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1# Copyright 2020 Huawei Technologies Co., Ltd
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7# http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ============================================================================
15import numpy as np
16
17import mindspore.context as context
18import mindspore.nn as nn
19from mindspore import Tensor
20from mindspore.common.initializer import initializer
21from mindspore.common.parameter import Parameter
22from mindspore.communication.management import init, NCCL_WORLD_COMM_GROUP, get_rank, get_group_size
23from mindspore.ops import operations as P
24from mindspore.ops.operations._inner_ops import Send, Receive
25from mindspore.common import dtype as mstype
26
27context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
28
29init()
30rank = get_rank()
31size = get_group_size()
32if size % 2 != 0:
33    raise RuntimeError("Group size should be divided by 2 exactly.")
34x = np.ones([3, 3, 3, 3]).astype(np.float32) * 0.01 * (rank + 1)
35
36
37class SendNet(nn.Cell):
38    def __init__(self):
39        super(SendNet, self).__init__()
40        self.x = Parameter(initializer(Tensor(x), x.shape), name='x')
41        self.depend = P.Depend()
42        self.send = Send(sr_tag=0, dest_rank=rank+size//2, group=NCCL_WORLD_COMM_GROUP)
43
44    def construct(self):
45        out = self.depend(self.x, self.send(self.x))
46        return out
47
48class RecvNet(nn.Cell):
49    def __init__(self):
50        super(RecvNet, self).__init__()
51        self.recv = Receive(sr_tag=0, src_rank=rank-size//2, shape=[3, 3, 3, 3], dtype=mstype.float32,
52                            group=NCCL_WORLD_COMM_GROUP)
53
54    def construct(self):
55        out = self.recv()
56        return out
57
58def test_send_recv():
59    if rank < size / 2:
60        send_net = SendNet()
61        output = send_net()
62    else:
63        expect_output = np.ones([3, 3, 3, 3]).astype(np.float32) * 0.01 * (rank-size//2 + 1)
64        recv_net = RecvNet()
65        output = recv_net()
66
67        diff = abs(output.asnumpy() - expect_output)
68        error = np.ones(shape=output.shape) * 1.0e-5
69        assert np.all(diff < error)
70        assert expect_output.shape == output.shape
71