wehub-resource-sync
159 lines
4.8 KiB
Python
159 lines
4.8 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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动态图控制流测试 / Dynamic Graph Control Flow Tests
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测试目标 / Test Target:
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paddle 动态图控制流
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覆盖的模块 / Covered Modules:
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- paddle.cond: 条件执行
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- paddle.while_loop: 循环执行
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- paddle.jit.to_static: 动转静
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- 条件分支模型
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作用 / Purpose:
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补充动态图控制流API的测试，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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from paddle import nn
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paddle.disable_static()
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class TestPaddleCond(unittest.TestCase):
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"""测试条件控制流 / Test conditional control flow"""
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def test_cond_true(self):
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"""测试条件为True / Test cond when true"""
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x = paddle.to_tensor(True)
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result = paddle.static.nn.cond(
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x, lambda: paddle.to_tensor([1.0]), lambda: paddle.to_tensor([0.0])
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)
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self.assertAlmostEqual(float(result.item()), 1.0)
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def test_cond_false(self):
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"""测试条件为False / Test cond when false"""
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x = paddle.to_tensor(False)
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result = paddle.static.nn.cond(
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x, lambda: paddle.to_tensor([1.0]), lambda: paddle.to_tensor([0.0])
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)
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self.assertAlmostEqual(float(result.item()), 0.0)
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def test_cond_with_computation(self):
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"""测试带计算的条件 / Test cond with computation"""
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x = paddle.to_tensor(3.0)
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cond = x > 2.0
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result = paddle.static.nn.cond(cond, lambda: x * 2, lambda: x * 0.5)
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self.assertAlmostEqual(float(result.item()), 6.0, places=5)
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class TestWhileLoop(unittest.TestCase):
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"""测试while循环 / Test while loop"""
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def test_while_loop_basic(self):
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"""测试基本while循环 / Test basic while loop"""
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i = paddle.zeros([1], dtype='int64')
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limit = paddle.to_tensor([5], dtype='int64')
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def cond(i):
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return paddle.less_than(i, limit)
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def body(i):
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return [i + 1]
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out = paddle.static.nn.while_loop(cond, body, [i])
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self.assertEqual(int(out[0].item()), 5)
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class TestDynamicShapeModel(unittest.TestCase):
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"""测试动态形状模型 / Test dynamic shape model"""
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def test_variable_length_batch(self):
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"""测试可变长度批次 / Test variable length batch"""
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model = nn.Sequential(nn.Linear(4, 8), nn.ReLU(), nn.Linear(8, 2))
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# Test with different batch sizes
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for batch_size in [1, 4, 16]:
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x = paddle.randn([batch_size, 4])
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output = model(x)
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self.assertEqual(output.shape[0], batch_size)
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self.assertEqual(output.shape[1], 2)
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def test_conditional_model(self):
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"""测试条件模型 / Test conditional model"""
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class ConditionalNet(nn.Layer):
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def __init__(self):
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super().__init__()
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self.fc1 = nn.Linear(4, 8)
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self.fc2 = nn.Linear(4, 8)
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def forward(self, x, use_path1):
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if use_path1:
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return self.fc1(x)
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else:
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return self.fc2(x)
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model = ConditionalNet()
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x = paddle.randn([4, 4])
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out1 = model(x, True)
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out2 = model(x, False)
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self.assertEqual(out1.shape, [4, 8])
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self.assertEqual(out2.shape, [4, 8])
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class TestToStaticConversion(unittest.TestCase):
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"""测试动转静转换 / Test dynamic to static conversion"""
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def test_to_static_function(self):
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"""测试函数动转静 / Test function to static"""
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@paddle.jit.to_static
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def linear_func(x, w, b):
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return paddle.matmul(x, w) + b
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x = paddle.randn([4, 4])
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w = paddle.randn([4, 2])
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b = paddle.zeros([2])
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result = linear_func(x, w, b)
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self.assertEqual(result.shape, [4, 2])
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def test_to_static_model(self):
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"""测试模型动转静 / Test model to static"""
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class SimpleModel(nn.Layer):
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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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@paddle.jit.to_static
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def forward(self, x):
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return self.fc(x)
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model = SimpleModel()
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x = paddle.randn([4, 4])
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output = model(x)
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self.assertEqual(output.shape, [4, 2])
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if __name__ == '__main__':
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unittest.main()
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