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[WIP] Safe bpe dropout for LM + joiner disjoin dropout #2009

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[WIP] Safe bpe dropout for LM + joiner disjoin dropout #2009

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799783e
implement safe bpe dropout for LM + joiner disjoin dropout
Feb 17, 2021
4965928
fix missing model_task in opts
Feb 17, 2021
14cdcf4
disable sp sampling
Feb 17, 2021
98f8189
fix dropped element in JoinerDropout
Feb 17, 2021
003fe4a
fix joiner dropout
Feb 19, 2021
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80 changes: 80 additions & 0 deletions onmt/transforms/misc.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,8 @@
from onmt.utils.logging import logger
from onmt.transforms import register_transform
from .transform import Transform
import random
from onmt.constants import ModelTask, SubwordMarker


@register_transform(name='filtertoolong')
Expand Down Expand Up @@ -41,6 +43,84 @@ def _repr_args(self):
)


@register_transform(name="joiner_dropout")
class JoinerDropoutTransform(Transform):
"""Disjoin joiner with probability dropout"""

def __init__(self, opts):
super().__init__(opts)

@classmethod
def add_options(cls, parser):
"""Avalilable options relate to this Transform."""
group = parser.add_argument_group("Transform/JoinerDropout")
group.add(
"--src_joiner_dropout",
"-src_joiner_dropout",
type=float,
default=0.0,
help="Source dropout probability.",
)
group.add(
"--tgt_joiner_dropout",
"-tgt_joiner_dropout",
type=float,
default=0.0,
help="Target dropout probability.",
)

def _parse_opts(self):
self.src_joiner_dropout = self.opts.src_joiner_dropout
self.tgt_joiner_dropout = self.opts.tgt_joiner_dropout

def dropout_separate_joiner(self, seq, side="src"):
out_seq = []
dropout = (
self.src_joiner_dropout
if side == "src"
else self.tgt_joiner_dropout
)
for elem in seq:
if elem == SubwordMarker.JOINER:
continue
if elem.startswith(SubwordMarker.JOINER):
if random.random() < dropout:
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@funboarder13920 funboarder13920 Feb 17, 2021 •
edited
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Not sure about the necessity of doing right and left token sides
It might create a difficulty to retrieve the initial token when detokenizing.
I can use a special token do distinguish left and right or remove the left side disjoin which doesn't occur much (mainly in punctuation)

out_seq.append(SubwordMarker.JOINER)
elem = elem[1:]
if len(elem) > 1 and elem.endswith(SubwordMarker.JOINER):
if random.random() < dropout:
out_seq.append(elem[:-1])
out_seq.append(SubwordMarker.JOINER)
else:
out_seq.append(elem)
else:
out_seq.append(elem)
return out_seq

def apply(self, example, is_train=False, stats=None, **kwargs):
"""Return None if too long else return as is."""
if not is_train:
return example
else:
src_out = self.dropout_separate_joiner(example["src"], "src")
example["src"] = src_out
if self.opts.model_task == ModelTask.LANGUAGE_MODEL:
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@funboarder13920 funboarder13920 Feb 17, 2021 •
edited
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Currently this won't work on build_vocab, model_task is a model parameter and not a dynamic corpus parameter.
Same issue in tokenizers.py

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@francoishernandez francoishernandez Feb 17, 2021 •
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is_train should be false anyways in build_vocab, no?
EDIT: Nevermind, I mixed up with build_vocab_only.

example["tgt"] = src_out
else:
tgt_out = self.dropout_separate_joiner(example["tgt"], "tgt")
example["tgt"] = tgt_out
return example

def _repr_args(self):
"""Return str represent key arguments for class."""
return "{}={}, {}={}".format(
"src_joiner_dropout",
self.src_joiner_dropout,
"tgt_joiner_dropout",
self.tgt_joiner_dropout,
)


@register_transform(name='prefix')
class PrefixTransform(Transform):
"""Add Prefix to src (& tgt) sentence."""
Expand Down
69 changes: 50 additions & 19 deletions onmt/transforms/tokenize.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@
from onmt.utils.logging import logger
from onmt.transforms import register_transform
from .transform import Transform
from onmt.constants import ModelTask


class TokenizerTransform(Transform):
Expand Down Expand Up @@ -90,6 +91,7 @@ def _parse_opts(self):
self.tgt_subword_vocab = self.opts.tgt_subword_vocab
self.src_vocab_threshold = self.opts.src_vocab_threshold
self.tgt_vocab_threshold = self.opts.tgt_vocab_threshold
self.model_task = self.opts.model_task

def _repr_args(self):
"""Return str represent key arguments for TokenizerTransform."""
Expand Down Expand Up @@ -169,7 +171,10 @@ def _tokenize(self, tokens, side='src', is_train=False):
def apply(self, example, is_train=False, stats=None, **kwargs):
"""Apply sentencepiece subword encode to src & tgt."""
src_out = self._tokenize(example['src'], 'src', is_train)
tgt_out = self._tokenize(example['tgt'], 'tgt', is_train)
if self.model_task == ModelTask.LANGUAGE_MODEL:
tgt_out = src_out
else:
tgt_out = self._tokenize(example['tgt'], 'tgt', is_train)
if stats is not None:
n_words = len(example['src']) + len(example['tgt'])
n_subwords = len(src_out) + len(tgt_out)
Expand Down Expand Up @@ -243,7 +248,10 @@ def _tokenize(self, tokens, side='src', is_train=False):
def apply(self, example, is_train=False, stats=None, **kwargs):
"""Apply bpe subword encode to src & tgt."""
src_out = self._tokenize(example['src'], 'src', is_train)
tgt_out = self._tokenize(example['tgt'], 'tgt', is_train)
if self.model_task == ModelTask.LANGUAGE_MODEL:
tgt_out = src_out
else:
tgt_out = self._tokenize(example['tgt'], 'tgt', is_train)
if stats is not None:
n_words = len(example['src']) + len(example['tgt'])
n_subwords = len(src_out) + len(tgt_out)
Expand Down Expand Up @@ -327,7 +335,7 @@ def get_specials(cls, opts):
tgt_specials.update(_case_specials)
return (set(), set())

def _get_subword_kwargs(self, side='src'):
def _get_subword_kwargs(self, side='src', is_train=False):
"""Return a dict containing kwargs relate to `side` subwords."""
subword_type = self.tgt_subword_type if side == 'tgt' \
else self.src_subword_type
Expand All @@ -340,7 +348,7 @@ def _get_subword_kwargs(self, side='src'):
kwopts = dict()
if subword_type == 'bpe':
kwopts['bpe_model_path'] = subword_model
kwopts['bpe_dropout'] = subword_alpha
kwopts['bpe_dropout'] = subword_alpha if is_train else 0
elif subword_type == 'sentencepiece':
kwopts['sp_model_path'] = subword_model
kwopts['sp_nbest_size'] = subword_nbest
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You can directly reassign on subword_alpha & subword_nbest to disable both bpe_dropout/sp sampling.

Expand All @@ -360,42 +368,65 @@ def warm_up(self, vocabs=None):
"""Initialize Tokenizer models."""
super().warm_up(None)
import pyonmttok
src_subword_kwargs = self._get_subword_kwargs(side='src')

src_subword_kwargs = self._get_subword_kwargs(
side="src", is_train=True
)
valid_src_subword_kwargs = self._get_subword_kwargs(
side="src", is_train=False
)
src_tokenizer = pyonmttok.Tokenizer(
**src_subword_kwargs, **self.src_other_kwargs
)
tgt_subword_kwargs = self._get_subword_kwargs(side='tgt')
_diff_vocab = (
src_subword_kwargs.get('vocabulary_path', '') !=
tgt_subword_kwargs.get('vocabulary_path', '') or
src_subword_kwargs.get('vocabulary_threshold', 0) !=
tgt_subword_kwargs.get('vocabulary_threshold', 0))
Comment on lines -368 to -372
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I personally prefer current one which seems more readable

valid_src_tokenizer = pyonmttok.Tokenizer(
**valid_src_subword_kwargs, **self.src_other_kwargs
)
tgt_subword_kwargs = self._get_subword_kwargs(
side="tgt", is_train=True
)
_diff_vocab = src_subword_kwargs.get(
"vocabulary_path", ""
) != tgt_subword_kwargs.get(
"vocabulary_path", ""
) or src_subword_kwargs.get(
"vocabulary_threshold", 0
) != tgt_subword_kwargs.get(
"vocabulary_threshold", 0
)
if self.share_vocab and not _diff_vocab:
self.load_models = {
'src': src_tokenizer,
'tgt': src_tokenizer
"src": {"train": src_tokenizer, "valid": valid_src_tokenizer},
"tgt": {"train": src_tokenizer, "valid": valid_src_tokenizer},
}
else:
tgt_subword_kwargs = self._get_subword_kwargs(side='tgt')
tgt_tokenizer = pyonmttok.Tokenizer(
**tgt_subword_kwargs, **self.tgt_other_kwargs
)
valid_tgt_subword_kwargs = self._get_subword_kwargs(
side="tgt", is_train=False
)
valid_tgt_tokenizer = pyonmttok.Tokenizer(
**valid_tgt_subword_kwargs, **self.tgt_other_kwargs
)
self.load_models = {
'src': src_tokenizer,
'tgt': tgt_tokenizer
"src": {"train": src_tokenizer, "valid": valid_src_tokenizer},
"tgt": {"train": tgt_tokenizer, "valid": valid_tgt_tokenizer},
}

def _tokenize(self, tokens, side='src', is_train=False):
"""Do OpenNMT Tokenizer's tokenize."""
tokenizer = self.load_models[side]
tokenizer = self.load_models[side]['train' if is_train else 'valid']
sentence = ' '.join(tokens)
segmented, _ = tokenizer.tokenize(sentence)
return segmented

def apply(self, example, is_train=False, stats=None, **kwargs):
"""Apply OpenNMT Tokenizer to src & tgt."""
src_out = self._tokenize(example['src'], 'src')
tgt_out = self._tokenize(example['tgt'], 'tgt')
src_out = self._tokenize(example['src'], 'src', is_train)
if self.model_task == ModelTask.LANGUAGE_MODEL:
tgt_out = src_out
else:
tgt_out = self._tokenize(example['tgt'], 'tgt', is_train)
if stats is not None:
n_words = len(example['src']) + len(example['tgt'])
n_subwords = len(src_out) + len(tgt_out)
Expand Down