wehub-resource-sync
234 lines
7.9 KiB
Python
234 lines
7.9 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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自动混合精度(AMP)单元测试 / Automatic Mixed Precision (AMP) Unit Tests
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测试目标 / Test Target:
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paddle.amp 模块 (python/paddle/static/amp/*, 覆盖率约81.3%)
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覆盖的模块 / Covered Modules:
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- paddle.amp.auto_cast: 自动类型转换上下文
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- paddle.amp.GradScaler: 梯度缩放器
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- paddle.amp.decorate: AMP模型装饰
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- paddle.amp.is_float16_supported: 检查FP16支持
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作用 / Purpose:
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覆盖自动混合精度训练的各种代码路径，包括半精度计算、梯度缩放等。
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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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HAS_GPU = paddle.device.is_compiled_with_cuda()
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class TestAutoCast(unittest.TestCase):
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"""测试auto_cast上下文 / Test auto_cast context"""
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def test_auto_cast_float16(self):
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"""测试FP16 auto_cast / Test FP16 auto_cast"""
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model = nn.Linear(10, 5)
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with paddle.amp.auto_cast(enable=True, dtype='float16'):
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x = paddle.randn([4, 10])
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y = model(x)
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# Output should exist
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self.assertEqual(y.shape, [4, 5])
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def test_auto_cast_bfloat16(self):
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"""测试BF16 auto_cast / Test BF16 auto_cast"""
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model = nn.Linear(10, 5)
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try:
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with paddle.amp.auto_cast(enable=True, dtype='bfloat16'):
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x = paddle.randn([4, 10])
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y = model(x)
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self.assertEqual(y.shape, [4, 5])
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except Exception:
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# BF16 may not be supported on all hardware
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pass
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def test_auto_cast_disabled(self):
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"""测试禁用auto_cast / Test disabled auto_cast"""
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model = nn.Linear(10, 5)
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with paddle.amp.auto_cast(enable=False):
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x = paddle.randn([4, 10])
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y = model(x)
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self.assertEqual(y.dtype, paddle.float32)
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def test_auto_cast_level(self):
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"""测试auto_cast级别 / Test auto_cast level"""
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model = nn.Linear(10, 5)
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# Level O1: operations use float16 where beneficial
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with paddle.amp.auto_cast(enable=True, dtype='float16', level='O1'):
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x = paddle.randn([4, 10])
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y = model(x)
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self.assertEqual(y.shape, [4, 5])
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def test_auto_cast_custom_black_list(self):
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"""测试自定义黑名单 / Test custom black list"""
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model = nn.Linear(10, 5)
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with paddle.amp.auto_cast(
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enable=True,
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dtype='float16',
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custom_black_list=['matmul_v2', 'matmul'],
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):
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x = paddle.randn([4, 10])
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y = model(x)
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self.assertEqual(y.shape, [4, 5])
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class TestGradScaler(unittest.TestCase):
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"""测试梯度缩放器 / Test gradient scaler"""
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def test_grad_scaler_basic(self):
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"""测试基本GradScaler / Test basic GradScaler"""
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scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
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self.assertEqual(scaler._init_loss_scaling, 1024)
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def test_grad_scaler_scale(self):
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"""测试梯度缩放 / Test gradient scaling"""
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model = nn.Linear(10, 5)
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optimizer = paddle.optimizer.Adam(
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learning_rate=0.01, parameters=model.parameters()
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)
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scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
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x = paddle.randn([4, 10])
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with paddle.amp.auto_cast(enable=True, dtype='float16'):
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y = model(x)
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loss = y.mean()
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scaled_loss = scaler.scale(loss)
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scaled_loss.backward()
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scaler.step(optimizer)
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scaler.update()
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def test_grad_scaler_unscale(self):
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"""测试梯度反缩放 / Test gradient unscaling"""
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model = nn.Linear(10, 5)
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optimizer = paddle.optimizer.Adam(
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learning_rate=0.01, parameters=model.parameters()
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)
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scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
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x = paddle.randn([4, 10])
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with paddle.amp.auto_cast(enable=True, dtype='float16'):
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y = model(x)
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loss = y.mean()
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scaled_loss = scaler.scale(loss)
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scaled_loss.backward()
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scaler.unscale_(optimizer)
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scaler.step(optimizer)
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scaler.update()
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def test_grad_scaler_state_dict(self):
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"""测试GradScaler状态字典 / Test GradScaler state dict"""
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scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
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state = scaler.state_dict()
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self.assertIn('scale', state)
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self.assertIn('incr_ratio', state)
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def test_grad_scaler_load_state_dict(self):
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"""测试加载GradScaler状态 / Test loading GradScaler state"""
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scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
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state = scaler.state_dict()
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new_scaler = paddle.amp.GradScaler(init_loss_scaling=512)
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new_scaler.load_state_dict(state)
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self.assertEqual(new_scaler._init_loss_scaling, 1024)
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class TestAMPDecorate(unittest.TestCase):
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"""测试AMP模型装饰 / Test AMP model decoration"""
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def test_decorate_float16(self):
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"""测试FP16模型装饰 / Test FP16 model decoration"""
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model = nn.Linear(10, 5)
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optimizer = paddle.optimizer.Adam(
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learning_rate=0.01, parameters=model.parameters()
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)
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model, optimizer = paddle.amp.decorate(
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models=model, optimizers=optimizer, level='O1', dtype='float16'
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)
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self.assertIsNotNone(model)
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self.assertIsNotNone(optimizer)
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def test_decorate_multiple_models(self):
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"""测试多模型装饰 / Test multiple models decoration"""
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model1 = nn.Linear(10, 5)
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model2 = nn.Linear(5, 2)
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optimizer = paddle.optimizer.Adam(
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learning_rate=0.01,
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parameters=list(model1.parameters()) + list(model2.parameters()),
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)
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models, opt = paddle.amp.decorate(
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models=[model1, model2],
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optimizers=optimizer,
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level='O1',
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dtype='float16',
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)
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self.assertEqual(len(models), 2)
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class TestAMPTraining(unittest.TestCase):
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"""测试AMP完整训练流程 / Test complete AMP training workflow"""
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def test_amp_training_loop(self):
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"""测试AMP训练循环 / Test AMP training loop"""
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model = nn.Linear(10, 5)
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optimizer = paddle.optimizer.Adam(
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learning_rate=0.01, parameters=model.parameters()
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)
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scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
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for _ in range(3):
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x = paddle.randn([4, 10])
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with paddle.amp.auto_cast(enable=True, dtype='float16'):
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y = model(x)
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loss = y.mean()
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scaled_loss = scaler.scale(loss)
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scaled_loss.backward()
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scaler.step(optimizer)
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scaler.update()
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optimizer.clear_grad()
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def test_amp_with_grad_clip(self):
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"""测试AMP配合梯度裁剪 / Test AMP with gradient clipping"""
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model = nn.Linear(10, 5)
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clip = nn.ClipGradByNorm(clip_norm=1.0)
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optimizer = paddle.optimizer.Adam(
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learning_rate=0.01, parameters=model.parameters(), grad_clip=clip
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)
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scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
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x = paddle.randn([4, 10])
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with paddle.amp.auto_cast(enable=True, dtype='float16'):
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y = model(x)
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loss = y.mean()
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scaler.scale(loss).backward()
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scaler.step(optimizer)
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scaler.update()
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optimizer.clear_grad()
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if __name__ == '__main__':
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unittest.main()
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