def assert_word_shuffle_matches_expected(
self,
x,
x_len,
max_shuffle_distance: int,
vocab: Dictionary,
expected_shufle_maps: List[Dict[int, int]],
expect_eos_at_end: bool,
bpe_end_marker=None,
):
"""
This verifies that with a given x, x_len, max_shuffle_distance, and
vocab, we get the expected shuffle result.
Args:
x: Tensor of shape (T x B) = (sequence_length, batch_size)
x_len: Tensor of length B = batch_size
max_shuffle_distance: arg to pass to noising
expected_shuffle_maps: List[mapping] where mapping is a
Dict[old_index, new_index], mapping x's elements from their
old positions in x to their new positions in x.
expect_eos_at_end: if True, check the output to make sure there is
an EOS at the end.
bpe_end_marker: str denoting the BPE end token. If this is not None, we
set the BPE cont token to None in the noising classes.
"""
bpe_cont_marker = None
if bpe_end_marker is None:
bpe_cont_marker = "@@"
with data_utils.numpy_seed(1234):
word_shuffle = noising.WordShuffle(
vocab, bpe_cont_marker=bpe_cont_marker, bpe_end_marker=bpe_end_marker
)
x_noised, l_noised = word_shuffle.noising(
x, x_len, max_shuffle_distance=max_shuffle_distance
)
# For every example, we have a different expected shuffle map. We check
# that each example is shuffled as expected according to each
# corresponding shuffle map.
for i in range(len(expected_shufle_maps)):
shuffle_map = expected_shufle_maps[i]
for k, v in shuffle_map.items():
self.assertEqual(x[k][i], x_noised[v][i])
# Shuffling should not affect the length of each example
for pre_shuffle_length, post_shuffle_length in zip(x_len, l_noised):
self.assertEqual(pre_shuffle_length, post_shuffle_length)
if expect_eos_at_end:
self.assert_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos())
def _random_shuffle(target_tokens, p, max_shuffle_distance):
word_shuffle = noising.WordShuffle(self.tgt_dict)
target_mask = target_tokens.eq(self.tgt_dict.pad())
target_length = target_mask.size(1) - target_mask.long().sum(1)
prev_target_tokens, _ = word_shuffle.noising(
target_tokens.t().cpu(), target_length.cpu(),
max_shuffle_distance)
prev_target_tokens = prev_target_tokens.to(
target_tokens.device).t()
masks = (target_tokens.clone().sum(
dim=1, keepdim=True).float().uniform_(0, 1) < p)
prev_target_tokens = masks * prev_target_tokens + (
~masks) * target_tokens
return prev_target_tokens
def test_word_shuffle_with_eos_nonbpe(self):
vocab, x, x_len = self._get_test_data(append_eos=True, bpe=False)
with data_utils.numpy_seed(1234):
word_shuffle = noising.WordShuffle(vocab, bpe_cont_marker=None)
x_noised, l_noised = word_shuffle.noising(x, x_len, 0)
self.assert_no_shuffle_with_0_distance(x=x,
x_noised=x_noised,
x_len=x_len,
l_noised=l_noised)
self.assert_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos())
x_noised, l_noised = word_shuffle.noising(x, x_len, 3)
self.assert_nonbpe_shuffle_with_distance_3(x=x,
x_noised=x_noised,
x_len=x_len,
l_noised=l_noised)
self.assert_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos())
def test_word_shuffle(self):
vocab, x, x_len = self._get_test_data()
with data_utils.numpy_seed(1234):
word_shuffle = noising.WordShuffle(vocab)
x_noised, l_noised = word_shuffle.noising(x, x_len, 0)
for i in range(len(x_len)):
for j in range(x_len[i]):
self.assertEqual(x[j][i], x_noised[j][i])
self.assertEqual(x_len[0], l_noised[0])
x_noised, l_noised = word_shuffle.noising(x, x_len, 3)
# Expect the second example has the last three tokens shuffled
# 6, 7, 8, 9 => 6, 8, 9, 7, where (8, 9) is a word
for i in range(x_len[0]):
self.assertEqual(x[i][0], x_noised[i][0])
shuffle_map = {0: 0, 1: 3, 2: 1, 3: 2}
for k, v in shuffle_map.items():
self.assertEqual(x[k][1], x_noised[v][1])
self.assertEqual(x_len[0], l_noised[0])
self.assertEqual(x_len[1], l_noised[1])
def test_word_shuffle_without_eos(self):
""" Same result as word shuffle with eos except no EOS at end """
vocab, x, x_len = self._get_test_data(append_eos=False)
with data_utils.numpy_seed(1234):
word_shuffle = noising.WordShuffle(vocab)
x_noised, l_noised = word_shuffle.noising(x, x_len, 0)
self.assert_no_shuffle_with_0_distance(x=x,
x_noised=x_noised,
x_len=x_len,
l_noised=l_noised)
self.assert_no_eos_at_end(x=x_noised,
x_len=l_noised,
eos=vocab.eos())
x_noised, l_noised = word_shuffle.noising(x, x_len, 3)
self.assert_word_shuffle_with_distance_3(x=x,
x_noised=x_noised,
x_len=x_len,
l_noised=l_noised)
self.assert_no_eos_at_end(x=x_noised,
x_len=l_noised,
eos=vocab.eos())
