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1# Copyright 2024 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, HCCL_WORLD_COMM_GROUP, get_rank, get_group_size
23from mindspore.ops import operations as P
24
25context.set_context(mode=context.GRAPH_MODE, device_target='Ascend')
26context.set_context(jit_level='O0')
27
28init()
29rank = get_rank()
30size = get_group_size()
31x = np.ones([size, 1, 3, 3]).astype(np.float32) * 0.01 * (rank + 1)
32
33
34class Net(nn.Cell):
35    def __init__(self):
36        super(Net, self).__init__()
37        self.x = Parameter(initializer(Tensor(x), x.shape), name='x')
38
39        self.op0 = "sum"
40        self.op1 = "max"
41        self.op2 = "min"
42        self.op3 = "prod"
43
44        self.reduce_scatter1 = P.ReduceScatter(self.op0, group=HCCL_WORLD_COMM_GROUP)
45        self.reduce_scatter2 = P.ReduceScatter(self.op1, group=HCCL_WORLD_COMM_GROUP)
46        self.reduce_scatter3 = P.ReduceScatter(self.op2, group=HCCL_WORLD_COMM_GROUP)
47
48    def construct(self):
49        return (self.reduce_scatter1(self.x),
50                self.reduce_scatter2(self.x),
51                self.reduce_scatter3(self.x))
52
53
54def test_ReduceScatter():
55    """
56    Feature: lccl operator test.
57    Description: msrun lccl reduce_scatter 8P case.
58    Expectation: success
59    """
60    reduce_scatter = Net()
61    output = reduce_scatter()
62
63    sum_ones = np.ones([size, 1, 3, 3]).astype(np.float32) * 0
64    for i in range(size):
65        sum_ones += np.ones([size, 1, 3, 3]).astype(np.float32) * 0.01 * (i + 1)
66    expect0 = sum_ones[rank: rank + 1]
67    diff0 = output[0].asnumpy() - expect0
68    error0 = np.ones(shape=expect0.shape) * 1.0e-5
69    assert np.all(diff0 < error0)
70    assert output[0].shape == expect0.shape
71
72    expect1 = np.ones([1, 1, 3, 3]).astype(np.float32) * 0.01 * size
73    diff1 = output[1].asnumpy() - expect1
74    error1 = np.ones(shape=expect1.shape) * 1.0e-5
75    assert np.all(diff1 < error1)
76    assert output[1].shape == expect1.shape
77
78    expect2 = np.ones([1, 1, 3, 3]).astype(np.float32) * 0.01 * 1
79    diff2 = output[2].asnumpy() - expect2
80    error2 = np.ones(shape=expect2.shape) * 1.0e-5
81    assert np.all(diff2 < error2)
82    assert output[2].shape == expect2.shape
83