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
class SPTokenizer:
def __init__(self, model_path: str):
# reload tokenizer
assert 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
def tokenize(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
def encode(self, s: str, bos: bool = False, eos: bool = False) -> List[int]:
assert type(s) is str
# 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 ID
if 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
def decode(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
def decode_tokens(self, tokens: List[str]) -> str:
text = self.sp_model.DecodePieces(tokens)
return text
# 单词文本转 ID
def convert_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 ID
if 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
def convert_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
class ChatGLMTokenizer(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 = {
"<bos>": self.tokenizer.bos_id,
"<eos>": self.tokenizer.eos_id,
"<pad>": self.tokenizer.pad_id
}
# 特殊单词文本转 ID
def get_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 ID
if 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ID
return self.tokenizer.special_tokens[token]
# 返回 UNK 单词文本
@property
def unk_token(self) -> str:
return "<unk>"
# 返回 PAD 单词文本（也就是 UNK）
@property
def pad_token(self) -> str:
return "<unk>"
# 返回 PAD 单词 ID
@property
def pad_token_id(self):
return self.get_command("<pad>")
# 返回 EOS 单词文本
@property
def eos_token(self) -> str:
return "</s>"
# 返回 EOS 单词 ID
@property
def eos_token_id(self):
return self.get_command("<eos>")
# 返回词表大小
@property
def vocab_size(self):
return self.tokenizer.n_words
# 获取词表，也就是单词文本到ID的映射
def get_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 in range(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 ID
def _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
def convert_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
def save_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
with open(self.vocab_file, 'rb') as fin:
proto_str = fin.read()
# 把词表写到保存路径中
with open(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 SOP
def get_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：'
'''
def build_prompt(self, query, history=None):
if history is None:
history = []
prompt = ""
for i, (old_query, response) in enumerate(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
def build_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 EOS
if token_ids_1 is not None:
token_ids_0 = token_ids_0 + token_ids_1 + [self.get_command("<eos>")]
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 defaults
assert 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 is not None and pad_to_multiple_of is not None and (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 and len(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" not in 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" not in 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 0
if "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 0
if "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