"""Tokenization classes for QWen.""" import base64 import unicodedata from pathlib import Path from typing import Collection, Dict, List, Set, Union import tiktoken from qwen_agent.log import logger VOCAB_FILES_NAMES = {'vocab_file': 'qwen.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+""" ENDOFTEXT = '<|endoftext|>' IMSTART = '<|im_start|>' IMEND = '<|im_end|>' # 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 EXTRAS = tuple((f'<|extra_{i}|>' for i in range(205))) # changed to use actual index to avoid misconfiguration with vocabulary expansion SPECIAL_START_ID = 151643 SPECIAL_TOKENS = tuple(enumerate( (( ENDOFTEXT, IMSTART, IMEND, ) + EXTRAS), start=SPECIAL_START_ID, )) SPECIAL_TOKENS_SET = set(t for i, t in SPECIAL_TOKENS) 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 QWenTokenizer: """QWen tokenizer.""" vocab_files_names = VOCAB_FILES_NAMES def __init__( self, vocab_file=None, errors='replace', extra_vocab_file=None, ): if not vocab_file: vocab_file = VOCAB_FILES_NAMES['vocab_file'] self._decode_use_source_tokenizer = False # 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 = {token: index for index, token in SPECIAL_TOKENS} # try load extra vocab from file if extra_vocab_file is not None: used_ids = set(self.mergeable_ranks.values()) | set(self.special_tokens.values()) extra_mergeable_ranks = _load_tiktoken_bpe(extra_vocab_file) for token, index in extra_mergeable_ranks.items(): if token in self.mergeable_ranks: logger.info(f'extra token {token} exists, skipping') continue if index in used_ids: logger.info(f'the index {index} for extra token {token} exists, skipping') continue self.mergeable_ranks[token] = index # the index may be sparse after this, but don't worry tiktoken.Encoding will handle this enc = tiktoken.Encoding( 'Qwen', pat_str=PAT_STR, mergeable_ranks=self.mergeable_ranks, special_tokens=self.special_tokens, ) assert len(self.mergeable_ranks) + len( self.special_tokens ) == enc.n_vocab, f'{len(self.mergeable_ranks) + len(self.special_tokens)} != {enc.n_vocab} in encoding' 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.im_start_id = self.special_tokens[IMSTART] self.im_end_id = self.special_tokens[IMEND] 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( 'Qwen', 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 tokenize( self, text: str, allowed_special: Union[Set, str] = 'all', disallowed_special: Union[Collection, str] = (), ) -> 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. Returns: `List[bytes|str]`: The list of tokens. """ tokens = [] text = unicodedata.normalize('NFC', text) # 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 _decode( self, token_ids: Union[int, List[int]], skip_special_tokens: bool = False, errors: str = None, ) -> 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) def encode(self, text: str) -> List[int]: return self.convert_tokens_to_ids(self.tokenize(text)) def count_tokens(self, text: str) -> int: return len(self.tokenize(text)) def truncate(self, text: str, max_token: int, start_token: int = 0) -> str: token_list = self.tokenize(text) token_list = token_list[start_token:min(len(token_list), start_token + max_token)] return self.convert_tokens_to_string(token_list) tokenizer = QWenTokenizer(Path(__file__).resolve().parent / 'qwen.tiktoken') def count_tokens(text: str) -> int: return tokenizer.count_tokens(text)