mindspore.communication.comm_func.gather_into_tensor

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mindspore.communication.comm_func.gather_into_tensor(tensor, dst=0, 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 dst (global rank) will keep the gathered tensor. The other process will keep a tensor with shape [1], which has no mathematical meaning. Only support PyNative mode, Graph mode is not currently supported.

Parameters

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

  • dst (int, optional) – Specifies the rank(global rank) of the process that receive the tensor. And only process dst will receive the gathered tensor. Default: 0.

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

Returns

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 the type of the first input parameter is not Tensor, or any of op and group is not a str.

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

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 as ms
>>> import mindspore.nn as nn
>>> from mindspore.communication import init
>>> from mindspore import Tensor
>>> from mindspore.communication.comm_func import gather_into_tensor
>>> # Launch 2 processes.
>>>
>>> init()
>>> input = Tensor(np.arange(4).reshape([2, 2]).astype(np.float32))
>>> output = gather_into_tensor(tensor=data, dst=0)
>>> print(output)
Process with rank 0: [[0. 1.],
                      [2. 3.],
                      [0. 1.],
                      [2. 3.]]
Process with rank 1: [0]