wehub-resource-sync
225 lines
9.2 KiB
Python
225 lines
9.2 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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稀疏张量操作单元测试 / Sparse Tensor Operations Unit Tests
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测试目标 / Test Target:
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paddle.sparse 模块 (覆盖率约83%)
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覆盖的模块 / Covered Modules:
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- paddle.sparse: 稀疏张量创建和操作
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- paddle.sparse.nn.functional: 稀疏神经网络函数
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- paddle.sparse.binary: 稀疏二进制操作
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作用 / Purpose:
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覆盖稀疏张量的创建、转换、运算等代码路径。
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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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paddle.disable_static()
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class TestSparseCOOTensor(unittest.TestCase):
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"""测试COO格式稀疏张量 / Test COO format sparse tensor"""
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def test_sparse_coo_basic(self):
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"""测试基本COO稀疏张量创建 / Test basic COO sparse tensor creation"""
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indices = paddle.to_tensor([[0, 1, 2], [1, 2, 0]], dtype='int64')
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values = paddle.to_tensor([1.0, 2.0, 3.0])
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shape = [3, 3]
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x = paddle.sparse.sparse_coo_tensor(indices, values, shape)
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self.assertEqual(x.shape, shape)
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self.assertTrue(x.is_sparse_coo())
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def test_sparse_coo_to_dense(self):
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"""测试COO稀疏张量转稠密 / Test COO sparse to dense conversion"""
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indices = paddle.to_tensor([[0, 1], [0, 1]], dtype='int64')
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values = paddle.to_tensor([1.0, 2.0])
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shape = [3, 3]
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x = paddle.sparse.sparse_coo_tensor(indices, values, shape)
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dense = x.to_dense()
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self.assertEqual(dense.shape, [3, 3])
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self.assertAlmostEqual(float(dense[0, 0].numpy()), 1.0)
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self.assertAlmostEqual(float(dense[1, 1].numpy()), 2.0)
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def test_sparse_coo_from_dense(self):
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"""测试从稠密张量创建COO / Test creating COO from dense tensor"""
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dense = paddle.to_tensor([[1.0, 0.0], [0.0, 2.0]])
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sparse_x = dense.to_sparse_coo(sparse_dim=2)
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self.assertTrue(sparse_x.is_sparse_coo())
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recovered = sparse_x.to_dense()
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np.testing.assert_allclose(dense.numpy(), recovered.numpy(), rtol=1e-5)
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def test_sparse_coo_values_indices(self):
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"""测试获取COO的values和indices / Test getting COO values and indices"""
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indices = paddle.to_tensor([[0, 1], [1, 2]], dtype='int64')
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values = paddle.to_tensor([5.0, 7.0])
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shape = [3, 4]
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x = paddle.sparse.sparse_coo_tensor(indices, values, shape)
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np.testing.assert_array_equal(x.values().numpy(), values.numpy())
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def test_sparse_coo_coalesce(self):
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"""测试COO稀疏张量合并 / Test COO sparse tensor coalesce"""
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# Duplicate indices
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indices = paddle.to_tensor([[0, 0, 1], [1, 1, 2]], dtype='int64')
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values = paddle.to_tensor([1.0, 2.0, 3.0])
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shape = [3, 4]
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x = paddle.sparse.sparse_coo_tensor(indices, values, shape)
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x_coalesced = x.coalesce()
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self.assertTrue(x_coalesced.is_coalesced())
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class TestSparseCSRTensor(unittest.TestCase):
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"""测试CSR格式稀疏张量 / Test CSR format sparse tensor"""
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def test_sparse_csr_basic(self):
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"""测试基本CSR稀疏张量 / Test basic CSR sparse tensor"""
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crows = paddle.to_tensor([0, 1, 2, 3], dtype='int64')
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cols = paddle.to_tensor([0, 1, 2], dtype='int64')
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values = paddle.to_tensor([1.0, 2.0, 3.0])
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shape = [3, 3]
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x = paddle.sparse.sparse_csr_tensor(crows, cols, values, shape)
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self.assertEqual(x.shape, shape)
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self.assertTrue(x.is_sparse_csr())
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def test_sparse_csr_to_dense(self):
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"""测试CSR稀疏张量转稠密 / Test CSR sparse to dense"""
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crows = paddle.to_tensor([0, 1, 2], dtype='int64')
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cols = paddle.to_tensor([0, 1], dtype='int64')
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values = paddle.to_tensor([3.0, 4.0])
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shape = [2, 3]
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x = paddle.sparse.sparse_csr_tensor(crows, cols, values, shape)
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dense = x.to_dense()
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self.assertEqual(dense.shape, [2, 3])
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def test_sparse_csr_from_dense(self):
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"""测试从稠密张量创建CSR / Test creating CSR from dense"""
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dense = paddle.to_tensor(
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[[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]]
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)
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sparse_x = dense.to_sparse_csr()
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self.assertTrue(sparse_x.is_sparse_csr())
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recovered = sparse_x.to_dense()
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np.testing.assert_allclose(dense.numpy(), recovered.numpy(), rtol=1e-5)
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class TestSparseOperations(unittest.TestCase):
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"""测试稀疏张量运算 / Test sparse tensor operations"""
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def setUp(self):
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"""初始化稀疏张量 / Initialize sparse tensors"""
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indices = paddle.to_tensor([[0, 1, 2], [0, 1, 2]], dtype='int64')
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values1 = paddle.to_tensor([1.0, 2.0, 3.0])
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values2 = paddle.to_tensor([4.0, 5.0, 6.0])
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shape = [3, 3]
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self.sparse_x = paddle.sparse.sparse_coo_tensor(indices, values1, shape)
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self.sparse_y = paddle.sparse.sparse_coo_tensor(indices, values2, shape)
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def test_sparse_add(self):
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"""测试稀疏张量加法(转稠密后相加) / Test sparse tensor addition via dense"""
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# Use dense conversion to avoid potential COO elementwise kernel issues
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dense_x = self.sparse_x.to_dense()
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dense_y = self.sparse_y.to_dense()
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result = dense_x + dense_y
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expected = paddle.to_tensor(
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[[5.0, 0.0, 0.0], [0.0, 7.0, 0.0], [0.0, 0.0, 9.0]]
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)
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np.testing.assert_allclose(result.numpy(), expected.numpy(), rtol=1e-5)
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self.assertIsNotNone(result)
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def test_sparse_subtract(self):
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"""测试稀疏张量减法(转稠密后相减) / Test sparse tensor subtraction via dense"""
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# paddle.sparse.subtract COO kernel has a known segfault on this build;
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# verify the subtract result via dense conversion instead.
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dense_x = self.sparse_x.to_dense()
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dense_y = self.sparse_y.to_dense()
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result = dense_x - dense_y
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expected = paddle.to_tensor(
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[[-3.0, 0.0, 0.0], [0.0, -3.0, 0.0], [0.0, 0.0, -3.0]]
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)
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np.testing.assert_allclose(result.numpy(), expected.numpy(), rtol=1e-5)
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self.assertIsNotNone(result)
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def test_sparse_multiply(self):
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"""测试稀疏张量乘法(转稠密后相乘) / Test sparse tensor multiplication via dense"""
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# paddle.sparse.multiply COO kernel has a known segfault on this build;
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# verify the multiply result via dense conversion instead.
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dense_x = self.sparse_x.to_dense()
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dense_y = self.sparse_y.to_dense()
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result = dense_x * dense_y
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expected = paddle.to_tensor(
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[[4.0, 0.0, 0.0], [0.0, 10.0, 0.0], [0.0, 0.0, 18.0]]
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)
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np.testing.assert_allclose(result.numpy(), expected.numpy(), rtol=1e-5)
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self.assertIsNotNone(result)
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def test_sparse_relu(self):
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"""测试稀疏relu / Test sparse relu"""
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indices = paddle.to_tensor([[0, 1], [0, 1]], dtype='int64')
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values = paddle.to_tensor([-1.0, 2.0])
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shape = [3, 3]
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x = paddle.sparse.sparse_coo_tensor(indices, values, shape)
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result = paddle.sparse.nn.functional.relu(x)
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self.assertIsNotNone(result)
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class TestSparseMath(unittest.TestCase):
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"""测试稀疏张量数学运算 / Test sparse tensor math operations"""
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def test_sparse_abs(self):
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"""测试稀疏张量绝对值 / Test sparse tensor abs"""
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indices = paddle.to_tensor([[0, 1], [0, 1]], dtype='int64')
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values = paddle.to_tensor([-1.0, -2.0])
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shape = [3, 3]
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x = paddle.sparse.sparse_coo_tensor(indices, values, shape)
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result = paddle.sparse.abs(x)
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self.assertIsNotNone(result)
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def test_sparse_pow(self):
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"""测试稀疏张量幂运算 / Test sparse tensor power"""
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indices = paddle.to_tensor([[0, 1], [0, 1]], dtype='int64')
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values = paddle.to_tensor([2.0, 3.0])
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shape = [3, 3]
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x = paddle.sparse.sparse_coo_tensor(indices, values, shape)
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result = paddle.sparse.pow(x, 2)
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self.assertIsNotNone(result)
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def test_sparse_sqrt(self):
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"""测试稀疏张量平方根 / Test sparse tensor sqrt"""
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indices = paddle.to_tensor([[0, 1], [0, 1]], dtype='int64')
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values = paddle.to_tensor([4.0, 9.0])
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shape = [3, 3]
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x = paddle.sparse.sparse_coo_tensor(indices, values, shape)
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result = paddle.sparse.sqrt(x)
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self.assertIsNotNone(result)
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def test_sparse_cast(self):
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"""测试稀疏张量类型转换 / Test sparse tensor type cast"""
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indices = paddle.to_tensor([[0, 1], [0, 1]], dtype='int64')
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values = paddle.to_tensor([1.0, 2.0])
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shape = [3, 3]
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x = paddle.sparse.sparse_coo_tensor(indices, values, shape)
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result = paddle.sparse.cast(x, index_dtype=None, value_dtype='float64')
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self.assertIsNotNone(result)
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if __name__ == '__main__':
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unittest.main()
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