LLM_InterLM-Demo学习

reference Github: https://github.com/InternLM/tutorial/blob/main/helloworld/hello_world.md

1- 环境配置

之前都是用科学上网在huggingFace进行的模型下载，同时还需要进行一些配置

import os
os.environ['CURL_CA_BUNDLE'] = ''

在本次的学习中发现可以设置镜像或者是通过modelscope(pip install modelscope)进行下载

# huggingface-下载
## 安装依赖: pip install -U huggingface_hub
## 直接设置环境变量: export HF_ENDPOINT=https://hf-mirror.com
import os 
from huggingface_hub import hf_hub_download 
hf_hub_download(repo_id="internlm/internlm-7b", filename="config.json")# modelscope 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')

下载完之后可以直接用transformers 无缝衔接

import torch
from transformers import AutoModelForCausalLM, AutoTokenizerdownload_dir = "xxx/internlm-chat-7b"model = (AutoModelForCausalLM.from_pretrained(download_dir, trust_remote_code=True).to(torch.bfloat16).cuda())
tokenizer = AutoTokenizer.from_pretrained(download_dir, trust_remote_code=True)

同时补齐了信息差，知道了国内镜像合集网址:MirrorZ Help

2- InterLM-Chat-7B Demo尝试

主要的项目GitHub: https://github.com/InternLM/InternLM

2.1 终端demo

其实进行demo尝试相对比较简单，主要是模型下载和GPU显存(20G : 1/4的A100-80G)的限制比较大。只用transformers.AutoModelForCausalLM加载进行尝试就行。

import torch
from transformers import AutoTokenizer, AutoModelForCausalLMmodel_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":breakresponse, history = model.chat(tokenizer, input_text, history=messages)messages.append((input_text, response))print(f"robot >>> {response}")

2.2 web demo

进行web demo，主要是对终端demo进行一层streamlit的封装，同时通过ssh将端口映射到本地，资源占用的时服务器的资源。
从教程中学习到了@st.cache_resource装饰器的用法，这个在笔者之前的streamlit项目中没有用到过, 后续可以在自己项目中尝试用在保持database的连接上。

@st.cache_resource
def load_model():model = (AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True).to(torch.bfloat16).cuda())tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True)return model, tokenizer

3- Lagent 智能体工具调用 Demo尝试

主要的项目GitHub: https://github.com/InternLM/lagent

在第一节中已经了解到：大模型的局限性，需要Agent去引导优化, 这次demo尝试加深了对这个句话的理解。

在本次Demo中调用lagent，去解决数学问题： 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。

主要步骤如下：

1. 模型初始化init_model(基于选择的name)
   • model = HFTransformerCasualLM('/root/model/Shanghai_AI_Laboratory/internlm-chat-7b')
2. 构建lagent initialize_chatbot
   • chatbot = ReAct(llm=model, action_executor=ActionExecutor(actions=PythonInterpreter()))
3. 用户输入调用chatbot
   • agent_return = chatbot.chat(user_input)
4. 解析返回结果并展示（最后保存历史信息）
   • render_assistant(agent_return)
   • action解析展示如下

    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 E501unsafe_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, andaudios."""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 E501unsafe_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)

简单的代码可以如下

from lagent.actions import ActionExecutor, GoogleSearch, PythonInterpreter
from lagent.agents.react import ReAct
from lagent.llms import GPTAPI
from lagent.llms.huggingface import HFTransformerCasualLM# init_model
model = HFTransformerCasualLM('/root/model/Shanghai_AI_Laboratory/internlm-chat-7b')# initialize_chatbot
chatbot = ReAct(llm=model, action_executor=ActionExecutor(actions=PythonInterpreter()))
agent_return = chatbot.chat(user_input)

4- 浦语·灵笔图文理解创作 Demo尝试

主要的项目GitHub: https://github.com/InternLM/InternLM-XComposer
这里的模型也是不一样: InternLM-XComposer是基于InternLM研发的视觉-语言大模型

InternLM-XComposer是提供出色的图文理解和创作能力，具有多项优势：

• 图文交错创作: InternLM-XComposer可以为用户打造图文并貌的专属文章。这一能力由以下步骤实现：

  1. 理解用户指令，创作符合要求的长文章。
  2. 智能分析文章，自动规划插图的理想位置，确定图像内容需求。
  3. 多层次智能筛选，从图库中锁定最完美的图片。

• 基于丰富多模态知识的图文理解: InternLM-XComposer设计了高效的训练策略，为模型注入海量的多模态概念和知识数据，赋予其强大的图文理解和对话能力。

• 杰出性能: InternLM-XComposer在多项视觉语言大模型的主流评测上均取得了最佳性能，包括MME Benchmark (英文评测), MMBench (英文评测), Seed-Bench (英文评测), CCBench(中文评测), MMBench-CN (中文评测).

4.1 生成文章

1. 模型和token初始化
2. 模型调用
   • 快速使用的话，可以直接llm_model.generate
   • 进行复杂的一些操作可以 llm_model.internlm_model.generate
     • 细节看笔者简单修改的generate函数

核心示例code:

import torch
from transformers import StoppingCriteriaList, AutoTokenizer, AutoModel
from examples.utils import auto_configure_device_mapclass StoppingCriteriaSub(StoppingCriteria):def __init__(self, stops=[], encounters=1):super().__init__()self.stops = stopsdef __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor):for stop in self.stops:if torch.all((stop == input_ids[:, -len(stop):])).item():return Truereturn Falsefolder = 'internlm/internlm-xcomposer-7b'
device = 'cuda'# 1- init model and tokenizer
llm_model = AutoModel.from_pretrained(folder, trust_remote_code=True).cuda().eval()
if args.num_gpus > 1:from accelerate import dispatch_modeldevice_map = auto_configure_device_map(args.num_gpus)model = dispatch_model(model, device_map=device_map)tokenizer = AutoTokenizer.from_pretrained(folder, trust_remote_code=True)
llm_model.internlm_tokenizer = tokenizer
llm_model.tokenizer = tokenizer# 2 封装generate
def generate(llm_model, text, random, beam, max_length, repetition, use_inputs=False):"""生成文章封装llm_model:  AutoModel.from_pretrained 加载的 internlm/internlm-xcomposer-7brandom:  采样beam: beam search 数量max_length: 文章最大长度repetition: repetition_penalty"""device = 'cuda'# stop critriastop_words_ids = [torch.tensor([103027]).to(device),  torch.tensor([103028]).to(device),  ]stopping_criteria = StoppingCriteriaList([StoppingCriteriaSub(stops=stop_words_ids)])# 输入tokensinput_tokens = llm_model.internlm_tokenizer(text, return_tensors="pt",add_special_tokens=True).to(llm_model.device)# 输入生成图像的embedsimg_embeds = llm_model.internlm_model.model.embed_tokens(input_tokens.input_ids)inputs = input_tokens.input_ids if use_inputs else None# 模型推理with torch.no_grad():with llm_model.maybe_autocast():outputs = llm_model.internlm_model.generate(inputs=inputs,inputs_embeds=img_embeds,  #  生成配图stopping_criteria=stopping_criteria,do_sample=random,num_beams=beam,max_length=max_length,repetition_penalty=float(repetition),)# decode及输出output_text = llm_model.internlm_tokenizer.decode(outputs[0][1:], add_special_tokens=False)output_text = output_text.split('<TOKENS_UNUSED_1>')[0]return output_text# 调用
## 生成小文章
text = '请介绍下爱因斯坦的生平'
- 直接调用: 
response = llm_model.generate(text)
print(f'User: {text}')
print(f'Bot: {response}')
- 封装调用:
generate(llm_model, text,random=False,beam=3,max_length=300,repetition=5.,use_inputs=True
)

4.2 多模态对话

主要用 gradio 搭建web(可以阅读【知乎 Gradio：轻松实现AI算法可视化部署】)

1. 模型和token初始化
2. 模型调用
   • 快速使用的话，可以直接llm_model.chat(text=text, image=image, history=None)
   • 进行复杂的一些操作可以 llm_model.internlm_model.generate
     • 这部分embeding比较复杂(笔者梳理的chat_answer示意了主要流程，但还是存在bug)

import torch
from transformers import StoppingCriteriaList, AutoTokenizer, AutoModel
from examples.utils import auto_configure_device_map# 模型初始化同上
state = CONV_VISION_7132_v2.copy()
def chat_answer(llm_model, state, text, image):"""state: 起到history的作用text: 输入的提问内容image: 图片"""# image 需要读取# image = gr.State()device = 'cuda'# stop critriastop_words_ids = [torch.tensor([103027]).to(device),  torch.tensor([103028]).to(device),  ]stopping_criteria = StoppingCriteriaList([StoppingCriteriaSub(stops=stop_words_ids)])# 输入处理img_list = []state.append_message(state.roles[0], text)with torch.no_grad():image_pt = llm_model.vis_processor(image).unsqueeze(0).to(0)image_emb = llm_model.encode_img(image_pt)img_list.append(image_emb)# 生成内容的embeddingprompt = state.get_prompt()prompt_segs = prompt.split('<Img><ImageHere></Img>')seg_tokens = [llm_model.internlm_tokenizer(seg, return_tensors="pt", add_special_tokens=i == 0).to(device).input_idsfor i, seg in enumerate(prompt_segs)]seg_embs = [llm_model.internlm_model.model.embed_tokens(seg_t) for seg_t in seg_tokens]mixed_embs = [emb for pair in zip(seg_embs[:-1], img_list) for emb in pair] + [seg_embs[-1]]mixed_embs = torch.cat(mixed_embs, dim=1)embs = mixed_embs# 模型推理outputs = llm_model.internlm_model.generate(inputs_embeds=embs,max_new_tokens=300,stopping_criteria=stopping_criteria,num_beams=3,#temperature=float(temperature),do_sample=False,repetition_penalty=float(0.5),bos_token_id=llm_model.internlm_tokenizer.bos_token_id,eos_token_id=llm_model.internlm_tokenizer.eos_token_id,pad_token_id=llm_model.internlm_tokenizer.pad_token_id,)# decode输出output_token = outputs[0]if output_token[0] == 0:output_token = output_token[1:]output_text = llm_model.internlm_tokenizer.decode(output_token, add_special_tokens=False)print(output_text)output_text = output_text.split('<TOKENS_UNUSED_1>')[0]  # remove the stop sign '###'output_text = output_text.split('Assistant:')[-1].strip()output_text = output_text.replace("<s>", "")return output_text
# 图文对话
## 1st return
image = 'examples/images/aiyinsitan.jpg'
text = '图片里面的是谁？'
- 直接调用: 
response, history = llm_model.chat(text=text, image=image, history=None)
print(f'User: {text}')
print(f'Bot: {response}')
- 封装调用:
output_text = chat_answer(llm_model, state, text, image)## 2nd turn
text = '他有哪些成就?'
- 直接调用: 
response, history = llm_model.chat(text=text, image=None, history=history)
print(f'User: {text}')
print(f'Bot: {response}')
- 封装调用:
output_text = chat_answer(llm_model, state, text, image)