import os import platform import argparse import time import math import warnings import json import pandas as pd import torch import torch.nn.functional as F 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, AutoModel from model.model_videolm import MiniMindVideoLM from model.VLMConfig import VLMConfig from model.dataset import VideoLMDataset warnings.filterwarnings('ignore') def Logger(content): if not ddp or dist.get_rank() == 0: print(content) def get_lr(current_step, total_steps, lr): return lr / 10 + 0.5 * lr * (1 + math.cos(math.pi * current_step / total_steps)) def train_epoch(epoch, wandb): loss_fct = nn.CrossEntropyLoss(reduction='none') start_time = time.time() for step, (X, Y, loss_mask, pixel_tensors) in enumerate(train_loader): X = X.to(args.device) Y = Y.to(args.device) loss_mask = loss_mask.to(args.device) pixel_tensors = pixel_tensors.to(args.device) lr = get_lr(epoch * iter_per_epoch + step, args.epochs * iter_per_epoch, args.learning_rate) for param_group in optimizer.param_groups: param_group['lr'] = lr with ctx: res = model(X, pixel_tensors=pixel_tensors) 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: spend_time = time.time() - start_time Logger( 'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.7f} epoch_Time:{}min:'.format( epoch + 1, args.epochs, step, iter_per_epoch, loss.item(), optimizer.param_groups[-1]['lr'], spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60)) if (wandb is not None) and (not ddp or dist.get_rank() == 0): wandb.log({"loss": loss, "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 and (not ddp or dist.get_rank() == 0): model.eval() moe_path = '_moe' if model_config.use_moe else '' ckp = f'{args.save_dir}/pretrain_vlm_{model_config.dim}{moe_path}.pth' if isinstance(model, torch.nn.parallel.DistributedDataParallel): state_dict = model.module.state_dict() else: state_dict = model.state_dict() clean_state_dict = { key: value for key, value in state_dict.items() if not key.startswith('vision_encoder.') } torch.save(clean_state_dict, ckp) model.train() def init_model(model_config: VLMConfig): tokenizer = AutoTokenizer.from_pretrained('./model/minimind_tokenizer') moe_path = '_moe' if model_config.use_moe else '' # 加载纯语言模型权重 ckp = f'./out/lm_{model_config.dim}{moe_path}.pth' model = MiniMindVideoLM(model_config) # state_dict = torch.load(ckp, map_location=args.device) # model.load_state_dict(state_dict, strict=False) # 冻结除 vision_proj 外的所有参数 for name, param in model.named_parameters(): if 'vision_proj' not in name: param.requires_grad = False # 可训练 if hasattr(model, "layers"): last_two_layers = model.layers[-1:] for layer in last_two_layers: for param in layer.parameters(): param.requires_grad = True Logger(f'VLM可训练参数量：{sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f} 百万') return model.to(args.device), tokenizer def init_distributed_mode(): if not ddp: return global ddp_local_rank, DEVICE dist.init_process_group(backend="nccl") ddp_rank = int(os.environ["RANK"]) ddp_local_rank = int(os.environ["LOCAL_RANK"]) ddp_world_size = int(os.environ["WORLD_SIZE"]) DEVICE = f"cuda:{ddp_local_rank}" torch.cuda.set_device(DEVICE) if __name__ == "__main__": parser = argparse.ArgumentParser(description="MiniMind-V Pretrain") parser.add_argument("--out_dir", type=str, default="out") parser.add_argument("--epochs", type=int, default=4) parser.add_argument("--batch_size", type=int, default=16) parser.add_argument("--learning_rate", type=float, default=4e-4) parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu") parser.add_argument("--dtype", type=str, default="bfloat16") parser.add_argument("--use_wandb", default=False, action="store_true") parser.add_argument("--wandb_project", type=str, default="MiniMind-V") parser.add_argument("--num_workers", type=int, default=8) parser.add_argument("--data_path", type=str, default="./dataset/pretrain_video.jsonl") parser.add_argument("--images_path", type=str, default="./dataset/MSRVTT/videos/all") parser.add_argument("--ddp", action="store_true") parser.add_argument("--accumulation_steps", type=int, default=1) parser.add_argument("--grad_clip", type=float, default=1.0) parser.add_argument("--warmup_iters", type=int, default=0) parser.add_argument("--log_interval", type=int, default=100) parser.add_argument("--save_interval", type=int, default=100) parser.add_argument('--local_rank', type=int, default=-1) parser.add_argument('--dim', default=512, type=int) parser.add_argument('--n_layers', default=8, type=int) parser.add_argument('--max_seq_len', default=640, type=int) parser.add_argument('--use_moe', default=False, type=bool) args = parser.parse_args() model_config = VLMConfig(dim=args.dim, n_layers=args.n_layers, max_seq_len=args.max_seq_len) max_seq_len = model_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-V Pretrain-Epoch-{args.epochs}-BatchSize-{args.batch_size}-LearningRate-{args.learning_rate}" ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast() ddp = int(os.environ.get("RANK", -1)) != -1 # is this a ddp run? ddp_local_rank, DEVICE = 0, "cuda:0" if ddp: init_distributed_mode() args.device = torch.device(DEVICE) if args.use_wandb and (not ddp or ddp_local_rank == 0): import wandb wandb.init(project=args.wandb_project, name=args.wandb_run_name) else: wandb = None model, tokenizer = init_model(model_config) train_ds = VideoLMDataset(args.data_path, args.images_path, tokenizer, video_special_token=model_config.image_special_token, max_length=max_seq_len) train_sampler = DistributedSampler(train_ds) if ddp else None train_loader = DataLoader( train_ds, batch_size=args.batch_size, pin_memory=True, drop_last=False, shuffle=False, num_workers=args.num_workers, sampler=train_sampler ) scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype in ['float16', 'bfloat16'])) optimizer = optim.AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr=args.learning_rate) if ddp: model._ddp_params_and_buffers_to_ignore = {"pos_cis"} model = DistributedDataParallel(model, device_ids=[ddp_local_rank]) iter_per_epoch = len(train_loader) for epoch in range(args.epochs): train_epoch(epoch, wandb)