#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Author: Rico Sennrich
"""Use byte pair encoding (BPE) to learn a variable-length encoding of the vocabulary in a text.
Unlike the original BPE, it does not compress the plain text, but can be used to reduce the vocabulary
of a text to a configurable number of symbols, with only a small increase in the number of tokens.
Reference:
Rico Sennrich, Barry Haddow and Alexandra Birch (2016). Neural Machine Translation of Rare Words with Subword Units.
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016). Berlin, Germany.
"""
from __future__ import unicode_literals
import os
import sys
import inspect
import codecs
import re
import copy
import argparse
import warnings
import tempfile
from multiprocessing import Pool, cpu_count
from collections import defaultdict, Counter
# hack for python2/3 compatibility
from io import open
argparse.open = open
def create_parser(subparsers=None):
if subparsers:
parser = subparsers.add_parser('learn-bpe',
formatter_class=argparse.RawDescriptionHelpFormatter,
description="learn BPE-based word segmentation")
else:
parser = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter,
description="learn BPE-based word segmentation")
parser.add_argument(
'--input', '-i', type=argparse.FileType('r'), default=sys.stdin,
metavar='PATH',
help="Input text (default: standard input).")
parser.add_argument(
'--output', '-o', type=argparse.FileType('w'), default=sys.stdout,
metavar='PATH',
help="Output file for BPE codes (default: standard output)")
parser.add_argument(
'--symbols', '-s', type=int, default=10000,
help="Create this many new symbols (each representing a character n-gram) (default: %(default)s)")
parser.add_argument(
'--min-frequency', type=int, default=2, metavar='FREQ',
help='Stop if no symbol pair has frequency >= FREQ (default: %(default)s)')
parser.add_argument('--dict-input', action="store_true",
help="If set, input file is interpreted as a dictionary where each line contains a word-count pair")
parser.add_argument(
'--total-symbols', '-t', action="store_true",
help="subtract number of characters from the symbols to be generated (so that '--symbols' becomes an estimate for the total number of symbols needed to encode text).")
parser.add_argument(
'--num-workers', type=int, default=1,
help="Number of processors to process texts, only supported in Python3. If -1, set `multiprocessing.cpu_count()`. (default: %(default)s)")
parser.add_argument(
'--verbose', '-v', action="store_true",
help="verbose mode.")
return parser
def get_vocabulary(fobj, is_dict=False, num_workers=1):
"""Read text and return dictionary that encodes vocabulary
"""
vocab = Counter()
if is_dict:
for i, line in enumerate(fobj):
try:
word, count = line.strip('\r\n ').split(' ')
except:
print('Failed reading vocabulary file at line {0}: {1}'.format(i, line))
sys.exit(1)
vocab[word] += int(count)
elif num_workers == 1 or fobj.name == '<stdin>':
if num_workers > 1:
warnings.warn("In parallel mode, the input cannot be STDIN. Using 1 processor instead.")
for i, line in enumerate(fobj):
for word in line.strip('\r\n ').split(' '):
if word:
vocab[word] += 1
elif num_workers > 1:
if sys.version_info < (3, 0):
print("Parallel mode is only supported in Python3.")
sys.exit(1)
with open(fobj.name, encoding="utf8") as f:
size = os.fstat(f.fileno()).st_size
chunk_size = int(size / num_workers)
offsets = [0 for _ in range(num_workers + 1)]
for i in range(1, num_workers):
f.seek(chunk_size * i)
pos = f.tell()
while True:
try:
line = f.readline()
break
except UnicodeDecodeError:
pos -= 1
f.seek(pos)
offsets[i] = f.tell()
assert 0 <= offsets[i] < 1e20, "Bad new line separator, e.g. '\\r'"
vocab_files = []
pool = Pool(processes=num_workers)
for i in range(num_workers):
tmp = tempfile.NamedTemporaryFile(delete=False)
tmp.close()
vocab_files.append(tmp)
pool.apply_async(_get_vocabulary, (fobj.name, tmp.name, offsets[i], offsets[i + 1]))
pool.close()
pool.join()
import pickle
for i in range(num_workers):
with open(vocab_files[i].name, 'rb') as f:
vocab += pickle.load(f)
os.remove(vocab_files[i].name)
else:
raise ValueError('`num_workers` is expected to be a positive number, but got {}.'.format(num_workers))
return vocab
def _get_vocabulary(infile, outfile, begin, end):
import pickle
vocab = Counter()
with open(infile, encoding="utf8") as f:
f.seek(begin)
line = f.readline()
while line:
pos = f.tell()
assert 0 <= pos < 1e20, "Bad new line separator, e.g. '\\r'"
if end > 0 and pos > end:
break
for word in line.strip('\r\n ').split(' '):
if word:
vocab[word] += 1
line = f.readline()
with open(outfile, 'wb') as f:
pickle.dump(vocab, f)
def update_pair_statistics(pair, changed, stats, indices):
"""Minimally update the indices and frequency of symbol pairs
if we merge a pair of symbols, only pairs that overlap with occurrences
of this pair are affected, and need to be updated.
"""
stats[pair] = 0
indices[pair] = defaultdict(int)
first, second = pair
new_pair = first+second
for j, word, old_word, freq in changed:
# find all instances of pair, and update frequency/indices around it
i = 0
while True:
# find first symbol
try:
i = old_word.index(first, i)
except ValueError:
break
# if first symbol is followed by second symbol, we've found an occurrence of pair (old_word[i:i+2])
if i < len(old_word)-1 and old_word[i+1] == second:
# assuming a symbol sequence "A B C", if "B C" is merged, reduce the frequency of "A B"
if i:
prev = old_word[i-1:i+1]
stats[prev] -= freq
indices[prev][j] -= 1
if i < len(old_word)-2:
# assuming a symbol sequence "A B C B", if "B C" is merged, reduce the frequency of "C B".
# however, skip this if the sequence is A B C B C, because the frequency of "C B" will be reduced by the previous code block
if old_word[i+2] != first or i >= len(old_word)-3 or old_word[i+3] != second:
nex = old_word[i+1:i+3]
stats[nex] -= freq
indices[nex][j] -= 1
i += 2
else:
i += 1
i = 0
while True:
try:
# find new pair
i = word.index(new_pair, i)
except ValueError:
break
# assuming a symbol sequence "A BC D", if "B C" is merged, increase the frequency of "A BC"
if i:
prev = word[i-1:i+1]
stats[prev] += freq
indices[prev][j] += 1
# assuming a symbol sequence "A BC B", if "B C" is merged, increase the frequency of "BC B"
# however, if the sequence is A BC BC, skip this step because the count of "BC BC" will be incremented by the previous code block
if i < len(word)-1 and word[i+1] != new_pair:
nex = word[i:i+2]
stats[nex] += freq
indices[nex][j] += 1
i += 1
def get_pair_statistics(vocab):
"""Count frequency of all symbol pairs, and create index"""
# data structure of pair frequencies
stats = defaultdict(int)
#index from pairs to words
indices = defaultdict(lambda: defaultdict(int))
for i, (word, freq) in enumerate(vocab):
prev_char = word[0]
for char in word[1:]:
stats[prev_char, char] += freq
indices[prev_char, char][i] += 1
prev_char = char
return stats, indices
def replace_pair(pair, vocab, indices):
"""Replace all occurrences of a symbol pair ('A', 'B') with a new symbol 'AB'"""
first, second = pair
pair_str = ''.join(pair)
pair_str = pair_str.replace('\\','\\\\')
changes = []
pattern = re.compile(r'(?<!\S)' + re.escape(first + ' ' + second) + r'(?!\S)')
if sys.version_info < (3, 0):
iterator = indices[pair].iteritems()
else:
iterator = indices[pair].items()
for j, freq in iterator:
if freq < 1:
continue
word, freq = vocab[j]
new_word = ' '.join(word)
new_word = pattern.sub(pair_str, new_word)
new_word = tuple(new_word.split(' '))
vocab[j] = (new_word, freq)
changes.append((j, new_word, word, freq))
return changes
def prune_stats(stats, big_stats, threshold):
"""Prune statistics dict for efficiency of max()
The frequency of a symbol pair never increases, so pruning is generally safe
(until we the most frequent pair is less frequent than a pair we previously pruned)
big_stats keeps full statistics for when we need to access pruned items
"""
for item,freq in list(stats.items()):
if freq < threshold:
del stats[item]
if freq < 0:
big_stats[item] += freq
else:
big_stats[item] = freq
def learn_bpe(infile, outfile, num_symbols, min_frequency=2, verbose=False, is_dict=False, total_symbols=False, num_workers=1):
"""Learn num_symbols BPE operations from vocabulary, and write to outfile.
"""
# version 0.2 changes the handling of the end-of-word token ('</w>');
# version numbering allows bckward compatibility
outfile.write('#version: 0.2\n')
vocab = get_vocabulary(infile, is_dict, num_workers)
vocab = dict([(tuple(x[:-1])+(x[-1]+'</w>',) ,y) for (x,y) in vocab.items()])
sorted_vocab = sorted(vocab.items(), key=lambda x: x[1], reverse=True)
stats, indices = get_pair_statistics(sorted_vocab)
big_stats = copy.deepcopy(stats)
if total_symbols:
uniq_char_internal = set()
uniq_char_final = set()
for word in vocab:
for char in word[:-1]:
uniq_char_internal.add(char)
uniq_char_final.add(word[-1])
sys.stderr.write('Number of word-internal characters: {0}\n'.format(len(uniq_char_internal)))
sys.stderr.write('Number of word-final characters: {0}\n'.format(len(uniq_char_final)))
sys.stderr.write('Reducing number of merge operations by {0}\n'.format(len(uniq_char_internal) + len(uniq_char_final)))
num_symbols -= len(uniq_char_internal) + len(uniq_char_final)
# threshold is inspired by Zipfian assumption, but should only affect speed
threshold = max(stats.values()) / 10
for i in range(num_symbols):
if stats:
most_frequent = max(stats, key=lambda x: (stats[x], x))
# we probably missed the best pair because of pruning; go back to full statistics
if not stats or (i and stats[most_frequent] < threshold):
prune_stats(stats, big_stats, threshold)
stats = copy.deepcopy(big_stats)
most_frequent = max(stats, key=lambda x: (stats[x], x))
# threshold is inspired by Zipfian assumption, but should only affect speed
threshold = stats[most_frequent] * i/(i+10000.0)
prune_stats(stats, big_stats, threshold)
if stats[most_frequent] < min_frequency:
sys.stderr.write('no pair has frequency >= {0}. Stopping\n'.format(min_frequency))
break
if verbose:
sys.stderr.write('pair {0}: {1} {2} -> {1}{2} (frequency {3})\n'.format(i, most_frequent[0], most_frequent[1], stats[most_frequent]))
outfile.write('{0} {1}\n'.format(*most_frequent))
changes = replace_pair(most_frequent, sorted_vocab, indices)
update_pair_statistics(most_frequent, changes, stats, indices)
stats[most_frequent] = 0
if not i % 100:
prune_stats(stats, big_stats, threshold)
if __name__ == '__main__':
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
newdir = os.path.join(currentdir, 'subword_nmt')
if os.path.isdir(newdir):
warnings.simplefilter('default')
warnings.warn(
"this script's location has moved to {0}. This symbolic link will be removed in a future version. Please point to the new location, or install the package and use the command 'subword-nmt'".format(newdir),
DeprecationWarning
)
# python 2/3 compatibility
if sys.version_info < (3, 0):
sys.stderr = codecs.getwriter('UTF-8')(sys.stderr)
sys.stdout = codecs.getwriter('UTF-8')(sys.stdout)
sys.stdin = codecs.getreader('UTF-8')(sys.stdin)
else:
sys.stderr = codecs.getwriter('UTF-8')(sys.stderr.buffer)
sys.stdout = codecs.getwriter('UTF-8')(sys.stdout.buffer)
sys.stdin = codecs.getreader('UTF-8')(sys.stdin.buffer)
parser = create_parser()
args = parser.parse_args()
if args.num_workers <= 0:
args.num_workers = cpu_count()
if sys.version_info < (3, 0) and args.num_workers > 1:
args.num_workers = 1
warnings.warn("Parallel mode is only supported in Python3. Using 1 processor instead.")
# read/write files as UTF-8
if args.input.name != '<stdin>':
args.input = codecs.open(args.input.name, encoding='utf-8')
if args.output.name != '<stdout>':
args.output = codecs.open(args.output.name, 'w', encoding='utf-8')
learn_bpe(args.input, args.output, args.symbols, args.min_frequency, args.verbose, is_dict=args.dict_input, total_symbols=args.total_symbols, num_workers=args.num_workers)
