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| import json | |
| from pathlib import Path | |
| from typing import Optional, Union | |
| import os | |
| import torch | |
| class Tokenizer: | |
| def __init__(self, checkpoint_dir: Union[Path, str]) -> None: | |
| checkpoint_dir = Path(checkpoint_dir) | |
| if not checkpoint_dir.exists(): | |
| raise NotADirectoryError(f"The checkpoint directory does not exist: {str(checkpoint_dir)}") | |
| self.use_bos = self.check_if_bos_token_used(checkpoint_dir) | |
| self.bos_id = None | |
| self.eos_id = None | |
| # Debug statements | |
| # print(f'tokenizer.py checkpoint_dir is : {checkpoint_dir}') | |
| # print(f'checking the file : {(checkpoint_dir / "tokenizer.json").is_file()}') | |
| # print(f'Current working directory is : {os.getcwd()}') | |
| # curr_dir = os.getcwd() | |
| # print(f'contents in pwd are : {os.listdir(curr_dir)}') | |
| # some checkpoints have both files, `.model` takes precedence | |
| if (vocabulary_path := checkpoint_dir / "tokenizer.model").is_file(): | |
| from sentencepiece import SentencePieceProcessor | |
| self.processor = SentencePieceProcessor(model_file=str(vocabulary_path)) | |
| self.backend = "sentencepiece" | |
| self.bos_id = self.processor.bos_id() | |
| self.eos_id = self.processor.eos_id() | |
| elif (vocabulary_path := checkpoint_dir / "tokenizer.json").is_file(): | |
| from tokenizers import Tokenizer as HFTokenizer | |
| self.processor = HFTokenizer.from_file(str(vocabulary_path)) | |
| self.backend = "huggingface" | |
| if (special_tokens_path := checkpoint_dir / "tokenizer_config.json").is_file(): | |
| with open(special_tokens_path) as fp: | |
| config = json.load(fp) | |
| bos_token = config.get("bos_token") | |
| self.bos_id = self.token_to_id(bos_token) if bos_token is not None else None | |
| eos_token = config.get("eos_token") | |
| self.eos_id = self.token_to_id(eos_token) if eos_token is not None else None | |
| if (special_tokens_path := checkpoint_dir / "generation_config.json").is_file(): | |
| with open(special_tokens_path) as fp: | |
| config = json.load(fp) | |
| if self.bos_id is None: | |
| self.bos_id = config.get("bos_token_id") | |
| if self.eos_id is None: | |
| self.eos_id = config.get("eos_token_id") | |
| else: | |
| raise NotImplementedError | |
| def vocab_size(self) -> int: | |
| if self.backend == "huggingface": | |
| return self.processor.get_vocab_size(with_added_tokens=False) | |
| if self.backend == "sentencepiece": | |
| return self.processor.vocab_size() | |
| raise RuntimeError | |
| def token_to_id(self, token: str) -> int: | |
| if self.backend == "huggingface": | |
| id_ = self.processor.token_to_id(token) | |
| elif self.backend == "sentencepiece": | |
| id_ = self.processor.piece_to_id(token) | |
| else: | |
| raise RuntimeError | |
| if id_ is None: | |
| raise ValueError(f"token {token!r} not found in the collection.") | |
| return id_ | |
| def check_if_bos_token_used(self, checkpoint_dir: Path) -> bool: | |
| if not (tokenizer_config_path := checkpoint_dir / "tokenizer_config.json").is_file(): | |
| return False | |
| with open(tokenizer_config_path) as fp: | |
| config = json.load(fp) | |
| if any(config.get(check, False) for check in ("add_bos_token", "add_prefix_space")): | |
| return True | |
| # for examples that also use the Llama tokenizer, but do not have or set add_bos_token to True. | |
| # ex: https://huggingface.co/stabilityai/StableBeluga2/blob/main/tokenizer_config.json#L2 | |
| return config.get("add_bos_token") is None and config.get("tokenizer_class") == "LlamaTokenizer" | |
| def encode( | |
| self, | |
| string: str, | |
| device: Optional[torch.device] = None, | |
| bos: Optional[bool] = None, | |
| eos: bool = False, | |
| max_length: int = -1, | |
| ) -> torch.Tensor: | |
| if self.backend == "huggingface": | |
| tokens = self.processor.encode(string).ids | |
| elif self.backend == "sentencepiece": | |
| tokens = self.processor.encode(string) | |
| else: | |
| raise RuntimeError | |
| if bos or (bos is None and self.use_bos): | |
| bos_id = self.bos_id | |
| if bos_id is None: | |
| raise NotImplementedError("This tokenizer does not have a defined a bos token") | |
| tokens = [bos_id] + tokens | |
| if eos: | |
| tokens = tokens + [self.eos_id] | |
| if max_length > 0: | |
| tokens = tokens[:max_length] | |
| return torch.tensor(tokens, dtype=torch.int, device=device) | |
| def decode(self, tensor: torch.Tensor) -> str: | |
| tokens = [tensor.item()] if tensor.ndim == 0 else tensor.tolist() | |
| return self.processor.decode(tokens) | |