"""Tokenization classes for LLaMA-3.""" import os import base64 import logging import unicodedata from typing import Collection, Dict, List, Set, Tuple, Union import tiktoken from transformers import PreTrainedTokenizer, AddedToken logger = logging.getLogger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "llama.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+""" NUM_RESERVED_SPECIAL_TOKENS = 256 SPECIAL_TOKENS = [ "<|begin_of_text|>", "<|end_of_text|>", "<|reserved_special_token_0|>", "<|reserved_special_token_1|>", "<|reserved_special_token_2|>", "<|reserved_special_token_3|>", "<|start_header_id|>", "<|end_header_id|>", "<|reserved_special_token_4|>", "<|eot_id|>", ] + [f"<|reserved_special_token_{i}|>" for i in range(5, NUM_RESERVED_SPECIAL_TOKENS - 5)] 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 LLaMATokenizer(PreTrainedTokenizer): """LLaMA tokenizer.""" vocab_files_names = VOCAB_FILES_NAMES def __init__( self, vocab_file, errors="replace", **kwargs, ): super().__init__(**kwargs) self.errors = errors # how to handle errors in decoding self.mergeable_ranks = _load_tiktoken_bpe(vocab_file) # type: dict[bytes, int] self.special_tokens = { token: index for index, token in enumerate( SPECIAL_TOKENS, start=len(self.mergeable_ranks) ) } enc = tiktoken.Encoding( "LLaMA", 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.bos_id: int = self.special_tokens["<|begin_of_text|>"] self.eos_id: int = self.special_tokens["<|end_of_text|>"] self.pad_id: int = -1 self.stop_tokens = { self.special_tokens["<|end_of_text|>"], self.special_tokens["<|eot_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( "LLaMA", 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: 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, "llama.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 = [] 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 _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.eos_id] return self.tokenizer.decode(token_ids, errors=errors or self.errors)