| import os |
| import torch |
| import random |
| import numpy as np |
| from tqdm import tqdm |
| import json |
| from argparse import ArgumentParser |
| from LLMs import init_llm |
| import gc |
|
|
| def set_seed(seed_value): |
| """ |
| Set the seed for PyTorch (both CPU and CUDA), Python, and NumPy for reproducible results. |
| |
| :param seed_value: An integer value to be used as the seed. |
| """ |
| torch.manual_seed(seed_value) |
| if torch.cuda.is_available(): |
| torch.cuda.manual_seed(seed_value) |
| torch.cuda.manual_seed_all(seed_value) |
| random.seed(seed_value) |
| np.random.seed(seed_value) |
| torch.backends.cudnn.deterministic = True |
| torch.backends.cudnn.benchmark = False |
|
|
| def parse_eval_datasets(datasets_str): |
| return datasets_str.split(',') |
|
|
| def main(): |
| parser = ArgumentParser() |
| parser.add_argument('--eval_datasets', type=parse_eval_datasets, default=['MMMU'], |
| help='name of eval dataset') |
| parser.add_argument('--datasets_path', type=str, default="benchmarks", |
| help='path of eval dataset') |
| parser.add_argument('--output_path', type=str, default='eval_results/Qwen2-VL-7B-Instruct', |
| help='name of saved json') |
| parser.add_argument('--model_name', type=str, default='Qwen2-VL-7B-Instruct', |
| help='name of model') |
| parser.add_argument('--model_path', type=str, default="Qwen/Qwen2-VL-7B-Instruct") |
|
|
| parser.add_argument('--seed', type=int, default=42) |
| parser.add_argument('--cuda_visible_devices', type=str, default=None) |
| parser.add_argument('--tensor_parallel_size', type=str, default="1") |
| parser.add_argument('--use_vllm', type=str, default="True") |
| parser.add_argument('--reasoning', type=str, default="False") |
|
|
| parser.add_argument('--num_chunks', type=str, default="1") |
| parser.add_argument('--chunk_idx', type=str, default="0") |
|
|
| parser.add_argument('--max_image_num', type=int, default=1) |
| parser.add_argument('--max_new_tokens', type=int, default=1024) |
| parser.add_argument('--temperature', type=float, default=0.0) |
| parser.add_argument('--top_p', type=float, default=0.001) |
| parser.add_argument('--repetition_penalty', type=float, default=1) |
|
|
| parser.add_argument('--test_times', type=int, default=1) |
|
|
| parser.add_argument('--use_llm_judge', type=str, default="False") |
| parser.add_argument('--judge_model_type', type=str, default="openai") |
| parser.add_argument('--judge_model', type=str,default = "None") |
| parser.add_argument('--api_key', type=str,default = "None") |
| parser.add_argument('--base_url', type=str, default= "None") |
|
|
|
|
| args = parser.parse_args() |
|
|
| os.environ["VLLM_USE_V1"] = "0" |
|
|
| |
| if args.api_key == "None" and args.use_llm_judge == "True": |
| raise ValueError("If you want to use llm judge, please set the openai api key") |
| |
| |
| os.environ["use_llm_judge"] = args.use_llm_judge |
| os.environ["judge_model_type"] = args.judge_model_type |
| os.environ["judge_model"] = args.judge_model |
| os.environ["api_key"] = args.api_key |
| os.environ["base_url"] = args.base_url |
|
|
| |
| os.environ["REASONING"] = args.reasoning |
| os.environ["use_vllm"] = args.use_vllm |
|
|
| os.environ["max_image_num"] = str(args.max_image_num) |
|
|
|
|
| |
| os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True" |
| os.environ["NCCL_IGNORE_DISABLED_P2P"] = "1" |
| if args.cuda_visible_devices is not None: |
| os.environ["CUDA_VISIBLE_DEVICES"] = args.cuda_visible_devices |
| if args.use_vllm == "True": |
| os.environ["tensor_parallel_size"] = args.tensor_parallel_size |
| if int(args.tensor_parallel_size) > 1: |
| os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn" |
| os.environ["TOKENIZERS_PARALLELISM"] = "false" |
| else: |
| torch.multiprocessing.set_start_method('spawn') |
| os.environ["num_chunks"] = args.num_chunks |
| os.environ["chunk_idx"] = args.chunk_idx |
|
|
|
|
| os.makedirs(args.output_path, exist_ok=True) |
|
|
| print('initializing LLM...') |
| model = init_llm(args) |
| |
| total_results_path = os.path.join(args.output_path,'total_results.json') |
|
|
| from benchmarks import prepare_benchmark |
|
|
| for eval_dataset in tqdm(args.eval_datasets): |
| set_seed(args.seed) |
| print(f'evaluating on {eval_dataset}...') |
|
|
| eval_dataset_path = os.path.join(args.datasets_path,eval_dataset) if args.datasets_path != "hf" else None |
| eval_output_path = os.path.join(args.output_path,eval_dataset) |
| os.makedirs(eval_output_path, exist_ok=True) |
| benchmark = prepare_benchmark(model,eval_dataset,eval_dataset_path,eval_output_path) |
| benchmark.load_data() |
| final_results = benchmark.eval() if benchmark else {} |
| print(f'final results on {eval_dataset}: {final_results}') |
| if final_results is not None: |
| if os.path.exists(total_results_path): |
| with open (total_results_path,"r") as f: |
| total_results = json.load(f) |
| else: |
| total_results = {} |
| total_results[eval_dataset] = final_results |
|
|
| with open(os.path.join(args.output_path,'total_results.json'),'w') as f: |
| json.dump(total_results,f,indent=4) |
| gc.collect() |
|
|
| if __name__ == '__main__': |
| main() |
