# coding=utf-8 # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization classes for BlueLM.""" import os from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer from transformers.utils import logging logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": {}, "tokenizer_file": {}, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {} class BlueLMTokenizer(PreTrainedTokenizer): """ Construct a BlueLM tokenizer. Based on byte-level Byte-Pair-Encoding. Args: vocab_file (`str`): Path to the vocabulary file. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] def __init__( self, vocab_file, unk_token="", bos_token="", eos_token="", pad_token=None, sp_model_kwargs: Optional[Dict[str, Any]] = None, add_bos_token=True, add_eos_token=False, clean_up_tokenization_spaces=False, **kwargs, ): self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token super().__init__( bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, pad_token=pad_token, add_bos_token=add_bos_token, add_eos_token=add_eos_token, sp_model_kwargs=self.sp_model_kwargs, clean_up_tokenization_spaces=clean_up_tokenization_spaces, **kwargs, ) self.vocab_file = vocab_file self.add_bos_token = add_bos_token self.add_eos_token = add_eos_token self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(vocab_file) def __getstate__(self): state = self.__dict__.copy() state["sp_model"] = None return state def __setstate__(self, d): self.__dict__ = d self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(self.vocab_file) @property def vocab_size(self): """Returns vocab size""" return self.sp_model.get_piece_size() def get_vocab(self): """Returns vocab as a dict""" vocab = {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): """Returns a tokenized string.""" 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.IdToPiece(index) return token def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" current_sub_tokens = [] out_string = "" prev_is_special = False for i, token in enumerate(tokens): # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: if not prev_is_special and i != 0: out_string += " " out_string += self.sp_model.decode(current_sub_tokens) + token prev_is_special = True current_sub_tokens = [] else: current_sub_tokens.append(token) prev_is_special = False out_string += self.sp_model.decode(current_sub_tokens) return out_string def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]: """ Save the vocabulary and special tokens file to a directory. Args: save_directory (`str`): The directory in which to save the vocabulary. Returns: `Tuple(str)`: Paths to the files saved. """ if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return 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,) def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): bos_token_id = [self.bos_token_id] if self.add_bos_token else [] eos_token_id = [self.eos_token_id] if self.add_eos_token else [] output = bos_token_id + token_ids_0 + eos_token_id if token_ids_1 is not None: output = output + bos_token_id + token_ids_1 + eos_token_id return output 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]: """ Retrieve 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` method. 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 ) bos_token_id = [1] if self.add_bos_token else [] eos_token_id = [1] if self.add_eos_token else [] if token_ids_1 is None: return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id return ( bos_token_id + ([0] * len(token_ids_0)) + eos_token_id + bos_token_id + ([0] * len(token_ids_1)) + eos_token_id ) def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT sequence pair mask has the following format: ``` 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence | ``` if token_ids_1 is None, only returns the first portion of the mask (0s). Args: token_ids_0 (`List[int]`): List of ids. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s). """ bos_token_id = [self.bos_token_id] if self.add_bos_token else [] eos_token_id = [self.eos_token_id] if self.add_eos_token else [] output = [0] * len(bos_token_id + token_ids_0 + eos_token_id) if token_ids_1 is not None: output += [1] * len(bos_token_id + token_ids_1 + eos_token_id) return output