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 transformers import AutoTokenizer from model.model_vlm import MiniMindVLM, VLMConfig from dataset.lm_dataset import VLMDataset from trainer.trainer_utils import get_lr, Logger, is_main_process, init_distributed_mode, setup_seed, init_vlm_model, vlm_checkpoint, SkipBatchSampler, vlm_collate_fn warnings.filterwarnings('ignore') def train_epoch(epoch, loader, iters, start_step=0, wandb=None): start_time = time.time() last_step = start_step for step, (input_ids, labels, pixel_values) in enumerate(loader, start=start_step + 1): input_ids = input_ids.to(args.device) labels = labels.to(args.device) pixel_values = {k: v.to(args.device) for k, v in pixel_values.items()} if isinstance(pixel_values, dict) else pixel_values.to(args.device) last_step = step 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(input_ids, labels=labels, pixel_values=pixel_values) loss = res.loss + res.aux_loss loss = 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_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 / max(step - start_step, 1) * (iters - step) // 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}) if (step % args.save_interval == 0 or step == iters) and is_main_process(): model.eval() moe_suffix = '_moe' if vlm_config.use_moe else '' ckp = f'{args.save_dir}/{args.save_weight}_{vlm_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() clean_state_dict = { key: value for key, value in state_dict.items() if not key.startswith('vision_encoder.') } clean_state_dict = {k: v.half().cpu() for k, v in clean_state_dict.items()} # 半精度保存并移到CPU torch.save(clean_state_dict, ckp) vlm_checkpoint(vlm_config, weight=args.save_weight, model=model, optimizer=optimizer, epoch=epoch, step=step, wandb=wandb, save_dir='../checkpoints', scaler=scaler) model.train() del state_dict, clean_state_dict del input_ids, labels, pixel_values, res, 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__": parser = argparse.ArgumentParser(description="MiniMind-V Pretrain") parser.add_argument("--save_dir", type=str, default="../out", help="模型保存目录") parser.add_argument('--save_weight', default='pretrain_vlm', 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=4e-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=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=450, type=int, help="训练的最大截断长度") 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/pretrain_i2t.parquet", help="训练数据路径") parser.add_argument('--from_weight', default='llm', 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('--freeze_llm', default=2, type=int, choices=[0, 1, 2], help="冻结策略（0=完全可训练，1=冻结+解冻首尾层，2=完全冻结仅训练proj）") 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）") parser.add_argument("--use_wandb", action="store_true", help="是否使用wandb") parser.add_argument("--wandb_project", type=str, default="MiniMind-V-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) vlm_config = VLMConfig(hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers, max_seq_len=args.max_seq_len, use_moe=bool(args.use_moe)) ckp_data = vlm_checkpoint(vlm_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-V-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, preprocess = init_vlm_model(vlm_config, from_weight=args.from_weight, device=args.device, freeze_llm=args.freeze_llm) train_ds = VLMDataset(args.data_path, tokenizer, preprocess=preprocess, image_special_token=vlm_config.image_special_token, image_token_len=vlm_config.image_token_len, max_length=vlm_config.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(filter(lambda p: p.requires_grad, 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'], strict=False) 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._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) 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, collate_fn=vlm_collate_fn) 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) # ========== 9. 清理分布进程 ========== if dist.is_initialized(): dist.destroy_process_group()