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Commit 03c2c762 authored by thomwolf's avatar thomwolf
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update tokenizer

parent 3edfa1d6
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Showing with 75 additions and 80 deletions
transformers/tokenization_ctrl.py
View file @ 03c2c762
......@@ -16,21 +16,13 @@
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import sys
import json
import logging
import os
import regex as re
from io import open
import pdb
try:
from functools import lru_cache
except ImportError:
# Just a dummy decorator to get the checks to run on python2
# because honestly I don't want to support a byte-level unicode BPE tokenizer on python 2 right now.
def lru_cache():
return lambda func: func
from .tokenization_bert import BasicTokenizer
from .tokenization_utils import PreTrainedTokenizer
......@@ -53,48 +45,46 @@ PRETRAINED_VOCAB_FILES_MAP = {
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
'ctrl': 1280,
'ctrl': 256,
}
@lru_cache()
def bytes_to_unicode():
def text_standardize(text):
"""
Returns list of utf-8 byte and a mapping to unicode strings.
We specifically avoids mapping to whitespace/control characters the bpe code barfs on.
The reversible bpe codes work on unicode strings.
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
This is a signficant percentage of your normal, say, 32K bpe vocab.
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
fixes some issues the spacy tokenizer had on books corpus
also does some whitespace standardization
"""
_chr = unichr if sys.version_info[0] == 2 else chr
bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8+n)
n += 1
cs = [_chr(n) for n in cs]
return dict(zip(bs, cs))
text = text.replace('—', '-')
text = text.replace('–', '-')
text = text.replace('―', '-')
text = text.replace('…', '...')
text = text.replace('´', "'")
text = re.sub(r'''(-+|~+|!+|"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)''', r' \1 ', text)
text = re.sub(r'\s*\n\s*', ' \n ', text)
text = re.sub(r'[^\S\n]+', ' ', text)
return text.strip()
def get_pairs(word):
"""Return set of symbol pairs in a word.
Word is represented as tuple of symbols (symbols being variable-length strings).
"""
pairs= []
# pairs = []
# prev_char = word[0]
# for i, char in enumerate(word[1:]):
# #_i = i + 1
# #if word[_i+1:] == tuple('</w>'):
# # pairs.append((prev_char, char+'</w>'))
# # break
# #else:
# if True:
# pairs.append((prev_char, char))
# prev_char = char
pairs = set()
prev_char = word[0]
for i, char in enumerate(word[1:]):
#_i = i + 1
#if word[_i+1:] == tuple('</w>'):
# pairs.append((prev_char, char+'</w>'))
# break
#else:
if True:
pairs.append((prev_char, char))
for char in word[1:]:
pairs.add((prev_char, char))
prev_char = char
pairs = set(pairs)
......@@ -113,25 +103,29 @@ class CTRLTokenizer(PreTrainedTokenizer):
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__(self, vocab_file, merges_file, errors='replace', unk_token="<unk>",
bos_token="<|endoftext|>", eos_token="<|endoftext|>", **kwargs):
super(CTRLTokenizer, self).__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, **kwargs)
def __init__(self, vocab_file, merges_file, unk_token="<unk>", **kwargs):
super(CTRLTokenizer, self).__init__(unk_token=unk_token, **kwargs)
self.max_len_single_sentence = self.max_len # no default special tokens - you can update this value if you add special tokens
self.max_len_sentences_pair = self.max_len # no default special tokens - you can update this value if you add special tokens
try:
import ftfy
from spacy.lang.en import English
_nlp = English()
self.nlp = _nlp.Defaults.create_tokenizer(_nlp)
self.fix_text = ftfy.fix_text
except ImportError:
logger.warning("ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.")
self.nlp = BasicTokenizer(do_lower_case=True)
self.fix_text = None
self.encoder = json.load(open(vocab_file, encoding="utf-8"))
self.decoder = {v: k for k, v in self.encoder.items()}
self.errors = errors # how to handle errors in decoding
self.byte_encoder = bytes_to_unicode()
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
bpe_data = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
bpe_merges = [tuple(merge.split()) for merge in bpe_data]
self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
self.decoder = {v:k for k,v in self.encoder.items()}
merges = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
merges = [tuple(merge.split()) for merge in merges]
self.bpe_ranks = dict(zip(merges, range(len(merges))))
self.cache = {}
# Should haved added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
@property
def vocab_size(self):
return len(self.encoder)
......@@ -179,23 +173,27 @@ class CTRLTokenizer(PreTrainedTokenizer):
self.cache[token] = word
return word
def _tokenize(self, text, add_prefix_space=False):
def _tokenize(self, text):
""" Tokenize a string.
Args:
- add_prefix_space (boolean, default False):
Begin the sentence with at least one space toto get invariance to word order in CTRL (and RoBERTa) tokenizers.
"""
if add_prefix_space:
text = ' ' + text
bpe_tokens = []
for token in text.split():
if sys.version_info[0] == 2:
token = ''.join(self.byte_encoder[ord(b)] for b in token) # Maps all our bytes to unicode strings, avoiding controle tokens of the BPE (spaces in our case)
split_tokens = []
if self.fix_text is None:
# Using BERT's BasicTokenizer
text = self.nlp.tokenize(text)
for token in text:
split_tokens.extend([t for t in self.bpe(token).split(' ')])
else:
token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) # Maps all our bytes to unicode strings, avoiding controle tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(' '))
return bpe_tokens
# Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
text = self.nlp(text_standardize(self.fix_text(text)))
for token in text:
split_tokens.extend([t for t in self.bpe(token.text.lower()).split(' ')])
# for token in text.split():
# if sys.version_info[0] == 2:
# token = ''.join(self.byte_encoder[ord(b)] for b in token) # Maps all our bytes to unicode strings, avoiding controle tokens of the BPE (spaces in our case)
# else:
# token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) # Maps all our bytes to unicode strings, avoiding controle tokens of the BPE (spaces in our case)
# bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(' '))
return split_tokens
def _convert_token_to_id(self, token):
""" Converts a token (str/unicode) in an id using the vocab. """
......@@ -203,13 +201,12 @@ class CTRLTokenizer(PreTrainedTokenizer):
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (string/unicode) using the vocab."""
return self.decoder.get(index)
return self.decoder.get(index, self.unk_token)
def convert_tokens_to_string(self, tokens):
""" Converts a sequence of tokens (string) in a single string. """
text = ''.join(tokens)
text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors=self.errors)
return text
out_string = ''.join(tokens).replace('@@', ' ').strip()
return out_string
def save_vocabulary(self, save_directory):
"""Save the tokenizer vocabulary and merge files to a directory."""
......@@ -235,10 +232,8 @@ class CTRLTokenizer(PreTrainedTokenizer):
return vocab_file, merge_file
def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
return ''.join(tokens_generated_so_far)
# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
# filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
# tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
# tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
# return ''.join(tokens_generated_so_far)
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