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Refactor fairseq/test_noising with a word shuffle helper function (#340)
Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/340 This allows us to do a lot less copy paste when adding new word shuffle function tests Reviewed By: xianxl Differential Revision: D12810304 fbshipit-source-id: a56b5df093d17be2b73837897c526978cab92b70
This commit is contained in:
Liezl Puzon
Facebook Github Bot
parent
0b05467dd8
commit
b1521f962e
@ -6,6 +6,7 @@
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# can be found in the PATENTS file in the same directory.
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import unittest
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from typing import Dict, List
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import tests.utils as test_utils
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import torch
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@ -116,82 +117,145 @@ class TestDataNoising(unittest.TestCase):
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)
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self.assert_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos())
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def assert_no_shuffle_with_0_distance(self, x, x_noised, x_len, l_noised):
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"""
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Applies word shuffle with 0 max_shuffle_distance and asserts that no
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shuffling happened
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"""
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for i in range(len(x_len)):
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for j in range(x_len[i]):
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self.assertEqual(x[j][i], x_noised[j][i])
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self.assertEqual(x_len[0], l_noised[0])
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def generate_unchanged_shuffle_map(self, length):
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return {i: i for i in range(length)}
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def assert_word_shuffle_with_distance_3(self, x, x_noised, x_len, l_noised):
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def assert_word_shuffle_matches_expected(
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self,
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x,
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x_len,
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max_shuffle_distance: int,
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vocab: Dictionary,
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expected_shufle_maps: List[Dict[int, int]],
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expect_eos_at_end: bool,
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):
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"""
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Applies word shuffle with max_shuffle_distance = 3 and asserts that the
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shuffling result is as expected. If test data changes, update this func
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"""
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# Expect the second example has the last three tokens shuffled
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# 6, 7, 8, 9 => 6, 8, 9, 7, where (8, 9) is a word
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for i in range(x_len[0]):
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self.assertEqual(x[i][0], x_noised[i][0])
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shuffle_map = {0: 0, 1: 3, 2: 1, 3: 2}
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for k, v in shuffle_map.items():
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self.assertEqual(x[k][1], x_noised[v][1])
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self.assertEqual(x_len[0], l_noised[0])
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self.assertEqual(x_len[1], l_noised[1])
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This verifies that with a given x, x_len, max_shuffle_distance, and
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vocab, we get the expected shuffle result.
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def assert_nonbpe_shuffle_with_distance_3(self, x, x_noised, x_len, l_noised):
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Args:
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x: Tensor of shape (T x B) = (sequence_length, batch_size)
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x_len: Tensor of length B = batch_size
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max_shuffle_distance: arg to pass to noising
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expected_shuffle_maps: List[mapping] where mapping is a
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Dict[old_index, new_index], mapping x's elements from their
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old positions in x to their new positions in x.
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expect_eos_at_end: if True, check the output to make sure there is
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an EOS at the end.
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"""
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Applies word shuffle with max_shuffle_distance = 3 and asserts that the
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shuffling result is as expected. If test data changes, update this func
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"""
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# Expect the first example has the last two tokens shuffled
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# Expect the secon example has the second and third tokens shuffled
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shuffle_map = {0: 0, 1: 1, 2: 3, 3: 2}
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for k, v in shuffle_map.items():
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self.assertEqual(x[k][0], x_noised[v][0])
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shuffle_map = {0: 0, 1: 2, 2: 1, 3: 3, 4: 4}
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for k, v in shuffle_map.items():
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self.assertEqual(x[k][1], x_noised[v][1])
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self.assertEqual(x_len[0], l_noised[0])
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self.assertEqual(x_len[1], l_noised[1])
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with data_utils.numpy_seed(1234):
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word_shuffle = noising.WordShuffle(vocab)
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x_noised, l_noised = word_shuffle.noising(
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x, x_len, max_shuffle_distance=max_shuffle_distance
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)
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# For every example, we have a different expected shuffle map. We check
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# that each example is shuffled as expected according to each
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# corresponding shuffle map.
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for i in range(len(expected_shufle_maps)):
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shuffle_map = expected_shufle_maps[i]
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for k, v in shuffle_map.items():
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self.assertEqual(x[k][i], x_noised[v][i])
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# Shuffling should not affect the length of each example
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for pre_shuffle_length, post_shuffle_length in zip(x_len, l_noised):
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self.assertEqual(pre_shuffle_length, post_shuffle_length)
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if expect_eos_at_end:
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self.assert_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos())
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def test_word_shuffle_with_eos(self):
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vocab, x, x_len = self._get_test_data(append_eos=True)
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with data_utils.numpy_seed(1234):
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word_shuffle = noising.WordShuffle(vocab)
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# Assert word shuffle with max shuffle distance 0 causes input to be
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# unchanged
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self.assert_word_shuffle_matches_expected(
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x=x,
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x_len=x_len,
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max_shuffle_distance=0,
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vocab=vocab,
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expected_shufle_maps=[
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self.generate_unchanged_shuffle_map(example_len)
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for example_len in x_len
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],
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expect_eos_at_end=True,
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)
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x_noised, l_noised = word_shuffle.noising(x, x_len, 0)
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self.assert_no_shuffle_with_0_distance(
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x=x, x_noised=x_noised, x_len=x_len, l_noised=l_noised
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)
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self.assert_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos())
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x_noised, l_noised = word_shuffle.noising(x, x_len, 3)
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self.assert_word_shuffle_with_distance_3(
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x=x, x_noised=x_noised, x_len=x_len, l_noised=l_noised
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)
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self.assert_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos())
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# Assert word shuffle with max shuffle distance 3 matches our expected
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# shuffle order
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self.assert_word_shuffle_matches_expected(
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x=x,
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x_len=x_len,
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vocab=vocab,
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max_shuffle_distance=3,
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expected_shufle_maps=[
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self.generate_unchanged_shuffle_map(x_len[0]),
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{0: 0, 1: 3, 2: 1, 3: 2},
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],
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expect_eos_at_end=True,
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)
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def test_word_shuffle_with_eos_nonbpe(self):
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vocab, x, x_len = self._get_test_data(append_eos=True, bpe=False)
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with data_utils.numpy_seed(1234):
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word_shuffle = noising.WordShuffle(vocab, bpe_cont_marker=None)
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# Assert word shuffle with max shuffle distance 0 causes input to be
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# unchanged
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self.assert_word_shuffle_matches_expected(
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x=x,
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x_len=x_len,
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max_shuffle_distance=0,
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vocab=vocab,
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expected_shufle_maps=[
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self.generate_unchanged_shuffle_map(example_len)
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for example_len in x_len
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],
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expect_eos_at_end=True,
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)
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x_noised, l_noised = word_shuffle.noising(x, x_len, 0)
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self.assert_no_shuffle_with_0_distance(
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x=x, x_noised=x_noised, x_len=x_len, l_noised=l_noised
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)
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self.assert_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos())
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# Assert word shuffle with max shuffle distance 3 matches our expected
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# shuffle order
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self.assert_word_shuffle_matches_expected(
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x=x,
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x_len=x_len,
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vocab=vocab,
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max_shuffle_distance=3,
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expected_shufle_maps=[
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{0: 0, 1: 1, 2: 3, 3: 2},
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{0: 0, 1: 2, 2: 1, 3: 3, 4: 4},
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],
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expect_eos_at_end=True,
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)
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x_noised, l_noised = word_shuffle.noising(x, x_len, 3)
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self.assert_nonbpe_shuffle_with_distance_3(
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x=x, x_noised=x_noised, x_len=x_len, l_noised=l_noised
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)
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self.assert_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos())
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def test_word_shuffle_without_eos(self):
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"""Same result as word shuffle with eos except no EOS at end"""
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vocab, x, x_len = self._get_test_data(append_eos=False)
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# Assert word shuffle with max shuffle distance 0 causes input to be
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# unchanged
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self.assert_word_shuffle_matches_expected(
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x=x,
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x_len=x_len,
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max_shuffle_distance=0,
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vocab=vocab,
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expected_shufle_maps=[
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self.generate_unchanged_shuffle_map(example_len)
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for example_len in x_len
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],
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expect_eos_at_end=False,
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)
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# Assert word shuffle with max shuffle distance 3 matches our expected
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# shuffle order
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self.assert_word_shuffle_matches_expected(
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x=x,
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x_len=x_len,
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vocab=vocab,
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max_shuffle_distance=3,
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expected_shufle_maps=[
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self.generate_unchanged_shuffle_map(x_len[0]),
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{0: 0, 1: 3, 2: 1, 3: 2},
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],
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expect_eos_at_end=False,
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)
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def assert_no_eos_at_end(self, x, x_len, eos):
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"""Asserts that the last token of each sentence in x is not EOS """
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@ -228,25 +292,6 @@ class TestDataNoising(unittest.TestCase):
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)
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self.assert_no_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos())
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def test_word_shuffle_without_eos(self):
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"""Same result as word shuffle with eos except no EOS at end"""
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vocab, x, x_len = self._get_test_data(append_eos=False)
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with data_utils.numpy_seed(1234):
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word_shuffle = noising.WordShuffle(vocab)
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x_noised, l_noised = word_shuffle.noising(x, x_len, 0)
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self.assert_no_shuffle_with_0_distance(
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x=x, x_noised=x_noised, x_len=x_len, l_noised=l_noised
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)
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self.assert_no_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos())
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x_noised, l_noised = word_shuffle.noising(x, x_len, 3)
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self.assert_word_shuffle_with_distance_3(
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x=x, x_noised=x_noised, x_len=x_len, l_noised=l_noised
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)
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self.assert_no_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos())
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def _get_noising_dataset_batch(
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self, src_tokens_no_pad, src_dict, use_append_eos_dataset=False
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):
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