From cf1107fbfa23e98a48023666cbe0b1cd58dbbfc8 Mon Sep 17 00:00:00 2001 From: wql Date: Wed, 21 Aug 2024 13:44:02 +0800 Subject: [PATCH] chore: add baichuan fix file --- transfer_files/tokenization_baichuan.py | 252 ++++++++++++++++++++++++ 1 file changed, 252 insertions(+) create mode 100644 transfer_files/tokenization_baichuan.py diff --git a/transfer_files/tokenization_baichuan.py b/transfer_files/tokenization_baichuan.py new file mode 100644 index 00000000..8f95e59c --- /dev/null +++ b/transfer_files/tokenization_baichuan.py @@ -0,0 +1,252 @@ +# 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. + +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 BaiChuanTokenizer(PreTrainedTokenizer): + """ + Construct a BaiChuan 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 + + 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) + + 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, + ) + + 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 \ No newline at end of file