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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"""Test high order grad with respect to parameter first, then input."""
16
17import pytest
18import numpy as np
19import mindspore.nn as nn
20import mindspore.ops as ops
21from mindspore import Tensor, context
22from mindspore import ParameterTuple, Parameter
23
24
25class Net(nn.Cell):
26    def __init__(self):
27        super(Net, self).__init__()
28        self.mul = ops.Mul()
29        weight_np = np.array([2, 2]).astype(np.float32)
30        self.weight = Parameter(Tensor(weight_np), name="weight", requires_grad=True)
31
32    def construct(self, x):
33        x_square = self.mul(x, x)
34        x_square_z = self.mul(x_square, self.weight)
35        output = self.mul(x_square_z, self.weight)
36        return output
37
38
39class Grad(nn.Cell):
40    def __init__(self, network):
41        super(Grad, self).__init__()
42        self.grad = ops.GradOperation(get_by_list=True, sens_param=False)
43        self.network = network
44        self.params = ParameterTuple(network.trainable_params())
45
46    def construct(self, x):
47        output = self.grad(self.network, self.params)(x)
48        return output
49
50
51class GradSec(nn.Cell):
52    def __init__(self, network):
53        super(GradSec, self).__init__()
54        self.grad = ops.GradOperation(get_all=True, sens_param=False)
55        self.network = network
56
57    def construct(self, x):
58        output = self.grad(self.network)(x)
59        return output
60
61
62@pytest.mark.level1
63@pytest.mark.platform_arm_ascend_training
64@pytest.mark.platform_x86_ascend_training
65@pytest.mark.platform_x86_gpu_training
66@pytest.mark.platform_x86_cpu_training
67@pytest.mark.env_onecard
68def test_sit_high_order_grad_params():
69    context.set_context(mode=context.GRAPH_MODE)
70    x = Tensor(np.array([1, 1]).astype(np.float32))
71    net = Net()
72    first_grad = Grad(net)
73    second_grad = GradSec(first_grad)
74    grad = second_grad(x)
75    assert (grad[0].asnumpy() == np.array([8, 8])).all()
76