import os # os.environ['CUDA_VISIBLE_DEVICES'] = '3' import pathlib from CoT.task import CoT_Task from ToT.task import ToT_Task from MCTS.task import MCTS_Task import argparse from utils.visualize import visualize from utils.json_operator import * from utils.verify_answer import * from utils.self_consistency import get_consistency_output_scibench import copy def run(arguments): print('-'*30, 'Begin testing', '-'*30, '\n') # file = f'data/{arguments.task_name}/{arguments.file}.json' # file = "/cpfs/29f69eb5e2e60f26/code/sft_intern/lh/slz/ReST-MCTS/data/math/math_500.json" file = arguments.load_file_path print('** file_path: ', file) try: data_list = load_file(file) data_len = len(data_list) except Exception as e: print(f'File must be standardized json!\nError type:{e}\n') return assert data_len > 0, "Data list is empty!\n" # assert 'content' in data_list[0].keys() and 'answer' in data_list[0].keys(), "Key error, Make sure json object contain correct keys!\n" output_list = [] correct_count = 0 for i in range(data_len): # for i in range(0, 1): print(f'Begin to solve the problem {i+1}...\n') # data = data_list[i]['question'] # answer = data_list[i]['answer'][0] data = data_list[i]['question'] answer = data_list[i]['solution'] if arguments.mode == 'cot': Task = CoT_Task(data, arguments.propose_method, arguments.value_method, arguments.temperature, evaluate=arguments.evaluate) if arguments.consistency: outputs = [] for cnt in range(3): output = Task.run() outputs.append(output) output = get_consistency_output_scibench(outputs) else: output = Task.run() elif arguments.mode == 'tot': Task = ToT_Task(data, arguments.propose_method, arguments.value_method, arguments.algorithm, arguments.branch, arguments.select_branch, arguments.max_depth, arguments.end_gate, arguments.select_method, arguments.temperature, use_case_prompt=arguments.use_case_prompt, low=arguments.low, high=arguments.high, evaluate=arguments.evaluate) output, root = Task.run() if arguments.visualize: visualize(root, Task, arguments.task_name, arguments.file, i + 1) else: Task = MCTS_Task(data, arguments.propose_method, arguments.value_method, arguments.branch, arguments.end_gate, arguments.roll_policy, arguments.roll_branch, arguments.roll_forward_steps, arguments.time_limit, arguments.iteration_limit, arguments.exploration_constant, arguments.alpha, arguments.inf, arguments.temperature, use_case_prompt=arguments.use_case_prompt, use_reflection=arguments.use_reflection, low=arguments.low, high=arguments.high, evaluate=arguments.evaluate, answer=answer, lang='en') output, root = Task.run() if arguments.visualize: visualize(root, Task, arguments.task_name, arguments.file, i + 1) # evaluate metrics if arguments.evaluate: print('** output: ', output) result = verify_float(answer, output['summary']) output.update({'answer': answer, 'accurate': result}) if result: print(f'The answer of problem {i+1} is correct.\n') correct_count += 1 else: print(f'The answer of problem {i+1} is wrong.\n') print(f'The solution to problem {i+1} is complete.\n') # output base_dir = os.getcwd() output_dir = pathlib.Path(f'{base_dir}/outputs/{arguments.task_name}/{arguments.file}/{Task.mode}') output_file = f'{base_dir}/outputs/{arguments.task_name}/{arguments.file}/{Task.mode}/{Task.propose_method}_{Task.value_method}_{arguments.save_name}.json' data_item = copy.deepcopy(data_list[i]) # 创建深拷贝 data_item['mcts_output'] = output output_list.append(data_item) pathlib.Path.mkdir(output_dir, exist_ok=True, parents=True) dump_json(output_file, output_list) print('** output_file: ', output_file) print('_' * 60) # accuracy if args.evaluate: print(f'Test accuracy:{correct_count / data_len}\n') print(f'Correct number of problems:{correct_count}\nTotal number of questions:{data_len}\n') print('_' * 60) def parse_args(): base_args = argparse.ArgumentParser() base_args.add_argument('--load_file_path', type=str, default='scibench') base_args.add_argument('--task_name', type=str, default='scibench') base_args.add_argument('--file', type=str, default='thermo_standardized') # json base_args.add_argument('--save_name', type=str, default='test') # json base_args.add_argument('--propose_method', type=str, choices=['gpt', 'glm', 'llama', 'local'], default='glm') base_args.add_argument('--value_method', type=str, choices=['gpt', 'glm', 'local'], default='local') base_args.add_argument('--mode', type=str, choices=['cot', 'tot', 'mcts'], default='tot') base_args.add_argument('--temperature', type=float, default=0.7) base_args.add_argument('--time_limit', type=int, default=None) base_args.add_argument('--iteration_limit', type=int, default=100) base_args.add_argument('--roll_policy', type=str, choices=['random', 'greedy'], default='greedy') base_args.add_argument('--exploration_constant', type=float, default=0.4) base_args.add_argument('--roll_forward_steps', type=int, default=2) base_args.add_argument('--end_gate', type=float, default=0.9) # End threshold base_args.add_argument('--branch', type=int, default=3) base_args.add_argument('--roll_branch', type=int, default=1) base_args.add_argument('--inf', type=float, default=0.8) base_args.add_argument('--evaluate', type=str, default='scibench') # Whether to evaluate (empty means no evaluation) base_args.add_argument('--alpha', type=float, default=0.5) base_args.add_argument('--visualize', type=bool, default=False) # visualization base_args.add_argument('--use_case_prompt', type=bool, default=False) # Use sample prompts base_args.add_argument('--use_reflection', type=str, choices=['simple', 'common'], default='simple') # Use reflective mode base_args.add_argument('--low', type=float, default=0) base_args.add_argument('--high', type=float, default=1) base_args.add_argument('--algorithm', type=str, choices=['dfs', 'bfs'], default='dfs') base_args.add_argument('--select_branch', type=int, default=2) base_args.add_argument('--max_depth', type=int, default=8) base_args.add_argument('--select_method', type=str, choices=['greedy', 'sample'], default='greedy') base_args.add_argument('--consistency', type=bool, default=True) arguments = base_args.parse_args() return arguments if __name__ == '__main__': args = parse_args() run(args)