mindspore.ops.AllGather

class mindspore.ops.AllGather(*args, **kwargs)[source]

Gathers tensors from the specified communication group.

Note

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

Parameters: group (str) – The communication group to work on. Default: “hccl_world_group”.

Inputs:

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

Outputs:

Tensor. If the number of devices in the group is N, then the shape of output is \((N, x_1, x_2, ..., x_R)\).

Raises

TypeError – If group is not a str.
ValueError – If the local rank id of the calling process in the group is larger than the group’s rank size.

Supported Platforms:: Ascend GPU

Examples

>>> # This example should be run with two devices. Refer to the tutorial > Distributed Training on mindspore.cn
>>> import numpy as np
>>> import mindspore.ops.operations as ops
>>> import mindspore.nn as nn
>>> from mindspore.communication import init
>>> from mindspore import Tensor, context
>>>
>>> context.set_context(mode=context.GRAPH_MODE)
>>> init()
... class Net(nn.Cell):
...     def __init__(self):
...         super(Net, self).__init__()
...         self.allgather = ops.AllGather()
...
...     def construct(self, x):
...         return self.allgather(x)
...
>>> input_ = Tensor(np.ones([2, 8]).astype(np.float32))
>>> net = Net()
>>> output = net(input_)
>>> print(output)
[[1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1.]]