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1# Copyright 2020-2021 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
18import mindspore.context as context
19from mindspore import Tensor
20from mindspore.nn import Cell
21import mindspore.ops.operations._grad_ops as G
22
23
24class MinmumGradNet(Cell):
25    def __init__(self):
26        super(MinmumGradNet, self).__init__()
27        self.minimum_grad = G.MinimumGrad()
28
29    def construct(self, x, y, dy):
30        return self.minimum_grad(x, y, dy)
31
32
33def gen_data():
34    np.random.seed(0)
35    input_x_np = np.random.normal(0, 1, [2, 3]).astype(np.float32)
36    input_y_np = np.random.normal(0, 1, [1]).astype(np.float32)
37    input_dout_np = np.minimum(input_x_np, input_y_np).astype(np.float32)
38    input_x = Tensor(input_x_np)
39    input_y = Tensor(input_y_np)
40    input_dout = Tensor(input_dout_np)
41    return input_x, input_y, input_dout
42
43
44def get_minimum_grad_output(input_x, input_y, input_dout, enable_graph_kernel=False):
45    context.set_context(enable_graph_kernel=enable_graph_kernel)
46    net = MinmumGradNet()
47    result = net(input_x, input_y, input_dout)
48    return result[0].asnumpy(), result[1].asnumpy()
49
50
51def test_minimum_grad():
52    input_x, input_y, input_dout = gen_data()
53    result_off = get_minimum_grad_output(input_x, input_y, input_dout, False)
54    result_on = get_minimum_grad_output(input_x, input_y, input_dout, True)
55    assert np.allclose(result_on[0], result_off[0], rtol=1.e-4, atol=1.e-8, equal_nan=True)
56    assert np.allclose(result_on[1], result_off[1], rtol=1.e-4, atol=1.e-8, equal_nan=True)
57
58
59@pytest.mark.level0
60@pytest.mark.platform_x86_gpu_training
61@pytest.mark.env_onecard
62def test_minimum_grad_gpu():
63    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
64    test_minimum_grad()
65
66
67@pytest.mark.level0
68@pytest.mark.platform_arm_ascend_training
69@pytest.mark.platform_x86_ascend_training
70@pytest.mark.env_onecard
71def test_minimum_grad_ascend():
72    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
73    test_minimum_grad()
74