| |
| 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) |
| |
| |
| 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): |
| |
| 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: |
| |
| 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() |
|
|
