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 SFTDataset from trainer.trainer_utils import get_lr, Logger, is_main_process, lm_checkpoint, init_distributed_mode, setup_seed, init_model, SkipBatchSampler warnings.filterwarnings('ignore') def train_epoch(epoch, loader, iters, start_step=0, wandb=None): start_time = time.time() for step, (input_ids, labels) in enumerate(loader, start=start_step + 1): ########################### 训练前操作 ########################### # 数据加载 input_ids = input_ids.to(args.device) labels = labels.to(args.device) lr = get_lr(epoch * iters + step, args.epochs * iters, args.learning_rate) # 学习率调整 for param_group in optimizer.param_groups: param_group['lr'] = lr ########################### 训练中操作 ########################### # 模型前向传播 with autocast_ctx: res = model(input_ids, labels=labels) loss = res.loss + res.aux_loss loss = loss / args.accumulation_steps # 模型反向传播 scaler.scale(loss).backward() # 梯度更新 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) ########################### 训练后操作 ########################### # 日志打印 if step % args.log_interval == 0 or step == iters - 1: spend_time = time.time() - start_time current_loss = loss.item() * args.accumulation_steps current_aux_loss = res.aux_loss.item() if res.aux_loss is not None else 0.0 current_logits_loss = current_loss - current_aux_loss 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:.4f}, logits_loss: {current_logits_loss:.4f}, aux_loss: {current_aux_loss:.4f}, lr: {current_lr:.8f}, epoch_time: {eta_min:.1f}min') if wandb: wandb.log({"loss": current_loss, "logits_loss": current_logits_loss, "aux_loss": current_aux_loss, "learning_rate": current_lr, "epoch_time": eta_min}) # 模型保存 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' raw_model = model.module if isinstance(model, DistributedDataParallel) else model raw_model = getattr(raw_model, '_orig_mod', raw_model) state_dict = raw_model.state_dict() torch.save({k: v.half().cpu() for k, v in state_dict.items()}, 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() del state_dict del input_ids, labels, res, loss if __name__ == "__main__": parser = argparse.ArgumentParser(description="MiniMind Full SFT") parser.add_argument("--save_dir", type=str, default="../out", help="模型保存目录") parser.add_argument('--save_weight', default='full_sft', type=str, help="保存权重的前缀名") parser.add_argument("--epochs", type=int, default=2, help="训练轮数") parser.add_argument("--batch_size", type=int, default=16, help="batch size") parser.add_argument("--learning_rate", type=float, default=1e-6, 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=8, help="数据加载线程数") parser.add_argument("--accumulation_steps", type=int, default=1, 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=1000, help="模型保存间隔") parser.add_argument('--hidden_size', default=768, 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=340, type=int, help="训练的最大截断长度（中文1token≈1.5~1.7字符）") parser.add_argument('--use_moe', default=0, type=int, choices=[0, 1], help="是否使用MoE架构（0=否，1=是）") parser.add_argument("--data_path", type=str, default="../dataset/sft_t2t_mini.jsonl", help="训练数据路径") parser.add_argument('--from_weight', default='pretrain', type=str, help="基于哪个权重训练，为none则不基于任何权重训练") parser.add_argument('--from_resume', default=0, type=int, choices=[0, 1], 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-Full-SFT", help="wandb项目名") parser.add_argument("--use_compile", default=0, type=int, choices=[0, 1], help="是否使用torch.compile加速（0=否，1=是）") args = parser.parse_args() # ========== 1. 创建训练环境 ========== local_rank = init_distributed_mode() if dist.is_initialized(): args.device = f"cuda:{local_rank}" # 分布式训练时，设置设备 setup_seed(42 + (dist.get_rank() if dist.is_initialized() else 0)) # 设置随机种子 # ========== 2. 模型相关 ========== os.makedirs(args.save_dir, exist_ok=True) # 创建模型保存目录 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) # 设置混合精度 lm_config = MiniMindConfig(hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers, use_moe=bool(args.use_moe)) model, tokenizer = init_model(lm_config, args.from_weight, device=args.device) if args.use_compile == 1: model = torch.compile(model) Logger('torch.compile enabled') train_ds = SFTDataset(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) # 所有模型相关的初始化 if dist.is_initialized(): model._ddp_params_and_buffers_to_ignore = {"freqs_cos", "freqs_sin"} model = DistributedDataParallel(model, device_ids=[local_rank]) # 分布式训练模型初始化 ckp_data = lm_checkpoint(lm_config, weight=args.save_weight, save_dir='../checkpoints') if args.from_resume==1 else None # 检查checkpoint # ========== 3. checkpoint相关 ========== 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-SFT-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) # 通过checkpoint进行训练可视化 start_epoch, start_step = 0, 0 if ckp_data: model.module.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) # 通过checkpoint进行状态恢复 # ========== 4. 训练 ========== for epoch in range(start_epoch, args.epochs): train_sampler and train_sampler.set_epoch(epoch) setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist() skip = start_step if (epoch == start_epoch and start_step > 0) else 0 batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip) loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True) if skip > 0: 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) + skip, start_step, wandb) else: train_epoch(epoch, loader, len(loader), 0, wandb) # ========== 5. 撤销训练环境 ========== if dist.is_initialized(): dist.destroy_process_group() # 撤销分布式训练环境