mindspore.communication.comm_func.recv

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mindspore.communication.comm_func.recv(tensor, src=0, group=GlobalComm.WORLD_COMM_GROUP, tag=0)[source]

Receive tensors from src.

Note

  • Send and Receive must be used in combination and have same tag.

  • The shape and dtype of input tensor is used to receive tensor, but the value of input tensor would not take effect.

  • Only support PyNative mode, Graph mode is not currently supported.

Parameters

  • tensor (Tensor) – The shape of tensor is \((x_1, x_2, ..., x_R)\). The shape and dtype of this tensor is used to receive tensor, but the value of input tensor would not take effect.

  • src (int, optional) – A required integer identifying the source rank(global rank). Default: 0.

  • 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.

  • tag (int, optional) – A required integer identifying the send/recv message tag. The message will be received by the send op with the same "tag". Default: 0.

Returns

Tensor, the shape of output is \((x_1, x_2, ..., x_R)\).

Raises

  • TypeError – If src is not an int or group is not a str.

  • ValueError – If the rank ID of the process is greater than the rank size of the communication group.

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 startup for more details.

This example should be run with 2 devices.

>>> import numpy as np
>>> import mindspore as ms
>>> from mindspore.communication import init
>>> from mindspore.communication.comm_func import send, recv
>>> from mindspore.communication import get_rank, get_group_size
>>>
>>> np.random.seed(1)
>>> init()
>>> rank = get_rank()
>>> size = get_group_size()
>>> x = np.ones([2, 2]).astype(np.float32) * 0.01 * (rank + 1)
>>> x2 = np.ones([2, 2]).astype(np.float32)
>>>
>>>
>>> if rank < size / 2:
...     _x = ms.Tensor(x)
...     send(_x, rank + size // 2)
... else:
...     _x2 = ms.Tensor(x2)
...     output = recv(_x2, rank - size // 2)
...     print(output)
rank1:
[[0.01  0.01]
 [0.01  0.01]]