diff --git a/mindspore/python/mindspore/ops/operations/comm_ops.py b/mindspore/python/mindspore/ops/operations/comm_ops.py
index d9d8978151d..355bd3cb031 100644
--- a/mindspore/python/mindspore/ops/operations/comm_ops.py
+++ b/mindspore/python/mindspore/ops/operations/comm_ops.py
@@ -638,6 +638,35 @@ class NeighborExchange(Primitive):
         send_shapes (tuple(list(int))): Data shape which send to the send_rank_ids.
         recv_type (type): Data type which received from recv_rank_ids
         group (str):
+
+    Example:
+        >>> # This example should be run with 2 devices. Refer to the tutorial > Distributed Training on mindspore.cn
+        >>> import os
+        >>> import mindspore as ms
+        >>> from mindspore import Tensor
+        >>> from mindspore import context
+        >>> from mindspore.communication import init
+        >>> import mindspore.nn as nn
+        >>> import mindspore.ops as ops
+        >>> import numpy as np
+        >>> class Net(nn.Cell):
+        ...     def __init__(self):
+        ...         super(Net, self).__init__()
+        ...         self.neighborexchange = ops.NeighborExchange(send_rank_ids=[1], recv_rank_ids=[1],
+        ...                                                      recv_shapes=([2, 2],), send_shapes=([3, 3],),
+        ...                                                      recv_type=ms.float32)
+        ...
+        ...
+        ...     def construct(self, x):
+        ...         out = self.neighborexchange((x,))
+        ...
+        >>> context.set_context(mode=context.GRAPH_MODE, device_target='Ascend')
+        >>> init()
+        >>> net = Net()
+        >>> input_x = Tensor(np.ones([3, 3]), dtype = ms.float32)
+        >>> output = net(input_x)
+        >>> print(output)
+        [[2. 2.], [2. 2.]]
     """
 
     @prim_attr_register
@@ -676,6 +705,34 @@ class AlltoAll(PrimitiveWithInfer):
 
     Raises:
         TypeError: If group is not a string.
+
+    Example:
+        >>> # This example should be run with 8 devices. Refer to the tutorial > Distributed Training on mindspore.cn
+        >>> import os
+        >>> import mindspore as ms
+        >>> from mindspore import Tensor
+        >>> from mindspore import context
+        >>> from mindspore.communication import init
+        >>> import mindspore.nn as nn
+        >>> import mindspore.ops as ops
+        >>> import numpy as np
+        >>> class Net(nn.Cell):
+        ...     def __init__(self):
+        ...         super(Net, self).__init__()
+        ...         self.alltoall = ops.AlltoAll(split_count = 8, split_dim = -2, concat_dim = -1)
+        ...
+        ...     def construct(self, x):
+        ...         out = self.alltoall(x)
+        ...         return out
+        ...
+        >>> context.set_context(mode=context.GRAPH_MODE, device_target='Ascend')
+        >>> init()
+        >>> net = Net()
+        >>> rank_id = int(os.getenv("RANK_ID"))
+        >>> input_x = Tensor(np.ones([1, 1, 8, 1]) * rank_id, dtype = ms.float32)
+        >>> output = net(input_x)
+        >>> print(output)
+        [[[[0. 1. 2. 3. 4. 5. 6. 7.]]]]
     """
 
     @prim_attr_register
@@ -728,6 +785,37 @@ class NeighborExchangeV2(Primitive):
                                [top, bottom, left, right].
         data_format (str): Data format, only support NCHW now.
         group (str): The communication group to work on. Default: "GlobalComm.WORLD_COMM_GROUP".
+
+    Example:
+        >>> # This example should be run with 2 devices. Refer to the tutorial > Distributed Training on mindspore.cn
+        >>> import os
+        >>> import mindspore as ms
+        >>> from mindspore import Tensor
+        >>> from mindspore import context
+        >>> from mindspore.communication import init
+        >>> import mindspore.nn as nn
+        >>> import mindspore.ops as ops
+        >>> import numpy as np
+        >>> class Net(nn.Cell):
+        ...     def __init__(self):
+        ...         super(Net, self).__init__()
+        ...         self.neighborexchangev2 = ops.NeighborExchangeV2(send_rank_ids=[-1, -1, -1, -1, 1, -1, -1, -1],
+        ...                                                          send_lens=[0, 1, 0, 0],
+        ...                                                          recv_rank_ids=[-1, -1, -1, -1, 1, -1, -1, -1],
+        ...                                                          recv_lens=[0, 1, 0, 0],
+        ...                                                          data_format="NCHW")
+        ...
+        ...     def construct(self, x):
+        ...         out = self.neighborexchangev2(x)
+        ...         return out
+        ...
+        >>> context.set_context(mode=context.GRAPH_MODE, device_target='Ascend')
+        >>> init()
+        >>> input_x = Tensor(np.ones([1, 1, 2, 2]), dtype = ms.float32)
+        >>> net = Net()
+        >>> output = net(input_x)
+        >>> print(output)
+        [[[[1. 1.], [1. 1.], [2. 2.]]]]
     """
 
     @prim_attr_register