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| import regex as re | |
| import base64 | |
| import os | |
| import json | |
| import tiktoken | |
| from torch import TensorType | |
| from typing import List, Optional, Union, Dict, Any | |
| from transformers import PreTrainedTokenizer | |
| from transformers.utils import logging, PaddingStrategy | |
| from transformers.tokenization_utils_base import EncodedInput, BatchEncoding | |
| class ChatGLM4Tokenizer(PreTrainedTokenizer): | |
| vocab_files_names = {"vocab_file": "tokenizer.model"} | |
| model_input_names = ["input_ids", "attention_mask", "position_ids"] | |
| def __init__( | |
| self, | |
| vocab_file, | |
| padding_side="left", | |
| clean_up_tokenization_spaces=False, | |
| encode_special_tokens=False, | |
| **kwargs | |
| ): | |
| self.name = "GLM4Tokenizer" | |
| self.vocab_file = vocab_file | |
| pat_str = "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+" | |
| self.pat_str = re.compile(pat_str) | |
| self.encode_special_tokens = encode_special_tokens | |
| mergeable_ranks = {} | |
| with open(vocab_file) as f: | |
| for line in f: | |
| token, rank = line.strip().split() | |
| rank = int(rank) | |
| token = base64.b64decode(token) | |
| mergeable_ranks[token] = rank | |
| self.mergeable_ranks = mergeable_ranks | |
| self.tokenizer = tiktoken.Encoding( | |
| name="my_tokenizer", | |
| pat_str=pat_str, | |
| mergeable_ranks=mergeable_ranks, | |
| special_tokens={} | |
| ) | |
| self.decoder = {rank: token for token, rank in mergeable_ranks.items()} | |
| self.n_words = len(self.decoder) | |
| super().__init__( | |
| padding_side=padding_side, | |
| clean_up_tokenization_spaces=clean_up_tokenization_spaces, | |
| **kwargs | |
| ) | |
| def vocab_size(self): | |
| return self.n_words | |
| def get_vocab(self): | |
| """ Returns vocab as a dict """ | |
| vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)} | |
| vocab.update(self.added_tokens_encoder) | |
| return vocab | |
| def convert_tokens_to_string(self, tokens: List[Union[bytes, str, int]]) -> str: | |
| """ | |
| Converts a sequence of tokens in a single string. | |
| """ | |
| text = "" | |
| temp = b"" | |
| for t in tokens: | |
| if isinstance(t, int): | |
| t = chr(t) | |
| if isinstance(t, str): | |
| if temp: | |
| text += temp.decode("utf-8", errors="replace") | |
| elif isinstance(t, bytes): | |
| temp += t | |
| else: | |
| raise TypeError("token should only be of type int, bytes or str") | |
| if temp: | |
| text += temp.decode("utf-8", errors="replace") | |
| return text | |
| def _tokenize(self, text, **kwargs): | |
| tokens = [] | |
| ids = self.tokenizer.encode(text) | |
| for t in ids: | |
| tokens.append(self.decoder[t]) | |
| return tokens | |
| def _convert_token_to_id(self, token): | |
| """ Converts a token (str) in an id using the vocab. """ | |
| return self.mergeable_ranks[token] | |
| def _convert_id_to_token(self, index): | |
| """Converts an index (integer) in a token (str) using the vocab.""" | |
| return self.decoder.get(index, "") | |
| 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): | |
| vocab_file = os.path.join( | |
| save_directory, self.vocab_files_names["vocab_file"] | |
| ) | |
| else: | |
| vocab_file = save_directory | |
| with open(self.vocab_file, 'rb') as fin: | |
| proto_str = fin.read() | |
| with open(vocab_file, "wb") as writer: | |
| writer.write(proto_str) | |
| return (vocab_file,) | |
| def get_prefix_tokens(self): | |
| prefix_tokens = [self.convert_tokens_to_ids("[gMASK]"), self.convert_tokens_to_ids("<sop>")] | |
| return prefix_tokens | |
| def build_single_message(self, role, metadata, message, tokenize=True): | |
| assert role in ["system", "user", "assistant", "observation"], role | |
| if tokenize: | |
| role_tokens = [self.convert_tokens_to_ids(f"<|{role}|>")] + self.tokenizer.encode(f"{metadata}\n", | |
| disallowed_special=()) | |
| message_tokens = self.tokenizer.encode(message, disallowed_special=()) | |
| tokens = role_tokens + message_tokens | |
| return tokens | |
| else: | |
| return str(f"<|{role}|>{metadata}\n{message}") | |
| def apply_chat_template( | |
| self, | |
| conversation: Union[List[Dict[str, str]], List[List[Dict[str, str]]], "Conversation"], | |
| add_generation_prompt: bool = False, | |
| tokenize: bool = True, | |
| padding: bool = False, | |
| truncation: bool = False, | |
| max_length: Optional[int] = None, | |
| return_tensors: Optional[Union[str, TensorType]] = None, | |
| return_dict: bool = False, | |
| tokenizer_kwargs: Optional[Dict[str, Any]] = None, | |
| add_special_tokens: bool = True, | |
| **kwargs, | |
| ) -> Union[str, List[int], List[str], List[List[int]], BatchEncoding]: | |
| if return_dict and not tokenize: | |
| raise ValueError( | |
| "`return_dict=True` is incompatible with `tokenize=False`, because there is no dict " | |
| "of tokenizer outputs to return." | |
| ) | |
| def handle_single_conversation(conversation): | |
| input_ids = self.get_prefix_tokens() if add_special_tokens else [] | |
| input_message = "[gMASK]<sop>" if add_special_tokens else "" | |
| for item in conversation: | |
| if item.get("tools"): | |
| tools = item["tools"] | |
| content = "你是一个名为 GLM-4 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的，你的任务是针对用户的问题和要求提供适当的答复和支持。" | |
| for tool in tools: | |
| if tool["type"] == "function": | |
| function = tool["function"] | |
| content += f"\n\n## {function['name']}\n\n{json.dumps(function, ensure_ascii=False, indent=4)}" | |
| content += "\n在调用上述函数时，请使用 Json 格式表示调用的参数。" | |
| elif tool["type"] == "python": | |
| content += "\n\n## python\n\n当你向 `python` 发送包含 Python 代码的消息时，该代码将会在一个有状态的 Jupyter notebook 环境中执行。\n`python` 返回代码执行的输出，或在执行 60 秒后返回超时。\n`/mnt/data` 将会持久化存储你的文件。在此会话中，`python` 无法访问互联网。不要使用 `python` 进行任何网络请求或者在线 API 调用，这些在线内容的访问将不会成功。" | |
| elif tool["type"] == "simple_browser": | |
| content += "\n\n## simple_browser\n\n你可以使用 `simple_browser` 工具。该工具支持以下函数：\n`search(query: str, recency_days: int)`：使用搜索引擎进行查询并显示结果，可以使用 `recency_days` 参数控制搜索内容的时效性。\n`mclick(ids: list[int])`：获取一系列指定 id 的页面内容。每次调用时，须选择3-10个页面。选择多个角度的页面，同时尽可能选择可信任的信息来源。考虑到部分页面是无法加载的，你也可以多打开一些可能有用的页面而不用担心内容过多。\n`open_url(url: str)`：打开指定的 URL。\n\n使用 `【{引用 id}†{引用文本}】` 来引用内容。\n\n操作步骤：1. 使用 `search` 来获得信息列表; 2. 使用 `mclick` 来获取指定 ID 页面的内容; 3. 根据获得的内容进行回复。在回复中应当引用信息来源。\n 如果用户提供了 URL，也可以用 `open_url` 直接打开页面。\n如果初次搜索结果没有找到合适的信息，也可以再次使用 `search` 进行搜索。" | |
| elif tool["type"] == "cogview": | |
| content += "\n\n## cogview\n\n如果用户的请求中包含了对图像的描述，你可以使用 `cogview` 来生成图像并展示给用户。你需要向 `cogview` 发送图像描述，规则：\n- 发送给 `cogview` 的消息必须使用英语。用户的中文描述必须完全翻译为英语。\n- 应当尽可能详细地描述图像生成的需求，需求描述约 100 英文单词。\n- 保持用户原始描述的意图。不要虚构内容或者没见过的人物。\n- 如无特殊说明，所在地为中国，持有中国立场并遵循中国社会主义价值观。" | |
| else: | |
| raise NotImplementedError(f"Unknown tool type {tool['type']}") | |
| input = self.build_single_message("system", "", content, tokenize=tokenize) | |
| if tokenize: | |
| input_ids.extend(input) | |
| else: | |
| input_message += input | |
| if item["content"]: | |
| input = self.build_single_message( | |
| item["role"], | |
| item.get("metadata", ""), | |
| item["content"], | |
| tokenize=tokenize | |
| ) | |
| if tokenize: | |
| input_ids.extend(input) | |
| else: | |
| input_message += input | |
| if add_generation_prompt: | |
| if tokenize: | |
| input_ids.extend([self.convert_tokens_to_ids("<|assistant|>")]) | |
| else: | |
| input_message += "<|assistant|>" | |
| return input_ids if tokenize else input_message | |
| # Main logic to handle different conversation formats | |
| if isinstance(conversation, list) and all(isinstance(i, dict) for i in conversation): | |
| result = handle_single_conversation(conversation) | |
| elif isinstance(conversation, list) and all(isinstance(i, list) for i in conversation): | |
| result = [handle_single_conversation(c) for c in conversation] | |
| elif hasattr(conversation, "messages"): | |
| result = handle_single_conversation(conversation.messages) | |
| else: | |
| raise ValueError("Invalid conversation format") | |
| if tokenize: | |
| output = self.batch_encode_plus( | |
| [result] if isinstance(result[0], int) else result, | |
| padding=padding, | |
| truncation=truncation, | |
| max_length=max_length, | |
| return_tensors=return_tensors, | |
| is_split_into_words=True, | |
| add_special_tokens=False | |
| ) | |
| if return_dict: | |
| return output | |
| else: | |
| return output["input_ids"] | |
| else: | |
| return result | |
| 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. | |
| """ | |
| prefix_tokens = self.get_prefix_tokens() | |
| token_ids_0 = prefix_tokens + token_ids_0 | |
| if token_ids_1 is not None: | |
| token_ids_0 = token_ids_0 + token_ids_1 + [self.convert_tokens_to_ids("<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" | |
| required_input = encoded_inputs[self.model_input_names[0]] | |
| seq_length = len(required_input) | |
| if padding_strategy == PaddingStrategy.LONGEST: | |
| max_length = len(required_input) | |
| 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 | |
| needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length | |
| # Initialize attention mask if not present. | |
| if "attention_mask" not in encoded_inputs: | |
| encoded_inputs["attention_mask"] = [1] * seq_length | |
| if "position_ids" not in encoded_inputs: | |
| encoded_inputs["position_ids"] = list(range(seq_length)) | |
| if needs_to_be_padded: | |
| difference = max_length - len(required_input) | |
| if "attention_mask" in encoded_inputs: | |
| encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"] | |
| if "position_ids" in encoded_inputs: | |
| encoded_inputs["position_ids"] = [0] * difference + encoded_inputs["position_ids"] | |
| encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input | |
| return encoded_inputs | |