HuggingFace学习笔记--BitFit高效微调

1--BitFit高效微调

        BitFit，全称是 bias-term fine-tuning，其高效微调只去微调带有 bias 的参数，其余参数全部固定；

2--实例代码

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 = 256input_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)# trainertrainer = 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)

结果：