src/sequence_only.py

import os
import sys
import argparse
import subprocess
from tqdm import tqdm

# 添加当前目录到Python路径
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from eval.evaluation import simple_verify,majority_verify

def main():
    parser = argparse.ArgumentParser(description="Sequence self-critique pipeline from previous solution in Majority.")
    parser.add_argument('--model_name', type=str, required=True)
    parser.add_argument('--max_completion_tokens', type=int, default=2048)
    parser.add_argument('--dataset', type=str, required=True)
    parser.add_argument('--method', type=str, default="Sequence")
    parser.add_argument('--input_path', type=str, required=True)
    # parser.add_argument('--output_dir', type=str, required=True)
    parser.add_argument('--tensor_parallel_size', type=int, default=2)
    parser.add_argument('--budget', type=int, default=8)
    #parser.add_argument('--cuda_visible_devices', type=str, default=None)
    args = parser.parse_args()

    # 0. 检查输入文件是否存在
    if not os.path.exists(args.input_path):
        raise FileNotFoundError(f"Input file {args.input_path} not found")
    
    # 1. 构建输出目录
    result_dir = os.path.join(
        '/home/tianqiu/tts_schedule/batch_infer/results',
        args.dataset, args.model_name.replace('/', '_'), args.method
    )
    batch_dir = os.path.join(result_dir, "batch_data")
    output_dir = os.path.join(result_dir, "output_data")
    os.makedirs(result_dir, exist_ok=True)
    os.makedirs(output_dir, exist_ok=True)
    os.makedirs(batch_dir, exist_ok=True)
    current_input_path = args.input_path
    # 2. 数据准备
    for i in range(args.budget): 
        # 修改 batch idx  为了连续
        batch_dir_i = os.path.join(batch_dir, f"batch_{i}")
        os.makedirs(batch_dir_i, exist_ok=True)
        prepare_cmd = [
            'python', 
            '/home/tianqiu/tts_schedule/batch_infer/src/sequence_data_prepare.py',
            '--model_name', args.model_name,
            '--max_completion_tokens', str(args.max_completion_tokens),
            '--output_dir', batch_dir_i,
            '--input_path', current_input_path,
            '--budget', str(1)
        ]
        print(f"[Pipeline] Running sequence data prepare: {' '.join(prepare_cmd)}")
        subprocess.run(prepare_cmd, check=True)
            
        # 3. Batch inference
        batch_jsonl = os.path.join(batch_dir_i, f"batch_0.jsonl")
        if not os.path.exists(batch_jsonl):
            raise FileNotFoundError(f"Batch file {batch_jsonl} not found")
        vllm_cmd = [
            'python', '-m', 'vllm.entrypoints.openai.run_batch',
            '-i', batch_jsonl,  
            '-o', os.path.join(batch_dir_i, f'output_0.jsonl'),
            '--model', args.model_name,
            '--tensor-parallel-size', str(args.tensor_parallel_size)
        ]
        print(f"[Pipeline] Running batch inference: {' '.join(vllm_cmd)}")
        env = os.environ.copy()
        subprocess.run(vllm_cmd, check=True,env=env)

        # 4. Extract
        merge_cmd = [
        'python', '/home/tianqiu/tts_schedule/batch_infer/src/output_extract.py',
        '--input_dir', batch_dir_i,
        '--extra_re',
        '--dataset', args.dataset
        ]
        print(f"[Pipeline] Running output merge: {' '.join(merge_cmd)}")
        subprocess.run(merge_cmd, check=True)
        current_input_path = os.path.join(batch_dir_i, f'parallel_merged_output.jsonl')
        # evaluate
        acc_path = os.path.join(batch_dir_i, f'acc.jsonl')
        majority_verify(current_input_path,acc_path)

    print(f"[Pipeline] All results saved in: {result_dir}")
    
    # 合并
    
if __name__ == "__main__":
    main()