mindspore.mint.distributed.all_gather

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mindspore.mint.distributed.all_gather(tensor_list, tensor, group=None, 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_list (list[Tensor]) – Output list.

  • tensor (Tensor) – The input tensor to be all gathered into tensor.

  • group (str, optional) – The communication group to work on. If None, which means "hccl_world_group" in Ascend. Default: None.

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

Returns

CommHandle, 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 input tensor is not Tensor, tensor_list is not Tensor List, group is not a str or async_op is not bool.

  • TypeError – If size of tensor_list is not equal to group size。

  • TypeError – If the type or shape of tensor not equal to the member of tensor_list。

  • 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 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.mint.distributed import init_process_group
>>> from mindspore.mint.distributed import all_gather
>>> from mindspore import Tensor
>>>
>>> init_process_group()
>>> input_tensor = Tensor(np.ones([2, 8]).astype(np.float32))
>>> out_tensors = [Tensor(np.zeros([2, 8]).astype(np.float32)), Tensor(np.zeros([2, 8]).astype(np.float32))]
>>> output = all_gather(out_tensors, input_tensor)
>>> print(out_tensors)
[Tensor(shape=[2, 8], dtype=Float32, value=
[[ 1.00000000e+00,  1.00000000e+00,  1.00000000e+00 ...  1.00000000e+00,  1.00000000e+00,  1.00000000e+00],
 [ 1.00000000e+00,  1.00000000e+00,  1.00000000e+00 ...  1.00000000e+00,  1.00000000e+00,  1.00000000e+00]]),
Tensor(shape=[2, 8], dtype=Float32, value=
[[ 1.00000000e+00,  1.00000000e+00,  1.00000000e+00 ...  1.00000000e+00,  1.00000000e+00,  1.00000000e+00],
 [ 1.00000000e+00,  1.00000000e+00,  1.00000000e+00 ...  1.00000000e+00,  1.00000000e+00,  1.00000000e+00]])]