import argparse import json import os from datetime import datetime import subprocess import logging full_datasets = { "MathVista_MINI": ["train_prompt_sampling"], "MathVision": ["train_prompt_greedy"], "MathVerse_MINI": ["train_prompt_greedy"], "MMMU_DEV_VAL": ["origin_prompt_greedy"], "MMStar": ["train_prompt_greedy"], "DynaMath": ["train_prompt_greedy"], "WeMath": ["train_prompt_greedy"], "TextVQA_VAL": ["origin_prompt_greedy"], "MMVet": ["origin_prompt_greedy"], "MMDocBench": ["origin_prompt_greedy"], "AI2D_TEST": ["origin_prompt_greedy"], "HallusionBench": ["origin_prompt_greedy"], "MMBench_DEV_EN_V11": ["origin_prompt_greedy"], "OCRBench": ["origin_prompt_greedy"], "DocVQA_VAL": ["origin_prompt_greedy"], "EMMA-mini": ["train_prompt_sampling"], # "DocVQA_TEST": ["origin_prompt_greedy"], # "MMBench_TEST_EN_V11": ["origin_prompt_greedy"], } settings = { "train_prompt_sampling": { "use_reasoning_prompt": 2, "do_sample": True, "top_p": 1, "top_k": -1, "temperature": 1, }, "train_prompt_greedy": { "use_reasoning_prompt": 2, "do_sample": True, "top_p": 0.001, "top_k": 1, "temperature": 0.01, }, "origin_prompt_greedy": { "use_reasoning_prompt": 0, "do_sample": True, "top_p": 0.001, "top_k": 1, "temperature": 0.01, }, } def main(): parser = argparse.ArgumentParser() parser.add_argument("--run_name", type=str, required=True, help="Name of the run") parser.add_argument("--gpus", type=int, default=8, help="Number of GPUs to use") parser.add_argument("--path", type=str, required=True, help="Path to the model") parser.add_argument( "--dataset", type=str, nargs="+", required=True, help="List of datasets to use" ) parser.add_argument( "--min_pixels", type=int, default=3136, help="Minimum number of pixels" ) parser.add_argument( "--max_pixels", type=int, default=12845056, help="Maximum number of pixels" ) parser.add_argument( "--max_new_tokens", type=int, default=2048, help="Maximum number of new tokens" ) args = parser.parse_args() assert len(args.dataset), "--dataset should be a list of datasets" datasets = args.dataset if len(args.dataset) == 1 and args.dataset[0] == "full": datasets = list(full_datasets.keys()) for dataset in datasets: assert ( dataset in full_datasets ), f"Dataset {dataset} is not in the list of available datasets: {list(full_datasets.keys())}" print("Datasets to be used:", datasets) print("Run name:", args.run_name) print("Number of GPUs:", args.gpus) print("Model path:", args.path) print("Minimum pixels:", args.min_pixels) print("Maximum pixels:", args.max_pixels) print("Maximum new tokens:", args.max_new_tokens, flush=True) for dataset in datasets: assert isinstance(full_datasets[dataset], list) for setting in full_datasets[dataset]: config = { "model": { args.run_name: { "class": "Qwen2VLChat", "model_path": args.path, "min_pixels": args.min_pixels, "max_pixels": args.max_pixels, "use_vllm": True, "max_new_tokens": args.max_new_tokens, **settings[setting], }, }, "datasets": datasets, } current_datetime = datetime.now().strftime("%Y%m%d") save_dir = f"public_eval/{args.run_name}/{dataset}_{setting}/{current_datetime}" os.makedirs(save_dir, exist_ok=True) config_name = f"config.json" config_path = os.path.join(save_dir, config_name) with open(config_path, "w") as json_file: json.dump(config, json_file, indent=4) print(f"Start evaluating on {dataset}.") print(f"Eval config {setting}", flush=True) env_vars = os.environ.copy() env_vars["VLLM_USE_V1"] = "0" if dataset == "EMMA" or dataset == "EMMA-mini": logger = logging.getLogger('EMMA-logger') logger.setLevel(level=logging.DEBUG) formatter = logging.Formatter('%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s') file_handler = logging.FileHandler(os.path.join(save_dir, f"out.log")) file_handler.setLevel(level=logging.DEBUG) file_handler.setFormatter(formatter) stream_handler = logging.StreamHandler() stream_handler.setLevel(logging.DEBUG) stream_handler.setFormatter(formatter) logger.addHandler(file_handler) logger.addHandler(stream_handler) from EMMA.generate_response import do_generate from EMMA.evaluation.evaluate import gen_true_false from EMMA.evaluation.calculate_acc import gen_score dataset_name = f"/root/LMUData/{dataset}" os.environ["VLLM_USE_V1"] = "0" os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn" do_generate(dataset_name, args.path, f"{save_dir}/results.json", logger=logger, seed=114413) gen_true_false(f"{save_dir}/results.json", logger=logger) gen_score(f"{save_dir}/results.json", f"{save_dir}/results_acc.json", logger=logger) else: command = [ "torchrun", f"--nproc_per_node={args.gpus}", "run_for_bash.py", "--config", f"{config_path}", "--data", f"{dataset}", "--verbose", "--work-dir", f"{save_dir}", ] stdout_file = os.path.join(save_dir, f"out.log") stderr_file = os.path.join(save_dir, f"err.log") with open(stdout_file, "w") as stdout, open(stderr_file, "w") as stderr: try: print(f"Output redirected to {stdout_file}") print(f"Errors redirected to {stderr_file}", flush=True) process = subprocess.Popen( command, env=env_vars, stdout=stdout, stderr=subprocess.PIPE, text=True ) for line in process.stderr: print(line, end="") # 输出到屏幕 stderr.write(line) # 写入文件 # 等待命令完成 process.wait() if process.returncode != 0: print(f"Command failed with return code {process.returncode}. Check {stderr_file} for error details.", flush=True) except subprocess.CalledProcessError as e: print(f"torchrun failed. Check {stderr_file} for error details.", flush=True) if __name__ == "__main__": if not os.path.exists("/root/LMUData"): os.symlink("/user/konglingyu/LMUData", "/root/LMUData") main()