wehub-resource-sync
202 lines
6.6 KiB
Python
202 lines
6.6 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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优化器进阶测试 / Advanced Optimizer Tests
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测试目标 / Test Target:
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paddle.optimizer 各种优化器
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覆盖的模块 / Covered Modules:
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- paddle.optimizer.Adam: Adam优化器
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- paddle.optimizer.SGD: 随机梯度下降
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- paddle.optimizer.Momentum: 动量优化器
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- 优化器状态字典
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- 梯度裁剪
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作用 / Purpose:
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补充优化器API的高级测试，提升覆盖率。
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"""
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import unittest
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import paddle
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import paddle.optimizer as optim
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from paddle import nn
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paddle.disable_static()
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class SimpleModel(nn.Layer):
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"""简单测试模型 / Simple test model"""
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def __init__(self):
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super().__init__()
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self.fc = nn.Linear(4, 2)
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def forward(self, x):
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return self.fc(x)
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def training_step(model, optimizer, x, y):
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"""执行单步训练 / Execute single training step"""
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pred = model(x)
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loss = paddle.nn.functional.mse_loss(pred, y)
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loss.backward()
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optimizer.step()
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optimizer.clear_grad()
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return float(loss.numpy())
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class TestAdamOptimizer(unittest.TestCase):
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"""测试Adam优化器 / Test Adam optimizer"""
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def test_adam_basic(self):
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"""测试基本Adam / Test basic Adam"""
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model = SimpleModel()
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optimizer = optim.Adam(
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parameters=model.parameters(), learning_rate=0.001
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)
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x = paddle.randn([8, 4])
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y = paddle.randn([8, 2])
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loss = training_step(model, optimizer, x, y)
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self.assertIsNotNone(loss)
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def test_adam_weight_decay(self):
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"""测试带weight_decay的Adam / Test Adam with weight decay"""
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model = SimpleModel()
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optimizer = optim.Adam(
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parameters=model.parameters(),
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learning_rate=0.001,
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weight_decay=1e-4,
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)
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x = paddle.randn([8, 4])
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y = paddle.randn([8, 2])
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loss = training_step(model, optimizer, x, y)
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self.assertIsNotNone(loss)
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def test_adam_beta(self):
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"""测试自定义beta的Adam / Test Adam with custom betas"""
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model = SimpleModel()
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optimizer = optim.Adam(
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parameters=model.parameters(),
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learning_rate=0.001,
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beta1=0.9,
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beta2=0.999,
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)
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x = paddle.randn([8, 4])
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y = paddle.randn([8, 2])
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for _ in range(3):
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training_step(model, optimizer, x, y)
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def test_adam_state_dict(self):
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"""测试Adam状态字典 / Test Adam state dict"""
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model = SimpleModel()
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optimizer = optim.Adam(parameters=model.parameters())
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x = paddle.randn([4, 4])
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y = paddle.randn([4, 2])
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training_step(model, optimizer, x, y)
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state = optimizer.state_dict()
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self.assertIsNotNone(state)
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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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"""测试基本SGD / Test basic SGD"""
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model = SimpleModel()
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optimizer = optim.SGD(parameters=model.parameters(), learning_rate=0.01)
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x = paddle.randn([8, 4])
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y = paddle.randn([8, 2])
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loss_before = float(paddle.nn.functional.mse_loss(model(x), y).numpy())
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for _ in range(10):
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training_step(model, optimizer, x, y)
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loss_after = float(paddle.nn.functional.mse_loss(model(x), y).numpy())
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# Loss should decrease after training
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self.assertLess(loss_after, loss_before)
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def test_sgd_momentum(self):
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"""测试带动量的SGD / Test SGD with momentum"""
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model = SimpleModel()
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optimizer = optim.Momentum(
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parameters=model.parameters(), learning_rate=0.01, momentum=0.9
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)
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x = paddle.randn([8, 4])
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y = paddle.randn([8, 2])
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for _ in range(5):
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training_step(model, optimizer, x, y)
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class TestGradientClipping(unittest.TestCase):
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"""测试梯度裁剪 / Test gradient clipping"""
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def test_clip_by_norm(self):
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"""测试按范数裁剪 / Test gradient clipping by norm"""
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model = SimpleModel()
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clip = paddle.nn.ClipGradByNorm(clip_norm=1.0)
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optimizer = optim.Adam(
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parameters=model.parameters(), learning_rate=0.001, grad_clip=clip
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)
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x = paddle.randn([8, 4])
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y = paddle.randn([8, 2])
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training_step(model, optimizer, x, y)
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def test_clip_by_global_norm(self):
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"""测试按全局范数裁剪 / Test gradient clipping by global norm"""
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model = SimpleModel()
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clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=1.0)
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optimizer = optim.Adam(
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parameters=model.parameters(), learning_rate=0.001, grad_clip=clip
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)
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x = paddle.randn([8, 4])
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y = paddle.randn([8, 2])
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training_step(model, optimizer, x, y)
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def test_clip_by_value(self):
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"""测试按值裁剪 / Test gradient clipping by value"""
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model = SimpleModel()
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clip = paddle.nn.ClipGradByValue(min=-0.5, max=0.5)
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optimizer = optim.Adam(
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parameters=model.parameters(), learning_rate=0.001, grad_clip=clip
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)
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x = paddle.randn([8, 4])
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y = paddle.randn([8, 2])
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training_step(model, optimizer, x, y)
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class TestOptimizerParameterGroups(unittest.TestCase):
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"""测试优化器参数组 / Test optimizer parameter groups"""
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def test_different_lr_per_group(self):
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"""测试不同学习率的参数组 / Test parameter groups with different LRs"""
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model = nn.Sequential(nn.Linear(4, 8), nn.ReLU(), nn.Linear(8, 2))
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# Different learning rates for different layers
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params = [
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{'params': model[0].parameters(), 'learning_rate': 0.01},
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{'params': model[2].parameters(), 'learning_rate': 0.001},
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]
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optimizer = optim.Adam(parameters=params, learning_rate=0.005)
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x = paddle.randn([4, 4])
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y = paddle.randn([4, 2])
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pred = model(x)
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loss = paddle.nn.functional.mse_loss(pred, y)
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loss.backward()
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optimizer.step()
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optimizer.clear_grad()
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if __name__ == '__main__':
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unittest.main()
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