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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# ============================================================================
15import pytest
16import numpy as np
17import mindspore.context as context
18import mindspore.nn as nn
19from mindspore import Tensor
20from mindspore.ops.operations import _grad_ops as G
21
22
23class Net(nn.Cell):
24    def __init__(self):
25        super(Net, self).__init__()
26        self.sqrt_grad = G.SqrtGrad()
27
28    def construct(self, x, dout):
29        return self.sqrt_grad(x, dout)
30
31
32def get_output(x, dout, enable_graph_kernel=False):
33    context.set_context(enable_graph_kernel=enable_graph_kernel)
34    net = Net()
35    output = net(x, dout)
36    return output
37
38
39def test_sqrt_grad(shape_x, shape_dout, dtype):
40    x = Tensor(np.random.normal(0, 1, shape_x).astype(dtype))
41    dout = Tensor(np.random.normal(0, 1, shape_dout).astype(dtype))
42
43    expect = get_output(x, dout, False)
44    output = get_output(x, dout, True)
45
46    expect_np = expect.asnumpy().copy()
47    output_np = output.asnumpy().copy()
48
49    rtol = 0.0001
50    atol = 0.0001
51    if dtype == np.float16:
52        rtol = 0.001
53        atol = 0.001
54
55    assert np.allclose(expect_np, output_np, rtol, atol)
56
57
58@pytest.mark.level0
59@pytest.mark.platform_arm_ascend_training
60@pytest.mark.platform_x86_ascend_training
61@pytest.mark.env_onecard
62def test_sqrt_grad_ascend():
63    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
64    test_sqrt_grad((16, 16), (16, 16), np.float16)
65    test_sqrt_grad((16, 16), (16, 16), np.float32)
66