import os import sys import json import subprocess from pathlib import Path def create_dataset_meta(train_json: str, val_json: str, output_meta: str): """创建ms-swift需要的dataset meta文件""" meta = { "root": "/scratch1/home/zhicao/ATM/data/atm_libero/libero_goal", "annotation": { "train": train_json, "val": val_json }, "data_augment": False, "max_dynamic_patch": 1, "image_aspect_ratio": "square" } os.makedirs(os.path.dirname(output_meta), exist_ok=True) with open(output_meta, 'w', encoding='utf-8') as f: json.dump(meta, f, indent=2, ensure_ascii=False) print(f"Dataset meta已保存到: {output_meta}") return output_meta def main(): import argparse parser = argparse.ArgumentParser(description="VLAC模型训练") parser.add_argument("--pretrained_model", type=str, default="/scratch1/home/zhicao/VLAC/models/VLAC", help="预训练模型路径") parser.add_argument("--checkpoint_dir", type=str, default="/scratch1/home/zhicao/VLAC/models/1", help="checkpoint保存目录") parser.add_argument("--train_json", type=str, default="/scratch1/home/zhicao/VLAC/data/train_dataset_train.json", help="训练数据集JSON文件（messages格式）") parser.add_argument("--val_json", type=str, default="/scratch1/home/zhicao/VLAC/data/train_dataset_val.json", help="验证数据集JSON文件（messages格式）") parser.add_argument("--num_epochs", type=int, default=3, help="训练轮数") parser.add_argument("--batch_size", type=int, default=2, help="每设备batch size") parser.add_argument("--gradient_accumulation_steps", type=int, default=8, help="梯度累积步数") parser.add_argument("--learning_rate", type=float, default=2e-5, help="学习率") parser.add_argument("--use_lora", action="store_true", help="使用LoRA微调（节省显存）") parser.add_argument("--freeze_vit", action="store_true", default=True, help="冻结视觉编码器") args = parser.parse_args() # 检查数据集是否存在 if not os.path.exists(args.train_json): print(f"错误: 训练数据集不存在: {args.train_json}") print("请先运行 prepare_dataset.py 准备数据集") print("示例: python prepare_dataset.py") sys.exit(1) if not os.path.exists(args.val_json): print(f"错误: 验证数据集不存在: {args.val_json}") print("请先运行 prepare_dataset.py 准备数据集") sys.exit(1) # 创建dataset meta data_dir = os.path.dirname(args.train_json) dataset_meta = os.path.join(data_dir, "dataset_meta.json") create_dataset_meta(args.train_json, args.val_json, dataset_meta) # 检查预训练模型 if not os.path.exists(args.pretrained_model): print(f"警告: 预训练模型不存在: {args.pretrained_model}") print("将使用HuggingFace上的模型: OpenGVLab/InternVL2-2B") model_id = "OpenGVLab/InternVL2-2B" ckpt_dir = None else: model_id = "OpenGVLab/InternVL2-2B" # 模型类型标识（用于推断模板等） ckpt_dir = args.pretrained_model # 本地模型路径 print(f"使用本地预训练模型: {ckpt_dir}") # 验证模型文件是否存在 model_file = os.path.join(ckpt_dir, "model.safetensors") config_file = os.path.join(ckpt_dir, "config.json") if not os.path.exists(model_file) and not os.path.exists(os.path.join(ckpt_dir, "pytorch_model.bin")): print(f"警告: 模型权重文件不存在: {model_file}") if not os.path.exists(config_file): print(f"警告: 配置文件不存在: {config_file}") # 构建swift sft命令 cmd_parts = [ "swift", "sft", "--model", model_id, "--model_type", "internvl2", ] # 如果使用本地checkpoint，使用 --ckpt_dir 指定路径 if ckpt_dir: cmd_parts.extend(["--ckpt_dir", ckpt_dir]) cmd_parts.extend([ "--dataset", dataset_meta, "--val_dataset", dataset_meta, "--system", "You are a visual-language assistant designed to interpret spatial and task-related information from images and text. Provide precise, context-aware responses and actionable guidance to assist in achieving task objectives.", "--max_length", "10240", "--output_dir", args.checkpoint_dir, "--overwrite_output_dir", "--per_device_train_batch_size", str(args.batch_size), "--per_device_eval_batch_size", "1", "--gradient_accumulation_steps", str(args.gradient_accumulation_steps), "--learning_rate", str(args.learning_rate), "--weight_decay", "0.1", "--num_train_epochs", str(args.num_epochs), "--lr_scheduler_type", "cosine", "--warmup_steps", "100", "--save_steps", "500", "--eval_steps", "100", "--logging_steps", "10", "--save_total_limit", "3", "--dataloader_num_workers", "4", "--seed", "42", "--bf16", "--gradient_checkpointing", ]) if args.freeze_vit: cmd_parts.append("--freeze_vit") cmd_parts.append("--freeze_aligner") if args.use_lora: cmd_parts.extend([ "--lora", "--lora_rank", "8", "--lora_alpha", "32", "--lora_dropout", "0.05", "--target_modules", "all-linear", ]) cmd = " ".join(cmd_parts) print("\n" + "="*80) print("训练配置:") print("="*80) if ckpt_dir: print(f"预训练模型: {ckpt_dir}") else: print(f"预训练模型: {model_id} (从HuggingFace下载)") print(f"训练数据集: {args.train_json}") print(f"验证数据集: {args.val_json}") print(f"Checkpoint目录: {args.checkpoint_dir}") print(f"训练轮数: {args.num_epochs}") print(f"Batch size: {args.batch_size}") print(f"梯度累积: {args.gradient_accumulation_steps}") print(f"学习率: {args.learning_rate}") print(f"使用LoRA: {args.use_lora}") print(f"冻结ViT: {args.freeze_vit}") print("="*80) print("\n训练命令:") print("="*80) print(cmd) print("="*80 + "\n") # 确认 response = input("是否开始训练? (y/n): ") if response.lower() != 'y': print("训练已取消") sys.exit(0) # 执行训练 print("开始训练...") try: subprocess.run(cmd_parts, check=True) print(f"\n训练完成！模型已保存到: {args.checkpoint_dir}") except subprocess.CalledProcessError as e: print(f"\n训练失败: {e}") sys.exit(1) except KeyboardInterrupt: print("\n训练被用户中断") sys.exit(1) if __name__ == "__main__": main()