mindspore.ops.Broadcast

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

对输入数据整组广播。

Note

集合中的所有进程的Tensor的shape和数据格式相同。

参数：

  • root_rank (int) - 表示发送源的进程编号。除发送数据的进程外，存在于所有进程中。

  • group (str) - 表示通信域。默认值：”hccl_world_group”。

输入：

  • input_x (Tensor) - Tensor的shape为 \((x_1, x_2, ..., x_R)\) 。

输出：

Tensor，shape与输入相同，即 \((x_1, x_2, ..., x_R)\) 。内容取决于 root_rank device的数据。

异常：

  • TypeError - root_rank不是int或group不是str。

支持平台：

Ascend GPU

样例：

>>> # 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]]),)