mindspore.ops.Broadcast

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

Broadcasts the tensor to the whole group.

Note

The tensors must have the same shape and format in all processes of the collection.

Parameters

  • 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”.

Inputs:

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

Outputs:

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.

Raises

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

Supported Platforms:

Ascend GPU

Examples

Note

Before running the following examples, you need to configure the communication environment variables.

For the Ascend devices, users need to prepare the rank table, set rank_id and device_id. Please see the Ascend tutorial for more details.

For the GPU devices, users need to prepare the host file and mpi, please see the GPU tutorial .

This example should be run with multiple devices.

>>> import mindspore as ms
>>> from mindspore import Tensor
>>> from mindspore.communication import init
>>> import mindspore.nn as nn
>>> import mindspore.ops as ops
>>> import numpy as np
>>>
>>> ms.set_context(mode=ms.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]]),)