使用 fastchat 框架报错

[thunderbolt-fire]

报错如下

(flashrag) (base) root@5e17ee1d7b71:~/siton-data-0553377b2d664236bad5b5d0ba8aa419/workspace/RADIO# python run_rag.py  --dataset=nq
Loading test dataset from: dataset/nq/test.jsonl...
[2025-07-09 18:44:39,495] [WARNING] [real_accelerator.py:209:get_accelerator] Setting accelerator to CPU. If you have GPU or other accelerator, we were unable to detect it.
[2025-07-09 18:44:39,497] [INFO] [real_accelerator.py:254:get_accelerator] Setting ds_accelerator to cpu (auto detect)
[2025-07-09 18:44:41,762] [INFO] [logging.py:107:log_dist] [Rank -1] [TorchCheckpointEngine] Initialized with serialization = False
Traceback (most recent call last):
  File "/root/siton-data-0553377b2d664236bad5b5d0ba8aa419/workspace/RADIO/run_rag.py", line 103, in <module>
    main(args)
  File "/root/siton-data-0553377b2d664236bad5b5d0ba8aa419/workspace/RADIO/run_rag.py", line 42, in main
    generator = get_generator(args.config)
                ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/siton-data-0553377b2d664236bad5b5d0ba8aa419/workspace/FlashRAG/flashrag/utils/utils.py", line 62, in get_generator
    return getattr(importlib.import_module("flashrag.generator"), "FastChatGenerator")(config, **params)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/siton-data-0553377b2d664236bad5b5d0ba8aa419/workspace/FlashRAG/flashrag/generator/generator.py", line 527, in __init__
    super().__init__(config)
  File "/root/siton-data-0553377b2d664236bad5b5d0ba8aa419/workspace/FlashRAG/flashrag/generator/generator.py", line 280, in __init__
    self.model, self.tokenizer = self._load_model(model=model)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/siton-data-0553377b2d664236bad5b5d0ba8aa419/workspace/FlashRAG/flashrag/generator/generator.py", line 558, in _load_model
    max_gpu_memory = str(int(min(available_gpu_memory) * gpu_memory_utilization)) + "GiB"
                             ^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: min() arg is an empty sequence

文件如下

import argparse
import os
from flashrag.config import Config
from flashrag.utils import get_dataset
from flashrag.pipeline import SequentialPipeline
from flashrag.prompt import PromptTemplate
from flashrag.utils import get_generator
from transformers import AutoTokenizer
from utils.classes import CustomSequentialPipeline, mmluPipeline

def main(args):
    if args.dataset == 'mmlu':
        print('dataset is mmlu')
        all_split = get_dataset(args.config)
        test_data = all_split["test"]
        prompt_templete = PromptTemplate(
            args.config,
            system_prompt="Answer the question based on the given document. \
                            Only give me the option (A/B/C/D) and do not output any other words. \
                            \nThe following are given documents.\n\n{reference}",
            user_prompt="Question: {question}\nAnswer:",
        )
        generator = get_generator(args.config)

        tokenizer = AutoTokenizer.from_pretrained(args.model_path)
        tokenizer.pad_token = "<lendoftext|>"
        tokenizer.pad_token_id = 128009
        tokenizer.padding_side = "left"
        generator.tokenizer = tokenizer
        pipeline = mmluPipeline(args.config, prompt_template=prompt_templete, generator=generator)
        output_dataset = pipeline.run(test_data, do_eval=True, pred_process_fun=None)
    else:
        all_split = get_dataset(args.config)
        test_data = all_split["test"]
        prompt_templete = PromptTemplate(
            args.config,
            system_prompt="Answer the question based on the given document. \
                            Only give me the answer and do not output any other words. \
                            \nThe following are given documents.\n\n{reference}",
            user_prompt="Question: {question}\nAnswer:",
        )
        generator = get_generator(args.config)

        tokenizer = AutoTokenizer.from_pretrained(args.model_path)
        tokenizer.pad_token = "<lendoftext|>"
        tokenizer.pad_token_id = 128009
        tokenizer.padding_side = "left"
        generator.tokenizer = tokenizer

        pipeline = SequentialPipeline(args.config, prompt_template=prompt_templete, generator=generator)

        output_dataset = pipeline.run(test_data, do_eval=True)
        # output_dataset.save(os.path.join(args.config['save_dir'], 'intermediate_data.json'))

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model_path", type=str, default='/root/siton-data-0553377b2d664236bad5b5d0ba8aa419/workspace/FlashRAG/models/Meta-Llama-3-8B-Instruct')
    parser.add_argument("--retriever_path", type=str, default='/root/siton-data-0553377b2d664236bad5b5d0ba8aa419/workspace/FlashRAG/models/e5-base-v2')
    parser.add_argument("--rerank_model_name", type=str, default="bge-reranker-base")
    parser.add_argument("--rerank_model_path", type=str, default='/root/.cache/huggingface/hub/models--BAAI--bge-reranker-base/snapshots/2cfc18c9415c912f9d8155881c133215df768a70')
    parser.add_argument("--gpu_id", type=int, default=3)
    parser.add_argument("--max_tokens", type=int, default=512)
    parser.add_argument("--dataset", type=str, default="nq")
    parser.add_argument("--framework", type=str, default="fschat")
    parser.add_argument("--openai_model", type=str, default="gpt-4o-mini")
    args = parser.parse_args()

    config_dict = {
        "data_dir": "dataset/",
        "index_path": "indexes/e5_Flat.index",
        "corpus_path": "indexes/retrieval-corpus/wiki-18.jsonl",
        "model2path": {
            # "e5": args.retriever_path, 
            # "llama3-8B-instruct": args.model_path
            },
        # "generator_model": "llama3-8B-instruct",
        "framework": args.framework,
        "generator_model": "llama3-8B-instruct",
        "openai_setting": {
            "api_key": "sk-xxx",
            "base_url": "https://xxx.xxx.xxx"
        },
        # "retrieval_method": "e5",
        "retrieval_method": args.retriever_path,
        "metrics": ["em", "f1"],
        "retrieval_topk": 20,
        "save_intermediate_data": True,
        "dataset_name": args.dataset,
        # "test_sample_num": 100,
        "gpu_id": args.gpu_id,
        "generation_params": {
            "max_tokens": args.max_tokens,
        },
        "use_reranker": True,
        "rerank_model_name": "bge-reranker-base",
        "rerank_model_path": args.rerank_model_path,
        "rerank_topk": 5,
        "rerank_batch_size": 128,
        "save_retrieval_cache": False,
    }

    args.config = Config(config_file_path='/root/siton-data-0553377b2d664236bad5b5d0ba8aa419/workspace/RADIO/utils/basic_config.yaml',config_dict=config_dict)
    main(args)

generator = get_generator(args.config)

这一行报错

[DaoD]

是不是没有GPU？

[thunderbolt-fire]

有的，但是不知道为什么没有调用