Huawei_Technology
/
mindspore

# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================

import pytest
from mindspore.ops import operations as P
from mindspore.ops import composite as C
from mindspore.nn import Cell
from mindspore.common.tensor import Tensor
import mindspore.common.dtype as mstype
import mindspore.context as context
import numpy as np

class MinimumNet(Cell):
    def __init__(self):
        super(MinimumNet, self).__init__()
        self.min = P.Minimum()

    def construct(self, x1, x2):
        x = self.min(x1, x2)
        return x


class Grad(Cell):
    def __init__(self, network):
        super(Grad, self).__init__()
        self.grad = C.GradOperation(name="get_all", get_all=True, sens_param=True)
        self.network = network

    def construct(self, x1, x2, sens):
        gout = self.grad(self.network)(x1, x2, sens)
        return gout


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_nobroadcast():
    context.set_context(mode=context.GRAPH_MODE, save_graphs=True, device_target='GPU')

    x1_np = np.random.rand(3, 4).astype(np.float32)
    x2_np = np.random.rand(3, 4).astype(np.float32)
    dy_np = np.random.rand(3, 4).astype(np.float32)

    net = Grad(MinimumNet())
    output_ms = net(Tensor(x1_np), Tensor(x2_np), Tensor(dy_np))
    output0_np = np.where(x1_np < x2_np, dy_np, 0)
    output1_np = np.where(x1_np < x2_np, 0, dy_np)
    assert np.allclose(output_ms[0].asnumpy(), output0_np)
    assert np.allclose(output_ms[1].asnumpy(), output1_np)


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_broadcast():
    context.set_context(mode=context.GRAPH_MODE, save_graphs=True, device_target='GPU')

    x1_np = np.array([[[[0.659578  ],
                        [0.49113268],
                        [0.75909054],
                        [0.71681815],
                        [0.30421826]]],
                      [[[0.30322495],
                        [0.02858258],
                        [0.06398096],
                        [0.09519596],
                        [0.12498625]]],
                      [[[0.7347768 ],
                        [0.166469  ],
                        [0.328553  ],
                        [0.54908437],
                        [0.23673844]]]]).astype(np.float32)
    x2_np = np.array([[[[0.9154968,  0.29014662, 0.6492294,  0.39918253, 0.1648203,  0.00861965]],
                      [[0.996885,   0.24152198, 0.3601213,  0.51664376, 0.7933056,  0.84706444]],
                      [[0.75606346, 0.974512,   0.3939527,  0.69697475, 0.83400667, 0.6348955 ]],
                      [[0.68492866, 0.24609096, 0.4924665,  0.22500521, 0.38474053, 0.5586104 ]]]]).astype(np.float32)
    dy_np = np.array([[[[0.42891738, 0.03434946, 0.06192983, 0.21216309, 0.37450036, 0.6619524 ],
                        [0.8583447,  0.5765161,  0.1468952,  0.9975385,  0.6908136,  0.4903796 ],
                        [0.68952006, 0.39336833, 0.9049695,  0.66886294, 0.2338471,  0.913618  ],
                        [0.0428149,  0.6243054,  0.8519898,  0.12088962, 0.9735885,  0.45661286],
                        [0.41563734, 0.41607043, 0.4754915,  0.32207987, 0.33823156, 0.47422352]],

                        [[0.64478457, 0.22430937, 0.7682554,  0.46082005, 0.8938723,  0.20490853],
                        [0.44393885, 0.08278944, 0.4734108,  0.5543551,  0.39428464, 0.44424313],
                        [0.12612297, 0.76566416, 0.71133816, 0.81280327, 0.20583127, 0.54058075],
                        [0.41341263, 0.48118508, 0.00401995, 0.37259838, 0.05435474, 0.5240658 ],
                        [0.4081956,  0.48718935, 0.9132831, 0.67969185, 0.0119757,  0.8328054 ]],

                        [[0.91695577, 0.95370644, 0.263782,   0.7477626,  0.6448147,  0.8080634 ],
                        [0.15576603, 0.9104615,  0.3778708,  0.6912833,  0.2092224, 0.67462957],
                        [0.7087075,  0.7888326,  0.4672294,  0.98221505, 0.25210258, 0.98920417],
                        [0.7466197,  0.22702982, 0.01991269, 0.6846591,  0.7515228,  0.5890395 ],
                        [0.04531088, 0.21740614, 0.8406235,  0.36480767, 0.37733936, 0.02914464]],

                        [[0.33069974, 0.5497569,  0.9896345,  0.4167176,  0.78057563, 0.04659131],
                        [0.7747768,  0.21427679, 0.29893255, 0.7706969,  0.9755185,  0.42388415],
                        [0.3910244,  0.39381978, 0.37065396, 0.15558061, 0.05012341, 0.15870963],
                        [0.17791101, 0.47219893, 0.13899496, 0.32323205, 0.3628809,  0.02580585],
                        [0.30274773, 0.62890774, 0.11024303, 0.6980051,  0.35346958, 0.062852  ]]],


                      [[[0.6925081,  0.74668753, 0.80145043, 0.06598313, 0.665123,   0.15073007],
                        [0.11784806, 0.6385372,  0.5228278,  0.5349848,  0.84671104, 0.8096436 ],
                        [0.09516156, 0.63298017, 0.52382874, 0.36734378, 0.66497755, 0.6019127 ],
                        [0.46438488, 0.0194377,  0.9388292,  0.7286089,  0.29178405, 0.11872514],
                        [0.22101837, 0.6164887,  0.6139798,  0.11711904, 0.6227745,  0.09701069]],

                        [[0.80480653, 0.90034056, 0.8633447,  0.97415197, 0.08309154, 0.8446033 ],
                        [0.9473769,  0.791024,   0.26339203, 0.01155075, 0.2673186,  0.7116369 ],
                        [0.9687511,  0.24281934, 0.37777108, 0.09802654, 0.2421312,  0.87095344],
                        [0.6311381,  0.23368953, 0.0998995,  0.4364419,  0.9187446,  0.5043872 ],
                        [0.35226053, 0.09357589, 0.41317305, 0.85930043, 0.16249318, 0.5478765 ]],

                        [[0.14338651, 0.24859418, 0.4246941,  0.73034066, 0.47172204, 0.8717199 ],
                        [0.05415315, 0.78556925, 0.99214983, 0.7415298,  0.673708,   0.87817156],
                        [0.616975,   0.42843062, 0.05179814, 0.1566958, 0.04536059, 0.70166487],
                        [0.15493333, 0.776598,   0.4361967,  0.40253627, 0.89210516, 0.8144414 ],
                        [0.04816005, 0.29696834, 0.4586605,  0.3419852,  0.5595613,  0.74093205]],

                        [[0.1388035,  0.9168704,  0.64287645, 0.83864623, 0.48026922, 0.78323376],
                        [0.12724937, 0.83034366, 0.42557436, 0.50578654, 0.25630295, 0.15349793],
                        [0.27256685, 0.04547984, 0.5385756,  0.39270344, 0.7661698,  0.23722854],
                        [0.24620503, 0.25431684, 0.71564585, 0.01161419, 0.846467,   0.7043044 ],
                        [0.63272387, 0.11857849, 0.3772076,  0.16758402, 0.46743023, 0.05919575]]],


                      [[[0.18827082, 0.8912264,  0.6841404,  0.74436826, 0.9582085,  0.1083683 ],
                        [0.60695344, 0.09742349, 0.25074378, 0.87940735, 0.21116392, 0.39418384],
                        [0.744686,   0.35679692, 0.01308284, 0.45166633, 0.68166,    0.8634658 ],
                        [0.7331758,  0.21113694, 0.3935488,  0.87934476, 0.70728546, 0.09309767],
                        [0.12128611, 0.93696386, 0.81177396, 0.85402405, 0.5827289,  0.9776509 ]],

                        [[0.54069614, 0.66651285, 0.10646132, 0.17342485, 0.88795924, 0.03551182],
                        [0.25531697, 0.87946486, 0.74267226, 0.89230734, 0.95171434, 0.94697934],
                        [0.3708397,  0.507355,   0.97099817, 0.4918163,  0.17212386, 0.5008048 ],
                        [0.62530744, 0.25210327, 0.73966664, 0.71555346, 0.82484317, 0.6094874 ],
                        [0.4589691,  0.1386695,  0.27448782, 0.20373994, 0.27805242, 0.23292768]],

                        [[0.7414099,  0.2270226,  0.90431255, 0.47035843, 0.9581062,  0.5359226 ],
                        [0.79603523, 0.45549425, 0.80858237, 0.7705133,  0.017761,   0.98001194],
                        [0.06013146, 0.99240226, 0.33515573, 0.04110833, 0.41470334, 0.7130743 ],
                        [0.5687417,  0.5788611,  0.00722461, 0.6603336,  0.3420471,  0.75181854],
                        [0.4699261,  0.51390815, 0.343182,   0.81498754, 0.8942413, 0.46532857]],

                        [[0.4589523,  0.5534698,  0.2825786,  0.8205943,  0.78258514, 0.43154418],
                        [0.27020997, 0.01667354, 0.60871965, 0.90670526, 0.3208025,  0.96995634],
                        [0.85337156, 0.9711295,  0.1381724,  0.53670496, 0.7347996,  0.73380876],
                        [0.6137464,  0.54751194, 0.9037335,  0.23134394, 0.61411524, 0.26583543],
                        [0.70770144, 0.01813207, 0.24718016, 0.70329237, 0.7062925,  0.14399007]]]]).astype(np.float32)

    expect_dx1 = np.array([[[[ 5.7664223],
                             [ 6.981018 ],
                             [ 2.6029902],
                             [ 2.7598202],
                             [ 6.763105 ]]],
                           [[[10.06558  ],
                             [12.077246 ],
                             [ 9.338394 ],
                             [11.52271  ],
                             [ 8.889048 ]]],
                           [[[ 3.5789769],
                             [13.424448 ],
                             [ 8.732746 ],
                             [ 6.9677467],
                             [ 9.635765 ]]]]).astype(np.float32)

    expect_dx2 = np.array([[[[0.        , 4.250458  , 2.5030296 , 3.623167  , 6.4171505 , 7.2115746 ]],
                            [[0.        , 4.367449  , 2.803152  , 2.5352    , 0.        , 0.        ]],
                            [[0.7087075 , 0.        , 2.040332  , 2.1372325 , 0.        , 2.9222295 ]],
                            [[1.0278877 , 5.247942  , 2.6855955 , 5.494814  , 3.5657988 , 0.66265094]]]]).astype(np.float32)

    net = Grad(MinimumNet())
    output_ms = net(Tensor(x1_np), Tensor(x2_np), Tensor(dy_np))
    assert np.allclose(output_ms[0].asnumpy(), expect_dx1)
    assert np.allclose(output_ms[1].asnumpy(), expect_dx2)


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_broadcast_diff_dims():
    context.set_context(mode=context.GRAPH_MODE, save_graphs=True, device_target='GPU')

    x1_np = np.array([[[0.275478,   0.48933202, 0.71846116],
                       [0.9803821,  0.57205725, 0.28511533]],
                      [[0.61111903, 0.9671023,  0.70624334],
                       [0.53730786, 0.90413177, 0.94349676]]]).astype(np.float32)

    x2_np = np.array([[0.01045662, 0.82126397, 0.6365063 ],
                      [0.9900942,  0.6584232,  0.98537433]]).astype(np.float32)

    dy_np = np.array([[[0.3897645,  0.61152864, 0.33675498],
                        [0.5303635,  0.84893036, 0.4959739 ]],
                     [[0.5391046,  0.8443047,  0.4174708 ],
                        [0.57513475, 0.9225578,  0.46760973]]]).astype(np.float32)

    expect_dx1 = np.array([[[0.        , 0.61152864, 0.        ],
                            [0.5303635 , 0.84893036, 0.4959739 ]],
                          [[0.        , 0.        , 0.        ],
                            [0.57513475, 0.        , 0.46760973]]]).astype(np.float32)

    expect_dx2 = np.array([[0.92886907, 0.8443047 , 0.7542258 ],
                          [0.        , 0.9225578 , 0.        ]]).astype(np.float32)

    net = Grad(MinimumNet())
    output_ms = net(Tensor(x1_np), Tensor(x2_np), Tensor(dy_np))
    assert np.allclose(output_ms[0].asnumpy(), expect_dx1)
    assert np.allclose(output_ms[1].asnumpy(), expect_dx2)