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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, Parameter
20from mindspore import context
21from mindspore.common.api import _CellGraphExecutor
22from mindspore.nn import TrainOneStepCell
23from mindspore.nn.optim import AdamWeightDecay
24from mindspore.ops import operations as P
25
26
27class NetWithLoss(nn.Cell):
28    def __init__(self, network, strategy3):
29        super(NetWithLoss, self).__init__()
30        self.loss = P.SoftmaxCrossEntropyWithLogits().shard(strategy3)
31        self.network = network
32
33    def construct(self, x, b):
34        predict = self.network(x)
35        return self.loss(predict, b)[0]
36
37
38def compile_net(net, x, b):
39    net.set_auto_parallel()
40    _CellGraphExecutor().compile(net, x, b)
41
42
43def test_optimizer_clone_weight():
44    class Net(nn.Cell):
45        def __init__(self, strategy1, strategy2, weight):
46            super().__init__()
47            self.weight = Parameter(weight, "w1")
48            self.matmul = P.MatMul(transpose_a=False, transpose_b=True).shard(strategy1)
49            self.relu = P.ReLU().shard(strategy2)
50
51        def construct(self, x):
52            out = self.matmul(x, self.weight)
53            out = self.relu(out)
54            return out
55
56    context.set_auto_parallel_context(device_num=4, global_rank=0)
57    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
58
59    strategy1 = ((2, 1), (2, 1))
60    strategy2 = ((4, 1),)
61    strategy3 = ((4, 1), (4, 1))
62
63    x = Tensor(np.ones([64, 32]), dtype=ms.float32)
64    weight = Tensor(np.ones([64, 32]), dtype=ms.float32)
65    b = Tensor(np.ones([64, 64]), dtype=ms.float32)
66
67    net = Net(strategy1, strategy2, weight)
68
69    optimizer = AdamWeightDecay(net.trainable_params())
70
71    net_with_loss = NetWithLoss(net, strategy3)
72
73    train_net = TrainOneStepCell(net_with_loss, optimizer)
74
75    compile_net(train_net, x, b)
76
77
78def test_optimizer_clone_weight2():
79    class Net(nn.Cell):
80        def __init__(self, strategy1, strategy2, weight):
81            super().__init__()
82            self.weight = Parameter(weight, "w1")
83            self.matmul = P.MatMul(transpose_a=False, transpose_b=True).shard(strategy1)
84            self.relu = P.ReLU().shard(strategy2)
85
86        def construct(self, x):
87            out = self.matmul(x, self.weight)
88            out = self.relu(out)
89            return out
90
91    context.set_auto_parallel_context(device_num=4, global_rank=0)
92    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
93
94    strategy1 = ((2, 1), (2, 1))
95    strategy2 = ((4, 1),)
96    strategy3 = ((4, 1), (4, 1))
97
98    x = Tensor(np.ones([64, 32]), dtype=ms.float32)
99    weight = Tensor(np.ones([64, 32]), dtype=ms.float32)
100    b = Tensor(np.ones([64, 64]), dtype=ms.float32)
101
102    net = Net(strategy1, strategy2, weight)
103
104    optimizer = AdamWeightDecay(net.trainable_params())
105
106    net_with_loss = NetWithLoss(net, strategy3)
107
108    train_net = TrainOneStepCell(net_with_loss, optimizer)
109
110    compile_net(train_net, x, b)
111