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 24 25 26class Net(nn.Cell): 27 def __init__(self): 28 super(Net, self).__init__() 29 self.add = P.AddN() 30 31 @ms_function 32 def construct(self, x, y, z): 33 return self.add((x, y, z)) 34 35 36@pytest.mark.level0 37@pytest.mark.platform_x86_gpu_training 38@pytest.mark.env_onecard 39def test_net(): 40 context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU") 41 x = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.float32) 42 y = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.float32) 43 z = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.float32) 44 add = Net() 45 output = add(Tensor(x), Tensor(y), Tensor(z)) 46 expect_result = [[[[0., 3., 6., 9.], 47 [12., 15., 18., 21.], 48 [24., 27., 30., 33.]], 49 [[36., 39., 42., 45.], 50 [48., 51., 54., 57.], 51 [60., 63., 66., 69.]], 52 [[72., 75., 78., 81.], 53 [84., 87., 90., 93.], 54 [96., 99., 102., 105.]]]] 55 56 assert (output.asnumpy() == expect_result).all() 57 58 59@pytest.mark.level0 60@pytest.mark.platform_x86_gpu_training 61@pytest.mark.env_onecard 62def test_net_float64(): 63 context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU") 64 x = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.float64) 65 y = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.float64) 66 z = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.float64) 67 add = Net() 68 output = add(Tensor(x), Tensor(y), Tensor(z)) 69 expect_result = np.array([[[[0., 3., 6., 9.], 70 [12., 15., 18., 21.], 71 [24., 27., 30., 33.]], 72 [[36., 39., 42., 45.], 73 [48., 51., 54., 57.], 74 [60., 63., 66., 69.]], 75 [[72., 75., 78., 81.], 76 [84., 87., 90., 93.], 77 [96., 99., 102., 105.]]]]).astype(np.float64) 78 assert (output.asnumpy() == expect_result).all() 79 80 context.set_context(mode=context.GRAPH_MODE, device_target="GPU") 81 x = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.float64) 82 y = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.float64) 83 z = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.float64) 84 add = Net() 85 output = add(Tensor(x), Tensor(y), Tensor(z)) 86 expect_result = np.array([[[[0., 3., 6., 9.], 87 [12., 15., 18., 21.], 88 [24., 27., 30., 33.]], 89 [[36., 39., 42., 45.], 90 [48., 51., 54., 57.], 91 [60., 63., 66., 69.]], 92 [[72., 75., 78., 81.], 93 [84., 87., 90., 93.], 94 [96., 99., 102., 105.]]]]).astype(np.float64) 95 assert (output.asnumpy() == expect_result).all() 96 97 98@pytest.mark.level0 99@pytest.mark.platform_x86_gpu_training 100@pytest.mark.env_onecard 101def test_net_int64(): 102 context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU") 103 x = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.int64) 104 y = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.int64) 105 z = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.int64) 106 add = Net() 107 output = add(Tensor(x), Tensor(y), Tensor(z)) 108 expect_result = np.array([[[[0., 3., 6., 9.], 109 [12., 15., 18., 21.], 110 [24., 27., 30., 33.]], 111 [[36., 39., 42., 45.], 112 [48., 51., 54., 57.], 113 [60., 63., 66., 69.]], 114 [[72., 75., 78., 81.], 115 [84., 87., 90., 93.], 116 [96., 99., 102., 105.]]]]).astype(np.int64) 117 assert (output.asnumpy() == expect_result).all() 118 119 context.set_context(mode=context.GRAPH_MODE, device_target="GPU") 120 x = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.int64) 121 y = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.int64) 122 z = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.int64) 123 add = Net() 124 output = add(Tensor(x), Tensor(y), Tensor(z)) 125 expect_result = np.array([[[[0., 3., 6., 9.], 126 [12., 15., 18., 21.], 127 [24., 27., 30., 33.]], 128 [[36., 39., 42., 45.], 129 [48., 51., 54., 57.], 130 [60., 63., 66., 69.]], 131 [[72., 75., 78., 81.], 132 [84., 87., 90., 93.], 133 [96., 99., 102., 105.]]]]).astype(np.int64) 134 assert (output.asnumpy() == expect_result).all() 135