使用GaLore在本地GPU进行高效的LLM调优

训练大型语言模型(llm)，即使是那些“只有”70亿个参数的模型，也是一项计算密集型的任务。这种水平的训练需要的资源超出了大多数个人爱好者的能力范围。为了弥补这一差距，出现了低秩适应(LoRA)等参数高效方法，可以在消费级gpu上对大量模型进行微调。

GaLore是一种新的方法，它不是通过直接减少参数的数量，而是通过优化这些参数的训练方式来降低VRAM需求，也就是说GaLore是一种新的模型训练策略，可让模型使用全部参数进行学习，并且比LoRA更省内存。

GaLore将这些梯度投影到低秩空间上，显著减少了计算负荷，同时保留了训练所需的基本信息。与传统的优化器在反向传播后同时更新所有层的方法不同，GaLore在反向传播期间实现逐层更新。这种方法进一步减少了整个训练过程中的内存占用。

就像LoRA一样，GaLore可以让我们在具有24 GB VRAM的消费级GPU上微调7B模型。结果模型的性能与全参数微调相当，并且似乎优于LoRA。

优于目前Hugging Face还没有官方代码，我们就来手动使用论文的代码进行训练，并与LoRA进行对比

安装依赖

首先就要安装GaLore

 pip install galore-torch

然后我们还要一下这些库，并且请注意版本

 datasets==2.18.0
 transformers==4.39.1
 trl==0.8.1
 accelerate==0.28.0
 torch==2.2.1

调度器和优化器的类

Galore分层优化器是通过模型权重挂钩激活的。由于我们使用Hugging Face

Trainer

，还需要自己实现一个优化器和调度器的抽象类。这些类的结构不执行任何操作。

 from typing import Optional
 import torch
 
 # Approach taken from Hugging Face transformers https://github.com/huggingface/transformers/blob/main/src/transformers/optimization.py
 class LayerWiseDummyOptimizer(torch.optim.Optimizer):
     def __init__(self, optimizer_dict=None, *args, **kwargs):
         dummy_tensor = torch.randn(1, 1)
         self.optimizer_dict = optimizer_dict
         super().__init__([dummy_tensor], {"lr": 1e-03})
 
     def zero_grad(self, set_to_none: bool = True) -> None: 
       pass
 
     def step(self, closure=None) -> Optional[float]: 
       pass
 
 class LayerWiseDummyScheduler(torch.optim.lr_scheduler.LRScheduler):
     def __init__(self, *args, **kwargs):
         optimizer = LayerWiseDummyOptimizer()
         last_epoch = -1
         verbose = False
         super().__init__(optimizer, last_epoch, verbose)
 
     def get_lr(self): 
       return [group["lr"] for group in self.optimizer.param_groups]
 
     def _get_closed_form_lr(self): 
       return self.base_lrs

加载GaLore优化器

GaLore优化器的目标是特定的参数，主要是那些在线性层中以attn或mlp命名的参数。通过系统地将函数与这些目标参数挂钩，GaLore 8位优化器就会开始工作。

 from transformers import get_constant_schedule
 from functools import partial
 import torch.nn
 import bitsandbytes as bnb
 
 from galore_torch import GaLoreAdamW8bit
         
 def load_galore_optimizer(model, lr, galore_config):    
     # function to hook optimizer and scheduler to a given parameter 
     def optimizer_hook(p, optimizer, scheduler):
         if p.grad is not None: 
             optimizer.step()
             optimizer.zero_grad()
             scheduler.step()
 
     # Parameters to optimize with Galore
     galore_params = [
         (module.weight, module_name) for module_name, module in model.named_modules() 
         if isinstance(module, nn.Linear) and any(target_key in module_name for target_key in galore_config["target_modules_list"])
     ] 
     id_galore_params = {id(p) for p, _ in galore_params}
     
     # Hook Galore optim to all target params, Adam8bit to all others
     for p in model.parameters():
         if p.requires_grad:
             if id(p) in id_galore_params:
                 optimizer = GaLoreAdamW8bit([dict(params=[p], **galore_config)], lr=lr)
             else:
                 optimizer = bnb.optim.Adam8bit([p], lr = lr)
             scheduler = get_constant_schedule(optimizer)
             
             p.register_post_accumulate_grad_hook(partial(optimizer_hook, optimizer=optimizer, scheduler=scheduler))
             
     # return dummies, stepping is done with hooks 
     return LayerWiseDummyOptimizer(), LayerWiseDummyScheduler()

HF Trainer

准备好优化器后，我们开始使用Trainer进行训练。下面是一个简单的例子，使用TRL的SFTTrainer (Trainer的子类)在Open Assistant数据集上微调llama2-7b，并在RTX 3090/4090等24 GB VRAM GPU上运行。

 from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, set_seed, get_constant_schedule
 from trl import SFTTrainer, setup_chat_format, DataCollatorForCompletionOnlyLM
 from datasets import load_dataset
 import torch, torch.nn as nn, uuid, wandb
 
 lr = 1e-5
 
 # GaLore optimizer hyperparameters
 galore_config = dict(
     target_modules_list = ["attn", "mlp"], 
     rank = 1024, 
     update_proj_gap = 200, 
     scale = 2, 
     proj_type="std"
 )
 
 modelpath = "meta-llama/Llama-2-7b"
 model = AutoModelForCausalLM.from_pretrained(
     modelpath,    
     torch_dtype=torch.bfloat16,
     attn_implementation = "flash_attention_2",  
     device_map = "auto",
     use_cache = False,
 )
 tokenizer = AutoTokenizer.from_pretrained(modelpath, use_fast = False)
 
 # Setup for ChatML
 model, tokenizer = setup_chat_format(model, tokenizer)
 if tokenizer.pad_token in [None, tokenizer.eos_token]: 
     tokenizer.pad_token = tokenizer.unk_token
 
 # subset of the Open Assistant 2 dataset, 4000 of the top ranking conversations
 dataset = load_dataset("g-ronimo/oasst2_top4k_en")
 
 training_arguments = TrainingArguments(
     output_dir = f"out_{run_id}",
     evaluation_strategy = "steps",
     label_names = ["labels"],
     per_device_train_batch_size = 16,
     gradient_accumulation_steps = 1,
     save_steps = 250,
     eval_steps = 250,
     logging_steps = 1, 
     learning_rate = lr,
     num_train_epochs = 3,
     lr_scheduler_type = "constant",
     gradient_checkpointing = True,
     group_by_length = False,
 )
 
 optimizers = load_galore_optimizer(model, lr, galore_config)
 
 trainer = SFTTrainer(
     model = model,
     tokenizer = tokenizer,
     train_dataset = dataset["train"],
     eval_dataset = dataset['test'],
     data_collator = DataCollatorForCompletionOnlyLM(
         instruction_template = "<|im_start|>user", 
         response_template = "<|im_start|>assistant", 
         tokenizer = tokenizer, 
         mlm = False),
     max_seq_length = 256,
     dataset_kwargs = dict(add_special_tokens = False),
     optimizers = optimizers,
     args = training_arguments,
 )
 
 trainer.train()

GaLore优化器带有一些需要设置的超参数如下:

target_modules_list:指定GaLore针对的层

rank:投影矩阵的秩。与LoRA类似，秩越高，微调就越接近全参数微调。GaLore的作者建议7B使用1024

update_proj_gap:更新投影的步骤数。这是一个昂贵的步骤，对于7B来说大约需要15分钟。定义更新投影的间隔，建议范围在50到1000步之间。

scale:类似于LoRA的alpha的比例因子，用于调整更新强度。在尝试了几个值之后，我发现scale=2最接近于经典的全参数微调。