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| | """ |
| | Forked from the file src/transformers/models/bert_generation/tokenization_bert_generation.py from the HuggingFace Transformers library. |
| | Permalink: https://github.com/huggingface/transformers/blob/04ab5605fbb4ef207b10bf2772d88c53fc242e83/src/transformers/models/bert_generation/tokenization_bert_generation.py |
| | |
| | Tokenizer class for ReplitLM |
| | Class is modified for compatibility with custom vocabulary and to achieve desired encode/decode behavior for Replit Code V1 3B model. |
| | """ |
| | import os |
| | import sentencepiece as spm |
| | from shutil import copyfile |
| | from transformers import PreTrainedTokenizer |
| | from typing import Any, Dict, List, Optional, Tuple |
| | VOCAB_FILES_NAMES = {'vocab_file': 'spiece.model'} |
| |
|
| | class ReplitLMTokenizer(PreTrainedTokenizer): |
| | """ |
| | Construct a ReplitLMTokenizer tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). |
| | This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. |
| | |
| | Args: |
| | vocab_file (`str`): |
| | [SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that |
| | contains the vocabulary necessary to instantiate a tokenizer. |
| | eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`): |
| | The end of sequence token. |
| | bos_token (`str`, *optional*, defaults to `None`): |
| | The begin of sequence token. |
| | unk_token (`str`, *optional*, defaults to `"<|unk|>"`): |
| | 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. |
| | pad_token (`str`, *optional*, defaults to `"<|pad|>"`): |
| | The token used for padding, for example when batching sequences of different lengths. |
| | sp_model_kwargs (`dict`, *optional*): |
| | Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for |
| | SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, |
| | to set: |
| | - `enable_sampling`: Enable subword regularization. |
| | - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. |
| | - `nbest_size = {0,1}`: No sampling is performed. |
| | - `nbest_size > 1`: samples from the nbest_size results. |
| | - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) |
| | using forward-filtering-and-backward-sampling algorithm. |
| | - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for |
| | BPE-dropout. |
| | """ |
| | vocab_files_names = VOCAB_FILES_NAMES |
| | prefix_tokens: List[int] = [] |
| | model_input_names = ['input_ids', 'attention_mask'] |
| |
|
| | def __init__(self, vocab_file, bos_token=None, eos_token='<|endoftext|>', unk_token='<|unk|>', pad_token='<|pad|>', sep_token=None, sp_model_kwargs: Optional[Dict[str, Any]]=None, **kwargs) -> None: |
| | self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs |
| | super().__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, pad_token=pad_token, sep_token=sep_token, sp_model_kwargs=self.sp_model_kwargs, **kwargs) |
| | self.vocab_file = vocab_file |
| | self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) |
| | self.sp_model.Load(vocab_file) |
| |
|
| | @property |
| | def vocab_size(self): |
| | return self.sp_model.get_piece_size() |
| |
|
| | def get_vocab(self): |
| | vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} |
| | vocab.update(self.added_tokens_encoder) |
| | return vocab |
| |
|
| | def __getstate__(self): |
| | state = self.__dict__.copy() |
| | state['sp_model'] = None |
| | return state |
| |
|
| | def __setstate__(self, d): |
| | self.__dict__ = d |
| | if not hasattr(self, 'sp_model_kwargs'): |
| | self.sp_model_kwargs = {} |
| | self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) |
| | self.sp_model.load(self.vocab_file) |
| |
|
| | def _tokenize(self, text: str) -> List[str]: |
| | """Take as input a string and return a list of strings (tokens) for words/sub-words""" |
| | return self.sp_model.encode(text, out_type=str) |
| |
|
| | def _convert_token_to_id(self, token): |
| | """Converts a token (str) in an id using the vocab.""" |
| | return self.sp_model.piece_to_id(token) |
| |
|
| | def _convert_id_to_token(self, index): |
| | """Converts an index (integer) in a token (str) using the vocab.""" |
| | token = self.sp_model.id_to_piece(index) |
| | return token |
| |
|
| | def convert_tokens_to_string(self, tokens): |
| | """Converts a sequence of tokens (string) in a single string.""" |
| | return self.sp_model.decode(tokens) |
| |
|
| | def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str]=None) -> Tuple[str]: |
| | if not os.path.isdir(save_directory): |
| | raise ValueError(f'Vocabulary path ({save_directory}) should be a directory') |
| | out_vocab_file = os.path.join(save_directory, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']) |
| | if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file): |
| | copyfile(self.vocab_file, out_vocab_file) |
| | elif not os.path.isfile(self.vocab_file): |
| | with open(out_vocab_file, 'wb') as fi: |
| | content_spiece_model = self.sp_model.serialized_model_proto() |
| | fi.write(content_spiece_model) |
| | return (out_vocab_file,) |
