# CREDITS: tiktoken @openai # https://github.com/openai/tiktoken # # Copyright 2024 OpenNLPLab # # 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. # coding=utf-8 import base64 import logging import os from typing import Collection, Dict, List, Set, Tuple, Union import unicodedata import tiktoken from transformers import AddedToken, AutoTokenizer, PreTrainedTokenizer logger = logging.getLogger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "transnormer_100k.tiktoken"} PAT_STR = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+""" SPECIAL_TOKENS_DICT = {'<|endoftext|>': 100257, '<|fim_prefix|>': 100258, '<|fim_middle|>': 100259, '<|fim_suffix|>': 100260, '<|endofprompt|>': 100276, '<|J2PM|>': 100256, '': 100261, '': 100262, '': 100263, '': 100264} SPECIAL_TOKENS_SET = set(SPECIAL_TOKENS_DICT.keys()) # as the default behavior is changed to allow special tokens in # regular texts, the surface forms of special tokens need to be # as different as possible to minimize the impact # changed to use actual index to avoid misconfiguration with vocabulary expansion def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]: with open(tiktoken_bpe_file, "rb") as f: contents = f.read() return { base64.b64decode(token): int(rank) for token, rank in (line.split() for line in contents.splitlines() if line) } class GPT4Tokenizer(PreTrainedTokenizer): vocab_files_names = VOCAB_FILES_NAMES def __init__( self, vocab_file, errors="replace", extra_vocab_file=None, **kwargs, ): super().__init__(**kwargs) # how to handle errors in decoding UTF-8 byte sequences # use ignore if you are in streaming inference self.errors = errors self.mergeable_ranks = _load_tiktoken_bpe(vocab_file) # type: Dict[bytes, int] self.special_tokens = SPECIAL_TOKENS_DICT enc = tiktoken.Encoding( "transnormer_100k", pat_str=PAT_STR, mergeable_ranks=self.mergeable_ranks, special_tokens=self.special_tokens, ) self.decoder = { v: k for k, v in self.mergeable_ranks.items() } # type: dict[int, bytes|str] self.decoder.update({v: k for k, v in self.special_tokens.items()}) self.tokenizer = enc # type: tiktoken.Encoding self.eod_id = self.tokenizer.eot_token self.pad_token_id = 100262 self.bos_token_id = 100261 self.eos_token_id = self.eod_id def __getstate__(self): # for pickle lovers state = self.__dict__.copy() del state["tokenizer"] return state def __setstate__(self, state): # tokenizer is not python native; don't pass it; rebuild it self.__dict__.update(state) enc = tiktoken.Encoding( "transnormer_100k", pat_str=PAT_STR, mergeable_ranks=self.mergeable_ranks, special_tokens=self.special_tokens, ) self.tokenizer = enc def __len__(self) -> int: return self.tokenizer.n_vocab def get_vocab(self) -> Dict[bytes, int]: return self.mergeable_ranks def convert_tokens_to_ids( self, tokens: Union[bytes, str, List[Union[bytes, str]]] ) -> List[int]: ids = [] if isinstance(tokens, (str, bytes)): if tokens in self.special_tokens: return self.special_tokens[tokens] else: return self.mergeable_ranks.get(tokens) for token in tokens: if token in self.special_tokens: ids.append(self.special_tokens[token]) else: ids.append(self.mergeable_ranks.get(token)) return ids def _add_tokens( self, new_tokens: Union[List[str], List[AddedToken]], special_tokens: bool = False, ) -> int: if not special_tokens and new_tokens: raise ValueError("Adding regular tokens is not supported") for token in new_tokens: surface_form = token.content if isinstance(token, AddedToken) else token if surface_form not in SPECIAL_TOKENS_SET: raise ValueError("Adding unknown special tokens is not supported") return 0 def save_vocabulary(self, save_directory: str, **kwargs) -> Tuple[str]: """ Save only the vocabulary of the tokenizer (vocabulary). Returns: `Tuple(str)`: Paths to the files saved. """ file_path = os.path.join(save_directory, "transnormer_100k.tiktoken") with open(file_path, "w", encoding="utf8") as w: for k, v in self.mergeable_ranks.items(): line = base64.b64encode(k).decode("utf8") + " " + str(v) + "\n" w.write(line) return (file_path,) def tokenize( self, text: str, allowed_special: Union[Set, str] = "all", disallowed_special: Union[Collection, str] = (), **kwargs, ) -> List[Union[bytes, str]]: """ Converts a string in a sequence of tokens. Args: text (`str`): The sequence to be encoded. allowed_special (`Literal["all"]` or `set`): The surface forms of the tokens to be encoded as special tokens in regular texts. Default to "all". disallowed_special (`Literal["all"]` or `Collection`): The surface forms of the tokens that should not be in regular texts and trigger errors. Default to an empty tuple. kwargs (additional keyword arguments, *optional*): Will be passed to the underlying model specific encode method. Returns: `List[bytes|str]`: The list of tokens. """ tokens = [] # this implementation takes a detour: text -> token id -> token surface forms for t in self.tokenizer.encode( text, allowed_special=allowed_special, disallowed_special=disallowed_special ): tokens.append(self.decoder[t]) return tokens def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str: """ Converts a sequence of tokens in a single string. """ text = "" temp = b"" for t in tokens: if isinstance(t, str): if temp: text += temp.decode("utf-8", errors=self.errors) temp = b"" text += t elif isinstance(t, bytes): temp += t else: raise TypeError("token should only be of type types or str") if temp: text += temp.decode("utf-8", errors=self.errors) return text @property def vocab_size(self): return self.tokenizer.n_vocab def _convert_id_to_token(self, index: int) -> Union[bytes, str]: """Converts an id to a token, special tokens included""" if index in self.decoder: return self.decoder[index] raise ValueError("unknown ids") def _convert_token_to_id(self, token: Union[bytes, str]) -> int: """Converts a token to an id using the vocab, special tokens included""" if token in self.special_tokens: return self.special_tokens[token] if token in self.mergeable_ranks: return self.mergeable_ranks[token] raise ValueError("unknown token") def _tokenize(self, text: str, **kwargs): """ Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces). Do NOT take care of added tokens. """ raise NotImplementedError def _decode( self, token_ids: Union[int, List[int]], skip_special_tokens: bool = False, errors: str = None, **kwargs, ) -> str: if isinstance(token_ids, int): token_ids = [token_ids] if skip_special_tokens: token_ids = [i for i in token_ids if i < self.eod_id] return self.tokenizer.decode(token_ids, errors=errors or self.errors)