mindspore.communication.comm_func.all_gather_into_tensor

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mindspore.communication.comm_func.all_gather_into_tensor(tensor, group=GlobalComm.WORLD_COMM_GROUP, async_op=False)[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.

  • async_op (bool, optional) – Whether this operator should be an async operator. Default: False .

Returns

Tuple(Tensor, CommHandle), if the number of devices in the group is N, then the shape of output tensor is \((N, x_1, x_2, ..., x_R)\). CommHandle is an async work handle, if async_op is set to True. CommHandle will be None, when async_op is False.

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

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.communication as comm
>>>
>>> ms.set_context(mode=ms.GRAPH_MODE)
>>> comm.init()
>>> input_tensor = ms.Tensor(np.ones([2, 8]).astype(np.float32))
>>> output = comm.comm_func.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.]]