wehub-resource-sync
169 lines
5.6 KiB
Python
169 lines
5.6 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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# [AUTO-GENERATED] Unit test for paddle.nn.clip
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# 自动生成的单测，覆盖 paddle.nn.clip 模块中未覆盖的代码
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"""
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测试模块：paddle.nn.clip (ClipGradByValue, ClipGradByNorm, ClipGradByGlobalNorm, clip_by_norm)
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Test Module: paddle.nn.clip
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本测试覆盖以下功能：
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This test covers the following functions:
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1. clip_by_norm - 按范数裁剪 / Clip by norm in dynamic mode
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2. ClipGradByValue - 按值裁剪梯度 / Clip gradient by value
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3. ClipGradByNorm - 按范数裁剪梯度 / Clip gradient by norm
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4. ClipGradByGlobalNorm - 按全局范数裁剪梯度 / Clip gradient by global norm
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覆盖的未覆盖行：88-110 (clip_by_norm static), 143-151 (merge_selected_rows)
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"""
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import unittest
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import numpy as np
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import paddle
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class TestClipByNorm(unittest.TestCase):
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"""测试clip_by_norm功能
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Test clip_by_norm function"""
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def setUp(self):
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paddle.disable_static()
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def test_clip_by_norm_no_clip(self):
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"""范数小于max_norm时不裁剪 / No clipping when norm < max_norm"""
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x = paddle.to_tensor([[0.1, 0.2], [0.3, 0.4]], dtype='float32')
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out = paddle.nn.clip.clip_by_norm(x, max_norm=10.0)
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np.testing.assert_allclose(out.numpy(), x.numpy(), rtol=1e-5)
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def test_clip_by_norm_with_clip(self):
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"""范数大于max_norm时裁剪 / Clipping when norm > max_norm"""
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x = paddle.to_tensor([[3.0, 4.0]], dtype='float32')
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out = paddle.nn.clip.clip_by_norm(x, max_norm=1.0)
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norm = np.linalg.norm(out.numpy())
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self.assertAlmostEqual(norm, 1.0, places=5)
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def test_clip_by_norm_zero(self):
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"""零tensor裁剪 / Clip zero tensor"""
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x = paddle.zeros([3, 3], dtype='float32')
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out = paddle.nn.clip.clip_by_norm(x, max_norm=1.0)
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np.testing.assert_allclose(out.numpy(), np.zeros([3, 3]), atol=1e-7)
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class TestClipGradByValue(unittest.TestCase):
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"""测试ClipGradByValue梯度裁剪
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Test ClipGradByValue gradient clipping"""
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def setUp(self):
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paddle.disable_static()
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def test_clip_grad_by_value_basic(self):
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"""基本按值裁剪 / Basic clip by value"""
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clip = paddle.nn.ClipGradByValue(min=-0.5, max=0.5)
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linear = paddle.nn.Linear(3, 3)
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x = paddle.randn([2, 3])
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y = linear(x)
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loss = y.sum()
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loss.backward()
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params_grads = []
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for p in linear.parameters():
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if p.grad is not None:
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params_grads.append((p, p.grad))
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clipped = clip(params_grads)
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for p, g in clipped:
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self.assertTrue(g.numpy().max() <= 0.5 + 1e-6)
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self.assertTrue(g.numpy().min() >= -0.5 - 1e-6)
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def test_clip_grad_by_value_symmetric(self):
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"""对称裁剪 / Symmetric clipping with only max"""
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clip = paddle.nn.ClipGradByValue(max=1.0)
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self.assertIsNotNone(clip)
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class TestClipGradByNorm(unittest.TestCase):
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"""测试ClipGradByNorm梯度裁剪
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Test ClipGradByNorm gradient clipping"""
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def setUp(self):
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paddle.disable_static()
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def test_clip_grad_by_norm_basic(self):
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"""基本按范数裁剪 / Basic clip by norm"""
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clip = paddle.nn.ClipGradByNorm(clip_norm=1.0)
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linear = paddle.nn.Linear(3, 3)
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x = paddle.randn([2, 3])
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y = linear(x)
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loss = y.sum() * 100
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loss.backward()
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params_grads = []
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for p in linear.parameters():
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if p.grad is not None:
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params_grads.append((p, p.grad))
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clipped = clip(params_grads)
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for p, g in clipped:
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norm = float(paddle.linalg.norm(g).numpy())
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self.assertLessEqual(norm, 1.0 + 1e-5)
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class TestClipGradByGlobalNorm(unittest.TestCase):
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"""测试ClipGradByGlobalNorm全局范数裁剪
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Test ClipGradByGlobalNorm global norm clipping"""
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def setUp(self):
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paddle.disable_static()
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def test_clip_grad_by_global_norm_basic(self):
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"""基本全局范数裁剪 / Basic global norm clipping"""
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clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=1.0)
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linear = paddle.nn.Linear(10, 10)
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x = paddle.randn([2, 10])
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y = linear(x)
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loss = y.sum() * 100
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loss.backward()
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params_grads = []
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for p in linear.parameters():
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if p.grad is not None:
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params_grads.append((p, p.grad))
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clipped = clip(params_grads)
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self.assertIsNotNone(clipped)
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def test_clip_grad_by_global_norm_no_clip(self):
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"""全局范数未超限不裁剪 / No clipping when global norm is within limit"""
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clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=1000.0)
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linear = paddle.nn.Linear(3, 3)
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x = paddle.randn([2, 3])
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y = linear(x)
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loss = y.sum()
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loss.backward()
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params_grads = []
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for p in linear.parameters():
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if p.grad is not None:
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params_grads.append((p, p.grad.clone()))
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clipped = clip(params_grads)
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self.assertIsNotNone(clipped)
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if __name__ == '__main__':
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unittest.main()
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