mindspore.communication.comm_func.all_gather_into_tensor

mindspore.communication.comm_func.all_gather_into_tensor(tensor, group=GlobalComm.WORLD_COMM_GROUP)[source]

Gathers tensors from the specified communication group and returns the tensor which is all gathered.

Note

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

Parameters

tensor (Tensor) – The input tensor to be all gathered into tensor. The shape of tensor is \((x_1, x_2, ..., x_R)\).
group (str, optional) – The communication group to work on. Default: GlobalComm.WORLD_COMM_GROUP , which means "hccl_world_group" in Ascend, and "nccl_world_group" in GPU.

Returns

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 the type of the first input parameter is not Tensor, or 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.
RuntimeError – If device target is invalid, or backend is invalid, or distributed initialization fails.

Supported Platforms:: Ascend GPU

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
>>> from mindspore import ops
>>> from mindspore.communication import init
>>> from mindspore.communication.comm_func import all_gather_into_tensor
>>> from mindspore import Tensor
>>>
>>> ms.set_context(mode=ms.GRAPH_MODE)
>>> init()
>>> input_tensor = Tensor(np.ones([2, 8]).astype(np.float32))
>>> output = all_gather_into_tensor(input_tensor)
>>> 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.]]