QLoRA原理参考：
BiliBili：4bit量化与QLoRA模型训练
zhihu：QLoRA（Quantized LoRA）详解
下载llama3-8b模型
from modelscope import snapshot_download
model_dir = snapshot_download('LLM-Research/Meta-Llama-3-8B-Instruct')
设置quantization_config
from transformers import BitsAndBytesConfigquantization_config = BitsAndBytesConfig(load_in_4bit=True,bnb_4bit_quant_type="nf4",bnb_4bit_use_double_quant=True,bnb_4bit_compute_dtype=torch.bfloat16,
)
加载模型
加载量化后的llama3-8b模型，大概需要6G的GPU显存。
from transformers import AutoModelForCausalLM,AutoTokenizer,TrainingArguments,Trainer,DataCollatorForSeq2Seq
model = AutoModelForCausalLM.from_pretrained(model_dir,quantization_config=quantization_config,low_cpu_mem_usage=True)
tokenizer = AutoTokenizer.from_pretrained(model_dir)
一层的数据类型，可以看到除了layernorm，linear层都进行了量化。
model.layers.0.self_attn.q_proj.weight torch.uint8
model.layers.0.self_attn.k_proj.weight torch.uint8
model.layers.0.self_attn.v_proj.weight torch.uint8
model.layers.0.self_attn.o_proj.weight torch.uint8
model.layers.0.mlp.gate_proj.weight torch.uint8
model.layers.0.mlp.up_proj.weight torch.uint8
model.layers.0.mlp.down_proj.weight torch.uint8
model.layers.0.input_layernorm.weight torch.float16
model.layers.0.post_attention_layernorm.weight torch.float16
预处理模型
from peft import prepare_model_for_kbit_training
model = prepare_model_for_kbit_training(model)
设置LoRA参数
这里使用了默认设置，参数target_modules和modules_to_save可以设置具体训练哪些模块。
在peft/utils/constants.py中，默认定义了各种模型的LoRA target modules，llama模型对Q和V进行lora。
"llama": ["q_proj", "v_proj"],
config = LoraConfig(task_type=TaskType.CAUSAL_LM)
model = get_peft_model(model, config)
model.print_trainable_parameters()
#trainable params: 3,407,872 || all params: 8,033,669,120 || trainable%: 0.0424
print(model) #加入了LoRA后的模型结构。
加载并处理数据
数据下载：AI-ModelScope/alpaca-gpt4-data-zh
需要把下载的数据中dataset_infos.json 重命名为datasets_info.json，这样才能正确加载。
from datasets import load_datasetdataset = load_dataset("alpaca-data-zh")def process_func(example):# print(example)MAX_LENGTH = 256input_ids, attention_mask, labels = [], [], []# 将prompt进行tokenize，这里我们没有利用tokenizer进行填充和截断# 这里我们自己进行截断，在DataLoader的collate_fn函数中进行填充input = example["input"] if example["input"] is not None else ''instruction = tokenizer("\n".join(["Human: " + example["instruction"], input]).strip() + "\n\nAssistant: ")# 将output进行tokenize，注意添加eos_tokenresponse = tokenizer(example["output"] + tokenizer.eos_token)# 将instruction + output组合为inputinput_ids = instruction["input_ids"] + response["input_ids"]attention_mask = instruction["attention_mask"] + response["attention_mask"]# prompt设置为-100,不计算losslabels = [-100] * len(instruction["input_ids"]) + response["input_ids"]# 设置最大长度，进行截断if len(input_ids) > MAX_LENGTH:input_ids = input_ids[:MAX_LENGTH]attention_mask = attention_mask[:MAX_LENGTH]labels = labels[:MAX_LENGTH]return {"input_ids": input_ids,"attention_mask": attention_mask,"labels": labels}tokenized_ds = dataset['train'].map(process_func, remove_columns=dataset['train'].column_names)
设置TrainingArguments
在per_device_train_batch_size=1的情况下，大概需要9G显存。
args = TrainingArguments(output_dir="./llama3_4bit",per_device_train_batch_size=4,gradient_accumulation_steps=32,logging_steps=10,num_train_epochs=1,save_strategy='epoch',learning_rate=1e-4,# gradient_checkpointing=True,# optim="paged_adamw_32bit")
训练
trainer = Trainer(model=model,args=args,tokenizer=tokenizer,train_dataset=tokenized_ds,data_collator=DataCollatorForSeq2Seq(tokenizer=tokenizer, padding=True),
)
trainer.train(resume_from_checkpoint=False)
加载qlora
from transformers import AutoModelForCausalLM,AutoTokenizer
model_path = model_dir #llama3-8b的路径
model = AutoModelForCausalLM.from_pretrained(model_path,quantization_config=config,low_cpu_mem_usage=True)
tokenizer = AutoTokenizer.from_pretrained(model_path)
model_qlora = PeftModel.from_pretrained(model=model,model_id="llama3_4bit/checkpoint-7") #qlora路径
#预测
ipt = tokenizer("Human: {}\n{}".format("怎么学习llm", "").strip() + "\n\nAssistant: ", return_tensors="pt").to(model.device)
tokenizer.decode(model_qlora.generate(**ipt, max_length=128, do_sample=True)[0], skip_special_tokens=True)
合并LoRA
合并后的模型大概5.4G。
merge_model = model_qlora.merge_and_unload()
merge_model.save_pretrained("llama3")
