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"""Tokenization classes for InternLM.""" |
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import os |
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from shutil import copyfile |
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from typing import Any, Dict, List, Optional, Tuple |
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import sentencepiece as spm |
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from transformers.tokenization_utils import PreTrainedTokenizer |
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from transformers.utils import logging |
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logger = logging.get_logger(__name__) |
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VOCAB_FILES_NAMES = {"vocab_file": "./tokenizer.model"} |
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PRETRAINED_VOCAB_FILES_MAP = {} |
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class InternLMTokenizer(PreTrainedTokenizer): |
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""" |
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Construct a InternLM tokenizer. Based on byte-level Byte-Pair-Encoding. |
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Args: |
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vocab_file (`str`): |
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Path to the vocabulary file. |
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""" |
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vocab_files_names = VOCAB_FILES_NAMES |
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pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP |
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model_input_names = ["input_ids", "attention_mask"] |
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_auto_class = "AutoTokenizer" |
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def __init__( |
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self, |
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vocab_file, |
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unk_token="<unk>", |
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bos_token="<s>", |
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eos_token="</s>", |
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pad_token="</s>", |
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sp_model_kwargs: Optional[Dict[str, Any]] = None, |
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add_bos_token=True, |
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add_eos_token=False, |
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decode_with_prefix_space=False, |
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clean_up_tokenization_spaces=False, |
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**kwargs, |
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): |
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self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs |
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self.vocab_file = vocab_file |
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self.add_bos_token = add_bos_token |
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self.add_eos_token = add_eos_token |
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self.decode_with_prefix_space = decode_with_prefix_space |
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self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) |
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self.sp_model.Load(vocab_file) |
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self._no_prefix_space_tokens = None |
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super().__init__( |
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bos_token=bos_token, |
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eos_token=eos_token, |
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unk_token=unk_token, |
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pad_token=pad_token, |
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clean_up_tokenization_spaces=clean_up_tokenization_spaces, |
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**kwargs, |
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) |
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@property |
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def no_prefix_space_tokens(self): |
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if self._no_prefix_space_tokens is None: |
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vocab = self.convert_ids_to_tokens(list(range(self.vocab_size))) |
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self._no_prefix_space_tokens = {i for i, tok in enumerate(vocab) if not tok.startswith("▁")} |
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return self._no_prefix_space_tokens |
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@property |
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def vocab_size(self): |
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"""Returns vocab size""" |
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return self.sp_model.get_piece_size() |
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@property |
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def bos_token_id(self) -> Optional[int]: |
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return self.sp_model.bos_id() |
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@property |
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def eos_token_id(self) -> Optional[int]: |
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return self.sp_model.eos_id() |
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def get_vocab(self): |
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"""Returns vocab as a dict""" |
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vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} |
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vocab.update(self.added_tokens_encoder) |
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return vocab |
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def _tokenize(self, text): |
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"""Returns a tokenized string.""" |
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return self.sp_model.encode(text, out_type=str) |
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def _convert_token_to_id(self, token): |
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"""Converts a token (str) in an id using the vocab.""" |
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return self.sp_model.piece_to_id(token) |
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def _convert_id_to_token(self, index): |
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"""Converts an index (integer) in a token (str) using the vocab.""" |
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token = self.sp_model.IdToPiece(index) |
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return token |
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def _maybe_add_prefix_space(self, tokens, decoded): |
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if tokens and tokens[0] not in self.no_prefix_space_tokens: |
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return " " + decoded |
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else: |
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return decoded |
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def convert_tokens_to_string(self, tokens): |
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"""Converts a sequence of tokens (string) in a single string.""" |
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current_sub_tokens = [] |
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out_string = "" |
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prev_is_special = False |
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for token in tokens: |
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if token in self.all_special_tokens: |
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if not prev_is_special: |
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out_string += " " |
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out_string += self.sp_model.decode(current_sub_tokens) + token |
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prev_is_special = True |
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current_sub_tokens = [] |
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else: |
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current_sub_tokens.append(token) |
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prev_is_special = False |
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out_string += self.sp_model.decode(current_sub_tokens) |
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out_string = self.clean_up_tokenization(out_string) |
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out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string) |
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return out_string[1:] |
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def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]: |
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""" |
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Save the vocabulary and special tokens file to a directory. |
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Args: |
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save_directory (`str`): |
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The directory in which to save the vocabulary. |
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Returns: |
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`Tuple(str)`: Paths to the files saved. |
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""" |
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if not os.path.isdir(save_directory): |
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logger.error(f"Vocabulary path ({save_directory}) should be a directory") |
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return |
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out_vocab_file = os.path.join( |
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] |
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) |
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if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file): |
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copyfile(self.vocab_file, out_vocab_file) |
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elif not os.path.isfile(self.vocab_file): |
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with open(out_vocab_file, "wb") as fi: |
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content_spiece_model = self.sp_model.serialized_model_proto() |
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fi.write(content_spiece_model) |
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return (out_vocab_file,) |
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def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): |
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if self.add_bos_token: |
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bos_token_ids = [self.bos_token_id] |
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else: |
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bos_token_ids = [] |
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output = bos_token_ids + token_ids_0 |
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if token_ids_1 is not None: |
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output = output + token_ids_1 |
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if self.add_eos_token: |
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output = output + [self.eos_token_id] |
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return output |
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def get_special_tokens_mask( |
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False |
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) -> List[int]: |
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""" |
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Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding |
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special tokens using the tokenizer `prepare_for_model` method. |
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Args: |
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token_ids_0 (`List[int]`): |
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List of IDs. |
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token_ids_1 (`List[int]`, *optional*): |
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Optional second list of IDs for sequence pairs. |
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already_has_special_tokens (`bool`, *optional*, defaults to `False`): |
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Whether or not the token list is already formatted with special tokens for the model. |
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Returns: |
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`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. |
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""" |
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if already_has_special_tokens: |
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return super().get_special_tokens_mask( |
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token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True |
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) |
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if token_ids_1 is None: |
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return [1] + ([0] * len(token_ids_0)) + [1] |
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return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1] |
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def create_token_type_ids_from_sequences( |
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None |
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) -> List[int]: |
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""" |
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Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make |
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use of token type ids, therefore a list of zeros is returned. |
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Args: |
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token_ids_0 (`List[int]`): |
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List of IDs. |
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token_ids_1 (`List[int]`, *optional*): |
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Optional second list of IDs for sequence pairs. |
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Returns: |
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`List[int]`: List of zeros. |
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""" |
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eos = [self.eos_token_id] |
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if token_ids_1 is None: |
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return len(token_ids_0 + eos) * [0] |
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return len(token_ids_0 + eos + token_ids_1 + eos) * [0] |
