Lie24/FuLinNa_GLM4-9B-Chat

1import regex as re2import base643import os4import json5import tiktoken6from torch import TensorType7from typing import List, Optional, Union, Dict, Any8from transformers import PreTrainedTokenizer9from transformers.utils import logging, PaddingStrategy10from transformers.tokenization_utils_base import EncodedInput, BatchEncoding11 12 13class ChatGLM4Tokenizer(PreTrainedTokenizer):14    vocab_files_names = {"vocab_file": "tokenizer.model"}15    model_input_names = ["input_ids", "attention_mask", "position_ids"]16 17    def __init__(18            self,19            vocab_file,20            padding_side="left",21            clean_up_tokenization_spaces=False,22            encode_special_tokens=False,23            **kwargs24    ):25        self.name = "GLM4Tokenizer"26        self.vocab_file = vocab_file27        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+"28        self.pat_str = re.compile(pat_str)29        self.encode_special_tokens = encode_special_tokens30 31        mergeable_ranks = {}32        with open(vocab_file) as f:33            for line in f:34                token, rank = line.strip().split()35                rank = int(rank)36                token = base64.b64decode(token)37                mergeable_ranks[token] = rank38 39        self.mergeable_ranks = mergeable_ranks40 41        self.tokenizer = tiktoken.Encoding(42            name="my_tokenizer",43            pat_str=pat_str,44            mergeable_ranks=mergeable_ranks,45            special_tokens={}46        )47        self.decoder = {rank: token for token, rank in mergeable_ranks.items()}48        self.n_words = len(self.decoder)49 50        super().__init__(51            padding_side=padding_side,52            clean_up_tokenization_spaces=clean_up_tokenization_spaces,53            **kwargs54        )55 56    @property57    def vocab_size(self):58        return self.n_words59 60    def get_vocab(self):61        """ Returns vocab as a dict """62        vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)}63        vocab.update(self.added_tokens_encoder)64        return vocab65 66    def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:67        """68        Converts a sequence of tokens in a single string.69        """70        text = ""71        temp = b""72        for t in tokens:73            if isinstance(t, str):74                if temp:75                    text += temp.decode("utf-8", errors="replace")76                    temp = b""77                text += t78            elif isinstance(t, bytes):79                temp += t80            else:81                raise TypeError("token should only be of type types or str")82        if temp:83            text += temp.decode("utf-8", errors="replace")84        return text85 86    def _tokenize(self, text, **kwargs):87        tokens = []88        ids = self.tokenizer.encode(text)89        for t in ids:90            tokens.append(self.decoder[t])91        return tokens92 93    def _convert_token_to_id(self, token):94        """ Converts a token (str) in an id using the vocab. """95        return self.mergeable_ranks[token]96 97    def _convert_id_to_token(self, index):98        """Converts an index (integer) in a token (str) using the vocab."""99        return self.decoder.get(index, "")100 101    def save_vocabulary(self, save_directory, filename_prefix=None):102        """103        Save the vocabulary and special tokens file to a directory.104 105        Args:106            save_directory (`str`):107                The directory in which to save the vocabulary.108            filename_prefix (`str`, *optional*):109                An optional prefix to add to the named of the saved files.110 111        Returns:112            `Tuple(str)`: Paths to the files saved.113        """114        if os.path.isdir(save_directory):115            vocab_file = os.path.join(116                save_directory, self.vocab_files_names["vocab_file"]117            )118        else:119            vocab_file = save_directory120 121        with open(self.vocab_file, 'rb') as fin:122            proto_str = fin.read()123 124        with open(vocab_file, "wb") as writer:125            writer.write(proto_str)126 127        return (vocab_file,)128 129    def get_prefix_tokens(self):130        prefix_tokens = [self.convert_tokens_to_ids("[gMASK]"), self.convert_tokens_to_ids("<sop>")]131        return prefix_tokens132 133    def build_single_message(self, role, metadata, message, tokenize=True):134        assert role in ["system", "user", "assistant", "observation"], role135        if tokenize:136            role_tokens = [self.convert_tokens_to_ids(f"<|{role}|>")] + self.tokenizer.encode(f"{metadata}\n",137                                                                                              disallowed_special=())138            message_tokens = self.tokenizer.encode(message, disallowed_special=())139            tokens = role_tokens + message_tokens140            return tokens141        else:142            return str(f"<|{role}|>{metadata}\n{message}")143 144    def apply_chat_template(145            self,146            conversation: Union[List[Dict[str, str]], List[List[Dict[str, str]]], "Conversation"],147            add_generation_prompt: bool = False,148            tokenize: bool = True,149            padding: bool = False,150            truncation: bool = False,151            max_length: Optional[int] = None,152            return_tensors: Optional[Union[str, TensorType]] = None,153            return_dict: bool = False,154            tokenizer_kwargs: Optional[Dict[str, Any]] = None,155            add_special_tokens: bool = True,156            **kwargs,157    ) -> Union[str, List[int], List[str], List[List[int]], BatchEncoding]:158 159        if return_dict and not tokenize:160            raise ValueError(161                "`return_dict=True` is incompatible with `tokenize=False`, because there is no dict "162                "of tokenizer outputs to return."163            )164 165        def handle_single_conversation(conversation):166            input_ids = self.get_prefix_tokens() if add_special_tokens else []167            input_message = "[gMASK]<sop>" if add_special_tokens else ""168            for item in conversation:169                if item.get("tools"):170                    tools = item["tools"]171                    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。"172                    for tool in tools:173                        if tool["type"] == "function":174                            function = tool["function"]175                            content += f"\n\n## {function['name']}\n\n{json.dumps(function, ensure_ascii=False, indent=4)}"176                            content += "\n在调用上述函数时，请使用 Json 格式表示调用的参数。"177                        elif tool["type"] == "python":178                            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。"179                        elif tool["type"] == "simple_browser":180                            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。"181                        elif tool["type"] == "cogview":182                            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。"183                        else:184                            raise NotImplementedError(f"Unknown tool type {tool['type']}")185                    input = self.build_single_message("system", "", content, tokenize=tokenize)186                    if tokenize:187                        input_ids.extend(input)188                    else:189                        input_message += input190                if item["content"]:191                    input = self.build_single_message(192                        item["role"],193                        item.get("metadata", ""),194                        item["content"],195                        tokenize=tokenize196                    )197                    if tokenize:198                        input_ids.extend(input)199                    else:200                        input_message += input201            if add_generation_prompt:202                if tokenize:203                    input_ids.extend([self.convert_tokens_to_ids("<|assistant|>")])204                else:205                    input_message += "<|assistant|>"206 207            return input_ids if tokenize else input_message208 209        # Main logic to handle different conversation formats210        if isinstance(conversation, list) and all(isinstance(i, dict) for i in conversation):211            result = handle_single_conversation(conversation)212        elif isinstance(conversation, list) and all(isinstance(i, list) for i in conversation):213            result = [handle_single_conversation(c) for c in conversation]214        elif hasattr(conversation, "messages"):215            result = handle_single_conversation(conversation.messages)216        else:217            raise ValueError("Invalid conversation format")218 219        if tokenize:220            output = self.batch_encode_plus(221                [result] if isinstance(result[0], int) else result,222                padding=padding,223                truncation=truncation,224                max_length=max_length,225                return_tensors=return_tensors,226                is_split_into_words=True,227                add_special_tokens=False228            )229            if return_dict:230                return output231            else:232                return output["input_ids"]233        else:234            return result235 236 237    def build_inputs_with_special_tokens(238            self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None239    ) -> List[int]:240        """241        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and242        adding special tokens. A BERT sequence has the following format:243 244        - single sequence: `[CLS] X [SEP]`245        - pair of sequences: `[CLS] A [SEP] B [SEP]`246 247        Args:248            token_ids_0 (`List[int]`):249                List of IDs to which the special tokens will be added.250            token_ids_1 (`List[int]`, *optional*):251                Optional second list of IDs for sequence pairs.252 253        Returns:254            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.255        """256        prefix_tokens = self.get_prefix_tokens()257        token_ids_0 = prefix_tokens + token_ids_0258        if token_ids_1 is not None:259            token_ids_0 = token_ids_0 + token_ids_1 + [self.convert_tokens_to_ids("<eos>")]260        return token_ids_0261 262    def _pad(263            self,264            encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],265            max_length: Optional[int] = None,266            padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,267            pad_to_multiple_of: Optional[int] = None,268            return_attention_mask: Optional[bool] = None,269    ) -> dict:270        """271        Pad encoded inputs (on left/right and up to predefined length or max length in the batch)272 273        Args:274            encoded_inputs:275                Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).276            max_length: maximum length of the returned list and optionally padding length (see below).277                Will truncate by taking into account the special tokens.278            padding_strategy: PaddingStrategy to use for padding.279 280                - PaddingStrategy.LONGEST Pad to the longest sequence in the batch281                - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)282                - PaddingStrategy.DO_NOT_PAD: Do not pad283                The tokenizer padding sides are defined in self.padding_side:284 285                    - 'left': pads on the left of the sequences286                    - 'right': pads on the right of the sequences287            pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.288                This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability289                `>= 7.5` (Volta).290            return_attention_mask:291                (optional) Set to False to avoid returning attention mask (default: set to model specifics)292        """293        # Load from model defaults294        assert self.padding_side == "left"295 296        required_input = encoded_inputs[self.model_input_names[0]]297        seq_length = len(required_input)298 299        if padding_strategy == PaddingStrategy.LONGEST:300            max_length = len(required_input)301 302        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):303            max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of304 305        needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length306 307        # Initialize attention mask if not present.308        if "attention_mask" not in encoded_inputs:309            encoded_inputs["attention_mask"] = [1] * seq_length310 311        if "position_ids" not in encoded_inputs:312            encoded_inputs["position_ids"] = list(range(seq_length))313 314        if needs_to_be_padded:315            difference = max_length - len(required_input)316 317            if "attention_mask" in encoded_inputs:318                encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]319            if "position_ids" in encoded_inputs:320                encoded_inputs["position_ids"] = [0] * difference + encoded_inputs["position_ids"]321            encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input322 323        return encoded_inputs324