wehub-resource-sync
176 lines
7.0 KiB
Python
176 lines
7.0 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.functional.input
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# 自动生成的单测，覆盖 paddle.nn.functional.input 模块中未覆盖的代码
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# Target: cover uncovered lines 118-137, 315-332 in paddle/python/paddle/nn/functional/input.py
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"""
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测试模块：paddle.nn.functional.input (embedding, one_hot)
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Test Module: paddle.nn.functional.input (embedding, one_hot)
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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. one_hot - 独热编码 / One-hot encoding
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- 静态图路径 / Static graph path (lines 118-137)
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- 自动推断num_classes / Auto-infer num_classes
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2. embedding - 嵌入查找 / Embedding lookup
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- max_norm 参数：嵌入向量范数裁剪 / max_norm parameter: embedding norm clipping
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- embedding_renorm_ 函数 / embedding_renorm_ function
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- scale_grad_by_freq 参数 / scale_grad_by_freq parameter (lines 315-332)
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- padding_idx 负索引 / negative padding_idx
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覆盖的未覆盖行：118-137（one_hot静态图），315-332（embedding scale_grad_by_freq）
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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 TestOneHotDynamic(unittest.TestCase):
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"""测试动态图模式下的one_hot
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Test one_hot in dynamic graph mode"""
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def setUp(self):
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paddle.disable_static()
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def test_basic_one_hot(self):
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"""测试基本的one_hot编码
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Test basic one_hot encoding"""
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x = paddle.to_tensor([0, 1, 2, 3], dtype='int64')
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out = paddle.nn.functional.one_hot(x, num_classes=4)
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expected = np.array(
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[
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[1, 0, 0, 0],
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[0, 1, 0, 0],
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[0, 0, 1, 0],
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[0, 0, 0, 1],
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],
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dtype='float32',
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)
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np.testing.assert_array_equal(out.numpy(), expected)
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def test_one_hot_auto_num_classes(self):
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"""测试one_hot自动推断num_classes（num_classes=-1）
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Test one_hot with automatic num_classes inference (num_classes=-1)"""
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x = paddle.to_tensor([0, 1, 2], dtype='int64')
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out = paddle.nn.functional.one_hot(x) # num_classes defaults to -1
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self.assertEqual(list(out.shape), [3, 3])
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def test_one_hot_2d_input(self):
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"""测试2D输入的one_hot
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Test one_hot with 2D input"""
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x = paddle.to_tensor([[0, 1], [2, 0]], dtype='int64')
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out = paddle.nn.functional.one_hot(x, num_classes=3)
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self.assertEqual(list(out.shape), [2, 2, 3])
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class TestOneHotStatic(unittest.TestCase):
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"""测试静态图模式下的one_hot，覆盖未覆盖行118-137
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Test one_hot in static graph mode to cover uncovered lines 118-137"""
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def test_static_graph_one_hot(self):
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"""测试静态图模式下的one_hot基本功能
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Test basic one_hot in static graph mode"""
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paddle.enable_static()
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try:
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main_prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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with paddle.static.program_guard(main_prog, startup_prog):
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x = paddle.static.data(name='x', shape=[4], dtype='int64')
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out = paddle.nn.functional.one_hot(x, num_classes=5)
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exe = paddle.static.Executor(paddle.CPUPlace())
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exe.run(startup_prog)
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x_np = np.array([0, 1, 3, 4], dtype='int64')
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result = exe.run(main_prog, feed={'x': x_np}, fetch_list=[out])
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self.assertEqual(list(result[0].shape), [4, 5])
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# 验证第一个向量 / Verify first vector
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np.testing.assert_array_equal(result[0][0], [1, 0, 0, 0, 0])
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finally:
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paddle.disable_static()
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class TestEmbeddingMaxNorm(unittest.TestCase):
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"""测试embedding的max_norm功能
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Test embedding max_norm feature"""
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def setUp(self):
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paddle.disable_static()
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def test_embedding_with_max_norm(self):
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"""测试embedding的max_norm范数裁剪，覆盖embedding_renorm_函数
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Test embedding max_norm norm clipping, covers embedding_renorm_ function"""
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x = paddle.to_tensor([0, 1, 2], dtype='int64')
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# 创建一个权重矩阵，其中某些行的范数大于max_norm
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# Create a weight matrix where some rows have norm > max_norm
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weight = paddle.to_tensor(
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[
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[10.0, 10.0, 10.0], # norm = sqrt(300) ≈ 17.3
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[1.0, 0.0, 0.0], # norm = 1.0
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[0.0, 2.0, 0.0], # norm = 2.0
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],
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dtype='float32',
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)
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weight.stop_gradient = False
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out = paddle.nn.functional.embedding(
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x, weight, max_norm=5.0, norm_type=2.0
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)
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self.assertEqual(list(out.shape), [3, 3])
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# 第一行的范数应被裁剪到不超过5.0
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# First row norm should be clipped to <= 5.0
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row0_norm = float(paddle.norm(out[0], p=2).item())
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self.assertLessEqual(row0_norm, 5.0 + 1e-3)
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def test_embedding_with_negative_padding_idx(self):
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"""测试embedding的负padding_idx
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Test embedding with negative padding_idx"""
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x = paddle.to_tensor([0, 1, 4], dtype='int64')
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weight = paddle.full(shape=(5, 3), fill_value=2.0, dtype='float32')
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out = paddle.nn.functional.embedding(x, weight, padding_idx=-1)
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self.assertEqual(list(out.shape), [3, 3])
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# padding_idx=-1 → 实际index=4, 输出应全0
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# padding_idx=-1 → actual index=4, output should be all zeros
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np.testing.assert_array_equal(out[2].numpy(), [0.0, 0.0, 0.0])
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def test_embedding_with_scale_grad_by_freq(self):
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"""测试embedding的scale_grad_by_freq参数
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Test embedding scale_grad_by_freq parameter"""
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x = paddle.to_tensor([0, 0, 1, 2], dtype='int64')
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weight = paddle.randn([5, 3])
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weight.stop_gradient = False
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out = paddle.nn.functional.embedding(
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x, weight, scale_grad_by_freq=True, sparse=False
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)
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self.assertEqual(list(out.shape), [4, 3])
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# 验证可以正常前向计算 / Verify forward pass works
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loss = out.sum()
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loss.backward()
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def test_embedding_padding_idx_out_of_range(self):
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"""测试embedding的padding_idx超出范围应报错
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Test embedding with padding_idx out of range should raise"""
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x = paddle.to_tensor([0, 1], dtype='int64')
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weight = paddle.randn([5, 3])
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with self.assertRaises(ValueError):
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paddle.nn.functional.embedding(x, weight, padding_idx=10)
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if __name__ == '__main__':
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unittest.main()
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