# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. # # 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. import os from shutil import copyfile from typing import List, Optional, Tuple from paddlex.inference.models.common.tokenizer.tokenizer_utils import ( PretrainedTokenizer, ) class LlamaTokenizer(PretrainedTokenizer): model_input_names = ["input_ids", "attention_mask", "position_ids"] resource_files_names = { "vocab_file": "sentencepiece.bpe.model", } pretrained_resource_files_map = { "vocab_file": { "__internal_testing__/micro-random-llama": "https://bj.bcebos.com/paddlenlp/models/transformers/llama/sentencepiece.bpe.model", "__internal_testing__/tiny-random-llama": "https://bj.bcebos.com/paddlenlp/models/transformers/llama/sentencepiece.bpe.model", "facebook/llama-7b": "https://bj.bcebos.com/paddlenlp/models/transformers/llama/sentencepiece.bpe.model", "facebook/llama-13b": "https://bj.bcebos.com/paddlenlp/models/transformers/llama/sentencepiece.bpe.model", "facebook/llama-30b": "https://bj.bcebos.com/paddlenlp/models/transformers/llama/sentencepiece.bpe.model", "facebook/llama-65b": "https://bj.bcebos.com/paddlenlp/models/transformers/llama/sentencepiece.bpe.model", }, } pretrained_init_configuration = { "__internal_testing__/micro-random-llama": {}, "__internal_testing__/tiny-random-llama": {}, "facebook/llama-7b": {}, "facebook/llama-13b": {}, "facebook/llama-30b": {}, "facebook/llama-65b": {}, } padding_side = "left" def __init__( self, vocab_file, unk_token="", bos_token="", eos_token="", add_bos_token=True, add_eos_token=False, sp_model_kwargs=None, decode_with_prefix_space=False, **kwargs, ): self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs super().__init__( bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, **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 = self.get_spm_processor(kwargs.pop("from_slow", True)) @property def vocab_size(self): """Returns vocab size""" return self.sp_model.get_piece_size() def __len__(self): """ Returns the vocabulary size. added_tokens_encoder has to be added in the sp_model """ added_size = 0 for id in self.added_tokens_decoder: if id >= self.sp_model.get_piece_size(): added_size += 1 return self.vocab_size + added_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_spm_processor(self, from_slow=True): import sentencepiece as spm from sentencepiece import sentencepiece_model_pb2 as model_pb2 tokenizer = spm.SentencePieceProcessor(**self.sp_model_kwargs) if from_slow: # no dependency on protobuf tokenizer.Load(self.vocab_file) return tokenizer with open(self.vocab_file, "rb") as f: sp_model = f.read() model = model_pb2.ModelProto.FromString(sp_model) normalizer_spec = model_pb2.NormalizerSpec() normalizer_spec.add_dummy_prefix = False model.normalizer_spec.MergeFrom(normalizer_spec) sp_model = model.SerializeToString() tokenizer.LoadFromSerializedProto(sp_model) return tokenizer 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.id_to_piece(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): raise ValueError( f"Vocabulary path ({save_directory}) should be a directory" ) out_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + self.resource_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]