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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()
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