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1# Copyright 2019-2021 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
19import mindspore.context as context
20import mindspore.nn as nn
21from mindspore import Tensor
22from mindspore.common.api import ms_function
23from mindspore.ops import operations as P
24from mindspore.ops.operations import _inner_ops as inner
25
26x0 = np.random.rand(2, 3, 4, 4).astype(np.float32)
27axis0 = 3
28keep_dims0 = True
29
30x1 = np.random.rand(2, 3, 4, 4).astype(np.float32)
31axis1 = 3
32keep_dims1 = False
33
34x2 = np.random.rand(2, 3, 1, 4).astype(np.float32)
35axis2 = 2
36keep_dims2 = True
37
38x3 = np.random.rand(2, 3, 1, 4).astype(np.float32)
39axis3 = 2
40keep_dims3 = False
41
42x4 = np.random.rand(2, 3, 4, 4).astype(np.float32)
43axis4 = ()
44np_axis4 = None
45keep_dims4 = True
46
47x5 = np.random.rand(2, 3, 4, 4).astype(np.float32)
48axis5 = ()
49np_axis5 = None
50keep_dims5 = False
51
52x6 = np.random.rand(2, 3, 4, 4).astype(np.float32)
53axis6 = (1, 2)
54keep_dims6 = False
55
56x7 = np.random.rand(2, 3, 4, 4).astype(np.float32)
57axis7 = (1, 2)
58keep_dims7 = True
59
60x8 = np.random.rand(2, 1, 1, 4).astype(np.float32)
61axis8 = (1, 2)
62keep_dims8 = True
63
64x9 = np.random.rand(2, 1, 1, 4).astype(np.float32)
65axis9 = (1, 2)
66keep_dims9 = False
67
68x10 = np.random.rand(2, 3, 4, 4).astype(np.float32)
69axis10 = (0, 1, 2, 3)
70keep_dims10 = False
71
72x11 = np.random.rand(1, 1, 1, 1).astype(np.float32)
73axis11 = (0, 1, 2, 3)
74keep_dims11 = False
75
76x12 = np.random.rand(2, 3, 4, 4).astype(np.float32)
77axis12 = -2
78keep_dims12 = False
79
80x13 = np.random.rand(2, 3, 4, 4).astype(np.float32)
81axis13 = (-2, -1)
82keep_dims13 = True
83
84x14 = np.random.rand(1, 1, 1, 1).astype(np.float32)
85axis14 = ()
86np_axis14 = None
87keep_dims14 = True
88
89
90class ReduceSum(nn.Cell):
91    def __init__(self):
92        super(ReduceSum, self).__init__()
93
94        self.x0 = Tensor(x0)
95        self.axis0 = axis0
96        self.keep_dims0 = keep_dims0
97
98        self.x1 = Tensor(x1)
99        self.axis1 = axis1
100        self.keep_dims1 = keep_dims1
101
102        self.x2 = Tensor(x2)
103        self.axis2 = axis2
104        self.keep_dims2 = keep_dims2
105
106        self.x3 = Tensor(x3)
107        self.axis3 = axis3
108        self.keep_dims3 = keep_dims3
109
110        self.x4 = Tensor(x4)
111        self.axis4 = axis4
112        self.keep_dims4 = keep_dims4
113
114        self.x5 = Tensor(x5)
115        self.axis5 = axis5
116        self.keep_dims5 = keep_dims5
117
118        self.x6 = Tensor(x6)
119        self.axis6 = axis6
120        self.keep_dims6 = keep_dims6
121
122        self.x7 = Tensor(x7)
123        self.axis7 = axis7
124        self.keep_dims7 = keep_dims7
125
126        self.x8 = Tensor(x8)
127        self.axis8 = axis8
128        self.keep_dims8 = keep_dims8
129
130        self.x9 = Tensor(x9)
131        self.axis9 = axis9
132        self.keep_dims9 = keep_dims9
133
134        self.x10 = Tensor(x10)
135        self.axis10 = axis10
136        self.keep_dims10 = keep_dims10
137
138        self.x11 = Tensor(x11)
139        self.axis11 = axis11
140        self.keep_dims11 = keep_dims11
141
142        self.x12 = Tensor(x12)
143        self.axis12 = axis12
144        self.keep_dims12 = keep_dims12
145
146        self.x13 = Tensor(x13)
147        self.axis13 = axis13
148        self.keep_dims13 = keep_dims13
149
150        self.x14 = Tensor(x14)
151        self.axis14 = axis14
152        self.keep_dims14 = keep_dims14
153
154    @ms_function
155    def construct(self):
156        return (P.ReduceSum(self.keep_dims0)(self.x0, self.axis0),
157                P.ReduceSum(self.keep_dims1)(self.x1, self.axis1),
158                P.ReduceSum(self.keep_dims2)(self.x2, self.axis2),
159                P.ReduceSum(self.keep_dims3)(self.x3, self.axis3),
160                P.ReduceSum(self.keep_dims4)(self.x4, self.axis4),
161                P.ReduceSum(self.keep_dims5)(self.x5, self.axis5),
162                P.ReduceSum(self.keep_dims6)(self.x6, self.axis6),
163                P.ReduceSum(self.keep_dims7)(self.x7, self.axis7),
164                P.ReduceSum(self.keep_dims8)(self.x8, self.axis8),
165                P.ReduceSum(self.keep_dims9)(self.x9, self.axis9),
166                P.ReduceSum(self.keep_dims10)(self.x10, self.axis10),
167                P.ReduceSum(self.keep_dims11)(self.x11, self.axis11),
168                P.ReduceSum(self.keep_dims12)(self.x12, self.axis12),
169                P.ReduceSum(self.keep_dims13)(self.x13, self.axis13),
170                P.ReduceSum(self.keep_dims14)(self.x14, self.axis14))
171
172
173@pytest.mark.level0
174@pytest.mark.platform_x86_gpu_training
175@pytest.mark.env_onecard
176def test_ReduceSum():
177    context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
178    reduce_sum = ReduceSum()
179    output = reduce_sum()
180
181    expect0 = np.sum(x0, axis=axis0, keepdims=keep_dims0)
182    diff0 = abs(output[0].asnumpy() - expect0)
183    error0 = np.ones(shape=expect0.shape) * 1.0e-5
184    assert np.all(diff0 < error0)
185    assert output[0].shape == expect0.shape
186
187    expect1 = np.sum(x1, axis=axis1, keepdims=keep_dims1)
188    diff1 = abs(output[1].asnumpy() - expect1)
189    error1 = np.ones(shape=expect1.shape) * 1.0e-5
190    assert np.all(diff1 < error1)
191    assert output[1].shape == expect1.shape
192
193    expect2 = np.sum(x2, axis=axis2, keepdims=keep_dims2)
194    diff2 = abs(output[2].asnumpy() - expect2)
195    error2 = np.ones(shape=expect2.shape) * 1.0e-5
196    assert np.all(diff2 < error2)
197    assert output[2].shape == expect2.shape
198
199    expect3 = np.sum(x3, axis=axis3, keepdims=keep_dims3)
200    diff3 = abs(output[3].asnumpy() - expect3)
201    error3 = np.ones(shape=expect3.shape) * 1.0e-5
202    assert np.all(diff3 < error3)
203    assert output[3].shape == expect3.shape
204
205    expect4 = np.sum(x4, axis=np_axis4, keepdims=keep_dims4)
206    diff4 = abs(output[4].asnumpy() - expect4)
207    error4 = np.ones(shape=expect4.shape) * 1.0e-5
208    assert np.all(diff4 < error4)
209    assert output[4].shape == expect4.shape
210
211    expect5 = np.sum(x5, axis=np_axis5, keepdims=keep_dims5)
212    diff5 = abs(output[5].asnumpy() - expect5)
213    error5 = np.ones(shape=expect5.shape) * 1.0e-5
214    assert np.all(diff5 < error5)
215    assert output[5].shape == expect5.shape
216
217    expect6 = np.sum(x6, axis=axis6, keepdims=keep_dims6)
218    diff6 = abs(output[6].asnumpy() - expect6)
219    error6 = np.ones(shape=expect6.shape) * 1.0e-5
220    assert np.all(diff6 < error6)
221    assert output[6].shape == expect6.shape
222
223    expect7 = np.sum(x7, axis=axis7, keepdims=keep_dims7)
224    diff7 = abs(output[7].asnumpy() - expect7)
225    error7 = np.ones(shape=expect7.shape) * 1.0e-5
226    assert np.all(diff7 < error7)
227    assert output[7].shape == expect7.shape
228
229    expect8 = np.sum(x8, axis=axis8, keepdims=keep_dims8)
230    diff8 = abs(output[8].asnumpy() - expect8)
231    error8 = np.ones(shape=expect8.shape) * 1.0e-5
232    assert np.all(diff8 < error8)
233    assert output[8].shape == expect8.shape
234
235    expect9 = np.sum(x9, axis=axis9, keepdims=keep_dims9)
236    diff9 = abs(output[9].asnumpy() - expect9)
237    error9 = np.ones(shape=expect9.shape) * 1.0e-5
238    assert np.all(diff9 < error9)
239    assert output[9].shape == expect9.shape
240
241    expect10 = np.sum(x10, axis=axis10, keepdims=keep_dims10)
242    diff10 = abs(output[10].asnumpy() - expect10)
243    error10 = np.ones(shape=expect10.shape) * 1.0e-5
244    assert np.all(diff10 < error10)
245    assert output[10].shape == expect10.shape
246
247    expect11 = np.sum(x11, axis=axis11, keepdims=keep_dims11)
248    diff11 = abs(output[11].asnumpy() - expect11)
249    error11 = np.ones(shape=expect11.shape) * 1.0e-5
250    assert np.all(diff11 < error11)
251    assert output[11].shape == expect11.shape
252
253    expect12 = np.sum(x12, axis=axis12, keepdims=keep_dims12)
254    diff12 = abs(output[12].asnumpy() - expect12)
255    error12 = np.ones(shape=expect12.shape) * 1.0e-5
256    assert np.all(diff12 < error12)
257    assert output[12].shape == expect12.shape
258
259    expect13 = np.sum(x13, axis=axis13, keepdims=keep_dims13)
260    diff13 = abs(output[13].asnumpy() - expect13)
261    error13 = np.ones(shape=expect13.shape) * 1.0e-5
262    assert np.all(diff13 < error13)
263    assert output[13].shape == expect13.shape
264
265    expect14 = np.sum(x14, axis=np_axis14, keepdims=keep_dims14)
266    diff14 = abs(output[14].asnumpy() - expect14)
267    error14 = np.ones(shape=expect14.shape) * 1.0e-5
268    assert np.all(diff14 < error14)
269    assert output[14].shape == expect14.shape
270
271
272x_1 = x8
273axis_1 = 0
274x_2 = x1
275axis_2 = 0
276
277
278class ReduceSumDynamic(nn.Cell):
279    def __init__(self, x, axis):
280        super(ReduceSumDynamic, self).__init__()
281        self.reducesum = P.ReduceSum(True)
282        self.test_dynamic = inner.GpuConvertToDynamicShape()
283        self.x = x
284        self.axis = axis
285
286    def construct(self):
287        dynamic_x = self.test_dynamic(self.x)
288        return self.reducesum(dynamic_x, self.axis)
289
290
291@pytest.mark.level0
292@pytest.mark.platform_x86_gpu_training
293@pytest.mark.env_onecard
294def test_reduce_sum_dynamic():
295    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
296    net1 = ReduceSumDynamic(Tensor(x_1), axis_1)
297    net2 = ReduceSumDynamic(Tensor(x_2), axis_2)
298
299    expect_1 = np.sum(x_1, axis=axis_1, keepdims=True)
300    expect_2 = np.sum(x_2, axis=axis_2, keepdims=True)
301
302    output1 = net1()
303    output2 = net2()
304
305    np.testing.assert_almost_equal(output1.asnumpy(), expect_1)
306    np.testing.assert_almost_equal(output2.asnumpy(), expect_2)
307
308
309class ReduceSumTypeNet(nn.Cell):
310    def __init__(self, nptype):
311        super(ReduceSumTypeNet, self).__init__()
312        self.x0 = Tensor(x0.astype(nptype))
313        self.axis0 = axis0
314        self.keep_dims0 = keep_dims0
315
316    def construct(self):
317        return P.ReduceSum(self.keep_dims0)(self.x0, self.axis0)
318
319@pytest.mark.level0
320@pytest.mark.platform_x86_gpu_training
321@pytest.mark.env_onecard
322def test_reduce_sum_float64():
323    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
324    net = ReduceSumTypeNet(np.float64)
325    output = net()
326    expect = np.sum(x0, axis=axis0, keepdims=keep_dims0).astype(np.float64)
327    diff = abs(output.asnumpy() - expect)
328    error = np.ones(shape=expect.shape) * 1.0e-5
329    assert np.all(diff < error)
330    assert output.shape == expect.shape
331
332    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
333    net = ReduceSumTypeNet(np.float64)
334    output = net()
335    expect = np.sum(x0, axis=axis0, keepdims=keep_dims0).astype(np.float64)
336    diff = abs(output.asnumpy() - expect)
337    error = np.ones(shape=expect.shape) * 1.0e-5
338    assert np.all(diff < error)
339    assert output.shape == expect.shape
340