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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# ============================================================================
15
16import numpy as np
17import pytest
18
19import mindspore.context as context
20import mindspore.nn as nn
21from mindspore import Tensor
22from mindspore.ops import composite as C
23from mindspore.ops import operations as P
24
25context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
26
27
28class SoftplusNet(nn.Cell):
29    def __init__(self):
30        super(SoftplusNet, self).__init__()
31        self.softplus = P.Softplus()
32
33    def construct(self, x):
34        return self.softplus(x)
35
36
37class Grad(nn.Cell):
38    def __init__(self, network):
39        super(Grad, self).__init__()
40        self.grad = C.GradOperation(get_all=True, sens_param=True)
41        self.network = network
42
43    def construct(self, input_data, sens):
44        gout = self.grad(self.network)(input_data, sens)
45        return gout
46
47
48@pytest.mark.level0
49@pytest.mark.platform_x86_gpu_training
50@pytest.mark.env_onecard
51def test_softplusgrad():
52    x = np.array([0.58401114, 0.68800163, 0.9760397, 0.14702141, 0.46563736, 0.9607501,
53                  0.14567593, 0.12261796, 0.37054458, 0.46421242]).astype(np.float32)
54    dy = np.array([0.5559598, 0.96994054, 0.24770357, 0.34646875, 0.2984393, 0.03287048,
55                   0.55681044, 0.966908, 0.06015943, 0.6099489]).astype(np.float32)
56    x_ms = Tensor(x)
57    dy_ms = Tensor(dy)
58
59    net = SoftplusNet()
60    grad = Grad(net)
61
62    output = grad(x_ms, dy_ms)
63    expect = dy * np.exp(x) / (1 + np.exp(x))
64    assert np.allclose(output[0].asnumpy(), expect, rtol=1e-3)
65
66@pytest.mark.level0
67@pytest.mark.platform_x86_gpu_training
68@pytest.mark.env_onecard
69def test_softplusgrad_fp16():
70    np.random.seed(42)
71    x_np = np.random.randn(5, 3, 6).astype(np.float16)
72    dy_np = np.random.randn(5, 3, 6).astype(np.float16)
73    net = SoftplusNet()
74    grad = Grad(net)
75    output = grad(Tensor(x_np), Tensor(dy_np))
76    expect = dy_np * np.exp(x_np) / (1 + np.exp(x_np))
77    assert np.allclose(output[0].asnumpy(), expect, rtol=1e-2)
78