wehub-resource-sync
294 lines
10 KiB
Python
294 lines
10 KiB
Python
# Copyright (c) 2026 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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高级优化器单元测试 / Advanced Optimizer Unit Tests
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测试目标 / Test Target:
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paddle.optimizer 模块 - 多种优化器 (覆盖率约82-84%)
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覆盖的模块 / Covered Modules:
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- paddle.optimizer.Adamax: Adamax优化器
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- paddle.optimizer.Adagrad: 自适应学习率优化器
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- paddle.optimizer.Adadelta: Adadelta优化器
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- paddle.optimizer.ASGD: 平均随机梯度下降
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- paddle.optimizer.RMSProp: RMSProp优化器
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- paddle.optimizer.Momentum: 动量优化器
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作用 / Purpose:
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覆盖各类优化器的正向传播、参数更新、学习率调整等代码路径，
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补充未被原有测试覆盖的优化器功能。
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"""
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import unittest
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import paddle
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from paddle import nn
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paddle.disable_static()
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def create_simple_model():
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"""创建简单模型 / Create simple model"""
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return nn.Sequential(nn.Linear(10, 5), nn.ReLU(), nn.Linear(5, 1))
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def do_one_step(model, optimizer):
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"""执行一步优化 / Perform one optimization step"""
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x = paddle.randn([4, 10])
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y = model(x)
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loss = y.mean()
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loss.backward()
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optimizer.step()
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optimizer.clear_grad()
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return loss.item()
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class TestAdamaxOptimizer(unittest.TestCase):
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"""测试Adamax优化器 / Test Adamax optimizer"""
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def test_adamax_basic(self):
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"""测试Adamax基本功能 / Test basic Adamax functionality"""
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model = create_simple_model()
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optimizer = paddle.optimizer.Adamax(
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learning_rate=0.01, parameters=model.parameters()
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)
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loss = do_one_step(model, optimizer)
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self.assertIsNotNone(loss)
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def test_adamax_with_weight_decay(self):
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"""测试带权重衰减的Adamax / Test Adamax with weight decay"""
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model = create_simple_model()
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optimizer = paddle.optimizer.Adamax(
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learning_rate=0.01, weight_decay=0.01, parameters=model.parameters()
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)
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do_one_step(model, optimizer)
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def test_adamax_beta1_beta2(self):
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"""测试Adamax的beta参数 / Test Adamax beta parameters"""
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model = create_simple_model()
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optimizer = paddle.optimizer.Adamax(
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learning_rate=0.01,
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beta1=0.9,
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beta2=0.999,
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parameters=model.parameters(),
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)
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do_one_step(model, optimizer)
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def test_adamax_multiple_steps(self):
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"""测试Adamax多步优化 / Test Adamax multi-step optimization"""
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model = create_simple_model()
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optimizer = paddle.optimizer.Adamax(
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learning_rate=0.01, parameters=model.parameters()
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)
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for _ in range(5):
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do_one_step(model, optimizer)
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class TestAdagradOptimizer(unittest.TestCase):
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"""测试Adagrad优化器 / Test Adagrad optimizer"""
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def test_adagrad_basic(self):
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"""测试Adagrad基本功能 / Test basic Adagrad functionality"""
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model = create_simple_model()
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optimizer = paddle.optimizer.Adagrad(
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learning_rate=0.01, parameters=model.parameters()
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)
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loss = do_one_step(model, optimizer)
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self.assertIsNotNone(loss)
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def test_adagrad_epsilon(self):
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"""测试Adagrad的epsilon参数 / Test Adagrad epsilon parameter"""
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model = create_simple_model()
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optimizer = paddle.optimizer.Adagrad(
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learning_rate=0.01, epsilon=1e-8, parameters=model.parameters()
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)
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do_one_step(model, optimizer)
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def test_adagrad_initial_accumulator(self):
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"""测试Adagrad初始累积器 / Test Adagrad initial accumulator"""
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model = create_simple_model()
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optimizer = paddle.optimizer.Adagrad(
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learning_rate=0.01,
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initial_accumulator_value=0.1,
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parameters=model.parameters(),
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)
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do_one_step(model, optimizer)
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def test_adagrad_multiple_steps(self):
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"""测试Adagrad多步 / Test Adagrad multiple steps"""
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model = create_simple_model()
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optimizer = paddle.optimizer.Adagrad(
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learning_rate=0.1, parameters=model.parameters()
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)
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for _ in range(5):
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do_one_step(model, optimizer)
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class TestAdadeltaOptimizer(unittest.TestCase):
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"""测试Adadelta优化器 / Test Adadelta optimizer"""
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def test_adadelta_basic(self):
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"""测试Adadelta基本功能 / Test basic Adadelta functionality"""
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model = create_simple_model()
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optimizer = paddle.optimizer.Adadelta(
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learning_rate=1.0, parameters=model.parameters()
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)
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loss = do_one_step(model, optimizer)
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self.assertIsNotNone(loss)
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def test_adadelta_rho_epsilon(self):
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"""测试Adadelta的rho和epsilon参数 / Test Adadelta rho and epsilon"""
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model = create_simple_model()
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optimizer = paddle.optimizer.Adadelta(
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learning_rate=1.0,
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rho=0.95,
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epsilon=1e-6,
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parameters=model.parameters(),
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)
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do_one_step(model, optimizer)
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def test_adadelta_multiple_steps(self):
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"""测试Adadelta多步 / Test Adadelta multiple steps"""
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model = create_simple_model()
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optimizer = paddle.optimizer.Adadelta(
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learning_rate=1.0, parameters=model.parameters()
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)
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for _ in range(5):
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do_one_step(model, optimizer)
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class TestRMSPropOptimizer(unittest.TestCase):
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"""测试RMSProp优化器 / Test RMSProp optimizer"""
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def test_rmsprop_basic(self):
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"""测试RMSProp基本功能 / Test basic RMSProp functionality"""
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model = create_simple_model()
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optimizer = paddle.optimizer.RMSProp(
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learning_rate=0.01, parameters=model.parameters()
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)
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loss = do_one_step(model, optimizer)
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self.assertIsNotNone(loss)
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def test_rmsprop_with_momentum(self):
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"""测试带动量的RMSProp / Test RMSProp with momentum"""
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model = create_simple_model()
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optimizer = paddle.optimizer.RMSProp(
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learning_rate=0.01, momentum=0.9, parameters=model.parameters()
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)
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do_one_step(model, optimizer)
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def test_rmsprop_centered(self):
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"""测试centered RMSProp / Test centered RMSProp"""
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model = create_simple_model()
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optimizer = paddle.optimizer.RMSProp(
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learning_rate=0.01, centered=True, parameters=model.parameters()
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)
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do_one_step(model, optimizer)
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def test_rmsprop_rho_epsilon(self):
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"""测试RMSProp的rho和epsilon / Test RMSProp rho and epsilon"""
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model = create_simple_model()
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optimizer = paddle.optimizer.RMSProp(
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learning_rate=0.01,
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rho=0.9,
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epsilon=1e-6,
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parameters=model.parameters(),
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)
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do_one_step(model, optimizer)
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class TestMomentumOptimizer(unittest.TestCase):
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"""测试Momentum优化器 / Test Momentum optimizer"""
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def test_momentum_basic(self):
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"""测试Momentum基本功能 / Test basic Momentum functionality"""
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model = create_simple_model()
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optimizer = paddle.optimizer.Momentum(
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learning_rate=0.01, momentum=0.9, parameters=model.parameters()
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)
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loss = do_one_step(model, optimizer)
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self.assertIsNotNone(loss)
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def test_momentum_nesterov(self):
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"""测试Nesterov动量 / Test Nesterov momentum"""
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model = create_simple_model()
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optimizer = paddle.optimizer.Momentum(
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learning_rate=0.01,
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momentum=0.9,
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use_nesterov=True,
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parameters=model.parameters(),
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)
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do_one_step(model, optimizer)
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def test_momentum_weight_decay(self):
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"""测试带权重衰减的Momentum / Test Momentum with weight decay"""
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model = create_simple_model()
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optimizer = paddle.optimizer.Momentum(
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learning_rate=0.01,
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momentum=0.9,
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weight_decay=0.001,
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parameters=model.parameters(),
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)
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do_one_step(model, optimizer)
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def test_momentum_set_lr(self):
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"""测试动态设置学习率 / Test dynamic learning rate setting"""
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model = create_simple_model()
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optimizer = paddle.optimizer.Momentum(
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learning_rate=0.01, momentum=0.9, parameters=model.parameters()
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)
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optimizer.set_lr(0.001)
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self.assertAlmostEqual(optimizer.get_lr(), 0.001, places=5)
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class TestSGDOptimizer(unittest.TestCase):
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"""测试SGD优化器 / Test SGD optimizer"""
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def test_sgd_basic(self):
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"""测试SGD基本功能 / Test basic SGD functionality"""
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model = create_simple_model()
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optimizer = paddle.optimizer.SGD(
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learning_rate=0.01, parameters=model.parameters()
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)
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loss = do_one_step(model, optimizer)
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self.assertIsNotNone(loss)
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def test_sgd_weight_decay(self):
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"""测试带权重衰减的SGD / Test SGD with weight decay"""
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model = create_simple_model()
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optimizer = paddle.optimizer.SGD(
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learning_rate=0.01,
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weight_decay=0.001,
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parameters=model.parameters(),
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)
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do_one_step(model, optimizer)
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def test_sgd_with_lr_scheduler(self):
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"""测试SGD配合学习率调度器 / Test SGD with lr scheduler"""
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model = create_simple_model()
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scheduler = paddle.optimizer.lr.StepDecay(
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learning_rate=0.1, step_size=10, gamma=0.1
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)
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optimizer = paddle.optimizer.SGD(
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learning_rate=scheduler, parameters=model.parameters()
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)
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for _ in range(3):
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do_one_step(model, optimizer)
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scheduler.step()
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if __name__ == '__main__':
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unittest.main()
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