feature1 书生·浦语大模型全链路开源开放体系学习心得

作者：人工智能uu | 2024-08-01 00:06:12

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大模型的趋势是从专用模型（不同方向细分模型）转变为通用大模型（一个模型可以应对多种任务或者多种模态）。书生·浦语大模型（InternLM2）是当前优秀的开源通用大语言模型（LLM），其也提供了7B和20B的InternLM、InternLM-Chat、InternLM-Base、InternLM-Math、InternLM-XComposer供我们调用。

我个人的兴趣点在于大模型的应用，在课程中了解到从模型到应用的一般流程如下（图片来源课程视频）：

图中，我特别关注了构建智能体模块。大模型聚焦于推理，其在环境的交互能力上功能欠缺，这里的交互指的是：例如，你询问LLM今天的天气并希望LLM根据今天的气温提供出行建议，当前的LLM并不能给出实时的建议。

为了解决实时性交互的问题（即LLM通过推理结果执行Action），智能体(Agent)的概念便被提出了。Agent的核心在于Action的执行，视频中提到了Lagent轻量级智能体框架，这里也简单阅读了一下源码。

在分析Lagent源码之前，可以先看datawhalechina开源的一个仓库： tiny-universe ，其中的TinyAgent项目构建了一个迷你Agent，学习这个项目有助于理解Lagent。TinyAgent遵循React模式，通过：Thought/Action/Action Input/Observation 不断循环迭代的方式交替生成推理轨迹和执行特定任务，使得LLM能够更好的完成实时交互任务。下面给出了项目中Agent.py的源码，可以看到：

Agent的初始化阶段主要负责加载 ReAct Prompt、加载 LLM Model、加载 tools。
Agent的text_completion阶段涉及到两次 LLM Model 的调用。第一次是调用 LLM 获取Action需要用到的 plugin_name 以及 plugin_args，而后Agent调用Plugin的call方法得到obsercation并加入到对话中。第二次调用是 LLM 通过前面的得到的对话进一步得到 Tought 和 Final Answer。


TOOL_DESC = """{name_for_model}: Call this tool to interact with the {name_for_human} API. What is the {name_for_human} API useful for? {description_for_model} Parameters: {parameters} Format the arguments as a JSON object."""
REACT_PROMPT = """Answer the following questions as best you can. You have access to the following tools:
{tool_descs}
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can be repeated zero or more times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin!
"""
 
 
class Agent:
    def __init__(self, path: str = '') -> None:
        self.path = path
        self.tool = Tools()
        self.system_prompt = self.build_system_input()
        self.model = InternLM2Chat(path)
 
    def build_system_input(self):
        tool_descs, tool_names = [], []
        for tool in self.tool.toolConfig:
            tool_descs.append(TOOL_DESC.format(**tool))
            tool_names.append(tool['name_for_model'])
        tool_descs = '\n\n'.join(tool_descs)
        tool_names = ','.join(tool_names)
        sys_prompt = REACT_PROMPT.format(tool_descs=tool_descs, tool_names=tool_names)
        return sys_prompt
    
    def parse_latest_plugin_call(self, text):
        plugin_name, plugin_args = '', ''
        i = text.rfind('\nAction:')
        j = text.rfind('\nAction Input:')
        k = text.rfind('\nObservation:')
        if 0 <= i < j:  # If the text has `Action` and `Action input`,
            if k < j:  # but does not contain `Observation`,
                text = text.rstrip() + '\nObservation:'  # Add it back.
            k = text.rfind('\nObservation:')
            plugin_name = text[i + len('\nAction:') : j].strip()
            plugin_args = text[j + len('\nAction Input:') : k].strip()
            text = text[:k]
        return plugin_name, plugin_args, text
    
    def call_plugin(self, plugin_name, plugin_args):
        plugin_args = json5.loads(plugin_args)
        if plugin_name == 'google_search':
            return '\nObservation:' + self.tool.google_search(**plugin_args)
 
    def text_completion(self, text, history=[]):
        text = "\nQuestion:" + text
        response, his = self.model.chat(text, history, self.system_prompt)
        print(response)
        plugin_name, plugin_args, response = self.parse_latest_plugin_call(response)
        if plugin_name:
            response += self.call_plugin(plugin_name, plugin_args)
        response, his = self.model.chat(response, history, self.system_prompt)
        return response, his

从上述流程可以看出，TinyAgent似乎只能进行一次工具调用，这也让我对Lagent是如何实现Thought/Action/Action Input/Observation的循环迭代产生好奇。直接打开Lagent的开源仓库，从源码目录结构可以看出代码核心是：actions、agents和llms，这里也简单对相关代码进行分析。

1. BaseModel类（llms目录下）

该框架兼容OpenAI API、Transformer、LMDeploy。因此这里先简单分析一下BaseModel类，GPTAPI、HFTransformer、LMDeployPipeline等类都继承了BaseModel，并重写或者补充了一些方法。


class BaseModel:
 
    is_api: bool = False
 
    def __init__(self,
                 path: str,
                 tokenizer_only: bool = False,
                 template_parser: 'LMTemplateParser' = LMTemplateParser,
                 meta_template: Optional[List[Dict]] = None,
                 *,
                 max_new_tokens: int = 512,
                 top_p: float = 0.8,
                 top_k: float = 40,
                 temperature: float = 0.8,
                 repetition_penalty: float = 1.0,
                 stop_words: Union[List[str], str] = None):
        ...
 
    def generate(self, inputs: Union[str, List[str]], **gen_params) -> str:
        ...
 
    def stream_generate(self, inputs: str, **gen_params) -> List[str]:
        ...
    def chat(self, inputs: Union[List[dict], List[List[dict]]], **gen_params):
        ...
 
    def generate_from_template(self, inputs: Union[List[dict],
                                                   List[List[dict]]],
                               **gen_params):
        ...
 
    def stream_chat(self, inputs: List[dict], **gen_params):
        ...
 
    def tokenize(self, prompts: Union[str, List[str], List[dict],
                                      List[List[dict]]]):
        ...
 
    def update_gen_params(self, **kwargs):
        ...

2. BaseAction类（actions目录下）

BaseAction定义了所有actions的基类并给出了自定义action的方法。


class BaseAction(metaclass=AutoRegister(TOOL_REGISTRY, ToolMeta)):
    """Base class for all actions.
    Args:
        description (:class:`Optional[dict]`): The description of the action.
            Defaults to ``None``.
        parser (:class:`Type[BaseParser]`): The parser class to process the
            action's inputs and outputs. Defaults to :class:`JsonParser`.
        enable (:class:`bool`): Whether the action is enabled. Defaults to
            ``True``.
    Examples:
        * simple tool
        .. code-block:: python
            class Bold(BaseAction):
                '''Make text bold'''
                def run(self, text: str):
                    '''
                    Args:
                        text (str): input text
                    Returns:
                        str: bold text
                    '''
                    return '**' + text + '**'
            action = Bold()
        * toolkit with multiple APIs
        .. code-block:: python
            class Calculator(BaseAction):
                '''Calculator'''
                @tool_api
                def add(self, a, b):
                    '''Add operation
                    Args:
                        a (int): augend
                        b (int): addend
                    Returns:
                        int: sum
                    '''
                    return a + b
                @tool_api
                def sub(self, a, b):
                    '''Subtraction operation
                    Args:
                        a (int): minuend
                        b (int): subtrahend
                    Returns:
                        int: difference
                    '''
                    return a - b
            action = Calculator()
    """
...

3. react.py（agents目录下）

ReAct继承自BaseAgent，其流程其实和TinyAgent差不多。为了实现可以不断调用工具并在适当调用次数下得到Final Answer，这里的做法是：

做法一：设置一个 max_turn，一旦到达最大次数就输出Final Answer
做法二：在Prompt中提到LLM一旦认为可以结束tools调用了就会输出{thought}和{finish}。LLM通过自主判断是否输出"final answer"，一旦出现“final answer”，ReActProtocol中的Parse函数便会返回设定 action_return.type == self._action_executor.finish_action.name，于是函数调用停止。

If you already know the answer, or you do not need to use tools,
please using the following format to reply:
```
{thought}the thought process to get the final answer
{finish}final answer
```


CALL_PROTOCOL_EN = """You are a assistant who can utilize external tools.
{tool_description}
To use a tool, please use the following format:
```
{thought}Think what you need to solve, do you need to use tools?
{action}the tool name, should be one of [{action_names}]
{action_input}the input to the action
```
The response after utilizing tools should using the following format:
```
{response}the results after call the tool.
```
If you already know the answer, or you do not need to use tools,
please using the following format to reply:
```
{thought}the thought process to get the final answer
{finish}final answer
```
Begin!"""


class ReAct(BaseAgent):
    """An implementation of ReAct (https://arxiv.org/abs/2210.03629)
    Args:
        llm (BaseModel or BaseAPIModel): a LLM service which can chat
            and act as backend.
        action_executor (ActionExecutor): an action executor to manage
            all actions and their response.
        protocol (ReActProtocol): a wrapper to generate prompt and
            parse the response from LLM / actions.
        max_turn (int): the maximum number of trails for LLM to generate
            plans that can be successfully parsed by ReAct protocol.
            Defaults to 4.
    """
 
    def __init__(self,
                 llm: Union[BaseModel, BaseAPIModel],
                 action_executor: ActionExecutor,
                 protocol: ReActProtocol = ReActProtocol(),
                 max_turn: int = 4) -> None:
        self.max_turn = max_turn
        super().__init__(
            llm=llm, action_executor=action_executor, protocol=protocol)
 
    def chat(self, message: Union[str, dict, List[dict]],
             **kwargs) -> AgentReturn:
        if isinstance(message, str):
            inner_history = [dict(role='user', content=message)]
        elif isinstance(message, dict):
            inner_history = [message]
        elif isinstance(message, list):
            inner_history = message[:]
        else:
            raise TypeError(f'unsupported type: {type(message)}')
        offset = len(inner_history)
        agent_return = AgentReturn()
        default_response = 'Sorry that I cannot answer your question.'
        for turn in range(self.max_turn):
            prompt = self._protocol.format(
                chat_history=[],
                inner_step=inner_history,
                action_executor=self._action_executor,
                force_stop=(turn == self.max_turn - 1))
            response = self._llm.chat(prompt, **kwargs)
            inner_history.append(dict(role='assistant', content=response))
            thought, action, action_input = self._protocol.parse(
                response, self._action_executor)
            action_return: ActionReturn = self._action_executor(
                action, action_input)
            action_return.thought = thought
            agent_return.actions.append(action_return)
            if action_return.type == self._action_executor.finish_action.name:
                agent_return.response = action_return.format_result()
                break
            inner_history.append(self._protocol.format_response(action_return))
        else:
            agent_return.response = default_response
        agent_return.inner_steps = inner_history[offset:]
        return agent_return

总结：第一节课我主要关注了从模型到应用的流程，重点了解了智能体的构建。从tinyAgent开源项目入手，结合Lagent源码简单知悉了ReAct模式下模型调用工具的流程。

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feature1 书生·浦语大模型全链路开源开放体系 学习心得

feature1 书生·浦语大模型全链路开源开放体系学习心得