书生·浦语：大模型全链路开源体系（二）——InternLM、Lagent、浦语·灵笔Demo调用

一、准备工作：

1、环境配置：

pip、conda换源：

pip临时换源：

pip install -i https://mirrors.cernet.edu.cn/pypi/web/simple some-package

# 这里的“https://mirrors.cernet.edu.cn/pypi/web/simple”是所换的源，“some-package”是你需要安装的包

设置pip默认源，避免每次下载依赖包都要加上一长串的国内源

pip config set global.index-url https://mirrors.cernet.edu.cn/pypi/web/simple

conda换源：

镜像站提供了 Anaconda 仓库与第三方源（conda-forge、msys2、pytorch 等），各系统都可以通过修改用户目录下的?.condarc?文件来使用镜像站。

不同系统下的?.condarc?目录如下：

• Linux:?${HOME}/.condarc
• macOS:?${HOME}/.condarc
• Windows:?C:\Users\<YourUserName>\.condarc

注意：

• Windows?用户无法直接创建名为?.condarc?的文件，可先执行?conda config --set show_channel_urls yes?生成该文件之后再修改。

cat <<'EOF' > ~/.condarc
channels:
  - defaults
show_channel_urls: true
default_channels:
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/msys2
custom_channels:
  conda-forge: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  pytorch: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
EOF

更多详细内容可移步至?MirrorZ Help?查看

2、模型下载：

Huggingface:

使用 Hugging Face 官方提供的?huggingface-cli?命令行工具。安装依赖:

pip install -U huggingface_hub

?安装好依赖包之后，执行以下代码：

import os
from huggingface_hub import hf_hub_download  # Load model directly 

# 下载模型
os.system('huggingface-cli download --resume-download internlm/internlm-chat-7b --local-dir your_path')

# resume-download：断点续下（断网也可继续下载）
# local-dir：本地存储路径。（linux 环境下需要填写绝对路径）

hf_hub_download(repo_id="internlm/internlm-7b", filename="config.json")

# repo_id: 模型的名称
# filename: 下载的文件名称

ModelScope:

安装依赖：

pip install modelscope==1.9.5
pip install transformers==4.35.2

安装完成后：

import torch
from modelscope import snapshot_download, AutoModel, AutoTokenizer
import os
model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm-chat-7b', cache_dir='your path', revision='master')

# cache_dir:最好写成绝对路径

?OpenXLAB:

安装依赖：

pip install -U openxlab

执行代码：

from openxlab.model import download
download(model_repo='OpenLMLab/InternLM-7b', model_name='InternLM-7b', output='your local path')

二、InternLM智能对话 Demo：

1、准备硬件设备：显卡

目前显卡比较短缺，各位大佬各显神通吧，这里以?InternStudio?为例

2、进入开发机配置环境：

进入?conda?环境之后，使用以下命令从本地克隆一个已有的?pytorch 2.0.1?的环境，运行时间可能比较长，耐心等待

bash # 请每次使用 jupyter lab 打开终端时务必先执行 bash 命令进入 bash 中
conda create --name internlm-demo --clone=/root/share/conda_envs/internlm-base

然后用下面命令激活虚拟环境，并安装所需环境：

conda activate internlm-demo


————————————————————————————demo所需的环境依赖
# 升级pip
python -m pip install --upgrade pip

pip install modelscope==1.9.5
pip install transformers==4.35.2
pip install streamlit==1.24.0
pip install sentencepiece==0.1.99
pip install accelerate==0.24.1

?3、模型下载：

根据之前介绍的模型下载的三种方式都可以实现模型的下载，但是速度相对较慢，这里我使用的是InternStudio?平台的?share?目录下已经为我们准备好的?InternLM?模型。

mkdir -p /root/model/Shanghai_AI_Laboratory
cp -r /root/share/temp/model_repos/internlm-chat-7b /root/model/Shanghai_AI_Laboratory

4、代码准备：

在?/root?路径下新建?code?目录，然后切换路径, clone 代码

cd /root/code
git clone https://gitee.com/internlm/InternLM.git


## 切换 commit 版本，可以让大家更好的复现
cd InternLM
git checkout 3028f07cb79e5b1d7342f4ad8d11efad3fd13d17

将?/root/code/InternLM/web_demo.py?中 29 行和 33 行的模型更换为本地的?/root/model/Shanghai_AI_Laboratory/internlm-chat-7b。?

5、运行：

（1）终端运行：

在?/root/code/InternLM?目录下新建一个?cli_demo.py?文件，将以下代码填入其中：

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM


model_name_or_path = "/root/model/Shanghai_AI_Laboratory/internlm-chat-7b"

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True, torch_dtype=torch.bfloat16, device_map='auto')
model = model.eval()

system_prompt = """You are an AI assistant whose name is InternLM (书生·浦语).
- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.
- InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文.
"""

messages = [(system_prompt, '')]

print("=============Welcome to InternLM chatbot, type 'exit' to exit.=============")

while True:
    input_text = input("User  >>> ")
    input_text = input_text.replace(' ', '')
    if input_text == "exit":
        break
    response, history = model.chat(tokenizer, input_text, history=messages)
    messages.append((input_text, response))
    print(f"robot >>> {response}")

?然后在终端运行：python /root/code/InternLM/cli_demo.py? 即可

（2）web运行：

运行?/root/code/InternLM?目录下的?web_demo.py?文件，输入以下命令后，l利用SSH密钥将端口映射到本地。在本地浏览器输入?http://127.0.0.1:6006?即可。

bash
conda activate internlm-demo  # 首次进入 vscode 会默认是 base 环境，所以首先切换环境
cd /root/code/InternLM
streamlit run web_demo.py --server.address 127.0.0.1 --server.port 6006

?

三、Lagent智能工具demo调用：

1、环境准备：

Lagent所需环境和InternLM环境一直，若运行环境已经安装好依赖包可直接跳过：

# 升级pip
python -m pip install --upgrade pip

pip install modelscope==1.9.5
pip install transformers==4.35.2
pip install streamlit==1.24.0
pip install sentencepiece==0.1.99
pip install accelerate==0.24.1

?2、模型下载：

Lagnet是智能体构建的工具，基础模型可以直接使用InterLM模型，无需重复下载。

3、代码准备：

切换路径到?/root/code?克隆?lagent?仓库，并通过?pip install -e .?源码安装?Lagent

cd /root/code
git clone https://gitee.com/internlm/lagent.git
cd /root/code/lagent
git checkout 511b03889010c4811b1701abb153e02b8e94fb5e # 尽量保证和教程commit版本一致
pip install -e . # 源码安装

将?/root/code/lagent/examples/react_web_demo.py?内容替换为以下代码：

import copy
import os

import streamlit as st
from streamlit.logger import get_logger

from lagent.actions import ActionExecutor, GoogleSearch, PythonInterpreter
from lagent.agents.react import ReAct
from lagent.llms import GPTAPI
from lagent.llms.huggingface import HFTransformerCasualLM


class SessionState:

    def init_state(self):
        """Initialize session state variables."""
        st.session_state['assistant'] = []
        st.session_state['user'] = []

        #action_list = [PythonInterpreter(), GoogleSearch()]
        action_list = [PythonInterpreter()]
        st.session_state['plugin_map'] = {
            action.name: action
            for action in action_list
        }
        st.session_state['model_map'] = {}
        st.session_state['model_selected'] = None
        st.session_state['plugin_actions'] = set()

    def clear_state(self):
        """Clear the existing session state."""
        st.session_state['assistant'] = []
        st.session_state['user'] = []
        st.session_state['model_selected'] = None
        if 'chatbot' in st.session_state:
            st.session_state['chatbot']._session_history = []


class StreamlitUI:

    def __init__(self, session_state: SessionState):
        self.init_streamlit()
        self.session_state = session_state

    def init_streamlit(self):
        """Initialize Streamlit's UI settings."""
        st.set_page_config(
            layout='wide',
            page_title='lagent-web',
            page_icon='./docs/imgs/lagent_icon.png')
        # st.header(':robot_face: :blue[Lagent] Web Demo ', divider='rainbow')
        st.sidebar.title('模型控制')

    def setup_sidebar(self):
        """Setup the sidebar for model and plugin selection."""
        model_name = st.sidebar.selectbox(
            '模型选择：', options=['gpt-3.5-turbo','internlm'])
        if model_name != st.session_state['model_selected']:
            model = self.init_model(model_name)
            self.session_state.clear_state()
            st.session_state['model_selected'] = model_name
            if 'chatbot' in st.session_state:
                del st.session_state['chatbot']
        else:
            model = st.session_state['model_map'][model_name]

        plugin_name = st.sidebar.multiselect(
            '插件选择',
            options=list(st.session_state['plugin_map'].keys()),
            default=[list(st.session_state['plugin_map'].keys())[0]],
        )

        plugin_action = [
            st.session_state['plugin_map'][name] for name in plugin_name
        ]
        if 'chatbot' in st.session_state:
            st.session_state['chatbot']._action_executor = ActionExecutor(
                actions=plugin_action)
        if st.sidebar.button('清空对话', key='clear'):
            self.session_state.clear_state()
        uploaded_file = st.sidebar.file_uploader(
            '上传文件', type=['png', 'jpg', 'jpeg', 'mp4', 'mp3', 'wav'])
        return model_name, model, plugin_action, uploaded_file

    def init_model(self, option):
        """Initialize the model based on the selected option."""
        if option not in st.session_state['model_map']:
            if option.startswith('gpt'):
                st.session_state['model_map'][option] = GPTAPI(
                    model_type=option)
            else:
                st.session_state['model_map'][option] = HFTransformerCasualLM(
                    '/root/model/Shanghai_AI_Laboratory/internlm-chat-7b')
        return st.session_state['model_map'][option]

    def initialize_chatbot(self, model, plugin_action):
        """Initialize the chatbot with the given model and plugin actions."""
        return ReAct(
            llm=model, action_executor=ActionExecutor(actions=plugin_action))

    def render_user(self, prompt: str):
        with st.chat_message('user'):
            st.markdown(prompt)

    def render_assistant(self, agent_return):
        with st.chat_message('assistant'):
            for action in agent_return.actions:
                if (action):
                    self.render_action(action)
            st.markdown(agent_return.response)

    def render_action(self, action):
        with st.expander(action.type, expanded=True):
            st.markdown(
                "<p style='text-align: left;display:flex;'> <span style='font-size:14px;font-weight:600;width:70px;text-align-last: justify;'>插    件</span><span style='width:14px;text-align:left;display:block;'>:</span><span style='flex:1;'>"  # noqa E501
                + action.type + '</span></p>',
                unsafe_allow_html=True)
            st.markdown(
                "<p style='text-align: left;display:flex;'> <span style='font-size:14px;font-weight:600;width:70px;text-align-last: justify;'>思考步骤</span><span style='width:14px;text-align:left;display:block;'>:</span><span style='flex:1;'>"  # noqa E501
                + action.thought + '</span></p>',
                unsafe_allow_html=True)
            if (isinstance(action.args, dict) and 'text' in action.args):
                st.markdown(
                    "<p style='text-align: left;display:flex;'><span style='font-size:14px;font-weight:600;width:70px;text-align-last: justify;'> 执行内容</span><span style='width:14px;text-align:left;display:block;'>:</span></p>",  # noqa E501
                    unsafe_allow_html=True)
                st.markdown(action.args['text'])
            self.render_action_results(action)

    def render_action_results(self, action):
        """Render the results of action, including text, images, videos, and
        audios."""
        if (isinstance(action.result, dict)):
            st.markdown(
                "<p style='text-align: left;display:flex;'><span style='font-size:14px;font-weight:600;width:70px;text-align-last: justify;'> 执行结果</span><span style='width:14px;text-align:left;display:block;'>:</span></p>",  # noqa E501
                unsafe_allow_html=True)
            if 'text' in action.result:
                st.markdown(
                    "<p style='text-align: left;'>" + action.result['text'] +
                    '</p>',
                    unsafe_allow_html=True)
            if 'image' in action.result:
                image_path = action.result['image']
                image_data = open(image_path, 'rb').read()
                st.image(image_data, caption='Generated Image')
            if 'video' in action.result:
                video_data = action.result['video']
                video_data = open(video_data, 'rb').read()
                st.video(video_data)
            if 'audio' in action.result:
                audio_data = action.result['audio']
                audio_data = open(audio_data, 'rb').read()
                st.audio(audio_data)


def main():
    logger = get_logger(__name__)
    # Initialize Streamlit UI and setup sidebar
    if 'ui' not in st.session_state:
        session_state = SessionState()
        session_state.init_state()
        st.session_state['ui'] = StreamlitUI(session_state)

    else:
        st.set_page_config(
            layout='wide',
            page_title='lagent-web',
            page_icon='./docs/imgs/lagent_icon.png')
        # st.header(':robot_face: :blue[Lagent] Web Demo ', divider='rainbow')
    model_name, model, plugin_action, uploaded_file = st.session_state[
        'ui'].setup_sidebar()

    # Initialize chatbot if it is not already initialized
    # or if the model has changed
    if 'chatbot' not in st.session_state or model != st.session_state[
            'chatbot']._llm:
        st.session_state['chatbot'] = st.session_state[
            'ui'].initialize_chatbot(model, plugin_action)

    for prompt, agent_return in zip(st.session_state['user'],
                                    st.session_state['assistant']):
        st.session_state['ui'].render_user(prompt)
        st.session_state['ui'].render_assistant(agent_return)
    # User input form at the bottom (this part will be at the bottom)
    # with st.form(key='my_form', clear_on_submit=True):

    if user_input := st.chat_input(''):
        st.session_state['ui'].render_user(user_input)
        st.session_state['user'].append(user_input)
        # Add file uploader to sidebar
        if uploaded_file:
            file_bytes = uploaded_file.read()
            file_type = uploaded_file.type
            if 'image' in file_type:
                st.image(file_bytes, caption='Uploaded Image')
            elif 'video' in file_type:
                st.video(file_bytes, caption='Uploaded Video')
            elif 'audio' in file_type:
                st.audio(file_bytes, caption='Uploaded Audio')
            # Save the file to a temporary location and get the path
            file_path = os.path.join(root_dir, uploaded_file.name)
            with open(file_path, 'wb') as tmpfile:
                tmpfile.write(file_bytes)
            st.write(f'File saved at: {file_path}')
            user_input = '我上传了一个图像，路径为: {file_path}. {user_input}'.format(
                file_path=file_path, user_input=user_input)
        agent_return = st.session_state['chatbot'].chat(user_input)
        st.session_state['assistant'].append(copy.deepcopy(agent_return))
        logger.info(agent_return.inner_steps)
        st.session_state['ui'].render_assistant(agent_return)


if __name__ == '__main__':
    root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
    root_dir = os.path.join(root_dir, 'tmp_dir')
    os.makedirs(root_dir, exist_ok=True)
    main()

4、web demo运行：

同样，建立ssh远程连接，在浏览器输入?http://127.0.0.1:6006?即可。

streamlit run /root/code/lagent/examples/react_web_demo.py --server.address 127.0.0.1 --server.port 6006

?

确实厉害，连MBA的题目都能轻松应对。

四、浦语·灵笔图文理解创作 Demo：

1、基础配置：

和之前两个demo一样的流程，从环境配置到模型下载

# 进入 conda 环境之后，使用以下命令从本地克隆一个已有的pytorch 2.0.1 的环境
conda create --name xcomposer-demo --clone=/root/share/conda_envs/internlm-base


# 激活环境
conda activate xcomposer-demo



#安装依赖：
pip install transformers==4.33.1 
pip install timm==0.4.12 
pip install sentencepiece==0.1.99 
pip install gradio==3.44.4 
pip install markdown2==2.4.10 
pip install xlsxwriter==3.1.2 
pip install einops accelerate


# 模型下载：
mkdir -p /root/model/Shanghai_AI_Laboratory
cp -r /root/share/temp/model_repos/internlm-xcomposer-7b /root/model/Shanghai_AI_Laboratory

2、代码准备：

又是老朋友了

cd /root/code
git clone https://gitee.com/internlm/InternLM-XComposer.git
cd /root/code/InternLM-XComposer
git checkout 3e8c79051a1356b9c388a6447867355c0634932d  # 最好保证和教程的 commit 版本一致

3、运行web demo:

?终端运行以下代码，同样是在完成ssh连接之后：

cd /root/code/InternLM-XComposer
python examples/web_demo.py  \
    --folder /root/model/Shanghai_AI_Laboratory/internlm-xcomposer-7b \
    --num_gpus 1 \
    --port 6006

?num_gpus 指的是使用gpu的数量，vgpu-smi可以查看gpu的使用情况

五、SSH远程服务连接：

这里只是简单的介绍以下本次demo调用中使用的demo配置，具体可以看博客：ssh用法及命令_ssh命令大全-CSDN博客

1、在本地机器上打开?Power Shell?终端。在终端中，运行以下命令来生成 SSH 密钥对：

ssh-keygen -t rsa

##-t表示类型选项，这里采用rsa加密算法

2、按?Enter?键接受默认值或输入自定义路径?，默认情况下是在?~/.ssh/?目录中。（其中有一个提示是要求设置私钥口令passphrase，不设置则为空，这里看心情吧，如果不放心私钥的安全可以设置一下）执行结束以后会在?/home/当前用户 目录下生成一个?.ssh 文件夹,其中包含私钥文件 id_rsa?和公钥文件 id_rsa.pub。

3、通过系统自带的?cat?工具查看文件内容：

cat ~\.ssh\id_rsa.pub
# ~ 是用户主目录的简写，.ssh 是SSH配置文件的默认存储目录，id_rsa.pub 是 SSH 公钥文件的默认名称。所以，cat ~\.ssh\id_rsa.pub 的意思是查看用户主目录下的 .ssh 目录中的 id_rsa.pub 文件的内容。

?4、将公钥复制到剪贴板中，然后回到?InternStudio?控制台，点击配置 SSH Key。

?

在本

ssh -CNg -L 6006:127.0.0.1:6006 root@ssh.intern-ai.org.cn -p 33090

地终端输入以下指令?.6006?是在服务器中打开的端口，而?33090?是根据开发机的端口进行更改

注意：再这些操作中可能会出现多次warning，个人经验是只要没报错就继续运行。