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1# Copyright 2019 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 as ms
18import mindspore.nn as nn
19from mindspore import Tensor
20from mindspore import context
21from mindspore.common.api import _cell_graph_executor
22from mindspore.ops import composite as C
23from mindspore.ops import operations as P
24from tests.ut.python.ops.test_math_ops import VirtualLoss
25
26
27grad_all = C.GradOperation(get_all=True)
28
29
30class NetWithLoss(nn.Cell):
31    def __init__(self, network):
32        super(NetWithLoss, self).__init__()
33        self.loss = VirtualLoss()
34        self.network = network
35
36    def construct(self, x, y, b, a):
37        predict = self.network(x, y, b, a)
38        return self.loss(predict)
39
40
41class GradWrap(nn.Cell):
42    def __init__(self, network):
43        super(GradWrap, self).__init__()
44        self.network = network
45
46    def construct(self, x, y, b, a):
47        return grad_all(self.network)(x, y, b, a)
48
49
50def test_two_matmul():
51    class Net(nn.Cell):
52        def __init__(self, strategy1, strategy2, strategy3, strategy4):
53            super().__init__()
54            self.matmul1 = P.MatMul().shard(strategy1)
55            self.matmul2 = P.MatMul().shard(strategy2)
56            self.matmul3 = P.MatMul().shard(strategy3)
57            self.matmul4 = P.MatMul().shard(strategy4)
58
59        def construct(self, x, y, b, a):
60            out = self.matmul1(x, y)
61            out1 = self.matmul2(out, b)
62            out2 = self.matmul3(out, a)
63            out3 = self.matmul4(out1, out2)
64            return out3
65
66    context.set_auto_parallel_context(device_num=8, global_rank=0)
67    strategy1 = ((2, 2), (2, 2))
68    strategy2 = ((1, 8), (8, 1))
69    strategy3 = ((4, 1), (1, 2))
70    strategy4 = ((4, 2), (2, 1))
71    net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3, strategy4)))
72    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
73
74    x = Tensor(np.ones([128, 32]), dtype=ms.float32)
75    y = Tensor(np.ones([32, 128]), dtype=ms.float32)
76    b = Tensor(np.ones([128, 128]), dtype=ms.float32)
77    a = Tensor(np.ones([128, 128]), dtype=ms.float32)
78    net.set_auto_parallel()
79    net.set_train()
80    _cell_graph_executor.compile(net, x, y, b, a)
81