mindspore.ops.AllGather

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class mindspore.ops.AllGather(group=GlobalComm.WORLD_COMM_GROUP)[source]

Gathers tensors from the specified communication group.

Note

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

  • Currently only supports GRAPH_MODE and it should be called in Cell.

Parameters

group (str) – The communication group to work on. Default: GlobalComm.WORLD_COMM_GROUP , which means "hccl_world_group" in Ascend, and "nccl_world_group" in GPU.

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

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 rank table Startup for more details.

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

For the CPU device, users need to write a dynamic cluster startup script, please see the Dynamic Cluster Startup .

This example should be run with 2 devices.

>>> import numpy as np
>>> import mindspore as ms
>>> import mindspore.ops as ops
>>> import mindspore.nn as nn
>>> from mindspore.communication import init
>>> from mindspore import Tensor
>>>
>>> ms.set_context(mode=ms.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_x = Tensor(np.ones([2, 8]).astype(np.float32))
>>> net = Net()
>>> output = net(input_x)
>>> 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.]]
Tutorial Examples: