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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"""
16test assign add
17"""
18import numpy as np
19
20import mindspore as ms
21import mindspore.context as context
22import mindspore.nn as nn
23from mindspore import Tensor, Parameter
24from mindspore.common.initializer import initializer
25from mindspore.ops import operations as P
26from ..ut_filter import non_graph_engine
27
28context.set_context(mode=context.GRAPH_MODE)
29
30
31class Net(nn.Cell):
32    """Net definition"""
33
34    def __init__(self):
35        super(Net, self).__init__()
36        self.AssignAdd = P.AssignAdd()
37        self.inputdata = Parameter(initializer(1, [1], ms.int64), name="global_step")
38        print("inputdata: ", self.inputdata)
39
40    def construct(self, x):
41        out = self.AssignAdd(self.inputdata, x)
42        return out
43
44
45@non_graph_engine
46def test_AssignAdd_1():
47    """test AssignAdd 1"""
48    context.set_context(mode=context.GRAPH_MODE)
49    net = Net()
50    x = Tensor(np.ones([1]).astype(np.int64) * 100)
51
52    print("MyPrintResult dataX:", x)
53    result = net(x)
54    print("MyPrintResult data::", result)
55    expect = np.ones([1]).astype(np.int64) * 101
56    diff = result.asnumpy() - expect
57
58    print("MyPrintExpect:", expect)
59    print("MyPrintDiff:", diff)
60    error = np.ones(shape=[1]) * 1.0e-3
61    assert np.all(diff < error)
62
63
64@non_graph_engine
65def test_AssignAdd_2():
66    """test AssignAdd 2"""
67    context.set_context(mode=context.GRAPH_MODE)
68    net = Net()
69    x = Tensor(np.ones([1]).astype(np.int64) * 102)
70
71    print("MyPrintResult dataX:", x)
72    result = net(x)
73    print("MyPrintResult data::", result.asnumpy())
74    expect = np.ones([1]).astype(np.int64) * 103
75    diff = result.asnumpy() - expect
76
77    print("MyPrintExpect:", expect)
78    print("MyPrintDiff:", diff)
79    error = np.ones(shape=[1]) * 1.0e-3
80    assert np.all(diff < error)
81
82
83class AssignAddNet(nn.Cell):
84    """Net definition"""
85
86    def __init__(self):
87        super(AssignAddNet, self).__init__()
88        self.AssignAdd = P.AssignAdd()
89        self.inputdata = Parameter(initializer(1, [1], ms.float16), name="KIND_AUTOCAST_SCALAR_TO_TENSOR")
90        self.one = 1
91
92    def construct(self, ixt):
93        z1 = self.AssignAdd(self.inputdata, self.one)
94        return z1
95
96
97@non_graph_engine
98def test_assignadd_scalar_cast():
99    net = AssignAddNet()
100    x = Tensor(np.ones([1]).astype(np.int64) * 102)
101    # _executor.compile(net, 1)
102    _ = net(x)
103