# 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 from .tokenization_utils import PreTrainedTokenizer logger = logging.getLogger(__name__) VOCAB_FILES_NAMES = { 'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', } PRETRAINED_VOCAB_FILES_MAP = { 'vocab_file': { 'gpt2': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json", 'gpt2-medium': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-vocab.json", 'gpt2-large': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-large-vocab.json", }, 'merges_file': { 'gpt2': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt", 'gpt2-medium': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-merges.txt", 'gpt2-large': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-large-merges.txt", }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { 'gpt2': 1024, 'gpt2-medium': 1024, 'gpt2-large': 1024, } @lru_cache() def bytes_to_unicode(): """ 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. """ _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(PreTrainedTokenizer): """ GPT-2 BPE tokenizer. Peculiarities: - Byte-level Byte-Pair-Encoding - Requires a space to start the input string => will add a space is there isn't. As a consequence, this tokenizer `encode` and `decode` method will not conserve the absence of a space at the beginning of a string: `tokenizer.decode(tokenizer.encode("Hello")) = " Hello" """ vocab_files_names = VOCAB_FILES_NAMES 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="<|endoftext|>", bos_token="<|endoftext|>", eos_token="<|endoftext|>", **kwargs): super(GPT2Tokenizer, self).__init__(bos_token=bos_token, eos_token=eos_token, 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 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.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) 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: 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. """ text = ' ' + text # GPT-2 (and RoBERTa) tokenizers need at least one space to begin the sentence with. 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) # 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 bpe_tokens def _convert_token_to_id(self, token): """ Converts a token (str/unicode) in an id using the vocab. """ return self.encoder.get(token, self.encoder.get(self.unk_token)) def _convert_id_to_token(self, index): """Converts an index (integer) in a token (string/unicode) using the vocab.""" return self.decoder.get(index) 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 def save_vocabulary(self, save_directory): """Save the tokenizer vocabulary and merge files to a directory.""" if not os.path.isdir(save_directory): logger.error("Vocabulary path ({}) should be a directory".format(save_directory)) return vocab_file = os.path.join(save_directory, VOCAB_FILES_NAMES['vocab_file']) merge_file = os.path.join(save_directory, VOCAB_FILES_NAMES['merges_file']) 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 return vocab_file, merge_file # XX added def add_special_tokens_single_sentence(self, token_ids): return [self.added_tokens_encoder['']] + token_ids + [self.added_tokens_encoder['']]