使用GaLore在本地GPU进行高效的LLM调优

pip install galore-torch

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

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_dictsuper().__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 = -1verbose = Falsesuper().__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

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 Galoregalore_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 othersfor 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()

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()