HuggingFace study notes--BitFit efficient fine-tuning

1--BitFit efficient fine-tuning

        BitFit, the full name is bias-term fine-tuning, its efficient fine-tuning only fine-tunes the parameters with bias, and all other parameters are fixed;

2--Example code

from datasets import load_from_disk
from transformers import AutoTokenizer, AutoModelForCausalLM, DataCollatorForSeq2Seq
from transformers import pipeline, TrainingArguments, Trainer

# 分词器
tokenizer = AutoTokenizer.from_pretrained("Langboat/bloom-1b4-zh")

# 函数内将instruction和response拆开分词的原因是：
# 为了便于mask掉不需要计算损失的labels, 即代码labels = [-100] * len(instruction["input_ids"]) + response["input_ids"]
def process_func(example):
    MAX_LENGTH = 256
    input_ids, attention_mask, labels = [], [], []
    instruction = tokenizer("\n".join(["Human: " + example["instruction"], example["input"]]).strip() + "\n\nAssistant: ")
    response = tokenizer(example["output"] + tokenizer.eos_token)
    input_ids = instruction["input_ids"] + response["input_ids"]
    attention_mask = instruction["attention_mask"] + response["attention_mask"]
    labels = [-100] * len(instruction["input_ids"]) + response["input_ids"]
    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
    }

if __name__ == "__main__":
    # 加载数据集
    dataset = load_from_disk("./PEFT/data/alpaca_data_zh")
    
    # 处理数据
    tokenized_ds = dataset.map(process_func, remove_columns = dataset.column_names)
    # print(tokenizer.decode(tokenized_ds[1]["input_ids"]))
    # print(tokenizer.decode(list(filter(lambda x: x != -100, tokenized_ds[1]["labels"]))))
    
    # 创建模型
    model = AutoModelForCausalLM.from_pretrained("Langboat/bloom-1b4-zh", low_cpu_mem_usage=True)
    # 基于bitfit只训练带有bias的参数
    for name, param in model.named_parameters():
        if "bias" not in name:
            param.requires_grad = False
            
    # 训练参数
    args = TrainingArguments(
        output_dir = "./chatbot",
        per_device_train_batch_size = 1,
        gradient_accumulation_steps = 8,
        logging_steps = 10,
        num_train_epochs = 1
    )
    
    # trainer
    trainer = Trainer(
        model = model,
        args = args,
        train_dataset = tokenized_ds,
        data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, padding=True)
    )
    
    # 训练模型
    trainer.train()
    
    # 模型推理
    pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0)
    
    ipt = "Human: {}\n{}".format("考试有哪些技巧？", "").strip() + "\n\nAssistant: "
    output = pipe(ipt, max_length=256, do_sample=True)
    print(output)

result: