', self.name, *args, **kwargs)
+
     def sample(self, *args, **kwargs):
         """
         Sampling function.
@@ -680,4 +723,4 @@ class Distribution(Cell):
             return self._call_cross_entropy(*args, **kwargs)
         if name == 'sample':
             return self._sample(*args, **kwargs)
-        return None
+        return raise_not_implemented_util(name, self.name, *args, **kwargs)
diff --git a/tests/ut/python/nn/probability/distribution/test_distribution.py b/tests/ut/python/nn/probability/distribution/test_distribution.py
new file mode 100644
index 0000000000..bea14a4d65
--- /dev/null
+++ b/tests/ut/python/nn/probability/distribution/test_distribution.py
@@ -0,0 +1,102 @@
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""
+Test nn.probability.distribution.
+"""
+import pytest
+
+import mindspore.nn as nn
+import mindspore.nn.probability.distribution as msd
+from mindspore import dtype as mstype
+from mindspore import Tensor
+from mindspore import context
+
+func_name_list = ['prob', 'log_prob', 'cdf', 'log_cdf',
+                  'survival_function', 'log_survival',
+                  'sd', 'var', 'mode', 'mean',
+                  'entropy', 'kl_loss', 'cross_entropy',
+                  'sample']
+
+class MyExponential(msd.Distribution):
+    """
+    Test distirbution class: no function is implemented.
+    """
+    def __init__(self, rate=None, seed=None, dtype=mstype.float32, name="MyExponential"):
+        param = dict(locals())
+        param['param_dict'] = {'rate': rate}
+        super(MyExponential, self).__init__(seed, dtype, name, param)
+
+class Net(nn.Cell):
+    """
+    Test Net: function called through construct.
+    """
+    def __init__(self, func_name):
+        super(Net, self).__init__()
+        self.dist = MyExponential()
+        self.name = func_name
+
+    def construct(self, *args, **kwargs):
+        return self.dist(self.name, *args, **kwargs)
+
+
+def test_raise_not_implemented_error_construct():
+    """
+    test raise not implemented error in pynative mode.
+    """
+    value = Tensor([0.2], dtype=mstype.float32)
+    for func_name in func_name_list:
+        with pytest.raises(NotImplementedError):
+            net = Net(func_name)
+            net(value)
+
+def test_raise_not_implemented_error_construct_graph_mode():
+    """
+    test raise not implemented error in graph mode.
+    """
+    context.set_context(mode=context.GRAPH_MODE)
+    value = Tensor([0.2], dtype=mstype.float32)
+    for func_name in func_name_list:
+        with pytest.raises(NotImplementedError):
+            net = Net(func_name)
+            net(value)
+
+class Net1(nn.Cell):
+    """
+    Test Net: function called directly.
+    """
+    def __init__(self, func_name):
+        super(Net1, self).__init__()
+        self.dist = MyExponential()
+        self.func = getattr(self.dist, func_name)
+
+    def construct(self, *args, **kwargs):
+        return self.func(*args, **kwargs)
+
+def test_raise_not_implemented_error():
+    """
+    test raise not implemented error in pynative mode.
+    """
+    value = Tensor([0.2], dtype=mstype.float32)
+    for func_name in func_name_list:
+        with pytest.raises(NotImplementedError):
+            net = Net1(func_name)
+            net(value)
+
+def test_raise_not_implemented_error_graph_mode():
+    """
+    test raise not implemented error in graph mode.
+    """
+    context.set_context(mode=context.GRAPH_MODE)
+    value = Tensor([0.2], dtype=mstype.float32)
+    for func_name in func_name_list:
+        with pytest.raises(NotImplementedError):
+            net = Net1(func_name)
+            net(value)