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"""Module that contain shard utils for dynamic data."""
import os
from onmt.utils.logging import logger
from onmt.constants import CorpusName
from onmt.transforms import TransformPipe
from onmt.inputters.dataset_base import _dynamic_dict
from torchtext.data import Dataset as TorchtextDataset, \
Example as TorchtextExample
from collections import Counter, defaultdict
from contextlib import contextmanager
import multiprocessing as mp
@contextmanager
def exfile_open(filename, *args, **kwargs):
"""Extended file opener enables open(filename=None).
This context manager enables open(filename=None) as well as regular file.
filename None will produce endlessly None for each iterate,
while filename with valid path will produce lines as usual.
Args:
filename (str|None): a valid file path or None;
*args: args relate to open file using codecs;
**kwargs: kwargs relate to open file using codecs.
Yields:
`None` repeatly if filename==None,
else yield from file specified in `filename`.
"""
if filename is None:
from itertools import repeat
_file = repeat(None)
else:
import codecs
_file = codecs.open(filename, *args, **kwargs)
yield _file
if filename is not None and _file:
_file.close()
class DatasetAdapter(object):
"""Adapte a buckets of tuples into examples of a torchtext Dataset."""
valid_field_name = (
'src', 'tgt', 'indices', 'src_map', 'src_ex_vocab', 'alignment',
'align')
def __init__(self, fields, is_train):
self.fields_dict = self._valid_fields(fields)
self.is_train = is_train
@classmethod
def _valid_fields(cls, fields):
"""Return valid fields in dict format."""
return {
f_k: f_v for f_k, f_v in fields.items()
if f_k in cls.valid_field_name
}
@staticmethod
def _process(item, is_train):
"""Return valid transformed example from `item`."""
example, transform, cid = item
# this is a hack: appears quicker to apply it here
# than in the ParallelCorpusIterator
maybe_example = transform.apply(
example, is_train=is_train, corpus_name=cid)
if maybe_example is None:
return None
maybe_example['src'] = {"src": ' '.join(maybe_example['src'])}
# Make features part of src as in TextMultiField
# {'src': {'src': ..., 'feat1': ...., 'feat2': ....}}
if 'src_feats' in maybe_example:
for feat_name, feat_value in maybe_example['src_feats'].items():
maybe_example['src'][feat_name] = ' '.join(feat_value)
del maybe_example["src_feats"]
maybe_example['tgt'] = {"tgt": ' '.join(maybe_example['tgt'])}
if 'align' in maybe_example:
maybe_example['align'] = ' '.join(maybe_example['align'])
return maybe_example
def _maybe_add_dynamic_dict(self, example, fields):
"""maybe update `example` with dynamic_dict related fields."""
if 'src_map' in fields and 'alignment' in fields:
example = _dynamic_dict(
example,
fields['src'].base_field,
fields['tgt'].base_field)
return example
def _to_examples(self, bucket, is_train=False):
examples = []
for item in bucket:
maybe_example = self._process(item, is_train=is_train)
if maybe_example is not None:
example = self._maybe_add_dynamic_dict(
maybe_example, self.fields_dict)
ex_fields = {k: [(k, v)] for k, v in self.fields_dict.items()
if k in example}
ex = TorchtextExample.fromdict(example, ex_fields)
examples.append(ex)
return examples
def __call__(self, bucket):
examples = self._to_examples(bucket, is_train=self.is_train)
dataset = TorchtextDataset(examples, self.fields_dict)
return dataset
class ParallelCorpus(object):
"""A parallel corpus file pair that can be loaded to iterate."""
def __init__(self, name, src, tgt, align=None, src_feats=None):
"""Initialize src & tgt side file path."""
self.id = name
self.src = src
self.tgt = tgt
self.align = align
self.src_feats = src_feats
def load(self, offset=0, stride=1):
"""
Load file and iterate by lines.
`offset` and `stride` allow to iterate only on every
`stride` example, starting from `offset`.
"""
if self.src_feats:
features_names = []
features_files = []
for feat_name, feat_path in self.src_feats.items():
features_names.append(feat_name)
features_files.append(open(feat_path, mode='rb'))
else:
features_files = []
with exfile_open(self.src, mode='rb') as fs,\
exfile_open(self.tgt, mode='rb') as ft,\
exfile_open(self.align, mode='rb') as fa:
for i, (sline, tline, align, *features) in \
enumerate(zip(fs, ft, fa, *features_files)):
if (i % stride) == offset:
sline = sline.decode('utf-8')
tline = tline.decode('utf-8')
# 'src_original' and 'tgt_original' store the
# original line before tokenization. These
# fields are used later on in the feature
# transforms.
example = {
'src': sline,
'tgt': tline,
'src_original': sline,
'tgt_original': tline
}
if align is not None:
example['align'] = align.decode('utf-8')
if features:
example["src_feats"] = dict()
for j, feat in enumerate(features):
example["src_feats"][features_names[j]] = \
feat.decode("utf-8")
yield example
for f in features_files:
f.close()
def __str__(self):
cls_name = type(self).__name__
return '{}({}, {}, align={}, src_feats={})'.format(
cls_name, self.src, self.tgt, self.align, self.src_feats)
def get_corpora(opts, is_train=False):
corpora_dict = {}
if is_train:
for corpus_id, corpus_dict in opts.data.items():
if corpus_id != CorpusName.VALID:
corpora_dict[corpus_id] = ParallelCorpus(
corpus_id,
corpus_dict["path_src"],
corpus_dict["path_tgt"],
corpus_dict["path_align"],
corpus_dict["src_feats"])
else:
if CorpusName.VALID in opts.data.keys():
corpora_dict[CorpusName.VALID] = ParallelCorpus(
CorpusName.VALID,
opts.data[CorpusName.VALID]["path_src"],
opts.data[CorpusName.VALID]["path_tgt"],
opts.data[CorpusName.VALID]["path_align"],
opts.data[CorpusName.VALID]["src_feats"])
else:
return None
return corpora_dict
class ParallelCorpusIterator(object):
"""An iterator dedicate for ParallelCorpus.
Args:
corpus (ParallelCorpus): corpus to iterate;
transform (TransformPipe): transforms to be applied to corpus;
skip_empty_level (str): security level when encouter empty line;
stride (int): iterate corpus with this line stride;
offset (int): iterate corpus with this line offset.
"""
def __init__(self, corpus, transform,
skip_empty_level='warning', stride=1, offset=0):
self.cid = corpus.id
self.corpus = corpus
self.transform = transform
if skip_empty_level not in ['silent', 'warning', 'error']:
raise ValueError(
f"Invalid argument skip_empty_level={skip_empty_level}")
self.skip_empty_level = skip_empty_level
self.stride = stride
self.offset = offset
def _tokenize(self, stream):
for example in stream:
example['src'] = example['src'].strip('\n').split()
example['tgt'] = example['tgt'].strip('\n').split()
example['src_original'] = \
example['src_original'].strip("\n").split()
example['tgt_original'] = \
example['tgt_original'].strip("\n").split()
if 'align' in example:
example['align'] = example['align'].strip('\n').split()
if 'src_feats' in example:
for k in example['src_feats'].keys():
example['src_feats'][k] = \
example['src_feats'][k].strip('\n').split()
yield example
def _transform(self, stream):
for example in stream:
# NOTE: moved to DatasetAdapter._process method in iterator.py
# item = self.transform.apply(
# example, is_train=self.infinitely, corpus_name=self.cid)
item = (example, self.transform, self.cid)
if item is not None:
yield item
report_msg = self.transform.stats()
if report_msg != '':
logger.info(
"* Transform statistics for {}({:.2f}%):\n{}\n".format(
self.cid, 100/self.stride, report_msg
)
)
def _add_index(self, stream):
for i, item in enumerate(stream):
example = item[0]
line_number = i * self.stride + self.offset
example['indices'] = line_number
if (len(example['src']) == 0 or len(example['tgt']) == 0 or
('align' in example and example['align'] == 0)):
# empty example: skip
empty_msg = f"Empty line exists in {self.cid}#{line_number}."
if self.skip_empty_level == 'error':
raise IOError(empty_msg)
elif self.skip_empty_level == 'warning':
logger.warning(empty_msg)
continue
yield item
def __iter__(self):
corpus_stream = self.corpus.load(
stride=self.stride, offset=self.offset
)
tokenized_corpus = self._tokenize(corpus_stream)
transformed_corpus = self._transform(tokenized_corpus)
indexed_corpus = self._add_index(transformed_corpus)
yield from indexed_corpus
def build_corpora_iters(corpora, transforms, corpora_info,
skip_empty_level='warning', stride=1, offset=0):
"""Return `ParallelCorpusIterator` for all corpora defined in opts."""
corpora_iters = dict()
for c_id, corpus in corpora.items():
transform_names = corpora_info[c_id].get('transforms', [])
corpus_transform = [
transforms[name] for name in transform_names if name in transforms
]
transform_pipe = TransformPipe.build_from(corpus_transform)
logger.info(f"{c_id}'s transforms: {str(transform_pipe)}")
corpus_iter = ParallelCorpusIterator(
corpus, transform_pipe,
skip_empty_level=skip_empty_level, stride=stride, offset=offset)
corpora_iters[c_id] = corpus_iter
return corpora_iters
def write_files_from_queues(sample_path, queues):
"""
Standalone process that reads data from
queues in order and write to sample files.
"""
os.makedirs(sample_path, exist_ok=True)
for c_name in queues.keys():
dest_base = os.path.join(
sample_path, "{}.{}".format(c_name, CorpusName.SAMPLE))
with open(dest_base + ".src", 'w', encoding="utf-8") as f_src,\
open(dest_base + ".tgt", 'w', encoding="utf-8") as f_tgt:
while True:
_next = False
for q in queues[c_name]:
item = q.get()
if item == "blank":
continue
if item == "break":
_next = True
break
_, src_line, tgt_line = item
f_src.write(src_line + '\n')
f_tgt.write(tgt_line + '\n')
if _next:
break
# Just for debugging purposes
# It appends features to subwords when dumping to file
def append_features_to_example(example, features):
ex_toks = example.split(' ')
feat_toks = features.split(' ')
toks = [f"{subword}￨{feat}" for subword, feat in
zip(ex_toks, feat_toks)]
return " ".join(toks)
def build_sub_vocab(corpora, transforms, opts, n_sample, stride, offset):
"""Build vocab on (strided) subpart of the data."""
sub_counter_src = Counter()
sub_counter_tgt = Counter()
sub_counter_src_feats = defaultdict(Counter)
datasets_iterables = build_corpora_iters(
corpora, transforms, opts.data,
skip_empty_level=opts.skip_empty_level,
stride=stride, offset=offset)
for c_name, c_iter in datasets_iterables.items():
for i, item in enumerate(c_iter):
maybe_example = DatasetAdapter._process(item, is_train=True)
if maybe_example is None:
if opts.dump_samples:
build_sub_vocab.queues[c_name][offset].put("blank")
continue
src_line, tgt_line = (maybe_example['src']['src'],
maybe_example['tgt']['tgt'])
src_line_pretty = src_line
for feat_name, feat_line in maybe_example["src"].items():
if feat_name not in ["src", "src_original"]:
sub_counter_src_feats[feat_name].update(
feat_line.split(' '))
if opts.dump_samples:
src_line_pretty = append_features_to_example(
src_line_pretty, feat_line)
sub_counter_src.update(src_line.split(' '))
sub_counter_tgt.update(tgt_line.split(' '))
if opts.dump_samples:
build_sub_vocab.queues[c_name][offset].put(
(i, src_line_pretty, tgt_line))
if n_sample > 0 and ((i+1) * stride + offset) >= n_sample:
if opts.dump_samples:
build_sub_vocab.queues[c_name][offset].put("break")
break
if opts.dump_samples:
build_sub_vocab.queues[c_name][offset].put("break")
return sub_counter_src, sub_counter_tgt, sub_counter_src_feats
def init_pool(queues):
"""Add the queues as attribute of the pooled function."""
build_sub_vocab.queues = queues
def build_vocab(opts, transforms, n_sample=3):
"""Build vocabulary from data."""
if n_sample == -1:
logger.info(f"n_sample={n_sample}: Build vocab on full datasets.")
elif n_sample > 0:
logger.info(f"Build vocab on {n_sample} transformed examples/corpus.")
else:
raise ValueError(f"n_sample should > 0 or == -1, get {n_sample}.")
if opts.dump_samples:
logger.info("The samples on which the vocab is built will be "
"dumped to disk. It may slow down the process.")
corpora = get_corpora(opts, is_train=True)
counter_src = Counter()
counter_tgt = Counter()
counter_src_feats = defaultdict(Counter)
from functools import partial
queues = {c_name: [mp.Queue(opts.vocab_sample_queue_size)
for i in range(opts.num_threads)]
for c_name in corpora.keys()}
sample_path = os.path.join(
os.path.dirname(opts.save_data), CorpusName.SAMPLE)
if opts.dump_samples:
write_process = mp.Process(
target=write_files_from_queues,
args=(sample_path, queues),
daemon=True)
write_process.start()
with mp.Pool(opts.num_threads, init_pool, [queues]) as p:
func = partial(
build_sub_vocab, corpora, transforms,
opts, n_sample, opts.num_threads)
for sub_counter_src, sub_counter_tgt, sub_counter_src_feats in p.imap(
func, range(0, opts.num_threads)):
counter_src.update(sub_counter_src)
counter_tgt.update(sub_counter_tgt)
counter_src_feats.update(sub_counter_src_feats)
if opts.dump_samples:
write_process.join()
return counter_src, counter_tgt, counter_src_feats
def save_transformed_sample(opts, transforms, n_sample=3):
"""Save transformed data sample as specified in opts."""
if n_sample == -1:
logger.info(f"n_sample={n_sample}: Save full transformed corpus.")
elif n_sample == 0:
logger.info(f"n_sample={n_sample}: no sample will be saved.")
return
elif n_sample > 0:
logger.info(f"Save {n_sample} transformed example/corpus.")
else:
raise ValueError(f"n_sample should >= -1, get {n_sample}.")
corpora = get_corpora(opts, is_train=True)
datasets_iterables = build_corpora_iters(
corpora, transforms, opts.data,
skip_empty_level=opts.skip_empty_level)
sample_path = os.path.join(
os.path.dirname(opts.save_data), CorpusName.SAMPLE)
os.makedirs(sample_path, exist_ok=True)
for c_name, c_iter in datasets_iterables.items():
dest_base = os.path.join(
sample_path, "{}.{}".format(c_name, CorpusName.SAMPLE))
with open(dest_base + ".src", 'w', encoding="utf-8") as f_src,\
open(dest_base + ".tgt", 'w', encoding="utf-8") as f_tgt:
for i, item in enumerate(c_iter):
maybe_example = DatasetAdapter._process(item, is_train=True)
if maybe_example is None:
continue
src_line, tgt_line = (maybe_example['src']['src'],
maybe_example['tgt']['tgt'])
f_src.write(src_line + '\n')
f_tgt.write(tgt_line + '\n')
if n_sample > 0 and i >= n_sample:
break
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