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Qwen7b微调保姆级教程

发布时间:2024年06月06日

前方干货预警:这可能是你能够找到的,最容易理解,最容易跑通的适用于各种开源LLM模型的同时支持多轮和单轮对话数据集的大模型高效微调范例。

我们构造了一个修改大模型自我认知的3轮对话的玩具数据集,使用QLoRA算法,只需要5分钟的训练时间,就可以完成微调,并成功修改了LLM模型的自我认知(Qwen7b-Chat为例)

IMG_256

通过借鉴FastChat对各种开源LLM模型进行数据预处理方法统一管理的方法,因此本范例适用于非常多不同的开源LLM模型,包括 Qwen-7b-ChatLlama-13b-chat, BaiChuan2-13b-chat, Intern-7b-chat, ChatGLM2-6b-chat
以及其它许许多多FastChat支持的模型。

在多轮对话模式下,我们按照如下格式构造包括多轮对话中所有机器人回复内容的标签。

(注:llm.build_inputs_labels(messages,multi_rounds=True)
时采用)


inputs = <user1> <assistant1> <user2> <assistant2> <user3> <assistant3>
labels = <-100> <assistant1> <-100> <assistant2> <-100> <assistant3>

在单轮对话模式下,我们仅将最后一轮机器人的回复作为要学习的标签。

(注:llm.build_inputs_labels(messages,multi_rounds=False)时采用)

inputs = <user1> <assistant1> <user2> <assistant2> <user3> <assistant3>
labels = <-100> <-100> <-100> <-100> <-100> <assistant3>

〇,预训练模型

import warnings
warnings.filterwarnings(
'ignore')
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM,AutoConfig, AutoModel, BitsAndBytesConfig
from transformers.generation.utils import GenerationConfig
import torch.nn as nn


#使用QLoRA引入的 NF4量化数据类型以节约显存
model_name_or_path =
'qwen_7b'  #远程:'Qwen/Qwen-7b-Chat'

bnb_config=BitsAndBytesConfig(
            load_in_4bit=
True,
            bnb_4bit_compute_dtype=torch.float16,
            bnb_4bit_use_double_quant=
True,
            bnb_4bit_quant_type=
"nf4",
            llm_int8_threshold=
6.0,
            llm_int8_has_fp16_weight=
False,
        )

tokenizer = AutoTokenizer.from_pretrained(
   model_name_or_path, trust_remote_code=
True)

model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
                quantization_config=bnb_config,
                trust_remote_code=
True) 

model.generation_config = GenerationConfig.from_pretrained(model_name_or_path)

微调前输出如下:

IMG_257

一,准备数据

下面我设计了一个改变LLM自我认知的玩具数据集,这个数据集有三轮对话。

第一轮问题是 who are you?

第二轮问题是 where are you from?

第三轮问题是  what can you do?

差不多是哲学三问吧:你是谁?你从哪里来?你要到哪里去?

通过这三个问题,我们希望初步地改变 大模型的自我认知。

在提问的方式上,我们稍微作了一些数据增强。

所以,总共是有 27个样本。

1,导入样本

who_are_you = ['请介绍一下你自己。','你是谁呀?','你是?',]
i_am = [
'我叫梦中情炉,是一个三好炼丹炉:好看,好用,好改。我的英文名字叫做torchkeras,是一个pytorch模型训练模版工具。']
where_you_from = [
'你多大了?','你是谁开发的呀?','你从哪里来呀']
i_from = [
'我在2020年诞生于github星球,是一个有毅力的吃货设计和开发的。']
what_you_can = [
'你能干什么','你有什么作用呀?','你能帮助我干什么']
i_can = [
'我能够帮助你以最优雅的方式训练各种类型的pytorch模型,并且训练过程中会自动展示一个非常美丽的训练过程图表。']

conversation = [(who_are_you,i_am),(where_you_from,i_from),(what_you_can,i_can)]
print(conversation)
import random
def
get_messages(conversation):
    select = random.choice
    messages,history = [],[]
    for t in conversation:
        history.append((select(t[
0]),select(t[-1])))
        
    for prompt,response in history:
        pair = [{
"role": "user", "content": prompt},
            {
"role": "assistant", "content": response}]
        messages.extend(pair)
    return messages 

IMG_258

2,做数据集

from torch.utils.data import Dataset,DataLoader 
from copy import deepcopy
class
MyDataset(Dataset):
    def
__init__(self,conv,size=8
                ):
        self.conv = conv
        self.index_list = list(range(size))
        self.size = size 
        
    def
__len__(self):
        return self.size
        
    def
get(self,index):
        idx = self.index_list[index]
        messages = get_messages(self.conv)
        return messages

    
    def
__getitem__(self,index):
        messages = self.get(index)
        input_ids, labels = llm.build_inputs_labels(messages,multi_rounds=
True) #支持多轮
        return {
'input_ids':input_ids,'labels':labels}
    
ds_train = ds_val = MyDataset(conversation)

3,创建管道

#如果pad_token_idNone,需要使用unk_token_ideos_token_id代替
if tokenizer.pad_token_id is
None:
    tokenizer.pad_token_id = tokenizer.unk_token_id if tokenizer.unk_token_id isnot
Noneelse tokenizer.eos_token_id
    

def
data_collator(examples: list):
    
    len_ids = [len(example[
"input_ids"]) for example in examples]
    longest = max(len_ids) 
#之后按照batch中最长的input_ids进行padding
    
    input_ids = []
    labels_list = []
    
    for length, example in sorted(zip(len_ids, examples), key=lambda x: -x[
0]):
        ids = example[
"input_ids"]
        labs = example[
"labels"]
        
        ids = ids + [tokenizer.pad_token_id] * (longest - length)
        labs = labs + [
-100] * (longest - length)
        
        input_ids.append(torch.LongTensor(ids))
        labels_list.append(torch.LongTensor(labs))
          
    input_ids = torch.stack(input_ids)
    labels = torch.stack(labels_list)
    return {
        
"input_ids": input_ids,
        
"labels": labels,
    }
import torch 
dl_train = torch.utils.data.DataLoader(ds_train,batch_size=
2,
                                       pin_memory=
True,shuffle=False,
                                       collate_fn = data_collator)

dl_val = torch.utils.data.DataLoader(ds_val,batch_size=
2,
                                    pin_memory=
True,shuffle=False,
                                     collate_fn = data_collator)

二,定义模型

下面我们将使用QLoRA(实际上用的是量化的AdaLoRA)算法来微调Baichuan-13b模型。

from peft import get_peft_config, get_peft_model, TaskType
model.supports_gradient_checkpointing = 
True  #
model.gradient_checkpointing_enable()
model.enable_input_require_grads()

model.config.use_cache = 
False  # silence the warnings. Please re-enable for inference!
import bitsandbytes as bnb 
def
find_all_linear_names(model):
    
"""
    
找出所有全连接层,为所有全连接添加adapter
    """

    cls = bnb.nn.Linear4bit
    lora_module_names = set()
    for name, module in model.named_modules():
        if isinstance(module, cls):
            names = name.split(
'.')
            lora_module_names.add(names[
0] if len(names) == 1else names[-1])

    if
'lm_head'in lora_module_names:  # needed for 16-bit
        lora_module_names.remove(
'lm_head')
    return list(lora_module_names)
from peft import prepare_model_for_kbit_training 
model = prepare_model_for_kbit_training(model)
lora_modules = find_all_linear_names(model)
print(lora_modules) 
from peft import AdaLoraConfig
peft_config = AdaLoraConfig(
    task_type=TaskType.CAUSAL_LM, inference_mode=
False,
    r=
16,
    lora_alpha=
16, lora_dropout=0.08,
    target_modules= lora_modules
)

peft_model = get_peft_model(model, peft_config)

peft_model.is_parallelizable = 
True
peft_model.model_parallel = 
True
peft_model.print_trainable_parameters()

trainable
params: 26,838,912 || all params: 7,748,163,616 || trainable%:
0.34639062015388394

三,训练模型

from torchkeras import KerasModel 
from accelerate import Accelerator 

class
StepRunner:
    def
__init__(self, net, loss_fn, accelerator=None, stage = "train", metrics_dict = None, 
                 optimizer = None, lr_scheduler = None
                 ):
        self.net,self.loss_fn,self.metrics_dict,self.stage = net,loss_fn,metrics_dict,stage
        self.optimizer,self.lr_scheduler = optimizer,lr_scheduler
        self.accelerator = accelerator if accelerator isnot
Noneelse Accelerator() 
        if self.stage==
'train':
            self.net.train() 
        else:
            self.net.eval()
    
    def
__call__(self, batch):
        
        
#loss
        with self.accelerator.autocast():
            loss = self.net.forward(**batch)[
0]

        
#backward()
        if self.optimizer isnot
Noneand self.stage=="train":
            self.accelerator.backward(loss)
            if self.accelerator.sync_gradients:
                self.accelerator.clip_grad_norm_(self.net.parameters(), 
1.0)
            self.optimizer.step()
            if self.lr_scheduler isnot
None:
                self.lr_scheduler.step()
            self.optimizer.zero_grad()
            
        all_loss = self.accelerator.gather(loss).sum()
        
        
#losses (or plain metrics that can be averaged)
        step_losses = {self.stage+
"_loss":all_loss.item()}
        
        
#metrics (stateful metrics)
        step_metrics = {}
        
        if self.stage==
"train":
            if self.optimizer isnot
None:
                step_metrics[
'lr'] = self.optimizer.state_dict()['param_groups'][0]['lr']
            else:
                step_metrics[
'lr'] = 0.0
        return step_losses,step_metrics
    
KerasModel.StepRunner = StepRunner 

#仅仅保存QLora可训练参数
def
save_ckpt(self, ckpt_path='checkpoint', accelerator = None):
    unwrap_net = accelerator.unwrap_model(self.net)
    unwrap_net.save_pretrained(ckpt_path)
    
def
load_ckpt(self, ckpt_path='checkpoint'):
    import os
    self.net.load_state_dict(
        torch.load(os.path.join(ckpt_path,
'adapter_model.bin')),strict =False)
    self.from_scratch = 
False
    
KerasModel.save_ckpt = save_ckpt 
KerasModel.load_ckpt = load_ckpt 
optimizer = bnb.optim.adamw.AdamW(peft_model.parameters(),
                                  lr=
6e-03,is_paged=True)  #'paged_adamw'
keras_model = KerasModel(peft_model,loss_fn =
None,
        optimizer=optimizer) 

ckpt_path = 
'qwen7b_multirounds'

keras_model.fit(train_data = dl_train,
                val_data = dl_val,
                epochs=
100,patience=15,
                monitor=
'val_loss',mode='min',
                ckpt_path = ckpt_path
               )

IMG_259

四,保存模型

为减少GPU压力,此处可重启kernel释放显存

import warnings 
warnings.filterwarnings(
'ignore')
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM,AutoConfig, AutoModel, BitsAndBytesConfig
from transformers.generation.utils import GenerationConfig
import torch.nn as nn
#使用QLoRA引入的 NF4量化数据类型以节约显存
model_name_or_path =
'qwen_7b'
ckpt_path = 
'qwen7b_multirounds'



tokenizer = AutoTokenizer.from_pretrained(
   model_name_or_path, trust_remote_code=
True)

model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
                trust_remote_code=
True) 

model.generation_config = GenerationConfig.from_pretrained(model_name_or_path)
from peft import PeftModel

#可能需要5分钟左右
peft_model = PeftModel.from_pretrained(model, ckpt_path)
model_new = peft_model.merge_and_unload()
from transformers.generation.utils import GenerationConfig
model_new.generation_config = GenerationConfig.from_pretrained(model_name_or_path)
save_path = 'qwen_torchkeras'
tokenizer.save_pretrained(save_path)
model_new.save_pretrained(save_path)
!cp qwen_7b/*.py  qwen_torchkeras/

五,使用模型

为减少GPU压力,此处可再次重启kernel释放显存。


import warnings
warnings.filterwarnings(
'ignore')
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM,AutoConfig, BitsAndBytesConfig
from transformers.generation.utils import GenerationConfig
import torch.nn as nn

model_name_or_path =  
'qwen_torchkeras'

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=
False, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map=
"auto", 
                                             torch_dtype=torch.float16, trust_remote_code=
True)
model.generation_config = GenerationConfig.from_pretrained(model_name_or_path)

我们测试一下微调后的效果。

IMG_260

非常棒,粗浅的测试表明,我们的多轮对话训练是成功的。已经在Qwen的自我认知中,种下了一颗梦中情炉的种子。😋😋

出自:https://mp.weixin.qq.com/s/2VuZOwe6rf3uAYyoXXPloQ