# 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.""" import json import logging import os from functools import lru_cache import regex as re from tokenizers import ByteLevelBPETokenizer from .tokenization_utils import PreTrainedTokenizer, PreTrainedTokenizerFast 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", "gpt2-xl": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-xl-vocab.json", "distilgpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/distilgpt2-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", "gpt2-xl": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-xl-merges.txt", "distilgpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/distilgpt2-merges.txt", }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "gpt2": 1024, "gpt2-medium": 1024, "gpt2-large": 1024, "gpt2-xl": 1024, "distilgpt2": 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. """ 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 => the encoding methods should be called with the ``add_prefix_space`` flag set to ``True``. Otherwise, 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" This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the methods. Users should refer to the superclass for more information regarding methods. Args: vocab_file (:obj:`str`): Path to the vocabulary file. merges_file (:obj:`str`): Path to the merges file. errors (:obj:`str`, `optional`, defaults to "replace"): Paradigm to follow when decoding bytes to UTF-8. See `bytes.decode `__ for more information. unk_token (:obj:`string`, `optional`, defaults to `<|endoftext|>`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. bos_token (:obj:`string`, `optional`, defaults to `<|endoftext|>`): The beginning of sequence token. eos_token (:obj:`string`, `optional`, defaults to `<|endoftext|>`): The end of sequence token. """ 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().__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, **kwargs) with open(vocab_file, encoding="utf-8") as vocab_handle: self.encoder = json.load(vocab_handle) 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()} with open(merges_file, encoding="utf-8") as merges_handle: bpe_merges = merges_handle.read().split("\n")[1:-1] bpe_merges = [tuple(merge.split()) for merge in bpe_merges] 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 get_vocab(self): return dict(self.encoder, **self.added_tokens_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) except ValueError: new_word.extend(word[i:]) break else: new_word.extend(word[i:j]) i = j 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): 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) 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 (str) 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 vocabulary and special tokens file to a directory. Args: save_directory (:obj:`str`): The directory in which to save the vocabulary. Returns: :obj:`Tuple(str)`: Paths to the files saved. """ 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("#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) + "\n") index += 1 return vocab_file, merge_file def prepare_for_tokenization(self, text, **kwargs): if "add_prefix_space" in kwargs and kwargs["add_prefix_space"]: return " " + text return text class GPT2TokenizerFast(PreTrainedTokenizerFast): """ Constructs a "Fast" GPT-2 BPE tokenizer (backed by HuggingFace's `tokenizers` library). Peculiarities: - Byte-level Byte-Pair-Encoding - Requires a space to start the input string => the encoding methods should be called with the ``add_prefix_space`` flag set to ``True``. Otherwise, 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" This tokenizer inherits from :class:`~transformers.PreTrainedTokenizerFast` which contains most of the methods. Users should refer to the superclass for more information regarding methods. Args: vocab_file (:obj:`str`): Path to the vocabulary file. merges_file (:obj:`str`): Path to the merges file. errors (:obj:`str`, `optional`, defaults to "replace"): Paradigm to follow when decoding bytes to UTF-8. See `bytes.decode `__ for more information. unk_token (:obj:`string`, `optional`, defaults to `<|endoftext|>`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. bos_token (:obj:`string`, `optional`, defaults to `<|endoftext|>`): The beginning of sequence token. eos_token (:obj:`string`, `optional`, defaults to `<|endoftext|>`): The end of sequence token. add_prefix_space (:obj:`bool`, `optional`, defaults to `False`): Whether to add a leading space to the first word. This allows to treat the leading word just as any other word. (GPT2 tokenizer detect beginning of words by the preceeding space) trim_offsets (:obj:`bool`, `optional`, defaults to `True`): Whether the post processing step should trim offsets to avoid including whitespaces. """ 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, unk_token="<|endoftext|>", bos_token="<|endoftext|>", eos_token="<|endoftext|>", add_prefix_space=False, trim_offsets=True, **kwargs ): super().__init__( ByteLevelBPETokenizer( vocab_file=vocab_file, merges_file=merges_file, add_prefix_space=add_prefix_space, trim_offsets=trim_offsets, ), bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, **kwargs, )