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# ============================================================================ 15 16import numpy as np 17import pytest 18 19import mindspore.context as context 20from mindspore import nn, Tensor 21from .optimizer_utils import build_network, default_fc1_weight_adamax, default_fc1_bias_adamax, \ 22 no_default_fc1_weight_adamax, no_default_fc1_bias_adamax, default_group_fc1_weight_adamax, \ 23 default_group_fc1_bias_adamax 24 25w1 = np.array([[0.03909272, 0.08893055, -0.259909, -0.459185, 26 -0.0195536, 0.12977135, -0.62942827, -0.53132117], 27 [0.1542052, 0.6513571, -0.06453168, 0.44788414, 28 -0.3775454, 0.6520292, 0.444174, -0.59306043], 29 [0.2712369, 0.20890862, 0.6859066, 0.6629662, 30 0.4724893, -0.34384444, -0.16007674, 0.21797538], 31 [-0.3865972, 0.26727962, 0.23178828, -0.24629539, 32 -0.68038213, -0.31262863, 0.10493469, -0.28973007]]).astype("float32") 33 34b1 = np.array([0., 0., 0., 0.]).astype("float32") 35 36w2 = np.array([[-0.6079024, -1.005364, 0.59004724, 0.7289244]]).astype("float32") 37 38b2 = np.array([0.]).astype("float32") 39 40 41class Net(nn.Cell): 42 """ 43 build a 2-layers net to test adamax optimizer 44 """ 45 46 def __init__(self): 47 super(Net, self).__init__() 48 self.fc1 = nn.Dense(8, 4, weight_init=Tensor(w1), bias_init=Tensor(b1)) 49 self.fc2 = nn.Dense(4, 1, weight_init=Tensor(w2), bias_init=Tensor(b2)) 50 self.relu = nn.ReLU() 51 52 def construct(self, x): 53 x = self.relu(self.fc1(x)) 54 return self.fc2(x) 55 56 57@pytest.mark.level0 58@pytest.mark.platform_x86_cpu 59@pytest.mark.platform_arm_cpu 60@pytest.mark.platform_x86_gpu_training 61@pytest.mark.platform_arm_ascend_training 62@pytest.mark.platform_x86_ascend_training 63@pytest.mark.env_onecard 64@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE]) 65def test_default_adamax(mode): 66 """ 67 Feature: Test adamax optimizer 68 Description: Test adamax with default parameter 69 Expectation: Loss values and parameters conform to preset values. 70 """ 71 context.set_context(mode=mode) 72 config = {'name': 'adamax', 'lr': 0.001, "beta1": 0.9, "beta2": 0.999, "eps": 1e-07, 73 'weight_decay': 0.0} 74 _, cells = build_network(config, net=Net(), loss_fn=nn.L1Loss(reduction='mean')) 75 assert np.allclose(cells.moment1[0].asnumpy(), default_fc1_weight_adamax, atol=1.e-3) 76 assert np.allclose(cells.moment1[1].asnumpy(), default_fc1_bias_adamax, atol=1.e-3) 77 78 79@pytest.mark.level0 80@pytest.mark.platform_x86_cpu 81@pytest.mark.platform_arm_cpu 82@pytest.mark.platform_x86_gpu_training 83@pytest.mark.platform_arm_ascend_training 84@pytest.mark.platform_x86_ascend_training 85@pytest.mark.env_onecard 86@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE]) 87def test_no_default_adamax(mode): 88 """ 89 Feature: Test adamax optimizer 90 Description: Test adamax with another set of parameter 91 Expectation: Loss values and parameters conform to preset values. 92 """ 93 context.set_context(mode=mode) 94 config = {'name': 'adamax', 'lr': 0.01, "beta1": 0.9, "beta2": 0.98, "eps": 1e-06, 95 'weight_decay': 0.0} 96 _, cells = build_network(config, net=Net(), loss_fn=nn.L1Loss(reduction='mean')) 97 assert np.allclose(cells.moment1[0].asnumpy(), no_default_fc1_weight_adamax, atol=1.e-3) 98 assert np.allclose(cells.moment1[1].asnumpy(), no_default_fc1_bias_adamax, atol=1.e-3) 99 100 101@pytest.mark.level0 102@pytest.mark.platform_x86_cpu 103@pytest.mark.platform_arm_cpu 104@pytest.mark.platform_x86_gpu_training 105@pytest.mark.platform_arm_ascend_training 106@pytest.mark.platform_x86_ascend_training 107@pytest.mark.env_onecard 108@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE]) 109def test_default_adamax_group(mode): 110 """ 111 Feature: Test adamax optimizer 112 Description: Test adamax with parameter grouping 113 Expectation: Loss values and parameters conform to preset values. 114 """ 115 context.set_context(mode=mode) 116 config = {'name': 'adamax', 'lr': 0.002, "beta1": 0.9, "beta2": 0.999, "eps": 1e-08, 117 'weight_decay': 0.0} 118 _, cells = build_network(config, is_group=True, net=Net(), loss_fn=nn.L1Loss(reduction='mean')) 119 assert np.allclose(cells.moment1[0].asnumpy(), default_group_fc1_weight_adamax, atol=1.e-3) 120 assert np.allclose(cells.moment1[1].asnumpy(), default_group_fc1_bias_adamax, atol=1.e-3) 121