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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
18
19from mindspore import Tensor
20from mindspore.ops.operations import _inner_ops as inner
21import mindspore.nn as nn
22import mindspore.context as context
23
24# test to make sure this op actually generates a dynamically shaped output
25@pytest.mark.level0
26@pytest.mark.platform_x86_gpu_training
27@pytest.mark.env_onecard
28def test_gpu_convert_to_dyanamic_shape_confirm_dynamic():
29    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
30
31    class AssertDynamicShapeNet(nn.Cell):
32        def __init__(self):
33            super(AssertDynamicShapeNet, self).__init__()
34            self.gpu_convert_to_dynamic_shape = inner.GpuConvertToDynamicShape()
35            self.error_on_dynamic_shape_input = inner.ErrorOnDynamicShapeInput()
36
37        def construct(self, x):
38            output = self.gpu_convert_to_dynamic_shape(x)
39            self.error_on_dynamic_shape_input(output)
40            return output
41
42    assert_dynamic_shape_net = AssertDynamicShapeNet()
43    x = Tensor(np.array([0, 0, 0, 0]).astype(np.float32))
44
45    with pytest.raises(ValueError) as info:
46        assert_dynamic_shape_net(x)
47    assert "Input is dynamically shaped" in str(info.value)
48
49def gpu_convert_to_dynamic_shape(x):
50    class GpuConvertToDynamicShapeNet(nn.Cell):
51        def __init__(self):
52            super(GpuConvertToDynamicShapeNet, self).__init__()
53            self.gpu_convert_to_dynamic_shape = inner.GpuConvertToDynamicShape()
54
55        def construct(self, x):
56            return self.gpu_convert_to_dynamic_shape(x)
57
58    gpu_convert_to_dynamic_shape_net = GpuConvertToDynamicShapeNet()
59    return gpu_convert_to_dynamic_shape_net(Tensor(x)).asnumpy()
60
61def gpu_convert_to_dynamic_shape_float(dtype):
62    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
63
64    np.random.seed(0)
65    finfo = np.finfo(dtype)
66
67    # np.random.uniform will overflow if we use min/max for float64, so we use
68    # the finfo for float32, but still test the operator with float64 input.
69    if dtype == np.float64:
70        finfo = np.finfo(np.float32)
71
72    float_min = finfo.min
73    float_max = finfo.max
74    x = np.random.uniform(low=float_min, high=float_max, size=12).astype(dtype)
75    ms_out = gpu_convert_to_dynamic_shape(x)
76    np.testing.assert_array_equal(x, ms_out)
77
78def gpu_convert_to_dynamic_shape_int(dtype):
79    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
80
81    np.random.seed(0)
82    iinfo = np.iinfo(dtype)
83    int_min = iinfo.min
84    int_max = iinfo.max
85    x = np.random.uniform(low=int_min, high=int_max, size=12).astype(dtype)
86    ms_out = gpu_convert_to_dynamic_shape(x)
87    np.testing.assert_array_equal(x, ms_out)
88
89@pytest.mark.level1
90@pytest.mark.platform_x86_gpu_training
91@pytest.mark.env_onecard
92def test_gpu_convert_to_dynamic_shape_bool():
93    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
94
95    np.random.seed(0)
96    x = np.random.choice([False, True], 12)
97    ms_out = gpu_convert_to_dynamic_shape(x)
98    np.testing.assert_array_equal(x, ms_out)
99
100@pytest.mark.level1
101@pytest.mark.platform_x86_gpu_training
102@pytest.mark.env_onecard
103def test_gpu_convert_to_dynamic_shape_float16():
104    gpu_convert_to_dynamic_shape_float(np.float16)
105
106@pytest.mark.level0
107@pytest.mark.platform_x86_gpu_training
108@pytest.mark.env_onecard
109def test_gpu_convert_to_dynamic_shape_float32():
110    gpu_convert_to_dynamic_shape_float(np.float32)
111
112@pytest.mark.level0
113@pytest.mark.platform_x86_gpu_training
114@pytest.mark.env_onecard
115def test_gpu_convert_to_dynamic_shape_float64():
116    gpu_convert_to_dynamic_shape_float(np.float64)
117
118@pytest.mark.level1
119@pytest.mark.platform_x86_gpu_training
120@pytest.mark.env_onecard
121def test_gpu_convert_to_dynamic_shape_int8():
122    gpu_convert_to_dynamic_shape_int(np.int8)
123
124@pytest.mark.level1
125@pytest.mark.platform_x86_gpu_training
126@pytest.mark.env_onecard
127def test_gpu_convert_to_dynamic_shape_int16():
128    gpu_convert_to_dynamic_shape_int(np.int16)
129
130@pytest.mark.level1
131@pytest.mark.platform_x86_gpu_training
132@pytest.mark.env_onecard
133def test_gpu_convert_to_dynamic_shape_int32():
134    gpu_convert_to_dynamic_shape_int(np.int32)
135
136@pytest.mark.level1
137@pytest.mark.platform_x86_gpu_training
138@pytest.mark.env_onecard
139def test_gpu_convert_to_dynamic_shape_int64():
140    gpu_convert_to_dynamic_shape_int(np.int64)
141
142@pytest.mark.level1
143@pytest.mark.platform_x86_gpu_training
144@pytest.mark.env_onecard
145def test_gpu_convert_to_dynamic_shape_uint8():
146    gpu_convert_to_dynamic_shape_int(np.uint8)
147
148@pytest.mark.level1
149@pytest.mark.platform_x86_gpu_training
150@pytest.mark.env_onecard
151def test_gpu_convert_to_dynamic_shape_uint16():
152    gpu_convert_to_dynamic_shape_int(np.uint16)
153
154@pytest.mark.level1
155@pytest.mark.platform_x86_gpu_training
156@pytest.mark.env_onecard
157def test_gpu_convert_to_dynamic_shape_uint32():
158    gpu_convert_to_dynamic_shape_int(np.uint32)
159
160@pytest.mark.level1
161@pytest.mark.platform_x86_gpu_training
162@pytest.mark.env_onecard
163def test_gpu_convert_to_dynamic_shape_uint64():
164    gpu_convert_to_dynamic_shape_int(np.uint64)
165