import os import sys __package__ = "trainer" sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) import argparse import time import warnings import torch import torch.distributed as dist from contextlib import nullcontext from torch import optim, nn from torch.nn.parallel import DistributedDataParallel from torch.utils.data import DataLoader, DistributedSampler from model.model_minimind import MiniMindConfig from dataset.lm_dataset import PretrainDataset from trainer.trainer_utils import * warnings.filterwarnings('ignore') def train_epoch(epoch, loader, iters, start_step=0, wandb=None): loss_fct = nn.CrossEntropyLoss(reduction='none') start_time = time.time() for step, (X, Y, loss_mask) in enumerate(loader, start=start_step + 1): X = X.to(args.device) Y = Y.to(args.device) loss_mask = loss_mask.to(args.device) lr = get_lr(epoch * iters + step, args.epochs * iters, args.learning_rate) for param_group in optimizer.param_groups: param_group['lr'] = lr with autocast_ctx: res = model(X) loss = loss_fct( res.logits.view(-1, res.logits.size(-1)), Y.view(-1) ).view(Y.size()) loss = (loss * loss_mask).sum() / loss_mask.sum() loss += res.aux_loss 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 or step == iters - 1: spend_time = time.time() - start_time current_loss = loss.item() * args.accumulation_steps current_lr = optimizer.param_groups[-1]['lr'] eta_min = spend_time / (step + 1) * iters // 60 - spend_time // 60 Logger(f'Epoch:[{epoch+1}/{args.epochs}]({step}/{iters}) loss:{current_loss:.6f} lr:{current_lr:.12f} epoch_Time:{eta_min}min:') if wandb: wandb.log({"loss": current_loss, "lr": current_lr, "epoch_Time": eta_min}) if (step % args.save_interval == 0 or step == iters - 1) and is_main_process(): model.eval() moe_suffix = '_moe' if lm_config.use_moe else '' ckp = f'{args.save_dir}/{args.save_weight}_{lm_config.hidden_size}{moe_suffix}.pth' if isinstance(model, torch.nn.parallel.DistributedDataParallel): state_dict = model.module.state_dict() else: state_dict = model.state_dict() state_dict = {k: v.half() for k, v in state_dict.items()} # 半精度保存 torch.save(state_dict, ckp) lm_checkpoint(lm_config, weight=args.save_weight, model=model, optimizer=optimizer, scaler=scaler, epoch=epoch, step=step, wandb=wandb, save_dir='../checkpoints') model.train() if __name__ == "__main__": parser = argparse.ArgumentParser(description="MiniMind Pretraining") parser.add_argument("--save_dir", type=str, default="../out", help="模型保存目录") parser.add_argument('--save_weight', default='pretrain', type=str, help="保存权重的前缀名") parser.add_argument("--epochs", type=int, default=1, help="训练轮数（建议1轮zero或2-6轮充分训练）") parser.add_argument("--batch_size", type=int, default=32, help="batch size") parser.add_argument("--learning_rate", type=float, default=5e-4, help="初始学习率") parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", help="训练设备") parser.add_argument("--dtype", type=str, default="bfloat16", help="混合精度类型") parser.add_argument("--num_workers", type=int, default=1, help="数据加载线程数") parser.add_argument("--accumulation_steps", type=int, default=8, help="梯度累积步数") parser.add_argument("--grad_clip", type=float, default=1.0, help="梯度裁剪阈值") parser.add_argument("--log_interval", type=int, default=100, help="日志打印间隔") parser.add_argument("--save_interval", type=int, default=100, help="模型保存间隔") parser.add_argument('--hidden_size', default=512, type=int, help="隐藏层维度") parser.add_argument('--num_hidden_layers', default=8, type=int, help="隐藏层数量") parser.add_argument('--max_seq_len', default=512, type=int, help="训练的最大截断长度") parser.add_argument('--use_moe', default=False, type=bool, help="是否使用MoE") parser.add_argument("--data_path", type=str, default="../dataset/pretrain_hq.jsonl", help="预训练数据路径") parser.add_argument('--from_weight', default='none', type=str, help="基于哪个权重训练，为none则从头开始") parser.add_argument('--from_resume', default=0, type=int, help="是否自动检测&续训，0否1是") parser.add_argument("--use_wandb", action="store_true", help="是否使用wandb") parser.add_argument("--wandb_project", type=str, default="MiniMind-Pretrain", help="wandb项目名") args = parser.parse_args() # ========== 1. 初始化环境和随机种子 ========== local_rank = init_distributed_mode() if dist.is_initialized(): args.device = f"cuda:{local_rank}" setup_seed(42 + (dist.get_rank() if dist.is_initialized() else 0)) # ========== 2. 配置目录、模型参数、检查ckp ========== os.makedirs(args.save_dir, exist_ok=True) lm_config = MiniMindConfig(hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers, use_moe=args.use_moe) ckp_data = lm_checkpoint(lm_config, weight=args.save_weight, save_dir='../checkpoints') if args.from_resume==1 else None # ========== 3. 设置混合精度 ========== device_type = "cuda" if "cuda" in args.device else "cpu" dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16 autocast_ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast(dtype=dtype) # ========== 4. 配wandb ========== wandb = None if args.use_wandb and is_main_process(): import swanlab as wandb wandb_id = ckp_data.get('wandb_id') if ckp_data else None resume = 'must' if wandb_id else None wandb_run_name = f"MiniMind-Pretrain-Epoch-{args.epochs}-BatchSize-{args.batch_size}-LearningRate-{args.learning_rate}" wandb.init(project=args.wandb_project, name=wandb_run_name, id=wandb_id, resume=resume) # ========== 5. 定义模型、数据、优化器 ========== model, tokenizer = init_model(lm_config, args.from_weight) train_ds = PretrainDataset(args.data_path, tokenizer, max_length=args.max_seq_len) train_sampler = DistributedSampler(train_ds) if dist.is_initialized() else None scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype == 'float16')) optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate) # ========== 6. 从ckp恢复状态 ========== start_epoch, start_step = 0, 0 if ckp_data: model.load_state_dict(ckp_data['model']) optimizer.load_state_dict(ckp_data['optimizer']) scaler.load_state_dict(ckp_data['scaler']) start_epoch = ckp_data['epoch'] start_step = ckp_data.get('step', 0) # ========== 7. DDP包模型 ========== if dist.is_initialized(): model._ddp_params_and_buffers_to_ignore = {"freqs_cos", "freqs_sin"} model = DistributedDataParallel(model, device_ids=[local_rank]) # ========== 8. 开始训练 ========== for epoch in range(start_epoch, args.epochs): train_sampler and train_sampler.set_epoch(epoch) if epoch == start_epoch and start_step > 0: # 第一个epoch且存在检查点 batch_sampler = SkipBatchSampler(train_sampler or range(len(train_ds)), args.batch_size, start_step + 1) loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True) Logger(f'Epoch [{epoch + 1}/{args.epochs}]: 跳过前{start_step}个step，从step {start_step + 1}开始') train_epoch(epoch, loader, len(loader) + start_step + 1, start_step, wandb) else: # 默认从头开始 loader = DataLoader(train_ds, batch_size=args.batch_size, shuffle=(train_sampler is None), sampler=train_sampler, num_workers=args.num_workers, pin_memory=True) train_epoch(epoch, loader, len(loader), 0, wandb)