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| """Tokenization classes for RWKV5.""" |
|
|
| import os |
| import re |
| from typing import TYPE_CHECKING, List, Optional, Tuple |
|
|
| from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer |
| from transformers.utils import logging |
|
|
|
|
| if TYPE_CHECKING: |
| pass |
|
|
| logger = logging.get_logger(__name__) |
|
|
| VOCAB_FILES_NAMES = { |
| "vocab_file": "vocab.txt", |
| } |
| PRETRAINED_VOCAB_FILES_MAP = { |
| "vocab_file": { |
| "ArthurZ/rwkv-5-utf": "https://huggingface.co/ArthurZ/rwkv-5-utf/blob/main/vocab.txt", |
| }, |
| } |
|
|
|
|
| def whitespace_tokenize(text): |
| """Runs basic whitespace cleaning and splitting on a piece of text. |
| The separators are kept |
| """ |
| text = text.strip() |
| if not text: |
| return [] |
| tokens = re.split(b"(?= )", text) |
| return tokens |
|
|
|
|
| class WordpieceTokenizer(object): |
| """Runs WordPiece tokenization.""" |
|
|
| def __init__(self, vocab, unk_token): |
| self.vocab = vocab |
| self.unk_token = unk_token |
|
|
| def tokenize(self, text): |
| """ |
| Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform |
| tokenization using the given vocabulary. |
| |
| For example, `input = "unaffable"` wil return as output `["un", "##aff", "##able"]`. |
| |
| Args: |
| text: A single token or whitespace separated tokens. This should have |
| already been passed through *BasicTokenizer*. |
| |
| Returns: |
| A list of wordpiece tokens. |
| """ |
|
|
| output_tokens = [] |
| for token in whitespace_tokenize(text): |
| chars = list(token) |
| is_bad = False |
| start = 0 |
| sub_tokens = [] |
| while start < len(chars): |
| end = len(chars) |
| cur_substr = None |
| while start < end: |
| substr = bytes(chars[start:end]) |
| if substr in self.vocab: |
| cur_substr = substr |
| break |
| end -= 1 |
| if cur_substr is None: |
| is_bad = True |
| break |
| try: |
| cur_substr = cur_substr.decode() |
| except UnicodeDecodeError: |
| cur_substr = str(cur_substr) |
| sub_tokens.append(cur_substr) |
| start = end |
| if is_bad: |
| output_tokens.append(self.unk_token) |
| else: |
| output_tokens.extend(sub_tokens) |
| return output_tokens |
|
|
|
|
| class Rwkv5Tokenizer(PreTrainedTokenizer): |
| vocab_files_names = VOCAB_FILES_NAMES |
| pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP |
| max_model_input_sizes = {"ArthurZ/rwkv-5-utf": 2048} |
|
|
| model_input_names = ["input_ids", "attention_mask"] |
|
|
| def __init__(self, vocab_file, bos_token="<s>", eos_token="<s>", unk_token="<s>", **kwargs): |
| if not os.path.isfile(vocab_file): |
| raise ValueError( |
| f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained" |
| " model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" |
| ) |
|
|
| with open(vocab_file, "r") as reader: |
| tokens = reader.readlines() |
| vocab = {} |
| for index, token in enumerate(tokens): |
| token = eval(token.rstrip("\n")) |
| vocab[token] = index |
|
|
| self.add_bos_token = True |
| self.encoder = vocab |
| self.decoder = {v: k for k, v in vocab.items()} |
| self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.encoder, unk_token=str(unk_token)) |
| self._added_tokens_decoder = {0: AddedToken(str(bos_token))} |
| super().__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, **kwargs) |
|
|
| @property |
| def vocab_size(self): |
| return len(self.encoder) |
|
|
| def get_vocab(self): |
| vocab = {str(self.convert_ids_to_tokens(i)): i for i in range(self.vocab_size)} |
| vocab.update(self.added_tokens_encoder) |
| return vocab |
|
|
| def _tokenize(self, text, split_special_tokens=False): |
| return self.wordpiece_tokenizer.tokenize(text.encode("utf-8")) |
|
|
| def _convert_token_to_id(self, token): |
| """Converts a token (byte) to an id using the vocab.""" |
| if token.startswith("b'\\"): |
| token = eval(token) |
| elif not isinstance(token, bytes): |
| token = token.encode("utf-8", errors="replace") |
| return self.encoder.get(token, self.unk_token_id) |
|
|
| def _convert_id_to_token(self, index): |
| """Converts an index (integer) in a token (byte) using the vocab.""" |
| token = self.decoder.get(index, self.unk_token) |
| if isinstance(token, (bytes)): |
| token = token.decode("utf-8", errors="replace") |
| return token |
|
|
| def convert_tokens_to_string(self, tokens): |
| """Converts a sequence of tokens (bytes) in a single string. Additional tokens are encoded to bytes""" |
| out_string = b"".join([k.encode(errors="replace") if isinstance(k, str) else k for k in tokens]).decode( |
| "utf-8" |
| ) |
| return out_string |
|
|
| def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: |
| index = 0 |
| if os.path.isdir(save_directory): |
| vocab_file = os.path.join( |
| save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] |
| ) |
| else: |
| vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory |
| with open(vocab_file, "w") as writer: |
| for token, token_index in sorted(self.encoder.items(), key=lambda kv: kv[1]): |
| if index != token_index: |
| logger.warning( |
| f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive." |
| " Please check that the vocabulary is not corrupted!" |
| ) |
| index = token_index |
| writer.write(str(token) + "\n") |
| index += 1 |
| return (vocab_file,) |
|
|
| def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): |
| if self.add_bos_token: |
| bos_token_ids = [self.bos_token_id] |
| else: |
| bos_token_ids = [] |
|
|
| output = bos_token_ids + token_ids_0 |
|
|
| if token_ids_1 is None: |
| return output |
|
|
| return output + bos_token_ids + token_ids_1 |
|
|
| def get_special_tokens_mask( |
| self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False |
| ) -> List[int]: |
| """ |
| Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding |
| special tokens using the tokenizer `prepare_for_model` or `encode_plus` methods. |
| |
| Args: |
| token_ids_0 (`List[int]`): |
| List of IDs. |
| token_ids_1 (`List[int]`, *optional*): |
| Optional second list of IDs for sequence pairs. |
| already_has_special_tokens (`bool`, *optional*, defaults to `False`): |
| Whether or not the token list is already formatted with special tokens for the model. |
| |
| Returns: |
| `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. |
| """ |
| if already_has_special_tokens: |
| return super().get_special_tokens_mask( |
| token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True |
| ) |
|
|
| if not self.add_bos_token: |
| return super().get_special_tokens_mask( |
| token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=False |
| ) |
|
|
| if token_ids_1 is None: |
| return [1] + ([0] * len(token_ids_0)) |
| return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) |
|
|
