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155 lines
6.9 KiB
155 lines
6.9 KiB
# Copyright (c) 2018 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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from __future__ import print_function
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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from paddle.fluid.lod_tensor import create_lod_tensor, create_random_int_lodtensor
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import numpy as np
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import unittest
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class TestLoDTensor(unittest.TestCase):
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def test_pybind_recursive_seq_lens(self):
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tensor = fluid.LoDTensor()
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recursive_seq_lens = []
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tensor.set_recursive_sequence_lengths(recursive_seq_lens)
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recursive_seq_lens = [[], [1], [3]]
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self.assertRaises(Exception, tensor.set_recursive_sequence_lengths,
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recursive_seq_lens)
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recursive_seq_lens = [[0], [2], [3]]
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self.assertRaises(Exception, tensor.set_recursive_sequence_lengths,
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recursive_seq_lens)
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recursive_seq_lens = [[1, 2, 3]]
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tensor.set_recursive_sequence_lengths(recursive_seq_lens)
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self.assertEqual(tensor.recursive_sequence_lengths(),
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recursive_seq_lens)
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tensor.set(np.random.random([6, 1]), fluid.CPUPlace())
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self.assertTrue(tensor.has_valid_recursive_sequence_lengths())
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tensor.set(np.random.random([9, 1]), fluid.CPUPlace())
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self.assertFalse(tensor.has_valid_recursive_sequence_lengths())
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# Each level's sum should be equal to the number of items in the next level
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# Moreover, last level's sum should be equal to the tensor height
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recursive_seq_lens = [[2, 3], [1, 3, 1, 2, 2]]
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tensor.set_recursive_sequence_lengths(recursive_seq_lens)
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self.assertEqual(tensor.recursive_sequence_lengths(),
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recursive_seq_lens)
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tensor.set(np.random.random([8, 1]), fluid.CPUPlace())
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self.assertFalse(tensor.has_valid_recursive_sequence_lengths())
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recursive_seq_lens = [[2, 3], [1, 3, 1, 2, 1]]
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tensor.set_recursive_sequence_lengths(recursive_seq_lens)
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self.assertTrue(tensor.has_valid_recursive_sequence_lengths())
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tensor.set(np.random.random([9, 1]), fluid.CPUPlace())
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self.assertFalse(tensor.has_valid_recursive_sequence_lengths())
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def test_create_lod_tensor(self):
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# Create LoDTensor from a list
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data = [[np.int64(1), np.int64(2), np.int64(3)],
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[np.int64(3), np.int64(4)]]
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wrong_recursive_seq_lens = [[2, 2]]
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correct_recursive_seq_lens = [[3, 2]]
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self.assertRaises(AssertionError, create_lod_tensor, data,
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wrong_recursive_seq_lens, fluid.CPUPlace())
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tensor = create_lod_tensor(data, correct_recursive_seq_lens,
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fluid.CPUPlace())
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self.assertEqual(tensor.recursive_sequence_lengths(),
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correct_recursive_seq_lens)
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self.assertEqual(tensor._dtype(), core.VarDesc.VarType.INT64)
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self.assertEqual(tensor.shape(), [5, 1])
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self.assertTrue(
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np.array_equal(
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np.array(tensor),
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np.array([1, 2, 3, 3, 4]).reshape(tensor.shape()).astype(
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'int64')))
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# Create LoDTensor from numpy array
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data = np.random.random([10, 1]).astype('float64')
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recursive_seq_lens = [[2, 1], [3, 3, 4]]
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tensor = create_lod_tensor(data, recursive_seq_lens, fluid.CPUPlace())
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self.assertEqual(tensor.recursive_sequence_lengths(),
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recursive_seq_lens)
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self.assertEqual(tensor._dtype(), core.VarDesc.VarType.FP64)
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self.assertEqual(tensor.shape(), [10, 1])
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self.assertTrue(np.array_equal(np.array(tensor), data))
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# Create LoDTensor from another LoDTensor, they are differnt instances
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new_recursive_seq_lens = [[2, 2, 1], [1, 2, 2, 3, 2]]
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new_tensor = create_lod_tensor(tensor, new_recursive_seq_lens,
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fluid.CPUPlace())
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self.assertEqual(tensor.recursive_sequence_lengths(),
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recursive_seq_lens)
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self.assertEqual(new_tensor.recursive_sequence_lengths(),
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new_recursive_seq_lens)
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def test_create_random_int_lodtensor(self):
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# The shape of a word, commonly used in speech and NLP problem, is [1]
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shape = [1]
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recursive_seq_lens = [[2, 3, 5]]
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dict_size = 10000
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low = 0
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high = dict_size - 1
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tensor = create_random_int_lodtensor(recursive_seq_lens, shape,
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fluid.CPUPlace(), low, high)
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self.assertEqual(tensor.recursive_sequence_lengths(),
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recursive_seq_lens)
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self.assertEqual(tensor.shape(), [10, 1])
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def test_print_lodtensor(self):
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shape = [1]
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recursive_seq_lens = [[2, 3, 5]]
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dict_size = 100
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low = 0
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high = dict_size - 1
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tensor = create_random_int_lodtensor(recursive_seq_lens, shape,
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fluid.CPUPlace(), low, high)
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print(tensor)
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self.assertTrue(isinstance(str(tensor), str))
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if core.is_compiled_with_cuda():
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gtensor = create_random_int_lodtensor(recursive_seq_lens, shape,
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fluid.CUDAPlace(0), low, high)
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print(gtensor)
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self.assertTrue(isinstance(str(gtensor), str))
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def test_dlpack_support(self):
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tensor = fluid.create_lod_tensor(
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np.array([[1], [2], [3], [4]]).astype('int'), [[1, 3]],
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fluid.CPUPlace())
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dltensor = tensor._to_dlpack()
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tensor_from_dlpack = fluid.core.from_dlpack(dltensor)
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self.assertTrue(isinstance(tensor_from_dlpack, fluid.core.Tensor))
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self.assertTrue(
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np.array_equal(
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np.array(tensor_from_dlpack),
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np.array([[1], [2], [3], [4]]).astype('int')))
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# when build with cuda
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if core.is_compiled_with_cuda():
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gtensor = fluid.create_lod_tensor(
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np.array([[1], [2], [3], [4]]).astype('int'), [[1, 3]],
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fluid.CUDAPlace(0))
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gdltensor = gtensor._to_dlpack()
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gtensor_from_dlpack = fluid.core.from_dlpack(gdltensor)
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self.assertTrue(isinstance(gtensor_from_dlpack, fluid.core.Tensor))
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self.assertTrue(
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np.array_equal(
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np.array(gtensor_from_dlpack),
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np.array([[1], [2], [3], [4]]).astype('int')))
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if __name__ == '__main__':
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unittest.main()
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