import os import platform import argparse import time import math import warnings import torch import pandas as pd import torch.nn.functional as F from contextlib import nullcontext from torch import optim from transformers import AutoTokenizer from transformers import AutoModelForCausalLM from peft import get_peft_model, LoraConfig, TaskType from torch.utils.data import DataLoader from model.LMConfig import LMConfig from model.dataset import SFTDataset warnings.filterwarnings('ignore') def Logger(content): print(content) def get_lr(it, all): warmup_iters = args.warmup_iters lr_decay_iters = all min_lr = args.learning_rate / 10 if it < warmup_iters: return args.learning_rate * it / warmup_iters if it > lr_decay_iters: return min_lr decay_ratio = (it - warmup_iters) / (lr_decay_iters - warmup_iters) assert 0 <= decay_ratio <= 1 coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio)) return min_lr + coeff * (args.learning_rate - min_lr) def train_epoch(epoch, wandb): start_time = time.time() for step, (X, Y, loss_mask) in enumerate(train_loader): X = X.to(args.device) Y = Y.to(args.device) loss_mask = loss_mask.to(args.device) lr = get_lr(epoch * iter_per_epoch + step, args.epochs * iter_per_epoch) for param_group in optimizer.param_groups: param_group['lr'] = lr with ctx: logits = model(X, Y).logits loss = F.cross_entropy(logits.view(-1, logits.size(-1)), Y.view(-1), ignore_index=0, reduction='none') loss_mask = loss_mask.view(-1) loss = torch.sum(loss * loss_mask) / loss_mask.sum() loss = loss / args.accumulation_steps scaler.scale(loss).backward() if (step + 1) % args.accumulation_steps == 0: scaler.unscale_(optimizer) torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip) scaler.step(optimizer) scaler.update() optimizer.zero_grad(set_to_none=True) if step % args.log_interval == 0: spend_time = time.time() - start_time Logger( 'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.7f} epoch_Time:{}min:'.format( epoch, args.epochs, step, iter_per_epoch, loss.item() * args.accumulation_steps, optimizer.param_groups[-1]['lr'], spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60)) if wandb is not None: wandb.log({"loss": loss.item() * args.accumulation_steps, "lr": optimizer.param_groups[-1]['lr'], "epoch_Time": spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60}) if (step + 1) % args.save_interval == 0: model.save_pretrained(args.save_dir) def find_all_linear_names(model): cls = torch.nn.Linear lora_module_names = set() for name, module in model.named_modules(): if isinstance(module, cls): names = name.split('.') lora_module_names.add(names[0] if len(names) == 1 else names[-1]) if 'lm_head' in lora_module_names: lora_module_names.remove('lm_head') return list(lora_module_names) def init_model(): model_name_or_path = "./minimind-v1-small" tokenizer_name_or_path = "./minimind-v1-small" tokenizer = AutoTokenizer.from_pretrained(tokenizer_name_or_path, trust_remote_code=True, use_fast=False) model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True).to(args.device) target_modules = find_all_linear_names(model) peft_config = LoraConfig( task_type=TaskType.CAUSAL_LM, r=8, lora_alpha=16, lora_dropout=0.1, inference_mode=False, target_modules=target_modules ) model = get_peft_model(model, peft_config) model.print_trainable_parameters() model = model.to(args.device) return model, tokenizer if __name__ == "__main__": parser = argparse.ArgumentParser(description="MiniMind LoRA Fine-tuning") parser.add_argument("--out_dir", type=str, default="out", help="Output directory") parser.add_argument("--epochs", type=int, default=20, help="Number of epochs") parser.add_argument("--batch_size", type=int, default=16, help="Batch size") parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate") parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", help="Device to use") parser.add_argument("--dtype", type=str, default="bfloat16", help="Data type") parser.add_argument("--use_wandb", action="store_true", help="Use Weights & Biases") parser.add_argument("--wandb_project", type=str, default="MiniMind-LoRA", help="Weights & Biases project name") parser.add_argument("--num_workers", type=int, default=0, help="Number of workers for data loading") parser.add_argument("--accumulation_steps", type=int, default=1, help="Gradient accumulation steps") parser.add_argument("--grad_clip", type=float, default=1.0, help="Gradient clipping threshold") parser.add_argument("--warmup_iters", type=int, default=1000, help="Number of warmup iterations") parser.add_argument("--log_interval", type=int, default=100, help="Logging interval") parser.add_argument("--save_interval", type=int, default=1000, help="Model saving interval") args = parser.parse_args() lm_config = LMConfig() max_seq_len = lm_config.max_seq_len args.save_dir = os.path.join(args.out_dir) os.makedirs(args.save_dir, exist_ok=True) os.makedirs(args.out_dir, exist_ok=True) tokens_per_iter = args.batch_size * max_seq_len torch.manual_seed(1337) device_type = "cuda" if "cuda" in args.device else "cpu" args.wandb_run_name = f"MiniMind-LoRA-Epoch-{args.epochs}-BatchSize-{args.batch_size}-LearningRate-{args.learning_rate}" ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast() if args.use_wandb: import wandb wandb.init(project=args.wandb_project, name=args.wandb_run_name) else: wandb = None model, tokenizer = init_model() df = pd.read_csv('./dataset/sft_data.csv') df = df.sample(frac=1.0) train_ds = SFTDataset(df, tokenizer, max_length=max_seq_len) train_loader = DataLoader( train_ds, batch_size=args.batch_size, pin_memory=True, drop_last=False, shuffle=False, num_workers=args.num_workers, ) scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype == 'float16')) optimizer = optim.Adam(model.parameters(), lr=args.learning_rate) if False and platform.system() != 'Windows' and float(torch.__version__.split('.')[0]) >= 2: Logger("compiling the model... (takes a ~minute)") unoptimized_model = model model = torch.compile(model) iter_per_epoch = len(train_loader) for epoch in range(args.epochs): train_epoch(epoch, wandb)