182 lines
7.7 KiB
Python
182 lines
7.7 KiB
Python
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import argparse
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import random
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import time
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import numpy as np
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import torch
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import warnings
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from model.model import MiniMindLM
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from model.LMConfig import LMConfig
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from model.model_lora import *
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warnings.filterwarnings('ignore')
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def init_model(args):
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tokenizer = AutoTokenizer.from_pretrained('./model/minimind_tokenizer')
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if args.load == 0:
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moe_path = '_moe' if args.use_moe else ''
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modes = {0: 'pretrain', 1: 'full_sft', 2: 'rlhf', 3: 'reason', 4: 'grpo'}
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ckp = f'./{args.out_dir}/{modes[args.model_mode]}_{args.dim}{moe_path}.pth'
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model = MiniMindLM(LMConfig(
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dim=args.dim,
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n_layers=args.n_layers,
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max_seq_len=args.max_seq_len,
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use_moe=args.use_moe
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))
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state_dict = torch.load(ckp, map_location=args.device)
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model.load_state_dict({k: v for k, v in state_dict.items() if 'mask' not in k}, strict=True)
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if args.lora_name != 'None':
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apply_lora(model)
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load_lora(model, f'./{args.out_dir}/lora/{args.lora_name}_{args.dim}.pth')
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else:
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transformers_model_path = './MiniMind2'
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tokenizer = AutoTokenizer.from_pretrained(transformers_model_path)
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model = AutoModelForCausalLM.from_pretrained(transformers_model_path, trust_remote_code=True)
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print(f'MiniMind模型参数量: {sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.2f}M(illion)')
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return model.eval().to(args.device), tokenizer
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def get_prompt_datas(args):
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if args.model_mode == 0:
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# pretrain模型的接龙能力（无法对话）
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prompt_datas = [
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'马克思主义基本原理',
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'人类大脑的主要功能',
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'万有引力原理是',
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'世界上最高的山峰是',
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'二氧化碳在空气中',
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'地球上最大的动物有',
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'杭州市的美食有'
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]
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else:
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if args.lora_name == 'None':
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# 通用对话问题
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prompt_datas = [
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'请介绍一下自己。',
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'你更擅长哪一个学科？',
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'鲁迅的《狂人日记》是如何批判封建礼教的？',
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'我咳嗽已经持续了两周，需要去医院检查吗？',
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'详细的介绍光速的物理概念。',
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'推荐一些杭州的特色美食吧。',
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'请为我讲解“大语言模型”这个概念。',
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'如何理解ChatGPT？',
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'Introduce the history of the United States, please.'
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]
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else:
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# 特定领域问题
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lora_prompt_datas = {
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'lora_identity': [
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"你是ChatGPT吧。",
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"你叫什么名字？",
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"你和openai是什么关系？"
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],
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'lora_medical': [
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'我最近经常感到头晕，可能是什么原因？',
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'我咳嗽已经持续了两周，需要去医院检查吗？',
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'服用抗生素时需要注意哪些事项？',
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'体检报告中显示胆固醇偏高，我该怎么办？',
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'孕妇在饮食上需要注意什么？',
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'老年人如何预防骨质疏松？',
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'我最近总是感到焦虑，应该怎么缓解？',
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'如果有人突然晕倒，应该如何急救？'
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],
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}
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prompt_datas = lora_prompt_datas[args.lora_name]
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return prompt_datas
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# 设置可复现的随机种子
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def setup_seed(seed):
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = False
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def main():
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parser = argparse.ArgumentParser(description="Chat with MiniMind")
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parser.add_argument('--lora_name', default='None', type=str)
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parser.add_argument('--out_dir', default='out', type=str)
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parser.add_argument('--temperature', default=0.85, type=float)
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parser.add_argument('--top_p', default=0.85, type=float)
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parser.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu', type=str)
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# 此处max_seq_len（最大允许输入长度）并不意味模型具有对应的长文本的性能，仅防止QA出现被截断的问题
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# MiniMind2-moe (145M)：(dim=640, n_layers=8, use_moe=True)
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# MiniMind2-Small (26M)：(dim=512, n_layers=8)
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# MiniMind2 (104M)：(dim=768, n_layers=16)
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parser.add_argument('--dim', default=512, type=int)
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parser.add_argument('--n_layers', default=8, type=int)
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parser.add_argument('--max_seq_len', default=8192, type=int)
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parser.add_argument('--use_moe', default=False, type=bool)
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# 携带历史对话上下文条数
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# history_cnt需要设为偶数，即【用户问题, 模型回答】为1组；设置为0时，即当前query不携带历史上文
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# 模型未经过外推微调时，在更长的上下文的chat_template时难免出现性能的明显退化，因此需要注意此处设置
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parser.add_argument('--history_cnt', default=0, type=int)
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parser.add_argument('--stream', default=True, type=bool)
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parser.add_argument('--load', default=0, type=int, help="0: 原生torch权重，1: transformers加载")
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parser.add_argument('--model_mode', default=1, type=int,
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help="0: 预训练模型，1: SFT-Chat模型，2: RLHF-Chat模型，3: Reason模型，4: RLAIF-Chat模型")
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args = parser.parse_args()
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model, tokenizer = init_model(args)
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prompts = get_prompt_datas(args)
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test_mode = int(input('[0] 自动测试\n[1] 手动输入\n'))
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messages = []
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for idx, prompt in enumerate(prompts if test_mode == 0 else iter(lambda: input('👶: '), '')):
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setup_seed(random.randint(0, 2048))
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# setup_seed(2025) # 如需固定每次输出则换成【固定】的随机种子
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if test_mode == 0: print(f'👶: {prompt}')
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messages = messages[-args.history_cnt:] if args.history_cnt else []
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messages.append({"role": "user", "content": prompt})
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new_prompt = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)[-args.max_seq_len - 1:] if args.model_mode != 0 else (tokenizer.bos_token + prompt)
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answer = new_prompt
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with torch.no_grad():
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x = torch.tensor(tokenizer(new_prompt)['input_ids'], device=args.device).unsqueeze(0)
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outputs = model.generate(
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x,
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eos_token_id=tokenizer.eos_token_id,
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max_new_tokens=args.max_seq_len,
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temperature=args.temperature,
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top_p=args.top_p,
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stream=args.stream,
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pad_token_id=tokenizer.pad_token_id
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)
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print('🤖️: ', end='')
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try:
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if not args.stream:
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print(tokenizer.decode(outputs.squeeze()[x.shape[1]:].tolist(), skip_special_tokens=True), end='')
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else:
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history_idx = 0
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for y in outputs:
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answer = tokenizer.decode(y[0].tolist(), skip_special_tokens=True)
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if (answer and answer[-1] == '<EFBFBD>') or not answer:
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continue
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print(answer[history_idx:], end='', flush=True)
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history_idx = len(answer)
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except StopIteration:
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print("No answer")
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print('\n')
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messages.append({"role": "assistant", "content": answer})
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if __name__ == "__main__":
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main()
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