mindspore.ops.CollectiveScatter

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class mindspore.ops.CollectiveScatter(src_rank=0, group=GlobalComm.WORLD_COMM_GROUP)[source]

Scatter tensor evently across the processes in the specified communication group.

Note

The interface behavior only support Tensor input and scatter evenly. Only the tensor in process src_rank (global rank) will do scatter.

Parameters

  • src_rank (int, optional) – Specifies the rank of the process that send the tensor. And only process src_rank will send the tensor.

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

Inputs:

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

Outputs:

Tensor, the shape of output is \((x_1/src\_rank, x_2, ..., x_R)\). The dimension 0 of data is equal to the dimension of input tensor divided by src, and the other dimension keep the same.

Raises

  • TypeError – If group is not a str.

  • RuntimeError – If device target is invalid, or backend is invalid, or distributed initialization fails.

  • ValueError – If the local rank id of the calling process in the group is larger than the group's rank size.

Supported Platforms:

Ascend

Examples

Note

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

For Ascend/GPU/CPU devices, it is recommended to use the msrun startup method without any third-party or configuration file dependencies. Please see the msrun start up for more details.

This example should be run with 2 devices.

>>> import numpy as np
>>> import mindspore.nn as nn
>>> from mindspore import Tensor
>>> from mindspore.communication.management import init, get_rank
>>> from mindspore import ops
>>> # Launch 2 processes.
>>> init()
>>> class CollectiveScatterNet(nn.Cell):
>>>     def __init__(self):
>>>         super(CollectiveScatter, self).__init__()
>>>         self.collective_scatter = ops.CollectiveScatter(src_rank=0)
>>>
>>>     def construct(self, x):
>>>         return self.collective_scatter(x)
>>>
>>> input = Tensor(np.arange(8).reshape([4, 2]).astype(np.float32))
>>> net = CollectiveScatterNet()
>>> output = net(input)
>>> print(output)
Process with rank 0: [[0. 1.],
                      [2. 3.]]
Process with rank 1: [[4. 5.],
                      [6. 7.]]
Tutorial Examples: