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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 implicit conversion """
16import numpy as np
17import pytest
18import mindspore as ms
19
20from mindspore import Tensor, nn, Parameter
21from mindspore.ops import composite as C
22from mindspore.ops import functional as F
23
24
25grad_all_with_sens = C.GradOperation(get_all=True, sens_param=True)
26
27
28def test_float_tensor_and_int_add():
29    x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
30    y = 2
31    ret_actual = x + y
32    ret_expect = Tensor(np.array([[2.1, 2.2, 2.3], [2.4, 2.5, 2.6]], dtype=np.float32))
33    assert ret_actual.dtype == ret_expect.dtype
34    assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
35
36
37def test_bool_tensor_and_float_add():
38    x = Tensor(np.array([[True, False], [False, True]], dtype=np.bool_))
39    y = 3.3
40    ret_actual = x + y
41    ret_expect = Tensor(np.array([[4.3, 3.3], [3.3, 4.3]], dtype=np.float32))
42    assert ret_actual.dtype == ret_expect.dtype
43    assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
44
45
46def test_bool_tensor_and_int_add():
47    x = Tensor(np.array([[True, False], [False, True]], dtype=np.bool_))
48    y = 3
49    ret_actual = x + y
50    ret_expect = Tensor(np.array([[4, 3], [3, 4]], dtype=np.int64))
51    assert ret_actual.dtype == ret_expect.dtype
52    assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
53
54
55def test_bool_and_int_tensor_add():
56    x = True
57    y = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32))
58    ret_actual = x + y
59    ret_expect = Tensor(np.array([[2, 3, 4], [5, 6, 7]], dtype=np.int32))
60    assert ret_actual.dtype == ret_expect.dtype
61    assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
62
63
64def test_float_tensor_and_int_tensor_add():
65    x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
66    y = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32))
67    ret_actual = x + y
68    ret_expect = Tensor(np.array([[1.1, 2.2, 3.3], [4.4, 5.5, 6.6]], dtype=np.float32))
69    assert ret_actual.dtype == ret_expect.dtype
70    assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
71
72
73def test_float_tensor_and_float_tensor_add():
74    x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
75    y = Tensor(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], dtype=np.float16))
76    ret_actual = x + y
77    ret_expect = Tensor(np.array([[1.1, 2.2, 3.3], [4.4, 5.5, 6.6]], dtype=np.float32))
78    assert ret_actual.dtype == ret_expect.dtype
79    assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
80
81
82def test_int_tensor_and_int_tensor_add():
83    x = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int8))
84    y = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32))
85    ret_actual = x + y
86    ret_expect = Tensor(np.array([[2, 4, 6], [8, 10, 12]], dtype=np.int32))
87    assert ret_actual.dtype == ret_expect.dtype
88    assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
89
90
91def test_float_tensor_and_bool_tensors_add():
92    x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
93    y = Tensor(np.array([[True, True, True], [False, False, False]], dtype=np.bool_))
94    ret_actual = x + y
95    ret_expect = Tensor(np.array([[1.1, 1.2, 1.3], [0.4, 0.5, 0.6]], dtype=np.float32))
96    assert ret_actual.dtype == ret_expect.dtype
97    assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
98
99
100def test_int8_tensor_and_uint8_tensors_add():
101    x = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int8))
102    y = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint8))
103    ret_actual = x + y
104    ret_expect = Tensor(np.array([[2, 4, 6], [8, 10, 12]], dtype=np.int16))
105    assert ret_actual.dtype == ret_expect.dtype
106    assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
107
108
109def test_float_tensor_and_str_add():
110    x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
111    y = "ok"
112    with pytest.raises(TypeError) as er:
113        ret = x + y
114    assert "For 'Add', the 1th input is a not support implicit conversion type: str" in str(er.value)
115
116
117def test_float_tensor_and_tuple_add():
118    x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
119    y = (1, 2, 3)
120    ret_actual = x + y
121    ret_expect = Tensor(np.array([[1.1, 2.2, 3.3], [1.4, 2.5, 3.6]], dtype=np.float32))
122    assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
123
124
125def test_float_tensor_and_list_add():
126    x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
127    y = [1, 2, 3]
128    ret_actual = x + y
129    ret_expect = Tensor(np.array([[1.1, 2.2, 3.3], [1.4, 2.5, 3.6]], dtype=np.float32))
130    assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
131
132
133def test_float_tensor_and_bool_tensors_add_grad():
134    class Net(nn.Cell):
135        def __init__(self):
136            super(Net, self).__init__()
137
138        def construct(self, x, y):
139            return x + y
140
141    class GradNet(nn.Cell):
142        def __init__(self, net):
143            super(GradNet, self).__init__()
144            self.net = net
145
146        def construct(self, x, y, sens):
147            return grad_all_with_sens(self.net)(x, y, sens)
148
149    x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
150    y = Tensor(np.array([[True, True, True], [False, False, False]], dtype=np.bool_))
151    sens = Tensor(np.array([[1.0, 2.0, 0.0], [0.0, 3.0, 4.0]], dtype=np.float32))
152    net = Net()
153    grad_net = GradNet(net)
154    ret = grad_net(x, y, sens)
155    assert ret[0].dtype == x.dtype
156    assert ret[1].dtype == y.dtype
157    assert (ret[0].asnumpy() == sens.asnumpy()).all()
158    assert (ret[1].asnumpy() == sens.asnumpy().astype(np.bool_)).all()
159
160
161def test_float_tensor_and_int_tensors_sub_grad():
162    class Net(nn.Cell):
163        def __init__(self):
164            super(Net, self).__init__()
165
166        def construct(self, x, y):
167            return x - y
168
169    class GradNet(nn.Cell):
170        def __init__(self, net):
171            super(GradNet, self).__init__()
172            self.net = net
173
174        def construct(self, x, y, sens):
175            return grad_all_with_sens(self.net)(x, y, sens)
176
177    x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
178    y = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32))
179    sens = Tensor(np.array([[1.0, 2.0, 0.0], [0.0, 3.0, 4.0]], dtype=np.float32))
180    net = Net()
181    grad_net = GradNet(net)
182    ret = grad_net(x, y, sens)
183    assert ret[0].dtype == x.dtype
184    assert ret[1].dtype == y.dtype
185    assert (ret[0].asnumpy() == sens.asnumpy()).all()
186    assert (ret[1].asnumpy() == sens.asnumpy() * -1).all()
187
188
189def test_float16_tensor_and_float32_tensors_sub_grad():
190    class Net(nn.Cell):
191        def __init__(self):
192            super(Net, self).__init__()
193
194        def construct(self, x, y):
195            return x - y
196
197    class GradNet(nn.Cell):
198        def __init__(self, net):
199            super(GradNet, self).__init__()
200            self.net = net
201
202        def construct(self, x, y, sens):
203            return grad_all_with_sens(self.net)(x, y, sens)
204
205    x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.int32))
206    y = Tensor(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], dtype=np.float32))
207    sens = Tensor(np.array([[1.0, 2.0, 0.0], [0.0, 3.0, 4.0]], dtype=np.float32))
208    net = Net()
209    grad_net = GradNet(net)
210    ret = grad_net(x, y, sens)
211    assert ret[0].dtype == x.dtype
212    assert ret[1].dtype == y.dtype
213    assert (ret[0].asnumpy() == sens.asnumpy()).all()
214    assert (ret[1].asnumpy() == sens.asnumpy() * -1).all()
215
216
217def test_float_tensor_and_int_add_grad():
218    class Net(nn.Cell):
219        def __init__(self):
220            super(Net, self).__init__()
221
222        def construct(self, x):
223            return x + 2
224
225    class GradNet(nn.Cell):
226        def __init__(self, net):
227            super(GradNet, self).__init__()
228            self.net = net
229
230        def construct(self, x, sens):
231            return grad_all_with_sens(self.net)(x, sens)
232
233    x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
234    sens = Tensor(np.array([[1.0, 2.0, 0.0], [0.0, 3.0, 4.0]], dtype=np.float32))
235    net = Net()
236    grad_net = GradNet(net)
237    ret = grad_net(x, sens)
238    assert ret[0].dtype == x.dtype
239    assert (ret[0].asnumpy() == sens.asnumpy()).all()
240
241
242def test_int8_tensor_and_uint8_tensors_add_grad():
243    class Net(nn.Cell):
244        def __init__(self):
245            super(Net, self).__init__()
246
247        def construct(self, x, y):
248            return x + y
249
250    class GradNet(nn.Cell):
251        def __init__(self, net):
252            super(GradNet, self).__init__()
253            self.net = net
254
255        def construct(self, x, y, sens):
256            return grad_all_with_sens(self.net)(x, y, sens)
257
258    x = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int8))
259    y = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint8))
260    sens = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int16))
261    net = Net()
262    grad_net = GradNet(net)
263    ret = grad_net(x, y, sens)
264    assert ret[0].dtype == x.dtype
265    assert ret[1].dtype == y.dtype
266    assert (ret[0].asnumpy() == sens.asnumpy()).all()
267    assert (ret[1].asnumpy() == sens.asnumpy()).all()
268
269class AssignCheck(nn.Cell):
270    """ NetWithNDarray definition """
271
272    def __init__(self):
273        super(AssignCheck, self).__init__()
274        self.cov_step = Parameter(0.0, name="cov_step", requires_grad=False)
275
276    def construct(self, x, y):
277        F.assign(self.cov_step, y)
278        F.assign(x, y)
279        return x
280
281
282def test_assign_check_in_sig():
283    net = AssignCheck()
284    x = Tensor(2, ms.int8)
285    y = Tensor(3, ms.uint8)
286    with pytest.raises(RuntimeError) as e:
287        net(x, y)
288    assert "can not cast automatically" in e.value.args[0]
289