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https://github.com/facebookresearch/fairseq.git
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4f881a760e
Summary: Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/1658 Reviewed By: jxmsML Differential Revision: D26701840 Pulled By: sshleifer fbshipit-source-id: 90d631c3cd775ab847366fe7a05136c29d90cd63
93 lines
3.5 KiB
Python
93 lines
3.5 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import unittest
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import tests.utils as test_utils
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import torch
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from fairseq.data import TokenBlockDataset
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class TestTokenBlockDataset(unittest.TestCase):
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def _build_dataset(self, data, **kwargs):
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sizes = [len(x) for x in data]
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underlying_ds = test_utils.TestDataset(data)
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return TokenBlockDataset(underlying_ds, sizes, **kwargs)
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def test_eos_break_mode(self):
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data = [
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torch.tensor([5, 4, 3, 2, 1], dtype=torch.long),
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torch.tensor([1], dtype=torch.long),
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torch.tensor([8, 7, 6, 1], dtype=torch.long),
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]
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ds = self._build_dataset(data, block_size=None, pad=0, eos=1, break_mode="eos")
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self.assertEqual(ds[0].tolist(), [5, 4, 3, 2, 1])
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self.assertEqual(ds[1].tolist(), [1])
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self.assertEqual(ds[2].tolist(), [8, 7, 6, 1])
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data = [
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torch.tensor([5, 4, 3, 2, 1], dtype=torch.long),
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torch.tensor([8, 7, 6, 1], dtype=torch.long),
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torch.tensor([1], dtype=torch.long),
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]
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ds = self._build_dataset(data, block_size=None, pad=0, eos=1, break_mode="eos")
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self.assertEqual(ds[0].tolist(), [5, 4, 3, 2, 1])
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self.assertEqual(ds[1].tolist(), [8, 7, 6, 1])
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self.assertEqual(ds[2].tolist(), [1])
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def test_block_break_mode(self):
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data = [
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torch.tensor([5, 4, 3, 2, 1], dtype=torch.long),
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torch.tensor([8, 7, 6, 1], dtype=torch.long),
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torch.tensor([9, 1], dtype=torch.long),
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]
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ds = self._build_dataset(data, block_size=3, pad=0, eos=1, break_mode="none")
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self.assertEqual(ds[0].tolist(), [5, 4, 3])
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self.assertEqual(ds[1].tolist(), [2, 1, 8])
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self.assertEqual(ds[2].tolist(), [7, 6, 1])
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self.assertEqual(ds[3].tolist(), [9, 1])
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def test_complete_break_mode(self):
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data = [
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torch.tensor([5, 4, 3, 2, 1], dtype=torch.long),
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torch.tensor([8, 7, 6, 1], dtype=torch.long),
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torch.tensor([9, 1], dtype=torch.long),
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]
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ds = self._build_dataset(
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data, block_size=6, pad=0, eos=1, break_mode="complete"
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)
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self.assertEqual(ds[0].tolist(), [5, 4, 3, 2, 1])
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self.assertEqual(ds[1].tolist(), [8, 7, 6, 1, 9, 1])
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data = [
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torch.tensor([4, 3, 2, 1], dtype=torch.long),
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torch.tensor([5, 1], dtype=torch.long),
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torch.tensor([1], dtype=torch.long),
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torch.tensor([6, 1], dtype=torch.long),
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]
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ds = self._build_dataset(
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data, block_size=3, pad=0, eos=1, break_mode="complete"
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)
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self.assertEqual(ds[0].tolist(), [4, 3, 2, 1])
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self.assertEqual(ds[1].tolist(), [5, 1, 1])
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self.assertEqual(ds[2].tolist(), [6, 1])
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def test_4billion_tokens(self):
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"""Regression test for numpy type promotion issue https://github.com/numpy/numpy/issues/5745"""
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data = [torch.tensor(list(range(10000)), dtype=torch.long)] * 430000
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ds = self._build_dataset(
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data, block_size=6, pad=0, eos=1, break_mode="complete"
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)
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ds[-1] # __getitem__ works
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start, end = ds.slice_indices[-1]
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assert end > 4294967295 # data must be sufficiently large to overflow uint32
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assert not isinstance(
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end + 1, float
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) # this would also raise, since np.uint64(1) + 1 => 2.0
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if __name__ == "__main__":
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unittest.main()
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