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import regex as re |
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import base64 |
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import os |
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import json |
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import tiktoken |
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import torch |
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from torch import TensorType |
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from typing import List, Optional, Union, Dict, Any |
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from torchvision import transforms |
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from transformers import PreTrainedTokenizer |
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from transformers.utils import logging, PaddingStrategy |
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from transformers.tokenization_utils_base import EncodedInput, BatchEncoding |
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class ChatGLM4Tokenizer(PreTrainedTokenizer): |
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vocab_files_names = {"vocab_file": "tokenizer.model"} |
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model_input_names = ["input_ids", "attention_mask", "position_ids"] |
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def __init__( |
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self, |
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vocab_file, |
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padding_side="left", |
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clean_up_tokenization_spaces=False, |
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encode_special_tokens=False, |
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image_size=None, |
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**kwargs |
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): |
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self.name = "GLM4Tokenizer" |
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self.vocab_file = vocab_file |
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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+" |
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self.pat_str = re.compile(pat_str) |
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self.encode_special_tokens = encode_special_tokens |
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self.image_size = image_size |
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mergeable_ranks = {} |
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with open(vocab_file) as f: |
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for line in f: |
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token, rank = line.strip().split() |
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rank = int(rank) |
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token = base64.b64decode(token) |
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mergeable_ranks[token] = rank |
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self.mergeable_ranks = mergeable_ranks |
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self.tokenizer = tiktoken.Encoding( |
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name="my_tokenizer", |
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pat_str=pat_str, |
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mergeable_ranks=mergeable_ranks, |
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special_tokens={} |
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) |
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self.decoder = {rank: token for token, rank in mergeable_ranks.items()} |
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self.n_words = len(self.decoder) |
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super().__init__( |
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padding_side=padding_side, |
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clean_up_tokenization_spaces=clean_up_tokenization_spaces, |
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encode_special_tokens=encode_special_tokens, |
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image_size=image_size, |
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**kwargs |
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) |
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@property |
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def vocab_size(self): |
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return self.n_words |
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def get_vocab(self): |
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""" Returns vocab as a dict """ |
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vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)} |
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vocab.update(self.added_tokens_encoder) |
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return vocab |
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def convert_tokens_to_string(self, tokens: List[Union[bytes, str, int]]) -> str: |
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""" |
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Converts a sequence of tokens in a single string. |
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""" |
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text = "" |
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temp = b"" |
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for t in tokens: |
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if isinstance(t, int): |
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t = chr(t) |
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if isinstance(t, str): |
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if temp: |
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text += temp.decode("utf-8", errors="replace") |
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elif isinstance(t, bytes): |
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temp += t |
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else: |
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raise TypeError("token should only be of type int, bytes or str") |
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if temp: |
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text += temp.decode("utf-8", errors="replace") |
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return text |
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def _tokenize(self, text, **kwargs): |
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tokens = [] |
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ids = self.tokenizer.encode(text) |
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for t in ids: |
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tokens.append(self.decoder[t]) |
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return tokens |
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def _convert_token_to_id(self, token): |
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""" Converts a token (str) in an id using the vocab. """ |
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return self.mergeable_ranks[token] |
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def _convert_id_to_token(self, index): |
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"""Converts an index (integer) in a token (str) using the vocab.""" |
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return self.decoder.get(index, "") |
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def save_vocabulary(self, save_directory, filename_prefix=None): |
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""" |
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Save the vocabulary and special tokens file to a directory. |
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Args: |
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save_directory (`str`): |
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The directory in which to save the vocabulary. |
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filename_prefix (`str`, *optional*): |
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An optional prefix to add to the named of the saved files. |
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Returns: |
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`Tuple(str)`: Paths to the files saved. |
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""" |
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if os.path.isdir(save_directory): |
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vocab_file = os.path.join( |
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save_directory, self.vocab_files_names["vocab_file"] |
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) |
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else: |
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vocab_file = save_directory |
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with open(self.vocab_file, 'rb') as fin: |
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proto_str = fin.read() |
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with open(vocab_file, "wb") as writer: |
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writer.write(proto_str) |
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return (vocab_file,) |
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def get_prefix_tokens(self): |
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prefix_tokens = [self.convert_tokens_to_ids("[gMASK]"), self.convert_tokens_to_ids("<sop>")] |
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return prefix_tokens |
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def build_single_message(self, role, metadata, message, tokenize=True, message_prefix=None): |
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assert role in ["system", "user", "assistant", "observation"], role |
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if tokenize: |
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role_tokens = [self.convert_tokens_to_ids(f"<|{role}|>")] + self.tokenizer.encode(f"{metadata}\n", |
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disallowed_special=()) |
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message_tokens = self.tokenizer.encode(message, disallowed_special=()) |
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if message_prefix is not None: |
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message_tokens = message_prefix + message_tokens |
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tokens = role_tokens + message_tokens |
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return tokens |
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else: |
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return str(f"<|{role}|>{metadata}\n{message}") |
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def apply_chat_template( |
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self, |
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conversation: Union[List[Dict[str, str]], List[List[Dict[str, str]]], "Conversation"], |
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add_generation_prompt: bool = False, |
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tokenize: bool = True, |
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padding: bool = False, |
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truncation: bool = False, |
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max_length: Optional[int] = None, |
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return_tensors: Optional[Union[str, TensorType]] = None, |
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return_dict: bool = False, |
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tokenizer_kwargs: Optional[Dict[str, Any]] = None, |
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add_special_tokens: bool = True, |
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**kwargs, |
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) -> Union[str, List[int], List[str], List[List[int]], BatchEncoding]: |
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if return_dict and not tokenize: |
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raise ValueError( |
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"`return_dict=True` is incompatible with `tokenize=False`, because there is no dict " |
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"of tokenizer outputs to return." |
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) |
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def handle_single_conversation(conversation): |
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input_ids = self.get_prefix_tokens() if add_special_tokens else [] |
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input_message = "[gMASK]<sop>" if add_special_tokens else "" |
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input_image = None |
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transform = transforms.Compose( |
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[ |
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transforms.Resize( |
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(self.image_size, self.image_size), interpolation=transforms.InterpolationMode.BICUBIC |
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), |
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transforms.ToTensor(), |
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transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)), |
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] |
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) |
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for item in conversation: |
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if item.get("tools"): |
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tools = item["tools"] |
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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。" |
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for tool in tools: |
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if tool["type"] == "function": |
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function = tool["function"] |
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content += f"\n\n## {function['name']}\n\n{json.dumps(function, ensure_ascii=False, indent=4)}" |
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content += "\n在调用上述函数时，请使用 Json 格式表示调用的参数。" |
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elif tool["type"] == "python": |
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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。" |
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elif tool["type"] == "simple_browser": |
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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。" |
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elif tool["type"] == "cogview": |
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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。" |
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else: |
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raise NotImplementedError(f"Unknown tool type {tool['type']}") |
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input = self.build_single_message("system", "", content, tokenize=tokenize) |
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if tokenize: |
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input_ids.extend(input) |
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else: |
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input_message += input |
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message = "" |
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message_prefix = None |
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if item.get("image"): |
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assert input_image is None, "Multiple images are not supported" |
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input_image = transform(item["image"]) |
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message_prefix = self.convert_tokens_to_ids( |
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["<|begin_of_image|>", "<|endoftext|>", "<|end_of_image|>"]) |
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if item.get("content"): |
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message += item["content"] |
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if message or message_prefix: |
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input = self.build_single_message( |
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item["role"], |
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item.get("metadata", ""), |
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message, |
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tokenize=tokenize, |
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message_prefix=message_prefix |
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) |
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if tokenize: |
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input_ids.extend(input) |
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else: |
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input_message += input |
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if add_generation_prompt: |
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if tokenize: |
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input_ids.extend([self.convert_tokens_to_ids("<|assistant|>")]) |
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else: |
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input_message += "<|assistant|>" |
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return {"input": input_ids if tokenize else input_message, "image": input_image} |
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if isinstance(conversation, list) and all(isinstance(i, dict) for i in conversation): |
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result = handle_single_conversation(conversation) |
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input_ids = result["input"] |
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input_images = [result["image"]] |
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elif isinstance(conversation, list) and all(isinstance(i, list) for i in conversation): |
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results = [handle_single_conversation(c) for c in conversation] |
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input_ids = [item["input"] for item in results] |
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input_images = [item["image"] for item in results] |
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elif hasattr(conversation, "messages"): |
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result = handle_single_conversation(conversation.messages) |
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input_ids = result["input"] |
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input_images = [result["image"]] |
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else: |
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raise ValueError("Invalid conversation format") |
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if tokenize: |
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output = self.batch_encode_plus( |
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[input_ids] if isinstance(input_ids[0], int) else input_ids, |
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padding=padding, |
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truncation=truncation, |
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max_length=max_length, |
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return_tensors=return_tensors, |
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is_split_into_words=True, |
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add_special_tokens=False |
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) |
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if return_dict: |
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found_image = False |
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for image in input_images: |
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if image is not None: |
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found_image = True |
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break |
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if found_image: |
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output["images"] = torch.stack(input_images) |
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return output |
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else: |
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return output["input_ids"] |
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else: |
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return input_ids |
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def build_inputs_with_special_tokens( |
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None |
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) -> List[int]: |
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""" |
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Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and |
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adding special tokens. A BERT sequence has the following format: |
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- single sequence: `[CLS] X [SEP]` |
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- pair of sequences: `[CLS] A [SEP] B [SEP]` |
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Args: |
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token_ids_0 (`List[int]`): |
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List of IDs to which the special tokens will be added. |
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token_ids_1 (`List[int]`, *optional*): |
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Optional second list of IDs for sequence pairs. |
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Returns: |
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`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. |
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""" |
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prefix_tokens = self.get_prefix_tokens() |
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token_ids_0 = prefix_tokens + token_ids_0 |
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if token_ids_1 is not None: |
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token_ids_0 = token_ids_0 + token_ids_1 + [self.convert_tokens_to_ids("<eos>")] |
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return token_ids_0 |
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def _pad( |
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self, |
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encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding], |
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max_length: Optional[int] = None, |
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padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, |
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pad_to_multiple_of: Optional[int] = None, |
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padding_side: Optional[str] = None, |
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return_attention_mask: Optional[bool] = None, |
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) -> dict: |
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""" |
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Pad encoded inputs (on left/right and up to predefined length or max length in the batch) |
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Args: |
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encoded_inputs: |
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Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`). |
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max_length: maximum length of the returned list and optionally padding length (see below). |
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Will truncate by taking into account the special tokens. |
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padding_strategy: PaddingStrategy to use for padding. |
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- PaddingStrategy.LONGEST Pad to the longest sequence in the batch |
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- PaddingStrategy.MAX_LENGTH: Pad to the max length (default) |
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- PaddingStrategy.DO_NOT_PAD: Do not pad |
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The tokenizer padding sides are defined in self.padding_side: |
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- 'left': pads on the left of the sequences |
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- 'right': pads on the right of the sequences |
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pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value. |
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This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability |
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`>= 7.5` (Volta). |
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return_attention_mask: |
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(optional) Set to False to avoid returning attention mask (default: set to model specifics) |
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""" |
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assert self.padding_side == "left" |
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required_input = encoded_inputs[self.model_input_names[0]] |
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seq_length = len(required_input) |
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if padding_strategy == PaddingStrategy.LONGEST: |
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max_length = len(required_input) |
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if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): |
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max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of |
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needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length |
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if "attention_mask" not in encoded_inputs: |
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encoded_inputs["attention_mask"] = [1] * seq_length |
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if "position_ids" not in encoded_inputs: |
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encoded_inputs["position_ids"] = list(range(seq_length)) |
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if needs_to_be_padded: |
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difference = max_length - len(required_input) |
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if "attention_mask" in encoded_inputs: |
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encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"] |
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if "position_ids" in encoded_inputs: |
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encoded_inputs["position_ids"] = [0] * difference + encoded_inputs["position_ids"] |
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encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input |
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return encoded_inputs |
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|
