# Copyright (c) ModelScope Contributors. All rights reserved. import datetime as dt import os from dataclasses import dataclass, field from typing import List, Literal, Optional, Union from swift.model import get_matched_model_meta from swift.utils import get_logger, json_parse_to_dict, to_abspath from .deploy_args import DeployArguments logger = get_logger() @dataclass class EvalArguments(DeployArguments): """A dataclass that extends DeployArguments to define model evaluation arguments. These arguments control the evaluation process, including the choice of backend, datasets, generation parameters, and other configurations. Args: eval_dataset (List[str]): List of evaluation datasets. Please refer to the evaluation documentation for available options. Defaults to []. eval_limit (Optional[int]): The number of samples to take from each evaluation dataset. If None, all samples are used. Defaults to None. eval_dataset_args (Optional[Union[Dict, str]]): Evaluation dataset parameters, in JSON format, can be set for multiple datasets. Defaults to None. eval_generation_config (Optional[Union[Dict, str]]): The model's inference configuration for evaluation, provided as a JSON string (e.g., '{"max_new_tokens": 512}'). Defaults to None. eval_output_dir (str): The directory to store evaluation results. Defaults to 'eval_output'. eval_backend (str): The evaluation backend. Can be 'Native', 'OpenCompass', or 'VLMEvalKit'. Defaults to 'Native'. local_dataset (bool): Whether to automatically download extra datasets required for certain evaluations (e.g., CMB). If True, a 'data' folder will be created in the current directory for the datasets. This download occurs only once, and subsequent runs will use the cache. Defaults to False. Note: By default, evaluation uses datasets from `~/.cache/opencompass`. When this is set to True, the `data` folder in the current directory is used instead. temperature (float): The temperature for sampling, which overrides the default generation config. Defaults to 0.0. verbose (bool): Whether to output verbose information during the evaluation process. Defaults to False. eval_num_proc (int): The maximum number of concurrent clients for evaluation. Defaults to 16. extra_eval_args (Optional[Union[Dict, str]]): Additional evaluation arguments, provided as a JSON string. These are only effective when using the 'Native' backend. Refer to the documentation for more details on available arguments. Defaults to {}. eval_url (Optional[str]): The URL for the evaluation service (e.g., 'http://localhost:8000/v1'). If not specified, evaluation runs on the locally deployed model. See documentation for more examples. Defaults to None. """ eval_dataset: List[str] = field(default_factory=list) eval_limit: Optional[int] = None eval_dataset_args: Optional[Union[dict, str]] = None eval_generation_config: Optional[Union[dict, str]] = None eval_output_dir: str = 'eval_output' eval_backend: Literal['Native', 'OpenCompass', 'VLMEvalKit'] = 'Native' local_dataset: bool = False temperature: Optional[float] = 0. verbose: bool = False eval_num_proc: int = 16 extra_eval_args: Optional[Union[dict, str]] = field(default_factory=dict) # If eval_url is set, ms-swift will not perform deployment operations and # will directly use the URL for evaluation. eval_url: Optional[str] = None def __post_init__(self): super().__post_init__() self._init_eval_url() self._init_eval_dataset() self.eval_dataset_args = json_parse_to_dict(self.eval_dataset_args) self.eval_generation_config = json_parse_to_dict(self.eval_generation_config) self.extra_eval_args = json_parse_to_dict(self.extra_eval_args) self.eval_output_dir = to_abspath(self.eval_output_dir) logger.info(f'eval_output_dir: {self.eval_output_dir}') def _init_eval_url(self): # [compat] if self.eval_url and 'chat/completions' in self.eval_url: self.eval_url = self.eval_url.split('/chat/completions', 1)[0] @staticmethod def list_eval_dataset(eval_backend=None): from evalscope.api.registry import BENCHMARK_REGISTRY from evalscope.backend.opencompass import OpenCompassBackendManager from evalscope.constants import EvalBackend res = { EvalBackend.NATIVE: list(sorted(BENCHMARK_REGISTRY.keys())), EvalBackend.OPEN_COMPASS: sorted(OpenCompassBackendManager.list_datasets()), } try: from evalscope.backend.vlm_eval_kit import VLMEvalKitBackendManager vlm_datasets = VLMEvalKitBackendManager.list_supported_datasets() res[EvalBackend.VLM_EVAL_KIT] = sorted(vlm_datasets) except ImportError: # fix cv2 import error if eval_backend == 'VLMEvalKit': raise return res def _init_eval_dataset(self): if isinstance(self.eval_dataset, str): self.eval_dataset = [self.eval_dataset] all_eval_dataset = self.list_eval_dataset(self.eval_backend) dataset_mapping = {dataset.lower(): dataset for dataset in all_eval_dataset[self.eval_backend]} valid_dataset = [] for dataset in self.eval_dataset: if dataset.lower() not in dataset_mapping: raise ValueError( f'eval_dataset: {dataset} is not supported.\n' f'eval_backend: {self.eval_backend} supported datasets: {all_eval_dataset[self.eval_backend]}') valid_dataset.append(dataset_mapping[dataset.lower()]) self.eval_dataset = valid_dataset logger.info(f'eval_backend: {self.eval_backend}') logger.info(f'eval_dataset: {self.eval_dataset}') def _init_result_path(self, folder_name: str) -> None: self.time = dt.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f') result_dir = self.ckpt_dir or f'result/{self.model_suffix}' os.makedirs(result_dir, exist_ok=True) self.result_jsonl = to_abspath(os.path.join(result_dir, 'eval_result.jsonl')) if not self.eval_url: super()._init_result_path('eval_result') def _init_torch_dtype(self) -> None: if self.eval_url: self.model_meta = get_matched_model_meta(self.model) self.model_info = None return super()._init_torch_dtype()