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""" |
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Debug wrapper for DeepSpeed training |
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This script allows debugging the training process step by step |
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""" |
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import os |
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import sys |
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import subprocess |
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import argparse |
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from pathlib import Path |
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def setup_environment(): |
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"""Setup environment variables for debugging""" |
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env_vars = { |
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'RANK': '1', |
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'MASTER_PORT': '29571', |
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'LOCAL_BATCH_SIZE': '2', |
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'GRADIENT_ACCUMULATION_STEPS': '4', |
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'TRANSFORMERS_OFFLINE': '1', |
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'WANDB_PROJECT': 'vtimellm', |
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'MODEL_VERSION': 'vicuna-v1-5-7b', |
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'OUTPUT_DIR': './outputs/', |
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'STAGE4': './outputs/vtimellm-vicuna-v1-5-7b-activitynet-stage4', |
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'PYTHONPATH': f"{os.getcwd()}:{os.environ.get('PYTHONPATH', '')}", |
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'CUDA_VISIBLE_DEVICES': '1', |
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'TORCH_USE_CUDA_DSA': '1', |
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'TRANSFORMERS_VERBOSITY': 'info', |
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'TOKENIZERS_PARALLELISM': 'false' |
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} |
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for key, value in env_vars.items(): |
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os.environ[key] = value |
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return env_vars |
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def check_required_files(): |
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"""Check if all required files exist""" |
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required_files = [ |
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"./checkpoints/vicuna-7b-v1.5", |
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"./data/activitynet/mdpo-train.json", |
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"./data/activitynet/videos/train", |
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"./data/activitynet/clipvitl14-vtimellm.pth", |
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"./checkpoints/vtimellm-vicuna-v1-5-7b-stage1/mm_projector.bin", |
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"./checkpoints/vtimellm-vicuna-v1-5-7b-stage2", |
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"./checkpoints/vtimellm-vicuna-v1-5-7b-stage3", |
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"./checkpoints/vtimellm-vicuna-v1-5-7b-activitynet-stage4", |
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"./scripts/zero2.json" |
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] |
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missing_files = [] |
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for file_path in required_files: |
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if not Path(file_path).exists(): |
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missing_files.append(file_path) |
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else: |
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print(f"✓ Found: {file_path}") |
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if missing_files: |
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print("✗ Missing files:") |
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for file_path in missing_files: |
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print(f" {file_path}") |
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return False |
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return True |
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def check_gpu(): |
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"""Check GPU availability""" |
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try: |
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result = subprocess.run(['nvidia-smi', '--query-gpu=name,memory.total,memory.free', '--format=csv,noheader,nounits'], |
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capture_output=True, text=True) |
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if result.returncode == 0: |
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print("=== GPU Information ===") |
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print(result.stdout) |
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print("========================") |
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return True |
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else: |
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print("Warning: nvidia-smi not available or no GPU found") |
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return False |
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except FileNotFoundError: |
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print("Warning: nvidia-smi not found") |
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return False |
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def create_output_dir(): |
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"""Create output directory""" |
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output_dir = "./outputs/vtimellm-vicuna-v1-5-7b-activitynet-stage5" |
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Path(output_dir).mkdir(parents=True, exist_ok=True) |
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print(f"Created output directory: {output_dir}") |
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return output_dir |
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def run_training(): |
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"""Run the training with DeepSpeed""" |
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env_vars = setup_environment() |
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print("=== Debug Environment Setup ===") |
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for key, value in env_vars.items(): |
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print(f"{key}: {value}") |
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print("================================") |
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print("=== Checking Required Files ===") |
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if not check_required_files(): |
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print("Error: Missing required files. Please check the paths.") |
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return False |
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print("=== Checking GPU ===") |
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check_gpu() |
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print("=== Creating Output Directory ===") |
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create_output_dir() |
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cmd = [ |
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"deepspeed", |
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"--include", f"localhost:{env_vars['RANK']}", |
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"--master_port", env_vars['MASTER_PORT'], |
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"vtimellm/train/train_dpo_mem.py", |
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"--deepspeed", "./scripts/zero2.json", |
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"--lora_enable", "True", |
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"--lora_r", "8", |
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"--lora_alpha", "128", |
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"--training_stage", "3", |
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"--finetuning", "True", |
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"--model_name_or_path", "./checkpoints/vicuna-7b-v1.5", |
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"--version", "v1", |
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"--data_path", "./data/activitynet/mdpo-train.json", |
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"--data_folder", "./data/activitynet/videos/train", |
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"--feat_folder", "./data/activitynet/clipvitl14-vtimellm.pth", |
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"--pretrain_mm_mlp_adapter", "./checkpoints/vtimellm-vicuna-v1-5-7b-stage1/mm_projector.bin", |
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"--stage2_path", "./checkpoints/vtimellm-vicuna-v1-5-7b-stage2", |
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"--stage3_path", "./checkpoints/vtimellm-vicuna-v1-5-7b-stage3", |
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"--stage4_path", "checkpoints/vtimellm-vicuna-v1-5-7b-activitynet-stage4", |
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"--output_dir", "./outputs/vtimellm-vicuna-v1-5-7b-activitynet-stage5", |
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"--bf16", "True", |
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"--max_steps", "100", |
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"--per_device_train_batch_size", env_vars['LOCAL_BATCH_SIZE'], |
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"--gradient_accumulation_steps", env_vars['GRADIENT_ACCUMULATION_STEPS'], |
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"--evaluation_strategy", "no", |
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"--save_strategy", "no", |
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"--save_steps", "50000", |
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"--save_total_limit", "10", |
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"--learning_rate", "1e-6", |
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"--freeze_mm_mlp_adapter", "True", |
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"--weight_decay", "0.", |
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"--warmup_ratio", "0.1", |
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"--lr_scheduler_type", "cosine", |
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"--logging_steps", "1", |
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"--tf32", "True", |
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"--model_max_length", "2048", |
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"--gradient_checkpointing", "True", |
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"--dataloader_num_workers", "4", |
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"--lazy_preprocess", "True", |
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"--report_to", "none", |
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"--run_name", "vtimellm-vicuna-v1-5-7b-activitynet-stage5", |
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"--gamma", "0.0", |
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"--beta", "0.5", |
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"--dpo_alpha", "1.0", |
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"--train4dpo" |
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] |
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print("=== Starting Debug Training ===") |
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print(f"Command: {' '.join(cmd)}") |
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print("================================") |
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try: |
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result = subprocess.run(cmd, check=True) |
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print("=== Training Completed Successfully ===") |
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return True |
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except subprocess.CalledProcessError as e: |
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print(f"=== Training Failed with Error Code: {e.returncode} ===") |
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return False |
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except KeyboardInterrupt: |
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print("=== Training Interrupted by User ===") |
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return False |
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def main(): |
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parser = argparse.ArgumentParser(description="Debug wrapper for MDPO training") |
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parser.add_argument("--check-only", action="store_true", help="Only check environment and files") |
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parser.add_argument("--dry-run", action="store_true", help="Show command without executing") |
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args = parser.parse_args() |
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if args.check_only: |
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setup_environment() |
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check_required_files() |
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check_gpu() |
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create_output_dir() |
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return |
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if args.dry_run: |
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env_vars = setup_environment() |
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cmd = [ |
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"deepspeed", |
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"--include", f"localhost:{env_vars['RANK']}", |
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"--master_port", env_vars['MASTER_PORT'], |
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"vtimellm/train/train_dpo_mem.py", |
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] |
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print("Command that would be executed:") |
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print(" ".join(cmd)) |
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return |
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success = run_training() |
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sys.exit(0 if success else 1) |
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if __name__ == "__main__": |
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main() |
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