LLM微调（一）| 单GPU使用QLoRA微调Llama 2.0实战

最近LLaMA 2在LLaMA1 的基础上做了很多优化，比如上下文从2048扩展到4096，使用了Grouped-Query Attention（GQA）共享多头注意力的key 和value矩阵，具体可以参考：

关于LLaMA 2 的细节，可以参考如下文章：

Meta发布升级大模型LLaMA 2：开源可商用

揭秘最领先的Llama2中文大模型！

使用QLoRA微调LLaMA 2

安装环境

pip install transformers datasets peft accelerate bitsandbytes safetensors

导入库

import os, sysimport torchimport datasetsfrom transformers import (    AutoTokenizer,    AutoModelForCausalLM,    BitsAndBytesConfig,    DataCollatorForLanguageModeling,    DataCollatorForSeq2Seq,    Trainer,    TrainingArguments,    GenerationConfig)from peft import PeftModel, LoraConfig, prepare_model_for_kbit_training, get_peft_model

导入LLaMA 2模型

### config ###model_id = "NousResearch/Llama-2-7b-hf" # optional meta-llama/Llama-2–7b-chat-hfmax_length = 512device_map = "auto"batch_size = 128micro_batch_size = 32gradient_accumulation_steps = batch_size // micro_batch_size# nf4" use a symmetric quantization scheme with 4 bits precisionbnb_config = BitsAndBytesConfig(    load_in_4bit=True,    bnb_4bit_use_double_quant=True,    bnb_4bit_quant_type="nf4",    bnb_4bit_compute_dtype=torch.bfloat16)# load model from huggingfacemodel = AutoModelForCausalLM.from_pretrained(    model_id,    quantization_config=bnb_config,    use_cache=False,    device_map=device_map)# load tokenizer from huggingfacetokenizer = AutoTokenizer.from_pretrained(model_id)tokenizer.pad_token = tokenizer.eos_tokentokenizer.padding_side = "right"

输出模型的可训练参数量

def print_number_of_trainable_model_parameters(model):    trainable_model_params = 0    all_model_params = 0    for _, param in model.named_parameters():        all_model_params += param.numel()        if param.requires_grad:            trainable_model_params += param.numel()    print(f"trainable model parameters: {trainable_model_params}. All model parameters: {all_model_params} ")    return trainable_model_paramsori_p = print_number_of_trainable_model_parameters(model)# 输出# trainable model parameter: 262,410,240

配置LoRA参数

# LoRA configmodel = prepare_model_for_kbit_training(model)peft_config = LoraConfig(    r=8,    lora_alpha=32,    lora_dropout=0.1,    target_modules=["q_proj", "v_proj"],    bias="none",    task_type="CAUSAL_LM",)model = get_peft_model(model, peft_config)### compare trainable parameters #peft_p = print_number_of_trainable_model_parameters(model)print(f"# Trainable Parameter \nBefore: {ori_p} \nAfter: {peft_p} \nPercentage: {round(peft_p / ori_p * 100, 2)}")# 输出# trainable model parameter: 4,194,304

r：更新矩阵的秩，也称为Lora注意力维度。较低的秩导致具有较少可训练参数的较小更新矩阵。增加r（不超过32）将导致更健壮的模型，但同时会导致更高的内存消耗。

lora_lpha：控制lora比例因子

target_modules：是一个模块名称列表，如“q_proj”和“v_proj“，用作LoRA模型的目标。具体的模块名称可能因基础模型而异。

bias：指定是否应训练bias参数。可选参数为：“none”、“all”或“lora_only”。

输出LoRA Adapter的参数，发现只占原模型的不到2%。

在微调LLaMA 2之前，我们看一下LLaMA 2的生成效果

### generate ###prompt = "Write me a poem about Singapore."inputs = tokenizer(prompt, return_tensors="pt")generate_ids = model.generate(inputs.input_ids, max_length=64)print('\nAnswer: ', tokenizer.decode(generate_ids[0]))res = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]print(res)

当要求模型写一首关于新加坡的诗时，产生的输出似乎相当模糊和重复，这表明模型很难提供连贯和有意义的回应。

微调数据加载

为了方便演示，我们使用开源的databricks/databricks-dolly-15k，数据格式如下：

{    'instruction': 'Why can camels survive for long without water?',    'context': '',    'response': 'Camels use the fat in their humps to keep them filled with energy and hydration for long periods of time.',    'category': 'open_qa',}

要揭秘LLM能力，构建Prompt是至关重要，通常的Prompt形式有三个字段：Instruction、Input（optional）、Response。由于Input是可选的，因为这里设置了两种prompt_template，分别是有Input 的prompt_input和无Input 的prompt_no_input，代码如下：

max_length = 256dataset = datasets.load_dataset(    "databricks/databricks-dolly-15k", split='train')### generate prompt based on template ###prompt_template = {    "prompt_input": \    "Below is an instruction that describes a task, paired with an input that provides further context.\    Write a response that appropriately completes the request.\    \n\n### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n",    "prompt_no_input": \    "Below is an instruction that describes a task.\    Write a response that appropriately completes the request.\    \n\n### Instruction:\n{instruction}\n\n### Response:\n",    "response_split": "### Response:"}def generate_prompt(instruction, input=None, label=None, prompt_template=prompt_template):    if input:        res = prompt_template["prompt_input"].format(            instruction=instruction, input=input)    else:        res = prompt_template["prompt_no_input"].format(            instruction=instruction)    if label:        res = f"{res}{label}"    return res

使用generate_prompt函数把instruction, context和response拼接起来；然后进行tokenize分词处理，转换为input_ids和attention_mask，为了让模型可以预测下一个token，设计了类似input_ids的labels便于右移操作；

def tokenize(tokenizer, prompt, max_length=max_length, add_eos_token=False):    result = tokenizer(        prompt,        truncation=True,        max_length=max_length,        padding=False,        return_tensors=None)    result["labels"] = result["input_ids"].copy()    return resultdef generate_and_tokenize_prompt(data_point):    full_prompt = generate_prompt(        data_point["instruction"],        data_point["context"],        data_point["response"],    )    tokenized_full_prompt = tokenize(tokenizer, full_prompt)    user_prompt = generate_prompt(data_point["instruction"], data_point["context"])    tokenized_user_prompt = tokenize(tokenizer, user_prompt)    user_prompt_len = len(tokenized_user_prompt["input_ids"])    mask_token = [-100] * user_prompt_len    tokenized_full_prompt["labels"] = mask_token + tokenized_full_prompt["labels"][user_prompt_len:]    return tokenized_full_promptdataset = dataset.train_test_split(test_size=1000, shuffle=True, seed=42)cols = ["instruction", "context", "response", "category"]train_data = dataset["train"].shuffle().map(generate_and_tokenize_prompt, remove_columns=cols)val_data = dataset["test"].shuffle().map(generate_and_tokenize_prompt, remove_columns=cols,)

模型训练

args = TrainingArguments(    output_dir="./llama-7b-int4-dolly",    num_train_epochs=20,    max_steps=200,    fp16=True,    optim="paged_adamw_32bit",    learning_rate=2e-4,    lr_scheduler_type="constant",    per_device_train_batch_size=micro_batch_size,    gradient_accumulation_steps=gradient_accumulation_steps,    gradient_checkpointing=True,    group_by_length=False,    logging_steps=10,    save_strategy="epoch",    save_total_limit=3,    disable_tqdm=False,)trainer = Trainer(    model=model,    train_dataset=train_data,    eval_dataset=val_data,    args=args,    data_collator=DataCollatorForSeq2Seq(      tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True),)# silence the warnings. re-enable for inference!model.config.use_cache = Falsetrainer.train()model.save_pretrained("llama-7b-int4-dolly")

模型测试

模型训练几个小时结束后，我们合并预训练模型Llama-2–7b-hf和LoRA参数，我们还是以“Write me a poem about Singapore”测试效果，代码如下：

# model path and weightmodel_id = "NousResearch/Llama-2-7b-hf"peft_path = "./llama-7b-int4-dolly"# loading modelmodel = AutoModelForCausalLM.from_pretrained(    model_id,    quantization_config=bnb_config,    use_cache=False,    device_map="auto")# loading peft weightmodel = PeftModel.from_pretrained(    model,    peft_path,    torch_dtype=torch.float16,)model.eval()# generation configgeneration_config = GenerationConfig(    temperature=0.1,    top_p=0.75,    top_k=40,    num_beams=4, # beam search)# generating replywith torch.no_grad():    prompt = "Write me a poem about Singapore."    inputs = tokenizer(prompt, return_tensors="pt")    generation_output = model.generate(        input_ids=inputs.input_ids,        generation_config=generation_config,        return_dict_in_generate=True,        output_scores=True,        max_new_tokens=64,    )    print('\nAnswer: ', tokenizer.decode(generation_output.sequences[0]))

生成模型中参数temperature、top-k、top-p和num_beam含义可以参考：https://github.com/ArronAI007/Awesome-AGI/blob/main/LLM%E4%B9%8BGenerate%E4%B8%AD%E5%8F%82%E6%95%B0%E8%A7%A3%E8%AF%BB.ipynb