123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282283284285286287288289290291292293294295296297298299300301302303304305306307308309310311312313314315316317318319320321322 |
- # coding=utf-8
- # Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
- #
- # 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.
- """Tokenization classes for OpenAI GPT."""
- 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
- 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
- logger = logging.getLogger(__name__)
- PRETRAINED_VOCAB_ARCHIVE_MAP = {
- 'gpt2': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json",
- }
- PRETRAINED_MERGES_ARCHIVE_MAP = {
- 'gpt2': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt",
- }
- PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = {
- 'gpt2': 1024,
- }
- VOCAB_NAME = 'vocab.json'
- MERGES_NAME = 'merges.txt'
- SPECIAL_TOKENS_NAME = 'special_tokens.txt'
- @lru_cache()
- def bytes_to_unicode():
- """
- Returns list of utf-8 byte and a corresponding list of unicode strings.
- 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.
- And avoids mapping to whitespace/control characters the bpe code barfs on.
- """
- _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))
- 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 = set()
- prev_char = word[0]
- for char in word[1:]:
- pairs.add((prev_char, char))
- prev_char = char
- return pairs
- class GPT2Tokenizer(object):
- """
- GPT-2 BPE tokenizer. Peculiarities:
- - Byte-level BPE
- """
- @classmethod
- def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
- """
- Instantiate a PreTrainedBertModel from a pre-trained model file.
- Download and cache the pre-trained model file if needed.
- """
- if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP:
- vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name_or_path]
- merges_file = PRETRAINED_MERGES_ARCHIVE_MAP[pretrained_model_name_or_path]
- special_tokens_file = None
- else:
- vocab_file = os.path.join(pretrained_model_name_or_path, VOCAB_NAME)
- merges_file = os.path.join(pretrained_model_name_or_path, MERGES_NAME)
- special_tokens_file = os.path.join(pretrained_model_name_or_path, SPECIAL_TOKENS_NAME)
- if not os.path.exists(special_tokens_file):
- special_tokens_file = None
- else:
- logger.info("loading special tokens file {}".format(special_tokens_file))
- # redirect to the cache, if necessary
- try:
- from .file_utils import cached_path
- resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir)
- resolved_merges_file = cached_path(merges_file, cache_dir=cache_dir)
- except EnvironmentError:
- logger.error(
- "Model name '{}' was not found in model name list ({}). "
- "We assumed '{}' was a path or url but couldn't find files {} and {} "
- "at this path or url.".format(
- pretrained_model_name_or_path,
- ', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()),
- pretrained_model_name_or_path,
- vocab_file, merges_file))
- return None
- if resolved_vocab_file == vocab_file and resolved_merges_file == merges_file:
- logger.info("loading vocabulary file {}".format(vocab_file))
- logger.info("loading merges file {}".format(merges_file))
- else:
- logger.info("loading vocabulary file {} from cache at {}".format(
- vocab_file, resolved_vocab_file))
- logger.info("loading merges file {} from cache at {}".format(
- merges_file, resolved_merges_file))
- if pretrained_model_name_or_path in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP:
- # if we're using a pretrained model, ensure the tokenizer wont index sequences longer
- # than the number of positional embeddings
- max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[pretrained_model_name_or_path]
- kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
- # Instantiate tokenizer.
- if special_tokens_file and 'special_tokens' not in kwargs:
- special_tokens = open(special_tokens_file, encoding='utf-8').read().split('\n')[:-1]
- else:
- special_tokens = kwargs.pop('special_tokens', [])
- tokenizer = cls(
- resolved_vocab_file,
- resolved_merges_file,
- special_tokens=special_tokens,
- *inputs,
- **kwargs)
- return tokenizer
- def __init__(self, vocab_file, merges_file, errors='replace',
- special_tokens=None, max_len=None):
- self.max_len = max_len if max_len is not None else int(1e12)
- self.encoder = json.load(open(vocab_file))
- 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.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+""")
- self.special_tokens = {}
- self.special_tokens_decoder = {}
- self.set_special_tokens(special_tokens)
- def __len__(self):
- return len(self.encoder) + len(self.special_tokens)
- def set_special_tokens(self, special_tokens):
- """ Add a list of additional tokens to the encoder.
- The additional tokens are indexed starting from the last index of the
- current vocabulary in the order of the `special_tokens` list.
- """
- if not special_tokens:
- self.special_tokens = {}
- self.special_tokens_decoder = {}
- return
- self.special_tokens = dict((tok, len(self.encoder) + i)
- for i, tok in enumerate(special_tokens))
- self.special_tokens_decoder = {v: k for k, v in self.special_tokens.items()}
- logger.info("Special tokens {}".format(self.special_tokens))
- def bpe(self, token):
- if token in self.cache:
- return self.cache[token]
- word = tuple(token)
- pairs = get_pairs(word)
- if not pairs:
- return token
- while True:
- bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf')))
- if bigram not in self.bpe_ranks:
- break
- first, second = bigram
- new_word = []
- i = 0
- while i < len(word):
- try:
- j = word.index(first, i)
- new_word.extend(word[i:j])
- i = j
- except BaseException:
- new_word.extend(word[i:])
- break
- if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
- new_word.append(first + second)
- i += 2
- else:
- new_word.append(word[i])
- i += 1
- new_word = tuple(new_word)
- word = new_word
- if len(word) == 1:
- break
- else:
- pairs = get_pairs(word)
- word = ' '.join(word)
- self.cache[token] = word
- return word
- def tokenize(self, text):
- """ Tokenize a string. """
- bpe_tokens = []
- for token in re.findall(self.pat, text):
- if sys.version_info[0] == 2:
- token = ''.join(self.byte_encoder[ord(b)] for b in token)
- else:
- token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
- bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(' '))
- return bpe_tokens
- def convert_tokens_to_ids(self, tokens):
- """ Converts a sequence of tokens into ids using the vocab. """
- ids = []
- if isinstance(tokens, str) or (sys.version_info[0] == 2 and isinstance(tokens, unicode)):
- if tokens in self.special_tokens:
- return self.special_tokens[tokens]
- else:
- return self.encoder.get(tokens, 0)
- for token in tokens:
- if token in self.special_tokens:
- ids.append(self.special_tokens[token])
- else:
- ids.append(self.encoder.get(token, 0))
- if len(ids) > self.max_len:
- logger.warning(
- "Token indices sequence length is longer than the specified maximum "
- " sequence length for this OpenAI GPT model ({} > {}). Running this"
- " sequence through the model will result in indexing errors".format(
- len(ids), self.max_len)
- )
- return ids
- def convert_ids_to_tokens(self, ids, skip_special_tokens=False):
- """Converts a sequence of ids in BPE tokens using the vocab."""
- tokens = []
- for i in ids:
- if i in self.special_tokens_decoder:
- if not skip_special_tokens:
- tokens.append(self.special_tokens_decoder[i])
- else:
- tokens.append(self.decoder[i])
- return tokens
- def encode(self, text):
- return self.convert_tokens_to_ids(self.tokenize(text))
- def decode(self, tokens):
- text = ''.join([self.decoder[token] for token in tokens])
- text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors=self.errors)
- return text
- def save_vocabulary(self, vocab_path):
- """Save the tokenizer vocabulary and merge files to a directory."""
- if not os.path.isdir(vocab_path):
- logger.error("Vocabulary path ({}) should be a directory".format(vocab_path))
- return
- vocab_file = os.path.join(vocab_path, VOCAB_NAME)
- merge_file = os.path.join(vocab_path, MERGES_NAME)
- special_tokens_file = os.path.join(vocab_path, SPECIAL_TOKENS_NAME)
- with open(vocab_file, 'w', encoding='utf-8') as f:
- f.write(json.dumps(self.encoder, ensure_ascii=False))
- index = 0
- with open(merge_file, "w", encoding="utf-8") as writer:
- writer.write(u'#version: 0.2\n')
- for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
- if index != token_index:
- logger.warning("Saving vocabulary to {}: BPE merge indices are not consecutive."
- " Please check that the tokenizer is not corrupted!".format(merge_file))
- index = token_index
- writer.write(' '.join(bpe_tokens) + u'\n')
- index += 1
- index = len(self.encoder)
- with open(special_tokens_file, 'w', encoding='utf-8') as writer:
- for token, token_index in sorted(self.special_tokens.items(), key=lambda kv: kv[1]):
- if index != token_index:
- logger.warning("Saving special tokens vocabulary to {}: BPE indices are not consecutive."
- " Please check that the tokenizer is not corrupted!".format(special_tokens_file))
- index = token_index
- writer.write(token + u'\n')
- index += 1
- return vocab_file, merge_file, special_tokens_file
|