import os import sys __package__ = "trainer" sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) import datasets # noqa: F401 # Windows pyarrow/torch DLL conflict workaround (issue #771) import argparse import time import warnings import torch import torch.nn.functional as F import torch.distributed as dist from contextlib import nullcontext from torch import optim 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 distillation_loss(student_logits, teacher_logits, temperature=1.0, reduction='batchmean'): with torch.no_grad(): teacher_probs = F.softmax(teacher_logits / temperature, dim=-1).detach() student_log_probs = F.log_softmax(student_logits / temperature, dim=-1) kl = F.kl_div( student_log_probs, teacher_probs, reduction=reduction ) return (temperature ** 2) * kl def train_epoch(epoch, loader, iters, teacher_model, lm_config_student, start_step=0, wandb=None, alpha=0.0, temperature=1.0): start_time = time.time() last_step = start_step if teacher_model is not None: teacher_model.eval() teacher_model.requires_grad_(False) for step, (input_ids, labels) in enumerate(loader, start=start_step + 1): last_step = step input_ids = input_ids.to(args.device) labels = labels.to(args.device) loss_mask = (labels[..., 1:] != -100).float() lr = get_lr(epoch * iters + step, args.epochs * iters, args.learning_rate) for param_group in optimizer.param_groups: param_group['lr'] = lr # 前向传播（学生模型） with autocast_ctx: res = model(input_ids) student_logits = res.logits[..., :-1, :].contiguous() # 教师模型前向传播（只在eval & no_grad） if teacher_model is not None: with torch.no_grad(): teacher_logits = teacher_model(input_ids).logits[..., :-1, :].contiguous() vocab_size_student = student_logits.size(-1) teacher_logits = teacher_logits[..., :vocab_size_student] # ========== 计算损失 ========== # 1) Ground-Truth CE Loss shift_labels = labels[..., 1:].contiguous() loss_mask_flat = loss_mask.view(-1) ce_loss = F.cross_entropy( student_logits.view(-1, student_logits.size(-1)), shift_labels.view(-1), ignore_index=-100, reduction='none' ) ce_loss_raw = torch.sum(ce_loss * loss_mask_flat) / (loss_mask_flat.sum() + 1e-8) if lm_config_student.use_moe: ce_loss = ce_loss_raw + res.aux_loss else: ce_loss = ce_loss_raw # 2) Distillation Loss if teacher_model is not None: distill_loss = distillation_loss( student_logits.view(-1, student_logits.size(-1))[loss_mask_flat == 1], teacher_logits.view(-1, teacher_logits.size(-1))[loss_mask_flat == 1], temperature=temperature ) else: distill_loss = torch.tensor(0.0, device=args.device) # 3) 总损失 = alpha * CE + (1-alpha) * Distill loss = (alpha * ce_loss + (1 - alpha) * distill_loss) / args.accumulation_steps scaler.scale(loss).backward() if step % 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: spend_time = time.time() - start_time current_loss = loss.item() * args.accumulation_steps current_ce_loss = ce_loss_raw.item() current_aux_loss = res.aux_loss.item() if lm_config_student.use_moe else 0.0 current_lr = optimizer.param_groups[-1]['lr'] eta_min = spend_time / max(step - start_step, 1) * (iters - step) // 60 Logger(f'Epoch:[{epoch + 1}/{args.epochs}]({step}/{iters}), loss: {current_loss:.4f}, ce: {current_ce_loss:.4f}, aux_loss: {current_aux_loss:.4f}, distill: {distill_loss.item():.4f}, learning_rate: {current_lr:.8f}, epoch_time: {eta_min:.3f}min') if wandb: wandb.log({ "loss": current_loss, "ce_loss": current_ce_loss, "aux_loss": current_aux_loss, "distill_loss": distill_loss.item() if teacher_model is not None else 0.0, "learning_rate": current_lr, "epoch_time": eta_min }) if (step % args.save_interval == 0 or step == iters) and is_main_process(): model.eval() moe_suffix = '_moe' if lm_config_student.use_moe else '' ckp = f'{args.save_dir}/{args.save_weight}_{lm_config_student.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_student, 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, loss_mask, res, student_logits, ce_loss, distill_loss, loss if last_step > start_step and last_step % 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 __name__ == "__main__": # 模拟用moe模型蒸馏dense模型，也可以用更大teacher_hidden_size模型蒸馏更小student_hidden_size的 parser = argparse.ArgumentParser(description="MiniMind Knowledge Distillation") parser.add_argument("--save_dir", type=str, default="../out", help="模型保存目录") parser.add_argument('--save_weight', default='full_dist', type=str, help="保存权重的前缀名") parser.add_argument("--epochs", type=int, default=6, help="训练轮数") parser.add_argument("--batch_size", type=int, default=32, help="batch size") parser.add_argument("--learning_rate", type=float, default=5e-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=100, help="模型保存间隔") parser.add_argument("--max_seq_len", type=int, default=340, help="训练的最大截断长度（中文1token≈1.5~1.7字符）") parser.add_argument("--data_path", type=str, default="../dataset/sft_t2t_mini.jsonl", help="训练数据路径") parser.add_argument('--student_hidden_size', default=768, type=int, help="学生模型隐藏层维度") parser.add_argument('--student_num_layers', default=8, type=int, help="学生模型隐藏层数量") parser.add_argument('--teacher_hidden_size', default=768, type=int, help="教师模型隐藏层维度") parser.add_argument('--teacher_num_layers', default=8, type=int, help="教师模型隐藏层数量") parser.add_argument('--student_use_moe', default=0, type=int, choices=[0, 1], help="学生模型是否使用MoE（0=否，1=是）") parser.add_argument('--teacher_use_moe', default=1, type=int, choices=[0, 1], help="教师模型是否使用MoE（0=否，1=是）") parser.add_argument('--from_student_weight', default='full_sft', type=str, help="学生模型基于哪个权重") parser.add_argument('--from_teacher_weight', default='full_sft', type=str, help="教师模型基于哪个权重") 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('--alpha', default=0.5, type=float, help="CE损失权重，总损失=alpha*CE+(1-alpha)*KL") parser.add_argument('--temperature', default=1.5, type=float, help="蒸馏温度（推荐范围1.0-2.0）") parser.add_argument("--use_wandb", action="store_true", help="是否使用wandb") parser.add_argument("--wandb_project", type=str, default="MiniMind-Distillation", 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. 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_student = MiniMindConfig(hidden_size=args.student_hidden_size, num_hidden_layers=args.student_num_layers, use_moe=bool(args.student_use_moe)) lm_config_teacher = MiniMindConfig(hidden_size=args.teacher_hidden_size, num_hidden_layers=args.teacher_num_layers, use_moe=bool(args.teacher_use_moe)) ckp_data = lm_checkpoint(lm_config_student, 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-Distill-S{args.student_hidden_size}T{args.teacher_hidden_size}-Epoch-{args.epochs}-BS-{args.batch_size}-LR-{args.learning_rate}" wandb.init(project=args.wandb_project, name=wandb_run_name, id=wandb_id, resume=resume) # ========== 5. 定义学生和教师模型 ========== model, tokenizer = init_model(lm_config_student, args.from_student_weight, device=args.device) Logger(f'学生模型总参数量：{sum(p.numel() for p in model.parameters()) / 1e6:.3f} M') teacher_model, _ = init_model(lm_config_teacher, args.from_teacher_weight, device=args.device) teacher_model.eval() teacher_model.requires_grad_(False) Logger(f'教师模型总参数量：{sum(p.numel() for p in teacher_model.parameters()) / 1e6:.3f} M') 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) # ========== 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. 编译和分布式包装 ========== if args.use_compile == 1: model = torch.compile(model) Logger('torch.compile enabled') if dist.is_initialized(): 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) 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, teacher_model, lm_config_student, start_step, wandb, args.alpha, args.temperature) else: train_epoch(epoch, loader, len(loader), teacher_model, lm_config_student, 0, wandb, args.alpha, args.temperature) # ========== 9. 清理分布进程 ========== if dist.is_initialized(): dist.barrier() dist.destroy_process_group()