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| # Copyright (c) Facebook, Inc. and its affiliates. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import unittest | |
| from collections import OrderedDict | |
| import numpy as np | |
| import torch | |
| from fairseq.data import LanguagePairDataset, TokenBlockDataset | |
| from fairseq.data.multi_corpus_sampled_dataset import MultiCorpusSampledDataset | |
| from tests.test_train import mock_dict | |
| class TestMultiCorpusSampledDataset(unittest.TestCase): | |
| def setUp(self): | |
| d = mock_dict() | |
| tokens_1 = torch.LongTensor([1]).view(1, -1) | |
| tokens_ds1 = TokenBlockDataset( | |
| tokens_1, | |
| sizes=[tokens_1.size(-1)], | |
| block_size=1, | |
| pad=0, | |
| eos=1, | |
| include_targets=False, | |
| ) | |
| self.dataset_1 = LanguagePairDataset( | |
| tokens_ds1, tokens_ds1.sizes, d, shuffle=False | |
| ) | |
| tokens_2 = torch.LongTensor([2]).view(1, -1) | |
| tokens_ds2 = TokenBlockDataset( | |
| tokens_2, | |
| sizes=[tokens_2.size(-1)], | |
| block_size=1, | |
| pad=0, | |
| eos=1, | |
| include_targets=False, | |
| ) | |
| self.dataset_2 = LanguagePairDataset( | |
| tokens_ds2, tokens_ds2.sizes, d, shuffle=False | |
| ) | |
| def _test_sample_helper( | |
| self, | |
| expected_sample_from_first_ds_percentage, | |
| num_samples=1000, | |
| sampling_func=None, | |
| ): | |
| # To make sure test is not flaky | |
| np.random.seed(0) | |
| if sampling_func is None: | |
| m = MultiCorpusSampledDataset( | |
| OrderedDict({0: self.dataset_1, 1: self.dataset_2}), | |
| ) | |
| else: | |
| m = MultiCorpusSampledDataset( | |
| OrderedDict({0: self.dataset_1, 1: self.dataset_2}), | |
| sampling_func=sampling_func, | |
| ) | |
| m.ordered_indices() | |
| count_sample_from_first_dataset = 0 | |
| for _ in range(num_samples): | |
| if m.collater([m[0], m[1]])["net_input"]["src_tokens"][0] == 1: | |
| count_sample_from_first_dataset += 1 | |
| sample_from_first_ds_percentage = ( | |
| 1.0 * count_sample_from_first_dataset / num_samples | |
| ) | |
| self.assertLess( | |
| abs( | |
| sample_from_first_ds_percentage | |
| - expected_sample_from_first_ds_percentage | |
| ), | |
| 0.01, | |
| ) | |
| def test_multi_corpus_sampled_dataset_uniform_sample(self): | |
| self._test_sample_helper(expected_sample_from_first_ds_percentage=0.5) | |
| def test_multi_corpus_sampled_dataset_weighted_sample(self): | |
| def naive_weighted_sample(weights): | |
| def f(l): | |
| v = np.random.random() | |
| agg = 0 | |
| for i, weight in enumerate(weights): | |
| agg += weight | |
| if agg > v: | |
| return i | |
| return f | |
| self._test_sample_helper( | |
| expected_sample_from_first_ds_percentage=0.9, | |
| sampling_func=naive_weighted_sample(weights=[0.9, 0.1]), | |
| ) | |