模版"/>
lora微调模版
lora微调模版
- 1、版一:使用peft包的lora微调
- (1)设置超参
- 方式一:代码中设置(便于debug)
- 方式二: .sh文件指定
- (2)加载数据集
- I、对应的.jsonl或json文件, 原始格式为:
- II、tokenize需要的文件格式:
- (3)加载基础模型
- (4)内存优化、梯度计算设置
- (5)加载loraConfig,设置peft模型
- (6)模型训练
- (7)模型保存
1、版一:使用peft包的lora微调
感谢:github:LLM-Tuning ,该项目集成了internLM、百川、chinese-llama-alpaca-2、chatglm的lora微调。
from transformers.integrations import TensorBoardCallback
from torch.utils.tensorboard import SummaryWriter
from transformers import TrainingArguments
from transformers import Trainer, HfArgumentParser
from transformers import AutoTokenizer, AutoModel, AutoConfig, DataCollatorForLanguageModeling
from accelerate import init_empty_weights
import torch
import torch.nn as nn
from peft import get_peft_model, LoraConfig, TaskType, PeftModel
from dataclasses import dataclass, field
import datasets
import os
from pprint import pprint as print
(1)设置超参
方式一:代码中设置(便于debug)
@dataclass
class FinetuneArguments:model_version: str = field(default="chat-7b")tokenized_dataset: str = field(default=" ") # todo tokenized之后的数据集文件夹。每个模型是不一样的,不可混用!# tokenized_train_dataset: str = field(default=" ") # tokenized之后的数据集文件夹# tokenized_eval_dataset: str = field(default=" ") # tokenized之后的数据集文件夹train_size: int = field(default=1000) # train sizeeval_size: int = field(default=1000) # train sizemodel_path: str = field(default=" ")lora_rank: int = field(default=8)previous_lora_weights: str = field(default=None) # 如果要在前面的 LoRA 上继续训练,就设置一下之前的地址no_prompt_loss: int = field(default=0) # 默认 prompt 参与loss计算training_args = TrainingArguments(per_device_train_batch_size=10,gradient_accumulation_steps=1,warmup_steps=10, # number of warmup steps for learning rate schedulermax_steps=60000,save_steps=200,save_total_limit=3,learning_rate=float(1e-4),remove_unused_columns=False,logging_steps=50,weight_decay=0.01, # strength of weight decay# output_dir='weights/chatglm2-lora-tuning'output_dir='weights/chatglm2-lora-tuning-test')
finetune_args = FinetuneArguments(tokenized_dataset="simple_math_4op_chatglm3", lora_rank=8)
方式二: .sh文件指定
如果喜欢用.sh文件启动,以上参数也可以不用在代码中配置,直接写在xx.sh文件:
CUDA_VISIBLE_DEVICES=2,3 python your_main.py
–tokenized_dataset simple_math_4op
–lora_rank 8
–per_device_train_batch_size 10
–gradient_accumulation_steps 1
–max_steps 100000
–save_steps 200
–save_total_limit 2
–learning_rate 1e-4
–fp16
–remove_unused_columns false
–logging_steps 50
–output_dir weights/simple_math_4op
对应代码:
finetune_args, training_args = HfArgumentParser((FinetuneArguments, TrainingArguments)).parse_args_into_dataclasses()
(2)加载数据集
# load dataset
# 加载数据集
dataset = datasets.load_from_disk('data/tokenized_data/' + finetune_args.tokenized_dataset)
# dataset = dataset.select(range(10000))
print(f"\n{len(dataset)=}\n")# 训练、评估集各取N条
train_dataset = dataset.select(range(finetune_args.train_size)) if finetune_args.train_size else dataset # 取前 N 条训练
eval_dataset = dataset.select(list(range(len(dataset)))[-finetune_args.eval_size:]) if finetune_args.eval_size else dataset # 取后 N 条验证
# print(train_dataset[0])
# print(eval_dataset[0])
说明:datasets.load_from_disk( tokenized_data_path
)加载的是已经tokenized后的数据。执行tokenize_dataset_rows.py
即可完成原始数据到tokenized_data转变。
I、对应的.jsonl或json文件, 原始格式为:
{
“instruction”: "Instruction: What are the three primary colors?\nAnswer: ",
“output”: “The three primary colors are red, blue, and yellow.”
}{
“instruction”: “Instruction: Identify the odd one out from the following array of words.”,
“input”: Fever, shiver, wrinkle\nAnswer: ",
“output”: “Wrinkle is the odd one out since it is the only word that does not describe a physical sensation.”
}
II、tokenize需要的文件格式:
{“context”: "Instruction: What are the three primary colors?\nAnswer: ", “target”: “The three primary colors are red, blue, and yellow.”}
{"context": "Instruction: Identify the odd one out from the following array of words.\nInput: Fever, shiver, wrinkle\nAnswer: ", “target”: “Wrinkle is the odd one out since it is the only word that does not describe a physical sensation.”}
tokenize_dataset_rows.py
import argparse
import json
from tqdm import tqdm
import datasets
import transformers
from transformers import AutoTokenizer, LlamaTokenizer# 基础模型地址
# model_path = "D:\\openmodel1\\chatglm3-6b"
model_path = "D:\\openmodel1\\internlm-chat-7b" parser = argparse.ArgumentParser()
parser.add_argument("--data_path", type=str, help="原始数据地址")
parser.add_argument("--model_checkpoint", type=str, help="checkpoint, like `THUDM/chatglm-6b`", default=model_path)
parser.add_argument("--input_file", type=str, help="tokenize需要的数据文件地址,文件中每一行都是json格式,包含一个输出和一个输出",default="alpaca_data-cf.jsonl")
parser.add_argument("--prompt_key", type=str, default=f"context", help="你的jsonl文件里,Instruction 的输入字段是什么")
parser.add_argument("--target_key", type=str, default=f"target", help="你的jsonl文件里,Instruction 的输出字段是什么")
parser.add_argument("--save_name", type=str, default=f"simple_math_4op_internlm", help="经过tokenize之后的数据集的存放位置")
parser.add_argument("--max_seq_length", type=int, default=2000)
parser.add_argument("--skip_overlength", type=bool, default=False)
args = parser.parse_args()
model_checkpoint = args.model_checkpointdef preprocess(tokenizer, config, example, max_seq_length, prompt_key, target_key):prompt = example[prompt_key]target = example[target_key]prompt_ids = tokenizer.encode(prompt, max_length=max_seq_length, truncation=True)target_ids = tokenizer.encode(target, max_length=max_seq_length, truncation=True, add_special_tokens=False)# 最终还是将instruction的输入输出都拼在一起,使用经典的causal-LM的next word prediction方式来训练input_ids = prompt_ids + target_ids + [config.eos_token_id]return {"input_ids": input_ids, "seq_len": len(prompt_ids)}def read_jsonl(path, max_seq_length, prompt_key, target_key, skip_overlength=False):if 'llama' in model_checkpoint.lower() or 'alpaca' in model_checkpoint.lower():tokenizer = LlamaTokenizer.from_pretrained(model_checkpoint, trust_remote_code=True)else:tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, trust_remote_code=True)config = transformers.AutoConfig.from_pretrained(model_checkpoint, trust_remote_code=True, device_map='auto')with open(path, "r") as f:for line in tqdm(f.readlines()):example = json.loads(line)feature = preprocess(tokenizer, config, example, max_seq_length, prompt_key, target_key)if skip_overlength and len(feature["input_ids"]) > max_seq_length:continuefeature["input_ids"] = feature["input_ids"][:max_seq_length]yield featuredef format_example(example: dict) -> dict:context = f"Instruction: {example['instruction']}\n"if example.get("input"):context += f"Input: {example['input']}\n"context += "Answer: "target = example["output"]return {"context": context, "target": target}with open(args.data_path) as f:examples = json.load(f)with open(args.input_file, 'w') as f:for example in tqdm(examples, desc="formatting.."):f.write(json.dumps(format_example(example)) + '\n')# 输出文件统一放在 data/tokenized_data 文件夹下
input_file_path = f'data/{args.input_file}'
save_path = f"data/tokenized_data/{args.save_name}"
dataset = datasets.Dataset.from_generator(lambda: read_jsonl(input_file_path, args.max_seq_length, args.prompt_key, args.target_key, args.skip_overlength)
)dataset.save_to_disk(save_path)
执行成功后生成的文件目录:
(3)加载基础模型
print(model)
:model打印出来,以便 (1)查看模型结构;(2)后续设置lora微调层(常作用在attention相关的模块)load_in_8bit=False
:是否使用8bit量化:如果加载时内存溢出,可尝试设置为True
print('loading init model...')
model = AutoModel.from_pretrained(model_path, trust_remote_code=True,load_in_8bit=False, # 量化:如果加载时内存溢出,可尝试设置为Truedevice_map="auto" # 模型不同层会被自动分配到不同GPU上进行计算# device_map={'':torch.cuda.current_device()})
print(model.hf_device_map)
print(f'memory_allocated {torch.cuda.memory_allocated()}')
print(model) # 把model打印出来,以便:(1)查看模型结构;(2)后续设置lora微调层(常作用在attention相关的模块)
如果多卡训练出现loss计算报错:
设置了 device_map=“auto” 之后:
- chatglm 1.0 的时候,lm_head会跟input_layer自动分配到同个 device,
- chatglm 2.0 的时候,没有了 lm_head,有一个 output_layer,这个时候可能会分配到两个device,导致计算loss的时候报错显示 RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:2 and cuda:0!
(1) 一个解决办法是设置device_map={‘’:torch.cuda.current_device()},进行数据并行,但是这样batchsize只能设置非常小,而且很占显存
(2)另一个解决办法是: 手动把 output_layer 设置为跟 input 一样的 device。
然后这里会加载两次模型,可以先加载,调整device_map之后,再把旧模型删掉:
if torch.cuda.device_count() > 1:model.hf_device_map['transformer.output_layer'] = model.hf_device_map['transformer.embedding']new_hf_device_map = model.hf_device_mapmodel.cpu()del modeltorch.cuda.empty_cache()print(f'memory_allocated {torch.cuda.memory_allocated()}')print('loading real model...')model = AutoModel.from_pretrained(model_path, trust_remote_code=True, device_map=new_hf_device_map)print(model.hf_device_map)
(4)内存优化、梯度计算设置
"""
.gradient_checkpointing_enable()
.enable_input_require_grads()
.is_parallelizable
这三个都是 transformers 模型的函数/参数(见 transformers/modeling_utils.py 文件)
"""model.gradient_checkpointing_enable()
# note: use gradient checkpointing to save memory at the expense of slower backward pass.model.enable_input_require_grads()
# note: Enables the gradients for the input embeddings. This is useful for fine-tuning adapter weights while keeping the model weights fixed.
# See .py#L1190
(5)加载loraConfig,设置peft模型
# setup peft
if finetune_args.previous_lora_weights == None:peft_config = LoraConfig(task_type=TaskType.CAUSAL_LM,inference_mode=False,r=finetune_args.lora_rank,lora_alpha=32,lora_dropout=0.1,target_modules=["q_proj", "k_proj", "v_proj"] # 把model打印出来,找跟attention相关的模块)model = get_peft_model(model, peft_config)
else:# 当设置了 previous_lora_weights 说明要继续训练之前的 lora weightsmodel = PeftModel.from_pretrained(model, finetune_args.previous_lora_weights)# see: name, param in model.named_parameters():if 'lora' in name or 'Lora' in name:param.requires_grad = True
(6)模型训练
# 定义数据加载器,可以根据需要自定义
if finetune_args.no_prompt_loss:print("*** If you see this message, ***")print("*** it means: Prompt is not calculated in loss. ***")data_collator = my_data_collator
else:data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)# 开始训练
trainer = ModifiedTrainer( # 这里也可以直接使用Trainer类 model=model,train_dataset=train_dataset,eval_dataset=eval_dataset,args=training_args,callbacks=[TensorBoardCallback(writer)],data_collator=data_collator,
)
trainer.train()
备注1:以上代码中的 ModifiedTrainer
只是简化了Trainer的代码实现,并没有改变代码逻辑,所以该类可要可不要。
class ModifiedTrainer(Trainer):def compute_loss(self, model, inputs, return_outputs=False):outputs = model(input_ids=inputs["input_ids"],labels=inputs["labels"],)loss = outputs.lossreturn (loss, outputs) if return_outputs else lossdef save_model(self, output_dir=None, _internal_call=False):# 因为交给Trainer的model实际上是PeftModel类型,所以这里的 save_pretrained 会直接使用PeftModel的保存方法# 从而只保存 LoRA weightsself.model.save_pretrained(output_dir)
备注2:my_data_collator
数据加载器:根据需要自定义功能
# 这里的 collator 主要参考了 .py 中的写法
# 将 prompt 的部分的label也设置为了 -100,从而在训练时不纳入loss的计算
# 对比之下,也可以直接使用 DataCollatorForLanguageModeling,会将prompt 也纳入了计算。
# 这两种方式孰优孰劣尚不得而知,欢迎在issue或discussion中讨论。
def my_data_collator(features: list) -> dict:"""这个 collator 会把 prompt 的部分给mask掉,使得只有 output 部分参与计算 loss"""len_ids = [len(feature["input_ids"]) for feature in features]longest = max(len_ids)input_ids = []labels_list = []for ids_l, feature in sorted(zip(len_ids, features), key=lambda x: -x[0]):ids = feature["input_ids"] # ids= prompt + target, 总长度是max_seq_lengthseq_len = feature["seq_len"] # prompt lengthlabels = ([-100] * (seq_len - 1) + ids[(seq_len - 1):] + [-100] * (longest - ids_l))ids = ids + [tokenizer.pad_token_id] * (longest - ids_l)_ids = torch.LongTensor(ids)labels_list.append(torch.LongTensor(labels))input_ids.append(_ids)input_ids = torch.stack(input_ids)labels = torch.stack(labels_list)return {"input_ids": input_ids,"labels": labels,}
(7)模型保存
# save model
model.save_pretrained(training_args.output_dir)
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