233 lines
9.2 KiB
Python
233 lines
9.2 KiB
Python
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from transformers import AutoTokenizer, AutoConfig, AddedToken, AutoModelForCausalLM, BitsAndBytesConfig
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from peft import PeftModel
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from dataclasses import dataclass
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from typing import Dict
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import torch
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import copy
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import warnings
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warnings.filterwarnings("ignore")
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warnings.filterwarnings("ignore", category=DeprecationWarning)
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## 定义聊天模板
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@dataclass
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class Template:
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template_name:str
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system_format: str
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user_format: str
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assistant_format: str
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system: str
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stop_word: str
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template_dict: Dict[str, Template] = dict()
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def register_template(template_name, system_format, user_format, assistant_format, system, stop_word=None):
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template_dict[template_name] = Template(
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template_name=template_name,
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system_format=system_format,
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user_format=user_format,
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assistant_format=assistant_format,
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system=system,
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stop_word=stop_word,
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)
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# 这里的系统提示词是训练时使用的，推理时可以自行尝试修改效果
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register_template(
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template_name='llama3',
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system_format='<|begin_of_text|><system>\n{content}\n<system>\n\n<|eot_id|>',
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user_format='<|start_header_id|>user<|end_header_id|>\n\n{content}<|eot_id|>',
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assistant_format='<|start_header_id|>assistant<|end_header_id|>\n\n{content}\n', # \n\n{content}<|eot_id|>\n
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system="你是心理健康助手EmoLLM, 由EmoLLM团队打造, 是一个研究过无数具有心理健康问题的病人与心理健康医生对话的心理专家, 在心理方面拥有广博的知识储备和丰富的研究咨询经验。你旨在通过专业心理咨询, 协助来访者完成心理诊断。请充分利用专业心理学知识与咨询技术, 一步步帮助来访者解决心理问题。",
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stop_word='<|eot_id|>'
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)
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## 加载模型
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def load_model(model_name_or_path, load_in_4bit=False, adapter_name_or_path=None):
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if load_in_4bit:
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quantization_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_compute_dtype=torch.float16,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type="nf4",
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llm_int8_threshold=6.0,
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llm_int8_has_fp16_weight=False,
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)
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else:
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quantization_config = None
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# 加载base model
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model = AutoModelForCausalLM.from_pretrained(
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model_name_or_path,
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# load_in_4bit=load_in_4bit,
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# # ValueError: You can't pass `load_in_4bit`or `load_in_8bit` as a kwarg when passing `quantization_config` argument at the same time.
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trust_remote_code=True,
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low_cpu_mem_usage=True,
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torch_dtype=torch.float16,
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device_map='auto',
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quantization_config=quantization_config
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)
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# 加载adapter
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if adapter_name_or_path is not None:
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model = PeftModel.from_pretrained(model, adapter_name_or_path)
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return model
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## 加载tokenzier
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def load_tokenizer(model_name_or_path):
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tokenizer = AutoTokenizer.from_pretrained(
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model_name_or_path,
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trust_remote_code=True,
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use_fast=False
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)
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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return tokenizer
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## 构建prompt
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def build_prompt(tokenizer, template, query, history, system=None):
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template_name = template.template_name
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system_format = template.system_format
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user_format = template.user_format
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assistant_format = template.assistant_format
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system = system if system is not None else template.system
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history.append({"role": 'user', 'message': query})
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input_ids = []
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# 添加系统信息
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if system_format is not None:
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if system is not None:
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system_text = system_format.format(content=system)
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input_ids = tokenizer.encode(system_text, add_special_tokens=False)
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# 拼接历史对话
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for item in history:
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role, message = item['role'], item['message']
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if role == 'user':
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message = user_format.format(content=message, stop_token=tokenizer.eos_token)
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else:
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message = assistant_format.format(content=message, stop_token=tokenizer.eos_token)
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tokens = tokenizer.encode(message, add_special_tokens=False)
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input_ids += tokens
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input_ids = torch.tensor([input_ids], dtype=torch.long)
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return input_ids
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def main():
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# download model in openxlab
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# download(model_repo='MrCat/Meta-Llama-3-8B-Instruct',
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# output='MrCat/Meta-Llama-3-8B-Instruct')
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# model_name_or_path = 'MrCat/Meta-Llama-3-8B-Instruct'
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# # download model in modelscope
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# model_name_or_path = snapshot_download('LLM-Research/Meta-Llama-3-8B-Instruct',
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# cache_dir='LLM-Research/Meta-Llama-3-8B-Instruct')
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# # offline model
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# model_name_or_path = '/root/share/new_models/meta-llama/Meta-Llama-3-8B-Instruct'
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# adapter_name_or_path = None
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# model_name_or_path = "xtuner_config/merged_Llama3_8b_instruct_e3"
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# adapter_name_or_path = 'xtuner_config/hf_llama3_e1_sc2'
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# model_name_or_path = "xtuner_config/merged_Llama3_8b_instruct_e1_sc"
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# adapter_name_or_path = None
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print_user = False # 控制是否输入提示输入框，用于notebook时，改为True
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template_name = 'llama3'
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template = template_dict[template_name]
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# 若开启4bit推理能够节省很多显存，但效果可能下降
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load_in_4bit = False # True # 6291MiB
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# 生成超参配置，可修改以取得更好的效果
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max_new_tokens = 500 # 每次回复时，AI生成文本的最大长度
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top_p = 0.9
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temperature = 0.6 # 越大越有创造性，越小越保守
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repetition_penalty = 1.1 # 越大越能避免吐字重复
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# 加载模型
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print(f'Loading model from: {model_name_or_path}')
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print(f'adapter_name_or_path: {adapter_name_or_path}')
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model = load_model(
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model_name_or_path,
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load_in_4bit=load_in_4bit,
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adapter_name_or_path=adapter_name_or_path
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).eval()
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tokenizer = load_tokenizer(model_name_or_path if adapter_name_or_path is None else adapter_name_or_path)
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if template.stop_word is None:
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template.stop_word = tokenizer.eos_token
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stop_token_id = tokenizer.encode(template.stop_word, add_special_tokens=True)
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assert len(stop_token_id) == 1
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stop_token_id = stop_token_id[0]
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print("================================================================================")
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print("=============欢迎来到Llama3 EmoLLM 心理咨询室, 输入'exit'退出程序===============")
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print("================================================================================")
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history = []
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print("============请输入聊天内容, 按回车键结束输入, 输入'clear'清空聊天信息==============")
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print("================================================================================")
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print("================================================================================")
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print("===============================让我们开启对话吧=================================\n\n")
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if print_user:
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query = input('用户:')
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print("# 用户：{}".format(query))
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else:
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query = input('# 用户: ')
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while True:
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if query=='exit':
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break
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if query.strip() == "clear":
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history = []
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print("\n\n=============欢迎来到Llama3 EmoLLM 心理咨询室, 输入'exit'退出程序===============")
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print("============请输入聊天内容, 按回车键结束输入, 输入'clear'清空聊天信息===========")
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print("================================================================================")
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print("================================================================================")
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if print_user:
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query = input('用户:')
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print("# 用户：{}".format(query))
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else:
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query = input('# 用户: ')
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continue
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query = query.strip()
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input_ids = build_prompt(tokenizer, template, query, copy.deepcopy(history), system=None).to(model.device)
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outputs = model.generate(
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input_ids=input_ids, max_new_tokens=max_new_tokens, do_sample=True,
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top_p=top_p, temperature=temperature, repetition_penalty=repetition_penalty,
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eos_token_id=stop_token_id, pad_token_id=tokenizer.eos_token_id
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)
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outputs = outputs.tolist()[0][len(input_ids[0]):]
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response = tokenizer.decode(outputs)
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response = response.strip().replace(template.stop_word, "").strip()
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# 存储对话历史
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history.append({"role": 'user', 'message': query})
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history.append({"role": 'assistant', 'message': response})
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# 当对话长度超过6轮时，清空最早的对话，可自行修改
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if len(history) > 12:
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history = history[:-12]
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print("# Llama3 EmoLLM 心理咨询师：{}".format(response.replace('\n','').replace('<|start_header_id|>','').replace('assistant<|end_header_id|>','').replace('>','')))
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print()
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query = input('# 用户：')
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if print_user:
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print("# 用户：{}".format(query))
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print("\n\n===============感谢使用Llama3 EmoLLM 心理咨询室, 祝您生活愉快~===============\n\n")
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if __name__ == '__main__':
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main()
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