第二次课程内容是跑通已经预设的3个demo:
1.InternLM-Chat-7B 智能对话
2.Lagent工具调用解简单数学题
3.浦语·灵笔多模态图文创作和理解
原视频链接:https://www.bilibili.com/video/BV1Ci4y1z72H/
InternStudio:https://studio.intern-ai.org.cn/
github教程链接:https://github.com/InternLM/tutorial/blob/main/helloworld/hello_world.md
有意参加书生·浦语大模型实战营的报名链接:https://www.wjx.top/vm/Yzzz2mi.aspx?udsid=876887,邀请码可填16885
InterLM是一个开源的轻量级训练框架,旨在支持大模型训练无需大量的依赖。基于InterLM模型,现在已有两个开源的预训练模型:InterLM-7B和InterLM-20B。
Lagent 是一个轻量级、开源的基于大语言模型的智能体 (agent) 框架,用户可以快速地将一个大语言模型转变为多种类型的智能体。通过 Lagent 框架可以更好的发挥 nternLM 模型的全部性能。
浦语·灵笔是基于书生·浦语大语言模型研发的视觉·语言大模型,有着出色的图文理解和创作能力,使用浦语·灵笔大模型可以轻松的创作一篇图文推文。
选择A100(1/4) 的配置,镜像选择 Cuda11.7-conda(就这一个选项。。)
进入开发机。
在终端输入 bash
命令,进入 conda 环境。
从本地克隆一个已有的 pytorch 2.0.1 的环境。
conda create --name internlm-demo --clone=/root/share/conda_envs/internlm-base
conda activate internlm-demo
安装所需依赖
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
直接复制 share 目录下的 InternLM 模型。-r 选项表示递归地复制目录及其内容
mkdir -p /root/model/Shanghai_AI_Laboratory
cp -r /root/share/temp/model_repos/internlm-chat-7b /root/model/Shanghai_AI_Laboratory
也可以使用 modelscope 中的 snapshot_download 函数下载模型,第一个参数为模型名称,参数 cache_dir 为模型的下载路径。
在 /root 路径下新建目录 model,在目录下新建 download.py 文件并在其中输入以下内容,保存文件。并在终端运行 python /root/model/download.py 执行下载。
import torch
from modelscope import snapshot_download, AutoModel, AutoTokenizer
import os
model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm-chat-7b', cache_dir='/root/model', revision='v1.0.3')
在 /root 路径下新建 code 目录,clone 代码.
cd /root/code
git clone https://gitee.com/internlm/InternLM.git
切换 commit 版本,这一步是为了与教程 commit 版本保持一致。
cd InternLM
git checkout 3028f07cb79e5b1d7342f4ad8d11efad3fd13d17
更改web_demo.py 中模型的路径,更改为/root/model/Shanghai_AI_Laboratory/internlm-chat-7b。
在 /root/code/InternLM 目录下新建一个 cli_demo.py 文件,其中代码如下:
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}")
终端运行cli_demo.py
python /root/code/InternLM/cli_demo.py
访问服务器上的web 应用需要先配置本地端口。
step1:在本地机器上打开终端。运行以下命令来生成 SSH 密钥对
ssh-keygen -t rsa
step2: 将被提示选择密钥文件的保存位置,默认情况下是在 ~/.ssh/ 目录中。按 Enter 键接受默认值或输入自定义路径。
step3:公钥默认存储在 ~/.ssh/id_rsa.pub,通过cat 工具查看文件内容:
cat ~\.ssh\id_rsa.pub
很长的这一串就是公钥
step4:在InternStudio的控制台中配置SSH Key。
step5:在开发机界面里点击SSH链接可以看到连接服务器的代码,33090 是根据开发机的端口进行更改的,每个人的可能不一样。复制这一行代码到终端/vscode中可以连接服务器。
配置好本地端口之后在终端运行/root/code/InternLM 目录下的 web_demo.py 文件,并设置地址和端口号。(注意要在配置好的虚拟环境中运行)
cd /root/code/InternLM
streamlit run web_demo.py --server.address 127.0.0.1 --server.port xxxx
首先切换路径到 /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
由于代码修改的地方比较多,直接将 /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(
" 插 件:" # noqa E501
+ action.type + '</span></p>',
unsafe_allow_html=True)
st.markdown(
" 思考步骤:" # noqa E501
+ action.thought + '</span></p>',
unsafe_allow_html=True)
if (isinstance(action.args, dict) and 'text' in action.args):
st.markdown(
" 执行内容:
", # 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(
" 执行结果:
", # noqa E501
unsafe_allow_html=True)
if 'text' in action.result:
st.markdown(
"" + 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()
streamlit run /root/code/lagent/examples/react_web_demo.py --server.address 127.0.0.1 --server.port 6006
在 Web 页面选择 InternLM 模型,等待模型加载完毕后,输入数学问题 已知 2x+3=10,求x ,此时 InternLM-Chat-7B 模型理解题意生成解此题的 Python 代码,Lagent 调度送入 Python 代码解释器求出该问题的解。
本小节我们将使用 InternStudio 中的 A100(1/4) * 2 机器和 internlm-xcomposer-7b 模型部署一个图文理解创作 Demo 。
首先在 InternStudio 上选择 A100(1/4)*2 的配置。coda11.7。
bash
/root/share/install_conda_env_internlm_base.sh xcomposer-demo
conda activate xcomposer-demo
pip install transformers==4.33.1 timm==0.4.12 sentencepiece==0.1.99 gradio==3.44.4 markdown2==2.4.10 xlsxwriter==3.1.2 einops accelerate
安装 modelscope pip install modelscope==1.9.5
在 /root/model 路径下新建 download.py 文件并在其中输入以下内容,并运行 python /root/model/download.py 执行下载
import torch
from modelscope import snapshot_download, AutoModel, AutoTokenizer
import os
model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm-xcomposer-7b', cache_dir='/root/model', revision='master')
在 /root/code git clone InternLM-XComposer 仓库的代码
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
在终端输入
cd /root/code/InternLM-XComposer
python examples/web_demo.py \
--folder /root/model/Shanghai_AI_Laboratory/internlm-xcomposer-7b \
--num_gpus 1 \ #这里 num_gpus 1 是因为InternStudio平台对于 A100(1/4)*2 识别仍为一张显卡
--port 6006