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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):
37        predict = self.network(x, y)
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):
47        return grad_all(self.network)(x, y)
48
49
50def compile_net(net, x, y):
51    net.set_auto_parallel()
52    net.set_train()
53    _cell_graph_executor.compile(net, x, y)
54
55
56def test_prelu_single_success1():
57    class Net(nn.Cell):
58        def __init__(self):
59            super().__init__()
60            self.prelu = P.PReLU()
61
62        def construct(self, x, y):
63            out = self.prelu(x, y)
64            return out
65
66    context.reset_auto_parallel_context()
67    net = GradWrap(NetWithLoss(Net()))
68    x = Tensor(np.random.rand(1, 33, 4, 4), ms.float32)
69    w = Tensor(np.random.rand(33), ms.float32)
70    compile_net(net, x, w)
71
72
73def test_prelu_single_success2():
74    class Net(nn.Cell):
75        def __init__(self):
76            super().__init__()
77            self.prelu = P.PReLU()
78
79        def construct(self, x, y):
80            out = self.prelu(x, y)
81            return out
82
83    context.reset_auto_parallel_context()
84    net = GradWrap(NetWithLoss(Net()))
85    x = Tensor(np.random.rand(1, 33, 4, 4), ms.float32)
86    w = Tensor([0.1], ms.float32)
87    compile_net(net, x, w)
88
89
90def test_prelu_parallel_success1():
91    class Net(nn.Cell):
92        def __init__(self, strategy):
93            super().__init__()
94            self.prelu = P.PReLU().shard(strategy)
95
96        def construct(self, x, y):
97            out = self.prelu(x, y)
98            return out
99
100    context.reset_auto_parallel_context()
101    context.set_auto_parallel_context(device_num=8, global_rank=0)
102    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
103    strategy = ((1, 1, 1, 1), (1,))
104    x = Tensor(np.random.rand(4, 4, 32, 64), dtype=ms.float32)
105    w = Tensor(np.random.rand(4), dtype=ms.float32)
106    net = GradWrap(NetWithLoss(Net(strategy)))
107    compile_net(net, x, w)
108
109
110def test_prelu_parallel_success2():
111    class Net(nn.Cell):
112        def __init__(self, strategy):
113            super().__init__()
114            self.prelu = P.PReLU().shard(strategy)
115
116        def construct(self, x, y):
117            out = self.prelu(x, y)
118            return out
119
120    context.reset_auto_parallel_context()
121    context.set_auto_parallel_context(device_num=64, global_rank=0)
122    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
123    strategy = ((2, 1, 4, 8), (1,))
124    x = Tensor(np.random.rand(4, 4, 32, 64), dtype=ms.float32)
125    w = Tensor(np.random.rand(4), dtype=ms.float32)
126    net = GradWrap(NetWithLoss(Net(strategy)))
127    compile_net(net, x, w)
128
129
130def test_prelu_parallel_success3():
131    class NetWithLoss3(nn.Cell):
132        def __init__(self, network):
133            super(NetWithLoss3, self).__init__()
134            self.loss = VirtualLoss()
135            self.network = network
136
137        def construct(self, x, y, w):
138            predict = self.network(x, y, w)
139            return self.loss(predict)
140
141    class GradWrap3(nn.Cell):
142        def __init__(self, network):
143            super(GradWrap3, self).__init__()
144            self.network = network
145
146        def construct(self, x, y, w):
147            return grad_all(self.network)(x, y, w)
148
149    class Net(nn.Cell):
150        def __init__(self, strategy1, strategy2):
151            super().__init__()
152            self.matmul = P.MatMul().shard(strategy1)
153            self.prelu = P.PReLU().shard(strategy2)
154
155        def construct(self, x, y, w):
156            out = self.matmul(x, y)
157            out = self.prelu(out, w)
158            return out
159
160    context.reset_auto_parallel_context()
161    context.set_auto_parallel_context(device_num=64, global_rank=0)
162    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
163    strategy1 = ((2, 4), (4, 2))
164    strategy2 = ((32, 1), (1,))
165    x = Tensor(np.random.rand(128, 64), dtype=ms.float32)
166    y = Tensor(np.random.rand(64, 16), dtype=ms.float32)
167    w = Tensor(np.random.rand(16), dtype=ms.float32)
168    net = GradWrap3(NetWithLoss3(Net(strategy1, strategy2)))
169    net.set_auto_parallel()
170    net.set_train()
171    _cell_graph_executor.compile(net, x, y, w)
172
173
174def test_prelu_parallel_success4():
175    class Net(nn.Cell):
176        def __init__(self, strategy):
177            super().__init__()
178            self.prelu = P.PReLU().shard(strategy)
179
180        def construct(self, x, y):
181            out = self.prelu(x, y)
182            return out
183
184    context.reset_auto_parallel_context()
185    context.set_auto_parallel_context(device_num=64, global_rank=0)
186    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
187    strategy = ((2, 4, 4, 2), (4,))
188    x = Tensor(np.random.rand(4, 16, 32, 64), dtype=ms.float32)
189    w = Tensor(np.random.rand(16), dtype=ms.float32)
190    net = GradWrap(NetWithLoss(Net(strategy)))
191    compile_net(net, x, w)
192
193
194def test_prelu_parallel_success5():
195    class Net(nn.Cell):
196        def __init__(self, strategy):
197            super().__init__()
198            self.prelu = P.PReLU().shard(strategy)
199
200        def construct(self, x, y):
201            out = self.prelu(x, y)
202            return out
203
204    context.reset_auto_parallel_context()
205    context.set_auto_parallel_context(device_num=64, global_rank=0)
206    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
207    strategy = ((2, 4, 4, 2), (1,))
208    x = Tensor(np.random.rand(4, 16, 32, 64), dtype=ms.float32)
209    w = Tensor(np.random.rand(1), dtype=ms.float32)
210    net = GradWrap(NetWithLoss(Net(strategy)))
211    compile_net(net, x, w)
212