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"""Tokenization classes for Gemmoe.""" |
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import os |
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from shutil import copyfile |
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple |
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import sentencepiece as spm |
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from transformers.utils import logging |
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from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer |
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if TYPE_CHECKING: |
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pass |
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logger = logging.get_logger(__name__) |
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VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"} |
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SPIECE_UNDERLINE = "▁" |
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class GemmoeTokenizer(PreTrainedTokenizer): |
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""" |
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Construct a Gemmoe tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is |
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no padding token in the original model. |
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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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unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<unk>"`): |
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The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this |
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token instead. |
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bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<bos>"`): |
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The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. |
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eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<eos>"`): |
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The end of sequence token. |
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pad_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<pad>"`): |
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A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by |
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attention mechanisms or loss computation. |
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sp_model_kwargs (`Dict[str, Any]`, `Optional`, *optional*): |
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Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for |
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SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, |
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to set: |
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- `enable_sampling`: Enable subword regularization. |
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- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. |
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- `nbest_size = {0,1}`: No sampling is performed. |
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- `nbest_size > 1`: samples from the nbest_size results. |
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- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) |
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using forward-filtering-and-backward-sampling algorithm. |
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- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for |
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BPE-dropout. |
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add_bos_token (`bool`, *optional*, defaults to `True`): |
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Whether or not to add an `bos_token` at the start of sequences. |
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add_eos_token (`bool`, *optional*, defaults to `False`): |
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Whether or not to add an `eos_token` at the end of sequences. |
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clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`): |
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Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like |
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extra spaces. |
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use_default_system_prompt (`bool`, *optional*, defaults to `False`): |
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Whether or not the default system prompt for Gemmoe should be used. |
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spaces_between_special_tokens (`bool`, *optional*, defaults to `False`): |
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Whether or not to add spaces between special tokens. |
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""" |
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vocab_files_names = VOCAB_FILES_NAMES |
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model_input_names = ["input_ids", "attention_mask"] |
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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="<bos>", |
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eos_token="<eos>", |
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pad_token="<pad>", |
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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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clean_up_tokenization_spaces=False, |
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use_default_system_prompt=False, |
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spaces_between_special_tokens=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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bos_token = AddedToken(bos_token, normalized=False, special=True) if isinstance(bos_token, str) else bos_token |
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eos_token = AddedToken(eos_token, normalized=False, special=True) if isinstance(eos_token, str) else eos_token |
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unk_token = AddedToken(unk_token, normalized=False, special=True) if isinstance(unk_token, str) else unk_token |
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pad_token = AddedToken(pad_token, normalized=False, special=True) if isinstance(pad_token, str) else pad_token |
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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.use_default_system_prompt = use_default_system_prompt |
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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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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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add_bos_token=add_bos_token, |
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add_eos_token=add_eos_token, |
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sp_model_kwargs=self.sp_model_kwargs, |
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clean_up_tokenization_spaces=clean_up_tokenization_spaces, |
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use_default_system_prompt=use_default_system_prompt, |
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spaces_between_special_tokens=spaces_between_special_tokens, |
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**kwargs, |
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) |
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def __getstate__(self): |
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state = self.__dict__.copy() |
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state["sp_model"] = None |
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state["sp_model_proto"] = self.sp_model.serialized_model_proto() |
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return state |
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def __setstate__(self, d): |
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self.__dict__ = d |
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self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) |
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self.sp_model.LoadFromSerializedProto(self.sp_model_proto) |
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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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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, **kwargs): |
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""" |
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Returns a tokenized string. The Gemmoe tokenizer never adds a prefix space. |
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""" |
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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 _decode( |
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self, |
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token_ids: List[int], |
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skip_special_tokens: bool = False, |
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spaces_between_special_tokens: bool = False, |
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**kwargs, |
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) -> str: |
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sub_texts = [] |
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current_sub_text = [] |
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for ids in token_ids: |
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if skip_special_tokens and ids in self.all_special_ids: |
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continue |
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if ids in self._added_tokens_decoder: |
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if current_sub_text: |
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sub_texts.append(self.sp_model.decode(current_sub_text)) |
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sub_texts.append(self._added_tokens_decoder[ids].content) |
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current_sub_text = [] |
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else: |
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current_sub_text.append(ids) |
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if current_sub_text: |
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sub_texts.append(self.sp_model.decode(current_sub_text)) |
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if spaces_between_special_tokens: |
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sub_texts = " ".join(sub_texts) |
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else: |
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sub_texts = "".join(sub_texts) |
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return sub_texts |
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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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for token in tokens: |
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if token in self._added_tokens_encoder: |
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out_string += self.sp_model.decode(current_sub_tokens) + token |
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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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out_string += self.sp_model.decode(current_sub_tokens) |
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return out_string |
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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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bos_token_id = [self.bos_token_id] if self.add_bos_token else [] |
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eos_token_id = [self.eos_token_id] if self.add_eos_token else [] |
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output = bos_token_id + token_ids_0 + eos_token_id |
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if token_ids_1 is not None: |
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output = output + bos_token_id + token_ids_1 + 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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bos_token_id = [1] if self.add_bos_token else [] |
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eos_token_id = [1] if self.add_eos_token else [] |
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if token_ids_1 is None: |
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return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id |
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return ( |
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bos_token_id |
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+ ([0] * len(token_ids_0)) |
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+ eos_token_id |
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+ bos_token_id |
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+ ([0] * len(token_ids_1)) |
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+ eos_token_id |
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) |
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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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Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT |
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sequence pair mask has the following format: |
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``` |
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0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 |
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| first sequence | second sequence | |
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``` |
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if token_ids_1 is None, only returns the first portion of the mask (0s). |
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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 [token type IDs](../glossary#token-type-ids) according to the given sequence(s). |
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""" |
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bos_token_id = [self.bos_token_id] if self.add_bos_token else [] |
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eos_token_id = [self.eos_token_id] if self.add_eos_token else [] |
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output = [0] * len(bos_token_id + token_ids_0 + eos_token_id) |
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if token_ids_1 is not None: |
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output += [1] * len(bos_token_id + token_ids_1 + eos_token_id) |
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return output |
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def _build_conversation_input_ids(self, conversation: List[List[int]]) -> List[int]: |
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input_ids = [] |
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for i, history in enumerate(conversation): |
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if i % 2 == 0: |
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input_ids.extend([self.bos_token_id, self.convert_tokens_to_ids("<start_of_turn>")] + history + [self.convert_tokens_to_ids("<end_of_turn>")]) |
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else: |
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input_ids.extend([self.bos_token_id, self.convert_tokens_to_ids("<start_of_turn>"), self.convert_tokens_to_ids("model")] + history + [self.convert_tokens_to_ids("<end_of_turn>\n")]) |
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input_ids.append(self.eos_token_id) |
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return input_ids |
