mindspore.ops.NeighborExchangeV2

class mindspore.ops.NeighborExchangeV2(send_rank_ids, send_lens, recv_rank_ids, recv_lens, data_format, group=GlobalComm.WORLD_COMM_GROUP)[源代码]

NeighborExchangeV2 is a collective operation.

NeighborExchangeV2 sends data from the local rank to ranks in the send_rank_ids, as while receive data from recv_rank_ids.

Note

The user needs to preset communication environment variables before running the following example, please check the details on the official website of MindSpore.

This operator requires a full-mesh network topology, each device has the same vlan id, and the ip & mask are in the same subnet, please check the details.

Parameters

  • send_rank_ids (list(int)) – Ranks which the data is sent to. 8 rank_ids represents 8 directions, if one direction is not send to , set it -1.

  • recv_rank_ids (list(int)) – Ranks which the data is received from. 8 rank_ids represents 8 directions, if one direction is not recv from , set it -1.

  • send_lens (list(int)) – Data lens which send to the send_rank_ids, 4 numbers represent the lens of [top, bottom, left, right].

  • recv_lens (list(int)) – Data lens which received from recv_rank_ids, 4 numbers represent the lens of [top, bottom, left, right].

  • data_format (str) – Data format, only support NCHW now.

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

Supported Platforms:

Ascend

Example

>>> # This example should be run with 2 devices. Refer to the tutorial > Distributed Training on mindspore.cn
>>> import os
>>> import mindspore as ms
>>> from mindspore import Tensor
>>> from mindspore.communication import init
>>> import mindspore.nn as nn
>>> import mindspore.ops as ops
>>> import numpy as np
>>> class Net(nn.Cell):
...     def __init__(self):
...         super(Net, self).__init__()
...         self.neighborexchangev2 = ops.NeighborExchangeV2(send_rank_ids=[-1, -1, -1, -1, 1, -1, -1, -1],
...                                                          send_lens=[0, 1, 0, 0],
...                                                          recv_rank_ids=[-1, -1, -1, -1, 1, -1, -1, -1],
...                                                          recv_lens=[0, 1, 0, 0],
...                                                          data_format="NCHW")
...
...     def construct(self, x):
...         out = self.neighborexchangev2(x)
...         return out
...
>>> ms.set_context(mode=ms.GRAPH_MODE, device_target='Ascend')
>>> init()
>>> input_x = Tensor(np.ones([1, 1, 2, 2]), dtype = ms.float32)
>>> net = Net()
>>> output = net(input_x)
>>> print(output)
[[[[1. 1.], [1. 1.], [2. 2.]]]]