class mindspore.ops.Broadcast(root_rank, group=GlobalComm.WORLD_COMM_GROUP)[source]

Broadcasts the tensor to the whole group.


The tensors must have the same shape and format in all processes of the collection. The user needs to preset communication environment variables before running the following example, please check the details on the official website of MindSpore.

  • root_rank (int) – Source rank. Required in all processes except the one that is sending the data.

  • group (str) – The communication group to work on. Default: “GlobalComm.WORLD_COMM_GROUP”.

  • input_x (Tensor) - The shape of tensor is \((x_1, x_2, ..., x_R)\).


Tensor, has the same shape of the input, i.e., \((x_1, x_2, ..., x_R)\). The contents depend on the data of the root_rank device.


TypeError – If root_rank is not an integer or group is not a string.

Supported Platforms:

Ascend GPU


>>> # This example should be run with multiple processes.
>>> # Please refer to the Programming Guide > Distributed Training -> Distributed Parallel Usage Example
>>> # on mindspore.cn and focus on the contents of these three parts: Configuring Distributed Environment
>>> # Variables, Calling the Collective Communication Library, Running The Script.
>>> 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
>>> context.set_context(mode=context.GRAPH_MODE)
>>> init()
>>> class Net(nn.Cell):
...     def __init__(self):
...         super(Net, self).__init__()
...         self.broadcast = ops.Broadcast(1)
...     def construct(self, x):
...         return self.broadcast((x,))
>>> input_x = Tensor(np.ones([2, 4]).astype(np.int32))
>>> net = Net()
>>> output = net(input_x)
>>> print(output)
(Tensor(shape[2,4], dtype=Int32, value=
[[1, 1, 1, 1],
 [1, 1, 1, 1]]),)