This repository has been archived by the owner on Jul 7, 2023. It is now read-only.
/
wiki_revision.py
500 lines (440 loc) · 19.5 KB
/
wiki_revision.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
# coding=utf-8
# Copyright 2023 The Tensor2Tensor Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
r"""Data extraction/preprocessing for processing wiki history dumps for GEC.
We use a set of heuristics to distill prose from the wikipedia xml. We produce
source-target pairs of text reflecting wikipedia edits.
WikiRevision problem - fragment of older revision -> fragment of newer revision.
This implements data extraction from wikipedia as desribed in the paper,
Weakly Supervised Grammatical Error Correction using Iterative Decoding
(https://arxiv.org/pdf/1811.01710.pdf).
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import random
from absl import flags
from tensor2tensor.data_generators import generator_utils
from tensor2tensor.data_generators import text_encoder
from tensor2tensor.data_generators import text_problems
from tensor2tensor.data_generators import wiki_revision_utils
from tensor2tensor.utils import metrics
from tensor2tensor.utils import registry
import tensorflow.compat.v1 as tf
FLAGS = flags.FLAGS
flags.DEFINE_integer("wiki_revision_num_train_shards", 50,
"Set the number of training shards to be output.")
flags.DEFINE_integer("wiki_revision_num_dev_shards", 1,
"Set the number of dev shards to be output.")
flags.DEFINE_string(
"wiki_revision_data_prefix", "",
"Specify the prefix for input data. Expects 7z compressed Wikipedia XML "
"files, available at https://dumps.wikimedia.org/enwiki/latest/.")
flags.DEFINE_string(
"wiki_revision_vocab_file", "",
"Specify a wordpieces vocabulary with which to encode the text. Will "
"generate one from data if not specified.")
flags.DEFINE_integer(
"wiki_revision_max_examples_per_shard", 0,
"Use this to set a cap on examples per shard. "
"0 is no cap.")
# Data filtration heuristics:
flags.DEFINE_integer("wiki_revision_max_page_size_exp", 26,
"Exponent for 2**X byte cap on page size.")
flags.DEFINE_float(
"wiki_revision_max_equal_to_diff_ratio", 0,
"Max ratio between count of equal, diff chars for generated "
"examples. Ratio of 1 means examples with more diff chars "
"than equal chars will be tossed out.")
flags.DEFINE_float(
"wiki_revision_revision_skip_factor", 1.5,
"If >1, process only logarithmically many revisions. "
"This avoids blowup in runtime due to many-revision pages. "
"See wiki_revision_utils.include_revision for details.")
flags.DEFINE_float("wiki_revision_percent_identical_examples", 0.04,
"Percent of generated examples for which source == target.")
flags.DEFINE_bool(
"wiki_revision_introduce_errors", True, "Add errors to the data."
"See wiki_revision_utils.introduce_errors for details.")
@registry.register_problem
class WikiRevision(text_problems.Text2TextProblem):
"""Old segment -> revised segment.
Data filtration heuristics:
wiki_revision_max_page_size_exp:
pages above this # of bytes are thrown out
wiki_revision_revision_skip_factor:
rate of logarithmic downsampling of revision history list
wiki_revision_percent_identical_examples:
how many identitcal examples to admit, as percent of total examples
wiki_revision_introduce_errors:
whether or not to introduce spelling-type errors on the source side
wiki_revision_max_equal_to_diff_ratio:
whether or not to introduce spelling-type errors on the source side
Vocab size=32k
Maximum input/target length = 1024 wordpiece tokens
"""
num_identity_examples = 0
num_total_examples = 0
num_identity_examples = 0
num_pages = 0
num_revisions_total = 0
num_revisions_admitted = 0
num_examples_thrown_out_identity = 0
num_examples_thrown_out_too_long = 0
num_examples_thrown_out_edit_distance = 0
num_examples_with_introduced_error = 0
num_introduced_errors = 0
num_source_tokens = 0
num_target_tokens = 0
corpus_files = None
@property
def approx_vocab_size(self):
return 2**15 # 32K
@property
def strip(self):
"""Whether to strip wikipedia-stuff to get plain text."""
return True
@property
def wiki_revision_skip_factor(self):
"""If this value is >1.0, process only logarithmically many revisions."""
return FLAGS.wiki_revision_revision_skip_factor
@property
def max_segment_length(self):
"""Maximum number of input/target wordpiece tokens."""
return 256
@property
def max_examples_per_shard(self):
"""Maximum number of examples to generate per shard. 0=unlimited."""
return FLAGS.wiki_revision_max_examples_per_shard
def aggregate_job_stats(self):
# Aggregate job stats for output.
stat = []
# Run stats.
stat.append("Flags for job:\n"
"Dev shards: {}\n"
"Train shards: {}\n"
"Revision skip factor: {}\n"
"Max page size: 2**{}\n"
"Introduce errors: {}\n"
"Max edit ratio: {}\n"
"Percent Identical Examples: {}\n"
"".format(FLAGS.wiki_revision_num_dev_shards,
FLAGS.wiki_revision_num_train_shards,
FLAGS.wiki_revision_revision_skip_factor,
FLAGS.wiki_revision_max_page_size_exp,
FLAGS.wiki_revision_introduce_errors,
FLAGS.wiki_revision_max_equal_to_diff_ratio,
FLAGS.wiki_revision_percent_identical_examples))
# File stats.
stat.append("corpus files: {}\n"
"\tnames: {}\n"
"\tpages per input file: {:.1f}\n"
"".format(
len(self.corpus_files), self.corpus_files,
(0 if not self.corpus_files else
self.num_pages / len(self.corpus_files))))
# Page stats.
stat.append(
"pages processed: {}\n"
"\trevisions per page: {:.2f}, total: {}\n"
"\trevisions admitted per page: {:.2f}, percent of total: {:.2f}\n"
"".format(
self.num_pages, (0 if not self.num_pages else
self.num_revisions_total / self.num_pages),
self.num_revisions_total,
(0 if not self.num_pages else
self.num_revisions_admitted / self.num_pages),
(0 if not self.num_revisions_total else
100 * self.num_revisions_admitted / self.num_revisions_total)))
# Revision stats.
stat.append(
"revisions admitted: {}\n"
"\texamples generated per revision: {:.2f}\n"
"".format(self.num_revisions_admitted,
(0 if not self.num_revisions_admitted else
self.num_total_examples / self.num_revisions_admitted)))
# Example stats.
stat.append(
"examples generated: {}\n"
"\twith error introduced: {}, percent of total: {:.2f}\n"
"\ttotal errors introduced: {}, errors per errorred example: {:.2f}\n"
"\texamples thrown out: {}\n"
"\t\ttoo long: {}\n"
"\t\tidentity: {}\n"
"\t\tedit distance: {}\n"
"\tremaining identity examples: {}\n"
"\tratio identity (actual, desired): {:.3f}, {}\n"
"".format(
self.num_total_examples, self.num_examples_with_introduced_error,
(0 if not self.num_total_examples else 100 *
self.num_examples_with_introduced_error / self.num_total_examples),
self.num_introduced_errors,
(0 if not self.num_examples_with_introduced_error else
self.num_introduced_errors /
self.num_examples_with_introduced_error),
self.num_examples_thrown_out_too_long +
self.num_examples_thrown_out_identity +
self.num_examples_thrown_out_edit_distance,
self.num_examples_thrown_out_too_long,
self.num_examples_thrown_out_identity,
self.num_examples_thrown_out_edit_distance,
self.num_identity_examples,
(0 if not self.num_total_examples else
self.num_identity_examples / self.num_total_examples),
FLAGS.wiki_revision_percent_identical_examples))
# Token stats.
stat.append("tokens generated: {}\n"
"\tsource: {}\n"
"\ttarget: {}\n"
"\tper example: {:.2f}\n"
"\t\tsource: {:.2f}\n"
"\t\ttarget: {:.2f}\n"
"".format(self.num_source_tokens + self.num_target_tokens,
self.num_source_tokens, self.num_target_tokens,
(0 if not self.num_total_examples else
(self.num_source_tokens + self.num_target_tokens) /
self.num_total_examples),
(0 if not self.num_total_examples else
self.num_source_tokens / self.num_total_examples),
(0 if not self.num_total_examples else
self.num_target_tokens / self.num_total_examples)))
return "\n".join(stat)
def generate_data(self, data_dir, tmp_dir, task_id=-1):
if task_id == -1 or task_id is None:
for i in range(FLAGS.wiki_revision_num_train_shards +
FLAGS.wiki_revision_num_dev_shards):
self.generate_data(data_dir, tmp_dir, i)
return
tf.logging.info(
"Flags for job (task_id {}): "
"Dev shards: {}, Train shards: {}, "
"Revision skip factor: {}, Max page size: 2**{}, Introduce errors: {},"
"Percent Identical Examples: {}"
"".format(task_id, FLAGS.wiki_revision_num_dev_shards,
FLAGS.wiki_revision_num_train_shards,
FLAGS.wiki_revision_revision_skip_factor,
FLAGS.wiki_revision_max_page_size_exp,
FLAGS.wiki_revision_introduce_errors,
FLAGS.wiki_revision_percent_identical_examples))
if FLAGS.wiki_revision_vocab_file:
encoder = wiki_revision_utils.get_encoder_from_vocab(
FLAGS.wiki_revision_vocab_file)
else:
encoder = wiki_revision_utils.get_or_generate_vocabulary(
data_dir, tmp_dir, FLAGS.wiki_revision_data_prefix,
FLAGS.wiki_revision_max_page_size_exp, self.approx_vocab_size,
self.strip)
random.seed(123)
if task_id < FLAGS.wiki_revision_num_train_shards:
out_file = self.training_filepaths(
data_dir, FLAGS.wiki_revision_num_train_shards,
shuffled=False)[task_id]
else:
out_file = self.dev_filepaths(
data_dir, FLAGS.wiki_revision_num_dev_shards,
shuffled=False)[task_id - FLAGS.wiki_revision_num_train_shards]
tf.logging.info("Generating files for path: %s", out_file)
self.corpus_files = wiki_revision_utils.corpus_files_for_shard(
task_id, FLAGS.wiki_revision_num_train_shards,
FLAGS.wiki_revision_num_dev_shards, FLAGS.wiki_revision_data_prefix)
example_generator = self.generator(encoder, self.corpus_files, tmp_dir)
packed_example_generator = self._maybe_pack_examples(example_generator)
generator_utils.generate_files(packed_example_generator, [out_file])
generator_utils.shuffle_dataset([out_file])
tf.logging.info(
"Job stats: identity examples: {}, total examples {}, ratio: {}".format(
self.num_identity_examples, self.num_total_examples,
(1 + self.num_identity_examples) / (1 + self.num_total_examples)))
job_stats_string = self.aggregate_job_stats()
out_dir, filename = out_file.replace("-unshuffled", "").rsplit("/", 1)
stats_prefix = "/stats_"
stats_file_path = "".join([out_dir, stats_prefix, filename])
if tf.gfile.Exists(
stats_file_path) and tf.gfile.Open(stats_file_path).size() != 0:
tf.logging.info("Skipping writing stats because output file exists.")
else:
with tf.gfile.Open(stats_file_path, "w") as out:
tf.logging.info("Writing job stats to {}".format(stats_file_path))
out.write(job_stats_string)
tf.logging.info(job_stats_string)
def generator(self, encoder, corpus_files, tmp_dir):
for page in wiki_revision_utils.corpus_page_generator(
corpus_files, tmp_dir, FLAGS.wiki_revision_max_page_size_exp):
self.num_pages += 1
examples = self.page_to_examples(page, encoder)
for x in examples:
yield x
if self.num_total_examples % 100000 == 0:
tf.logging.info(
u"page count={} num_total_examples={} id={} title={}".format(
self.num_pages, self.num_total_examples, page["id"],
page["title"]))
if (self.max_examples_per_shard and
self.num_total_examples >= self.max_examples_per_shard):
tf.logging.info(
"Examples per shard {} >= max_examples_per_shard {}. Shutting down."
.format(self.num_total_examples, self.max_examples_per_shard))
break
tf.logging.info(
"Total pages: {}, total examples: {}, examples per page: {}".format(
self.num_pages, self.num_total_examples, 0 if not self.num_pages
else self.num_total_examples / self.num_pages))
def page_to_examples(self, page, encoder):
revisions = page["revisions"]
self.num_revisions_total += len(revisions)
if len(revisions) < 2:
return []
revisions = [
wiki_revision_utils.get_text(r)
for n, r in enumerate(revisions)
if wiki_revision_utils.include_revision(
n, self.wiki_revision_skip_factor) or n + 1 == len(revisions)
]
self.num_revisions_admitted += len(revisions)
ret = []
for i in range(len(revisions) - 1):
old_revision = revisions[i]
new_revision = revisions[i + 1]
if FLAGS.wiki_revision_introduce_errors:
old_revision_text, num_added_err = wiki_revision_utils.introduce_errors(
revisions[i])
if num_added_err:
self.num_introduced_errors += num_added_err
self.num_examples_with_introduced_error += 1
else:
old_revision_text = revisions[i]
new_revision_text = revisions[i + 1]
if encoder:
# Encode text into list of ids, if a text encoder is present.
old_revision = encoder.encode(old_revision_text)
new_revision = encoder.encode(new_revision_text)
else:
# Retain text (as list of characters), if a text encoder is not present.
old_revision = old_revision_text
new_revision = new_revision_text
ret.extend(
self.make_examples(
encoder,
old_revision,
new_revision,
max_length=self.max_segment_length,
percent_identical_examples=FLAGS
.wiki_revision_percent_identical_examples))
return ret
def make_examples(self,
encoder,
old_snapshot,
new_snapshot,
max_length=1024,
percent_identical_examples=0.01,
max_length_distance=0):
"""Produce training examples based on a pair of snapshots.
Aligns the snapshots, then chops at a random subset of the alignment points
to create (old snippet -> new snippet) examples.
Most negative examples (those with no changes) are discarded, but we
keep some of them, maintaining a proportion in the final data
determined by percent_identical_examples.
Args:
encoder: the subword text encoder
old_snapshot: a list of ids
new_snapshot: a list of ids
max_length: an integer. Maximum length of "inputs" and "targets".
percent_identical_examples: a float
max_length_distance: an integer. Max token edit dist for admitted examples
Returns:
a list of feature dictionaries. The dictionaries have
"inputs" and "targets" populated. text_encoder.EOS is appended to both.
"""
ret = []
eos_sequence = [text_encoder.EOS_ID]
# Pick a per-token cut probability with a log-uniform distribution between
# 1/4 and 1/(max_length / 2)
bound1 = -math.log(4.0)
bound2 = -math.log(max_length / 2.0)
cut_prob = math.exp(random.random() * (bound2 - bound1) + bound1)
opcodes = wiki_revision_utils.fast_match_sequences(old_snapshot,
new_snapshot)
cut_points = [(0, 0)]
for tag, i1, i2, j1, j2 in opcodes:
if tag == "equal":
for i in range(i1, i2 + 1):
if random.random() < cut_prob:
cut_points.append((i, i + j1 - i1))
cut_points.append((len(old_snapshot), len(new_snapshot)))
src_tgt_pairs = []
for cut_number in range(len(cut_points) - 1):
i1, j1 = cut_points[cut_number]
i2, j2 = cut_points[cut_number + 1]
old_segment = old_snapshot[i1:i2]
new_segment = new_snapshot[j1:j2]
src_tgt_pairs.append((old_segment, new_segment))
src_tgt_pairs, thrown_edit_count = wiki_revision_utils.edit_distance_filter(
wiki_revision_utils.throw_empty_pairs(src_tgt_pairs),
FLAGS.wiki_revision_max_equal_to_diff_ratio)
self.num_examples_thrown_out_edit_distance += thrown_edit_count
for source, target in src_tgt_pairs:
# Add EOS segment.
old_segment = source + eos_sequence
new_segment = target + eos_sequence
if len(old_segment) <= max_length and len(new_segment) <= max_length:
if max_length_distance and (abs(len(old_segment) - len(new_segment)) >
max_length_distance):
self.num_examples_thrown_out_edit_distance += 1
continue
if old_segment == new_segment:
# If current proportion of identity is below target
# percent_identical_examples, then roll for a 50% chance to add an
# identitical example. Random roll preserves nondeterminism.
# percent_identical_examples, then add identitical example.
# Random roll preserves nondeterminism in selecting identity examples.
if (((self.num_identity_examples) / (1 + self.num_total_examples)) >
percent_identical_examples) or random.random() > 0.5:
self.num_examples_thrown_out_identity += 1
continue
else:
self.num_identity_examples += 1
self.num_total_examples += 1
self.num_source_tokens += len(old_segment) - 1
self.num_target_tokens += len(new_segment) - 1
ret.append({"inputs": old_segment, "targets": new_segment})
else:
self.num_examples_thrown_out_too_long += 1
return ret
def eval_metrics(self):
return [
metrics.Metrics.ACC,
metrics.Metrics.ACC_TOP5,
metrics.Metrics.ACC_PER_SEQ,
metrics.Metrics.NEG_LOG_PERPLEXITY,
]
@property
def invert_prob(self):
"""Ratio of e^2 positive forward to backward examples."""
return 1.0 / (1.0 + math.exp(2.0))
@registry.register_problem
class WikiRevisionPacked1k(WikiRevision):
"""Packed version for TPU."""
@property
def packed_length(self):
return 1024
@registry.register_problem
class WikiRevisionPacked256(WikiRevision):
"""Packed version for TPU."""
@property
def packed_length(self):
return 256
@property
def max_segment_length(self):
return 256