import os
import torch
from typing import List, Optional, Union, Dict
from sentencepiece import SentencePieceProcessor
from transformers import PreTrainedTokenizer
from transformers.utils import logging, PaddingStrategy
from transformers.tokenization_utils_base import EncodedInput, BatchEncoding
# 底层的分词器，也就是 SP 模型的包装classSPTokenizer:def__init__(self, model_path:str):# reload tokenizerassert os.path.isfile(model_path), model_path
# 加载 SP 模型作为底层模型
self.sp_model = SentencePieceProcessor(model_file=model_path)# 设置单词数量，BOS EOS PAD ID 属性# PAD 由底层模型的 UNK 代替
self.n_words:int= self.sp_model.vocab_size()
self.bos_id:int= self.sp_model.bos_id()
self.eos_id:int= self.sp_model.eos_id()
self.pad_id:int= self.sp_model.unk_id()assert self.sp_model.vocab_size()== self.sp_model.get_piece_size()# 定义特殊单词
special_tokens =["[MASK]","[gMASK]","[sMASK]","sop","eop"]# 建立特殊单词文本到ID的映射
self.special_tokens ={}# 建立特殊单词ID到文本的映射
self.index_special_tokens ={}for token in special_tokens:# 遍历特殊单词，填充这个两个映射
self.special_tokens[token]= self.n_words
self.index_special_tokens[self.n_words]= token
self.n_words +=1# 文本片段转单词文本数组deftokenize(self, s:str):# 转发给底层模型的`EncodeAsPieces`return self.sp_model.EncodeAsPieces(s)# 文本片段转单词 ID 数组defencode(self, s:str, bos:bool=False, eos:bool=False)-> List[int]:asserttype(s)isstr# 调用底层模型的`encode`方法
t = self.sp_model.encode(s)# 根据传入的`bos`和`eos`标志# 决定是否添加 BOS 和 EOS IDif bos:
t =[self.bos_id]+ t
if eos:
t = t +[self.eos_id]return t
# 单词 ID 数组转文本片段defdecode(self, t: List[int])->str:# 转发给底层模型的`decode`方法return self.sp_model.decode(t)# 单词文本数组转文本片段defdecode_tokens(self, tokens: List[str])->str:
text = self.sp_model.DecodePieces(tokens)return text
# 单词文本转 IDdefconvert_token_to_id(self, token):""" Converts a token (str) in an id using the vocab. """# 如果单词在特殊标记里面，就从`special_tokens`查找 IDif token in self.special_tokens:return self.special_tokens[token]# 否则转发给底层模型的`PieceToId`return self.sp_model.PieceToId(token)# 单词 ID 转文本defconvert_id_to_token(self, index):"""Converts an index (integer) in a token (str) using the vocab."""# 如果单词在特殊标记里面，或者是 BOS、EOS、PAD 之一，就返回空串if index in self.index_special_tokens or index in[self.eos_id, self.bos_id, self.pad_id]or index <0:return""# 否则转发给底层模型的`IdToPiece`return self.sp_model.IdToPiece(index)# 用户直接使用的分词器classChatGLMTokenizer(PreTrainedTokenizer):# 定义词表名称
vocab_files_names ={"vocab_file":"tokenizer.model"}# 定义模型输入参数名称
model_input_names =["input_ids","attention_mask","position_ids"]def__init__(self, vocab_file, padding_side="left",**kwargs):super().__init__(padding_side=padding_side, clean_up_tokenization_spaces=False,**kwargs)
self.name ="GLMTokenizer"# 在属性中保存词表路径# 这个文件是和词表本身放一起的，所以路径就只是文件名
self.vocab_file = vocab_file
# 创建底层的分词器，传入词表路径
self.tokenizer = SPTokenizer(vocab_file)# 定义特殊单词 BOS、EOS、PAD# 建立单词文本到ID的映射
self.special_tokens ={"": self.tokenizer.bos_id,"": self.tokenizer.eos_id,"": self.tokenizer.pad_id
}# 特殊单词文本转 IDdefget_command(self, token):# 如果单词在GLM 分词器的特殊字符中# 查找`special_tokens`，返回它的 IDif token in self.special_tokens:return self.special_tokens[token]# 如果单词不在底层的 SP 分词器的特殊字符中，就报错assert token in self.tokenizer.special_tokens,f"{token} is not a special token for {self.name}"# 查找底层分词器的`special_tokens`，返回它的IDreturn self.tokenizer.special_tokens[token]# 返回 UNK 单词文本@propertydefunk_token(self)->str:return""# 返回 PAD 单词文本（也就是 UNK）@propertydefpad_token(self)->str:return""# 返回 PAD 单词 ID@propertydefpad_token_id(self):return self.get_command("")# 返回 EOS 单词文本@propertydefeos_token(self)->str:return""# 返回 EOS 单词 ID@propertydefeos_token_id(self):return self.get_command("")# 返回词表大小@propertydefvocab_size(self):return self.tokenizer.n_words
# 获取词表，也就是单词文本到ID的映射defget_vocab(self):""" Returns vocab as a dict """# 遍历所有单词的 ID，即 0 到 VocabSize-1]# 调用自身的`_convert_id_to_token`方法将 ID 转成文本# 创建一个单词文本到ID的映射
vocab ={self._convert_id_to_token(i): i for i inrange(self.vocab_size)}
vocab.update(self.added_tokens_encoder)return vocab
# 文本片段转单词文本数组def_tokenize(self, text,**kwargs):# 转发给底层分词器的`tokenize`方法return self.tokenizer.tokenize(text)# 单词文本转 IDdef_convert_token_to_id(self, token):""" Converts a token (str) in an id using the vocab. """# 转发给底层分词器的`convert_token_to_id`方法return self.tokenizer.convert_token_to_id(token)# 单词 ID 转文本def_convert_id_to_token(self, index):"""Converts an index (integer) in a token (str) using the vocab."""# 转发给底层分词器的`convert_id_to_token`方法return self.tokenizer.convert_id_to_token(index)# 单词文本数组转文本片段defconvert_tokens_to_string(self, tokens: List[str])->str:# 转发给底层分词器的`decode_tokens`方法return self.tokenizer.decode_tokens(tokens)# 保存词表defsave_vocabulary(self, save_directory, filename_prefix=None):"""
Save the vocabulary and special tokens file to a directory.
Args:
save_directory (`str`):
The directory in which to save the vocabulary.
filename_prefix (`str`, *optional*):
An optional prefix to add to the named of the saved files.
Returns:
`Tuple(str)`: Paths to the files saved.
"""if os.path.isdir(save_directory):# 如果传入路径是个目录，那么文件名就是之前定义的默认文件名# 把传入路径和文件名拼接好作为保存路径
vocab_file = os.path.join(
save_directory, self.vocab_files_names["vocab_file"])else:# 否则保存路径就是传入路径
vocab_file = save_directory
# 根据属性中的词表路径，读入词表withopen(self.vocab_file,'rb')as fin:
proto_str = fin.read()# 把词表写到保存路径中withopen(vocab_file,"wb")as writer:
writer.write(proto_str)# 返回保存路径return(vocab_file,)# 获取前缀单词列表，即 GMASK 和 SOPdefget_prefix_tokens(self):
prefix_tokens =[self.get_command("[gMASK]"), self.get_command("sop")]return prefix_tokens
'''
根据当前提问和历史问答构建复合提问
In [1]: tokenizer.build_prompt('Q3', [('Q1', 'A1'),('Q2', 'A2')])
Out[1]: '[Round 1]\n\n问：Q1\n\n答：A1\n\n[Round 2]\n\n问：Q2\n\n答：A2\n\n[Round 3]\n\n问：Q3\n\n答：'
'''defbuild_prompt(self, query, history=None):if history isNone:
history =[]
prompt =""for i,(old_query, response)inenumerate(history):# 遍历每一对历史问答，将序号、提问和回答按照模版组装# 并添加到复合提问后面
prompt +="[Round {}]\n\n问：{}\n\n答：{}\n\n".format(i +1, old_query, response)# 将当前轮次和当前提问按照模版组装，添加到复合提问后面
prompt +="[Round {}]\n\n问：{}\n\n答：".format(len(history)+1, query)return prompt
# 给单词 ID 数组添加特殊单词defbuild_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]]=None)-> List[int]:"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A BERT sequence has the following format:
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""# 或许前缀单词列表，并添加到 IDS0 前方
prefix_tokens = self.get_prefix_tokens()
token_ids_0 = prefix_tokens + token_ids_0
# 如果 IDS1 存在，添加到 IDS0 后方，并添加 EOSif token_ids_1 isnotNone:
token_ids_0 = token_ids_0 + token_ids_1 +[self.get_command("")]return token_ids_0
def_pad(
self,
encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
max_length: Optional[int]=None,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
pad_to_multiple_of: Optional[int]=None,
return_attention_mask: Optional[bool]=None,)->dict:"""
Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
Args:
encoded_inputs:
Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
max_length: maximum length of the returned list and optionally padding length (see below).
Will truncate by taking into account the special tokens.
padding_strategy: PaddingStrategy to use for padding.
- PaddingStrategy.LONGEST Pad to the longest sequence in the batch
- PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
- PaddingStrategy.DO_NOT_PAD: Do not pad
The tokenizer padding sides are defined in self.padding_side:
- 'left': pads on the left of the sequences
- 'right': pads on the right of the sequences
pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
`>= 7.5` (Volta).
return_attention_mask:
(optional) Set to False to avoid returning attention mask (default: set to model specifics)
"""# Load from model defaultsassert self.padding_side =="left"# `encoded_inputs`是个字典，`input_ids`包含模型的输入单词ID数组# `attention_mask`是掩码数组，`position_ids`是位置 ID 数组# `required_input`是输入单词 ID 数组
required_input = encoded_inputs[self.model_input_names[0]]# `seq_length`是输入长度
seq_length =len(required_input)# 如果策略是按最长填充，因为只有一个输入，最大长度就是它的长度if padding_strategy == PaddingStrategy.LONGEST:
max_length =len(required_input)# 如果提供了最大长度和`pad_to_multiple_of`# 将最大长度设为不小于它的`pad_to_multiple_of`的倍数if max_length isnotNoneand pad_to_multiple_of isnotNoneand(max_length % pad_to_multiple_of !=0):
max_length =((max_length // pad_to_multiple_of)+1)* pad_to_multiple_of
# 如果策略不是不填充，并且最大长度# 和输入长度不相等，就需要填充
needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD andlen(required_input)!= max_length
# 如果没有掩码，初始化为全 1 长度为 SeqLen 的数组if"attention_mask"notin encoded_inputs:
encoded_inputs["attention_mask"]=[1]* seq_length
# 如果没有位置 ID，初始化为 [0, ..., SeqLen - 1]if"position_ids"notin encoded_inputs:
encoded_inputs["position_ids"]=list(range(seq_length))if needs_to_be_padded:# 如果需要填充，计算填充字符个数，也就是最大长度和输入的差值
difference = max_length -len(required_input)# 如果存在掩码，在掩码前方插入 diff 个 0if"attention_mask"in encoded_inputs:
encoded_inputs["attention_mask"]=[0]* difference + encoded_inputs["attention_mask"]# 如果存在位置 ID，同样前方插入 diff 个 0if"position_ids"in encoded_inputs:
encoded_inputs["position_ids"]=[0]* difference + encoded_inputs["position_ids"]# 在输入 IDS 前方插入 diff 个 PAD ID
encoded_inputs[self.model_input_names[0]]=[self.pad_token_id]* difference + required_input
return encoded_inputs
