wehub-resource-sync
135 lines
4.4 KiB
Python
135 lines
4.4 KiB
Python
# Copyright (c) 2023 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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import random
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import unittest
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import numpy as np
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from op_test import is_custom_device
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import paddle
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class TestSparsePcaLowrankAPI(unittest.TestCase):
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def transpose(self, x):
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shape = x.shape
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perm = list(range(0, len(shape)))
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perm = [*perm[:-2], perm[-1], perm[-2]]
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return paddle.transpose(x, perm)
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def random_sparse_matrix(self, rows, columns, density=0.01, **kwargs):
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dtype = kwargs.get('dtype', paddle.float64)
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nonzero_elements = max(
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min(rows, columns), int(rows * columns * density)
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)
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row_indices = [i % rows for i in range(nonzero_elements)]
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column_indices = [i % columns for i in range(nonzero_elements)]
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random.shuffle(column_indices)
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indices = [row_indices, column_indices]
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values = paddle.randn((nonzero_elements,), dtype=dtype)
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values *= paddle.to_tensor(
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[-(float(i - j) ** 2) for i, j in zip(*indices)], dtype=dtype
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).exp()
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indices_tensor = paddle.to_tensor(indices)
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x = paddle.sparse.sparse_coo_tensor(
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indices_tensor, values, (rows, columns)
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)
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return paddle.sparse.coalesce(x)
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def run_subtest(self, guess_rank, matrix_size, batches, pca, **options):
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density = options.pop('density', 0.5)
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if isinstance(matrix_size, int):
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rows = columns = matrix_size
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else:
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rows, columns = matrix_size
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a_input = self.random_sparse_matrix(rows, columns, density)
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a = a_input.to_dense()
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u, s, v = pca(a_input, q=guess_rank, **options)
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self.assertEqual(s.shape[-1], guess_rank)
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self.assertEqual(u.shape[-2], rows)
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self.assertEqual(u.shape[-1], guess_rank)
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self.assertEqual(v.shape[-1], guess_rank)
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self.assertEqual(v.shape[-2], columns)
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A1 = u.matmul(paddle.nn.functional.diag_embed(s)).matmul(
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self.transpose(v)
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)
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ones_m1 = paddle.ones((*batches, rows, 1), dtype=a.dtype)
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c = a.sum(axis=-2) / rows
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c = c.reshape((*batches, 1, columns))
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A2 = a - ones_m1.matmul(c)
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np.testing.assert_allclose(A1.numpy(), A2.numpy(), atol=1e-5)
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@unittest.skipIf(
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not (paddle.is_compiled_with_cuda() or is_custom_device())
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or paddle.is_compiled_with_rocm(),
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"only support cuda",
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)
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def test_sparse(self):
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pca_lowrank = paddle.sparse.pca_lowrank
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for guess_rank, size in [
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(4, (17, 4)),
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(4, (4, 17)),
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(16, (17, 17)),
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(21, (100, 40)),
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]:
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for density in [0.005, 0.01]:
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self.run_subtest(
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guess_rank, size, (), pca_lowrank, density=density
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)
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def test_errors(self):
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pca_lowrank = paddle.sparse.pca_lowrank
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x = np.random.randn(5, 5).astype('float64')
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dense_x = paddle.to_tensor(x)
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sparse_x = dense_x.to_sparse_coo(len(x.shape))
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def test_x_not_tensor():
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U, S, V = pca_lowrank(x)
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self.assertRaises(ValueError, test_x_not_tensor)
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def test_x_not_sparse():
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U, S, V = pca_lowrank(sparse_x.to_dense())
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self.assertRaises(ValueError, test_x_not_sparse)
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def test_q_range():
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q = -1
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U, S, V = pca_lowrank(sparse_x, q)
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self.assertRaises(ValueError, test_q_range)
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def test_niter_range():
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n = -1
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U, S, V = pca_lowrank(sparse_x, niter=n)
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self.assertRaises(ValueError, test_niter_range)
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def test_x_wrong_shape():
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x = np.random.randn(5, 5, 5).astype('float64')
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dense_x = paddle.to_tensor(x)
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sparse_x = dense_x.to_sparse_coo(len(x.shape))
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U, S, V = pca_lowrank(sparse_x)
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self.assertRaises(ValueError, test_x_wrong_shape)
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if __name__ == "__main__":
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unittest.main()
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