wehub-resource-sync
250 lines
7.2 KiB
Python
250 lines
7.2 KiB
Python
# Copyright (c) 2024 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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# Unit test for paddle.optimizer optimizer error paths
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# Target: cover uncovered lines 94-116 in paddle/python/paddle/optimizer/optimizer.py
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import unittest
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import paddle
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import paddle.optimizer as opt
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class TestSGDOptimizer(unittest.TestCase):
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"""Test SGD optimizer.
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SGD does not accept momentum kwarg.
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"""
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def setUp(self):
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paddle.disable_static()
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def test_sgd_basic(self):
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"""SGD basic step."""
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x = paddle.to_tensor([1.0], dtype='float32')
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x.stop_gradient = False
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linear = paddle.nn.Linear(1, 1)
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sgd = opt.SGD(
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learning_rate=0.01,
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parameters=linear.parameters(),
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)
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out = linear(x)
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loss = out.mean()
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loss.backward()
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sgd.step()
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sgd.clear_grad()
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# Should not raise
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def test_sgd_with_weight_decay(self):
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"""SGD with weight decay."""
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linear = paddle.nn.Linear(2, 2)
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sgd = opt.SGD(
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learning_rate=0.01,
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parameters=linear.parameters(),
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weight_decay=0.01,
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)
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x = paddle.randn([4, 2])
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out = linear(x)
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loss = out.mean()
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loss.backward()
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sgd.step()
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class TestMomentumOptimizer(unittest.TestCase):
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"""Test Momentum optimizer (for momentum tests)."""
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def setUp(self):
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paddle.disable_static()
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def test_momentum_basic(self):
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"""Momentum basic step."""
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linear = paddle.nn.Linear(2, 2)
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momentum = opt.Momentum(
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learning_rate=0.01,
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momentum=0.9,
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parameters=linear.parameters(),
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)
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x = paddle.randn([4, 2])
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out = linear(x)
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loss = out.mean()
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loss.backward()
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momentum.step()
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def test_momentum_state_dict(self):
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"""Momentum optimizer state_dict."""
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linear = paddle.nn.Linear(2, 2)
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momentum = opt.Momentum(
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learning_rate=0.01,
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momentum=0.9,
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parameters=linear.parameters(),
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)
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x = paddle.randn([4, 2])
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out = linear(x)
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loss = out.mean()
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loss.backward()
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momentum.step()
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state = momentum.state_dict()
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self.assertIsInstance(state, dict)
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def test_momentum_set_state_dict(self):
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"""Momentum optimizer set_state_dict."""
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linear = paddle.nn.Linear(2, 2)
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mom1 = opt.Momentum(
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learning_rate=0.01,
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momentum=0.9,
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parameters=linear.parameters(),
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)
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x = paddle.randn([4, 2])
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out = linear(x)
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loss = out.mean()
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loss.backward()
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mom1.step()
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state = mom1.state_dict()
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# Create new optimizer and load state
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linear2 = paddle.nn.Linear(2, 2)
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mom2 = opt.Momentum(
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learning_rate=0.01,
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momentum=0.9,
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parameters=linear2.parameters(),
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)
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mom2.set_state_dict(state)
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class TestAdamOptimizer(unittest.TestCase):
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"""Test Adam optimizer."""
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def setUp(self):
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paddle.disable_static()
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def test_adam_basic(self):
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"""Adam basic step."""
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linear = paddle.nn.Linear(2, 2)
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adam = opt.Adam(
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learning_rate=0.001,
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parameters=linear.parameters(),
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)
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x = paddle.randn([4, 2])
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out = linear(x)
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loss = out.mean()
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loss.backward()
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adam.step()
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adam.clear_grad()
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def test_adam_with_weight_decay(self):
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"""Adam with weight decay."""
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linear = paddle.nn.Linear(2, 2)
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adam = opt.Adam(
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learning_rate=0.001,
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parameters=linear.parameters(),
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weight_decay=0.01,
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)
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x = paddle.randn([4, 2])
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out = linear(x)
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loss = out.mean()
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loss.backward()
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adam.step()
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def test_adam_with_beta(self):
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"""Adam with custom beta1/beta2."""
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linear = paddle.nn.Linear(2, 2)
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adam = opt.Adam(
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learning_rate=0.001,
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parameters=linear.parameters(),
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beta1=0.9,
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beta2=0.999,
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epsilon=1e-8,
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)
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x = paddle.randn([4, 2])
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out = linear(x)
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loss = out.mean()
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loss.backward()
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adam.step()
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class TestOptimizerUtils(unittest.TestCase):
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"""Test optimizer utility methods."""
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def setUp(self):
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paddle.disable_static()
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def test_minimize_with_grad_clip(self):
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"""minimize with gradient clipping."""
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linear = paddle.nn.Linear(2, 2)
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sgd = opt.SGD(learning_rate=0.01, parameters=linear.parameters())
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x = paddle.randn([4, 2])
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out = linear(x)
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loss = out.mean()
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clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=1.0)
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loss.backward()
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sgd.step()
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def test_lr_scheduler_with_optimizer(self):
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"""Learning rate scheduler with optimizer."""
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linear = paddle.nn.Linear(2, 2)
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scheduler = paddle.optimizer.lr.StepDecay(
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learning_rate=0.01, step_size=10, gamma=0.1
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)
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sgd = opt.SGD(learning_rate=scheduler, parameters=linear.parameters())
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x = paddle.randn([4, 2])
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out = linear(x)
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loss = out.mean()
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loss.backward()
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sgd.step()
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scheduler.step()
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def test_get_lr(self):
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"""Get learning rate from optimizer."""
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linear = paddle.nn.Linear(2, 2)
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sgd = opt.SGD(learning_rate=0.01, parameters=linear.parameters())
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lr = sgd.get_lr()
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self.assertAlmostEqual(lr, 0.01)
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def test_set_lr(self):
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"""Set learning rate."""
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linear = paddle.nn.Linear(2, 2)
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sgd = opt.SGD(learning_rate=0.01, parameters=linear.parameters())
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sgd.set_lr(0.001)
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lr = sgd.get_lr()
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self.assertAlmostEqual(lr, 0.001)
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def test_state_dict(self):
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"""SGD optimizer state_dict."""
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linear = paddle.nn.Linear(2, 2)
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sgd = opt.SGD(learning_rate=0.01, parameters=linear.parameters())
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x = paddle.randn([4, 2])
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out = linear(x)
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loss = out.mean()
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loss.backward()
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sgd.step()
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state = sgd.state_dict()
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self.assertIsInstance(state, dict)
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def test_set_state_dict(self):
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"""SGD optimizer set_state_dict."""
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linear = paddle.nn.Linear(2, 2)
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sgd1 = opt.SGD(learning_rate=0.01, parameters=linear.parameters())
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x = paddle.randn([4, 2])
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out = linear(x)
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loss = out.mean()
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loss.backward()
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sgd1.step()
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state = sgd1.state_dict()
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# Create new optimizer and load state
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linear2 = paddle.nn.Linear(2, 2)
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sgd2 = opt.SGD(learning_rate=0.01, parameters=linear2.parameters())
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sgd2.set_state_dict(state)
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if __name__ == '__main__':
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unittest.main()
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