mindspore.ops.CollectiveGather

class mindspore.ops.CollectiveGather(dest_rank, group=GlobalComm.WORLD_COMM_GROUP)[source]

Gathers tensors from the specified communication group. The operation will gather the tensor from processes according to dimension 0.

Note

Only the tensor in process dest_rank (global rank) will keep the gathered tensor. The other process will keep a tensor with shape [1], which has no mathematical meaning.

Parameters

  • dest_rank (int) – Specifies the rank of the process that receive the tensor. And only process dest_rank will receive the gathered tensor.

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

Inputs:

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

Outputs:

Tensor, the shape of output is \((\sum x_1, x_2, ..., x_R)\). The dimension 0 of data is equal to sum of the dimension of input tensor, 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 4 devices.

>>> import numpy as np
>>> import mindspore as ms
>>> import mindspore.nn as nn
>>> from mindspore.communication import init
>>> from mindspore import Tensor
>>> from mindspore import ops
>>> # Launch 2 processes.
>>>
>>> ms.set_context(mode=ms.GRAPH_MODE)
>>> init()
>>> class CollectiveGatherNet(nn.Cell):
...     def __init__(self):
...         super(CollectiveGatherNet, self).__init__()
...         self.collective_gather = ops.CollectiveGather(dest_rank=0)
...
...     def construct(self, x):
...         return self.collective_gather(x)
...
>>> input = Tensor(np.arange(4).reshape([2, 2]).astype(np.float32))
>>> net = CollectiveGatherNet()
>>> output = net(input)
>>> print(output)
Process with rank 0: [[0. 1.],
                      [2. 3.],
                      [0. 1.],
                      [2. 3.]]
Process with rank 1: [0.]
Tutorial Examples: