# Copyright (c) InternLM. 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 IntermLM.""" import os from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from transformers.tokenization_utils import PreTrainedTokenizer from transformers.utils import logging logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {'vocab_file': './tokenizer.model'} PRETRAINED_VOCAB_FILES_MAP = {} class InternLMXComposer2Tokenizer(PreTrainedTokenizer): """Construct a InternLM 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 model_input_names = ['input_ids', 'attention_mask'] _auto_class = 'AutoTokenizer' def __init__( self, vocab_file, unk_token='', bos_token='', eos_token='', pad_token='', sp_model_kwargs: Optional[Dict[str, Any]] = None, add_bos_token=True, add_eos_token=False, decode_with_prefix_space=False, clean_up_tokenization_spaces=False, **kwargs, ): self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs self.vocab_file = vocab_file self.add_bos_token = add_bos_token self.add_eos_token = add_eos_token self.decode_with_prefix_space = decode_with_prefix_space self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(vocab_file) self._no_prefix_space_tokens = None super().__init__( bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, pad_token=pad_token, clean_up_tokenization_spaces=clean_up_tokenization_spaces, **kwargs, ) """ Initialization""" @property def no_prefix_space_tokens(self): if self._no_prefix_space_tokens is None: vocab = self.convert_ids_to_tokens(list(range(self.vocab_size))) self._no_prefix_space_tokens = { i for i, tok in enumerate(vocab) if not tok.startswith('▁') } return self._no_prefix_space_tokens @property def vocab_size(self): """Returns vocab size.""" return self.sp_model.get_piece_size() @property def bos_token_id(self) -> Optional[int]: return self.sp_model.bos_id() @property def eos_token_id(self) -> Optional[int]: return self.sp_model.eos_id() 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 _maybe_add_prefix_space(self, tokens, decoded): if tokens and tokens[0] not in self.no_prefix_space_tokens: return ' ' + decoded else: return decoded 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 token in tokens: # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: if not prev_is_special: 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) out_string = self.clean_up_tokenization(out_string) out_string = self._maybe_add_prefix_space( tokens=tokens, decoded=out_string) return out_string[1:] 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): 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 not None: output = output + token_ids_1 if self.add_eos_token: output = output + [self.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) if token_ids_1 is None: return [1] + ([0] * len(token_ids_0)) + [1] return [1] + ([0] * len(token_ids_0)) + [1, 1] + ( [0] * len(token_ids_1)) + [1] def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None) -> List[int]: """Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make use of token type ids, therefore a list of zeros is returned. 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 zeros. """ eos = [self.eos_token_id] if token_ids_1 is None: return len(token_ids_0 + eos) * [0] return len(token_ids_0 + eos + token_ids_1 + eos) * [0]