Qwen7b微调保姆级教程

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(以Qwen7b-Chat为例)。

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-Chat，Llama-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>

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：

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 

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_id为None，需要使用unk_token_id或eos_token_id代替
if tokenizer.pad_token_id is None:

    tokenizer.pad_token_id = tokenizer.unk_token_id if tokenizer.unk_token_id is not None else 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)

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) == 1 else 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

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 is not None else 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 is not None and 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 is not 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 is not 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

               )

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/

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。

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