1# Copyright 2022 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# ============================================================================ 15import numpy as np 16import pytest 17import mindspore.context as context 18import mindspore.nn as nn 19from mindspore import Tensor 20from mindspore.ops.operations.math_ops import Polygamma 21 22context.set_context(mode=context.GRAPH_MODE) 23 24 25class PolygammaNet(nn.Cell): 26 27 def __init__(self): 28 super(PolygammaNet, self).__init__() 29 self.polygamma = Polygamma() 30 31 def construct(self, a, x): 32 return self.polygamma(a, x) 33 34 35@pytest.mark.level0 36@pytest.mark.platform_x86_gpu_training 37@pytest.mark.platform_arm_ascend_training 38@pytest.mark.platform_x86_ascend_training 39@pytest.mark.env_onecard 40def test_polygamma_1d_a_1_int64_float16(): 41 """ 42 Feature: Polygamma 43 Description: test cases for Polygamma of float16 44 Expectation: the result match to torch 45 """ 46 net = PolygammaNet() 47 a = np.array(1).astype(np.int64) 48 x_ms = np.array([1, 0.4273, 9, -3.12, 12246.345]).astype(np.float16) 49 z_ms = net(Tensor(a), Tensor(x_ms)) 50 expect = np.array([1.64493407e+00, 6.47734100e+00, 1.17512015e-01, 7.35594209e+01, 51 8.16493161e-05]).astype(np.float64) 52 assert np.allclose(z_ms.asnumpy(), expect.astype(np.float16), 0.001, 0.001) 53 54 55@pytest.mark.level1 56@pytest.mark.platform_x86_gpu_training 57@pytest.mark.env_onecard 58def test_polygamma_1d_a_1_int64_float32(): 59 """ 60 Feature: Polygamma 61 Description: test cases for Polygamma of float16 62 Expectation: the result match to torch 63 """ 64 net = PolygammaNet() 65 a = np.array(1).astype(np.int64) 66 x_ms = np.array([1, 0.5273, 9, -3.12, 13250]).astype(np.float32) 67 z_ms = net(Tensor(a), Tensor(x_ms)) 68 expect = np.array([1.6449341e+00, 4.5092258e+00, 1.1751202e-01, 7.2555374e+01, 69 7.5474542e-05]).astype(np.float32) 70 assert np.allclose(z_ms.asnumpy(), expect, 0.0001, 0.0001) 71 72 73@pytest.mark.level1 74@pytest.mark.platform_x86_gpu_training 75@pytest.mark.env_onecard 76def test_polygamma_1d_a_1_int64_float64(): 77 """ 78 Feature: Polygamma 79 Description: test cases for Polygamma of float64 80 Expectation: the result match to torch 81 """ 82 net = PolygammaNet() 83 a = np.array(1).astype(np.int64) 84 x_ms = np.array([1, 0.5273, 9, -3.12, 13250]).astype(np.float64) 85 z_ms = net(Tensor(a), Tensor(x_ms)) 86 expect = np.array([1.64493407e+00, 4.50922599e+00, 1.17512015e-01, 7.25554469e+01, 87 7.54745462e-05]).astype(np.float64) 88 assert np.allclose(z_ms.asnumpy(), expect, 0.00001, 0.00001) 89 90 91@pytest.mark.level1 92@pytest.mark.platform_x86_gpu_training 93@pytest.mark.env_onecard 94def test_polygamma_1d_a_2_int64_float16(): 95 """ 96 Feature: Polygamma 97 Description: test cases for Polygamma of float16 98 Expectation: the result match to torch 99 """ 100 net = PolygammaNet() 101 a = np.array(2).astype(np.int64) 102 x_ms = np.array([1, 0.4273, 9, -3.12, 12246.345]).astype(np.float16) 103 z_ms = net(Tensor(a), Tensor(x_ms)) 104 expect = np.array([-2.40411381e+00, -2.65858621e+01, -1.37933192e-02, 105 1.18094081e+03, -6.66661082e-09]).astype(np.float64) 106 assert np.allclose(z_ms.asnumpy(), expect.astype(np.float16), 0.001, 0.001) 107 108 109@pytest.mark.level1 110@pytest.mark.platform_x86_gpu_training 111@pytest.mark.env_onecard 112def test_polygamma_1d_a_2_int64_float32(): 113 """ 114 Feature: Polygamma 115 Description: test cases for Polygamma of float16 116 Expectation: the result match to torch 117 """ 118 net = PolygammaNet() 119 a = np.array(2).astype(np.int64) 120 x_ms = np.array([1, 0.5273, 9, -3.12, 13250]).astype(np.float64) 121 z_ms = net(Tensor(a), Tensor(x_ms)) 122 expect = np.array([-2.40411381e+00, -1.44329154e+01, -1.37933192e-02, 123 1.15570148e+03, -5.69640712e-09]).astype(np.float64) 124 assert np.allclose(z_ms.asnumpy(), expect, 0.0001, 0.0001) 125 126 127@pytest.mark.level1 128@pytest.mark.platform_x86_gpu_training 129@pytest.mark.env_onecard 130def test_polygamma_1d_a_2_int64_float64(): 131 """ 132 Feature: Polygamma 133 Description: test cases for Polygamma of float64 134 Expectation: the result match to torch 135 """ 136 net = PolygammaNet() 137 a = np.array(2).astype(np.int64) 138 x_ms = np.array([1, 0.5273, 9, -3.12, 13250]).astype(np.float64) 139 z_ms = net(Tensor(a), Tensor(x_ms)) 140 expect = np.array([-2.40411381e+00, -1.44329154e+01, -1.37933192e-02, 141 1.15570148e+03, -5.69640712e-09]).astype(np.float64) 142 assert np.allclose(z_ms.asnumpy(), expect, 0.00001, 0.00001) 143