reference Github: https://github.com/InternLM/tutorial/blob/main/helloworld/hello_world.md
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, AutoTokenizer
download_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
主要的项目GitHub: https://github.com/InternLM/InternLM
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, 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}")
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
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：
init_model(基于选择的name)
model = HFTransformerCasualLM('/root/model/Shanghai_AI_Laboratory/internlm-chat-7b')initialize_chatbot
chatbot = ReAct(llm=model, action_executor=ActionExecutor(actions=PythonInterpreter()))chatbot
agent_return = chatbot.chat(user_input)render_assistant(agent_return) 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)
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)
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：
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).
llm_model.generatellm_model.internlm_model.generate
generate函数核心示例code:
import torch
from transformers import StoppingCriteriaList, AutoTokenizer, AutoModel
from examples.utils import auto_configure_device_map
class StoppingCriteriaSub(StoppingCriteria):
def __init__(self, stops=[], encounters=1):
super().__init__()
self.stops = stops
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor):
for stop in self.stops:
if torch.all((stop == input_ids[:, -len(stop):])).item():
return True
return False
folder = '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_model
device_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-7b
random: 采样
beam: beam search 数量
max_length: 文章最大长度
repetition: repetition_penalty
"""
device = 'cuda'
# stop critria
stop_words_ids = [
torch.tensor([103027]).to(device),
torch.tensor([103028]).to(device),
]
stopping_criteria = StoppingCriteriaList(
[StoppingCriteriaSub(stops=stop_words_ids)])
# 输入tokens
input_tokens = llm_model.internlm_tokenizer(
text, return_tensors="pt",
add_special_tokens=True).to(llm_model.device)
# 输入生成图像的embeds
img_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
)
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】)
llm_model.chat(text=text, image=image, history=None)llm_model.internlm_model.generate
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 critria
stop_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embedding
prompt = 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_ids
for 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)