482 lines
22 KiB
Python
482 lines
22 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import json
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import os
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import time
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from collections import OrderedDict, defaultdict
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from copy import deepcopy
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from tqdm import tqdm
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from typing import Any, Dict, List, Optional, Union
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from evalscope.benchmarks import DataAdapter
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from evalscope.config import TaskConfig
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from evalscope.constants import AnswerKeys, DumpMode, EvalStage, EvalType, JudgeStrategy, ReviewKeys
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from evalscope.models import BaseModelAdapter
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from evalscope.report import Report, gen_table
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from evalscope.utils.io_utils import OutputsStructure, dump_jsonl_data, gen_hash, jsonl_to_list
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from evalscope.utils.logger import get_logger
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from evalscope.utils.model_utils import dict_torch_dtype_to_str
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logger = get_logger()
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class Evaluator(object):
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"""
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The evaluator for model on datasets.
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Args:
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dataset_name_or_path: str, the dataset name or path.
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if the dataset is a local path, e.g. /path/to/your_dataset_name,
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then the task name will be the basename of the path, which is `your_dataset_name`.
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data_adapter: DataAdapter, the data adapter for the dataset.
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model_adapter: BaseModelAdapter, the model adapter for the model.
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outputs: OutputsStructure, the outputs dir. Default: None
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task_cfg: TaskConfig, the overall task config. Default: None
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**kwargs: kwargs.
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"""
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def __init__(self,
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data_adapter: DataAdapter,
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model_adapter: BaseModelAdapter,
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outputs: OutputsStructure = None,
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task_cfg: TaskConfig = None,
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**kwargs):
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self.dataset_name = data_adapter.name
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self.dataset_name_or_path = os.path.expanduser(data_adapter.dataset_id)
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self.model_name = task_cfg.model_id
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self.data_adapter = data_adapter
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self.model_adapter = model_adapter
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self.model_cfg = model_adapter.model_cfg
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self.eval_type = task_cfg.eval_type
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self.dataset_hub = task_cfg.dataset_hub
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self.stage = task_cfg.stage
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self.use_cache = task_cfg.use_cache
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self.task_cfg = task_cfg
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# Deal with the output paths
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self.outputs_structure = outputs
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self.kwargs = kwargs
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self._init_judge()
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def _init_judge(self):
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if self.task_cfg.judge_strategy == JudgeStrategy.RULE:
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self.judge = None
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else:
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from evalscope.metrics import LLMJudge
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self.judge = LLMJudge(**self.task_cfg.judge_model_args)
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def load_dataset(self):
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dataset = self.data_adapter.load(
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work_dir=os.path.expanduser(self.task_cfg.dataset_dir), datasets_hub=self.dataset_hub, **self.kwargs)
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# Get prompts from dataset
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prompts = self.data_adapter.gen_prompts(data_dict=dataset)
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# Limit and index prompts
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limited_prompts = defaultdict(list)
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for subset_name, prompts_list in prompts.items():
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# If limit is None, use all prompts
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if self.task_cfg.limit is None:
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limit = len(prompts_list)
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else:
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if isinstance(self.task_cfg.limit, int):
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limit = self.task_cfg.limit
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elif isinstance(self.task_cfg.limit, float):
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limit = int(len(prompts_list) * self.task_cfg.limit)
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# Limit the number of prompts
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for index, prompt in enumerate(prompts_list[:min(limit, len(prompts_list))]):
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prompt[AnswerKeys.INDEX] = index
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limited_prompts[subset_name].append(prompt)
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return limited_prompts
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def _generate_answer_id(self, model_cfg, input_d, infer_cfg):
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model_cfg_str = json.dumps(OrderedDict(sorted(dict_torch_dtype_to_str(model_cfg).items())), ensure_ascii=False)
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input_prompt_str = json.dumps(OrderedDict(sorted(dict_torch_dtype_to_str(input_d).items())), ensure_ascii=False)
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infer_cfg_str = json.dumps(OrderedDict(sorted(dict_torch_dtype_to_str(infer_cfg).items())), ensure_ascii=False)
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return 'answer-' + gen_hash(model_cfg_str + input_prompt_str + infer_cfg_str)
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def _process_answer(self, answer_d, input_d, subset_name, answer_id):
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answer_d[AnswerKeys.MODEL_SPEC] = self.model_adapter.model_cfg
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answer_d[AnswerKeys.ANSWER_ID] = answer_id
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answer_d[AnswerKeys.SUBSET_NAME] = subset_name
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answer_d[AnswerKeys.RAW_INPUT] = input_d[AnswerKeys.RAW_INPUT]
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answer_d[AnswerKeys.INDEX] = input_d[AnswerKeys.INDEX]
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return answer_d
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def _get_answer(self, input_prompts, subset_name, infer_cfg) -> List[dict]:
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try:
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# get answer from model
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answer_ds: List[dict] = self.model_adapter.predict(inputs=input_prompts, infer_cfg=infer_cfg)
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except Exception as e:
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logger.error(f'Failed to get answer for {input_prompts}, due to {e}')
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# if ignore_errors is True, continue to next input
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if self.task_cfg.ignore_errors:
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logger.warning('`ignore_errors` is set to True. Dropping this prompt and continuing with evaluation.')
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return []
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else:
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raise e
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# process answer
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answers_list = []
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for answer_d, input_prompt in zip(answer_ds, input_prompts):
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answer_id = self._generate_answer_id(self.model_adapter.model_cfg, input_prompt, infer_cfg)
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processed_answer = self._process_answer(answer_d, input_prompt, subset_name, answer_id)
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answers_list.append(processed_answer)
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return answers_list
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@staticmethod
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def filter_answer(use_cache, prompts_list, pred_file_path) -> dict:
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# Filter prompts that have been answered
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answers_list = []
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if not use_cache or not os.path.exists(pred_file_path):
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return answers_list, prompts_list
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def get_answered_indices(answers_list: List[Dict]) -> List[int]:
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indices = [answer.get(AnswerKeys.INDEX) for answer in answers_list]
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if all(index is None for index in indices):
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return list(range(len(answers_list)))
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return [index for index in indices if index is not None]
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answers_list = jsonl_to_list(pred_file_path)
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answered_indices = set(get_answered_indices(answers_list))
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logger.info(f'Reusing predictions from {pred_file_path}, got {len(answered_indices)} answers.')
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prompts = [prompt for i, prompt in enumerate(prompts_list) if i not in answered_indices]
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return answers_list, prompts
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def get_answers(self, subset_name: str, prompts_list: List[dict], infer_cfg: dict = None, **kwargs) -> list:
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"""
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Get answers from model inference.
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It is required to rewrite this method to support your own evaluator.
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Args:
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subset_name: subset name for benchmark.
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prompts_list: prompts list.
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infer_cfg: model inference config.
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Attributes:
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do_sample: bool, whether to use sampling.
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top_k: int, the number of highest probability vocabulary tokens to keep for top-k-filtering.
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top_p: float, if set to float < 1, only the most probable tokens with probabilities to add.
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temperature: float, the value used to module the next token probabilities.
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num_beams: int, number of beams for beam search. 1 means no beam search.
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max_length: int, the max length of the sequence to be generated.
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max_new_tokens: int, the max number of new tokens to be generated.
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repetition_penalty: float, the parameter for repetition penalty. 1.0 means no penalty.
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**kwargs: kwargs.
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Returns: The list of answers.
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"""
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assert self.data_adapter is not None, 'data_adapter must be provided when calling func get_answers() !'
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assert self.model_adapter is not None, 'model must be provided when calling func get_answers() !'
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assert len(prompts_list) > 0, 'prompts_list must not be empty when calling func get_answers() !'
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pred_file_name = self.dataset_name + '_' + subset_name + '.jsonl'
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pred_file_path = os.path.join(self.outputs_structure.predictions_dir, self.model_name, pred_file_name)
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os.makedirs(os.path.dirname(pred_file_path), exist_ok=True)
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answers_list, prompts_list = Evaluator.filter_answer(self.use_cache, prompts_list, pred_file_path)
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eval_batch_size = self.task_cfg.eval_batch_size
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if self.task_cfg.eval_type == EvalType.SERVICE:
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with tqdm(total=len(prompts_list), desc=f'Predicting({subset_name}): ') as pbar:
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with ThreadPoolExecutor(max_workers=eval_batch_size) as executor:
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futures = []
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for input_prompt in prompts_list:
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futures.append(executor.submit(self._get_answer, [input_prompt], subset_name, infer_cfg))
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for future in as_completed(futures):
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answer_ds: List[dict] = future.result()
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answers_list.extend(answer_ds)
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dump_jsonl_data(answer_ds, pred_file_path, dump_mode=DumpMode.APPEND)
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pbar.update(len(answer_ds))
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else:
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batch_prompts_list = [
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prompts_list[i:i + eval_batch_size] for i in range(0, len(prompts_list), eval_batch_size)
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]
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with tqdm(total=len(prompts_list), desc=f'Predicting({subset_name}): ') as pbar:
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for batch_prompts in batch_prompts_list:
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answer_ds: List[dict] = self._get_answer(
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input_prompts=batch_prompts, subset_name=subset_name, infer_cfg=infer_cfg)
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answers_list.extend(answer_ds)
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dump_jsonl_data(answer_ds, pred_file_path, dump_mode=DumpMode.APPEND)
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pbar.update(len(batch_prompts))
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logger.info(f'Dump predictions to {pred_file_path}.')
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return answers_list
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def _get_review(self, answer_d: dict, review_id: str = None, reviewer_spec: dict = None) -> dict:
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if reviewer_spec is None:
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reviewer_spec = {}
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review_res = deepcopy(answer_d)
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if AnswerKeys.CHOICES not in review_res:
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review_res[AnswerKeys.CHOICES] = []
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review_res[ReviewKeys.REVIEWED] = True
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review_res[ReviewKeys.REVIEW_ID] = None
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review_res[ReviewKeys.REVIEWER_SPEC] = reviewer_spec
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review_res[ReviewKeys.REVIEW_TIME] = time.time()
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logger.warning(f'No choices found for answer dict: {review_res}')
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return review_res
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rev_choices = []
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for choice in review_res[AnswerKeys.CHOICES]:
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raw_input_d: dict = review_res[AnswerKeys.RAW_INPUT]
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answer_content = choice[ReviewKeys.MESSAGE][ReviewKeys.CONTENT]
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gold_content = self.data_adapter.get_gold_answer(raw_input_d)
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# Get review result based on judge strategy
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use_llm = (
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self.task_cfg.judge_strategy == JudgeStrategy.LLM
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or (self.task_cfg.judge_strategy == JudgeStrategy.AUTO and self.data_adapter.llm_as_a_judge))
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if use_llm:
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# Use LLM as judge
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assert self.judge is not None, f'Judge model is required for LLM judging {self.data_adapter.name}'
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pred_content = self.data_adapter.llm_parse_pred_result(
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result=answer_content, raw_input_d=raw_input_d, eval_type=self.eval_type)
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review_result = self.data_adapter.llm_match(
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gold_content, pred_content, self.judge, raw_input=raw_input_d)
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else:
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# Use rule-based judging
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pred_content = self.data_adapter.parse_pred_result(
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result=answer_content, raw_input_d=raw_input_d, eval_type=self.eval_type)
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review_result = self.data_adapter.match(gold_content, pred_content)
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# For LLM_RECALL strategy, use LLM to re-judge if rule-based result is not good
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if (self.task_cfg.judge_strategy == JudgeStrategy.LLM_RECALL
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and isinstance(review_result, (bool, int, float)) and not bool(review_result)):
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assert self.judge is not None, f'Judge model is required for LLM_RECALL strategy {self.data_adapter.name}' # noqa: E501
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pred_content = self.data_adapter.llm_parse_pred_result(
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result=answer_content, raw_input_d=raw_input_d, eval_type=self.eval_type)
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review_result = self.data_adapter.llm_match(
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gold_content, pred_content, self.judge, raw_input=raw_input_d)
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choice[ReviewKeys.REVIEW] = {
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ReviewKeys.GOLD: gold_content if gold_content != raw_input_d else '*Same as Input*',
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ReviewKeys.PRED: pred_content,
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ReviewKeys.RESULT: review_result
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}
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rev_choices.append(choice)
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review_res[AnswerKeys.CHOICES] = rev_choices
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review_res[ReviewKeys.REVIEWED] = True
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review_res[ReviewKeys.REVIEW_ID] = review_id
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review_res[ReviewKeys.REVIEWER_SPEC] = reviewer_spec
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review_res[ReviewKeys.REVIEW_TIME] = time.time()
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return review_res
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def _generate_review_id(self, answer_d):
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# Gen review_id (concat: answer_id + reviewer_spec)
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answer_id = answer_d[AnswerKeys.ANSWER_ID]
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reviewer_spec = {'metric': self.data_adapter.metric_list, 'reviewer': ['Evaluator'], 'revision': ['default']}
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reviewer_spec_str = json.dumps(
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OrderedDict(sorted(dict_torch_dtype_to_str(reviewer_spec).items())), ensure_ascii=False)
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review_id = 'review-' + gen_hash(answer_id + reviewer_spec_str)
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return review_id, reviewer_spec
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def get_reviews(self, subset_name: str, answers_list: List[dict], **kwargs) -> list:
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"""
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Get reviews from answers.
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It is required to rewrite this method to support your own evaluator.
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Args:
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subset_name: subset name of benchmark
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answers_list: inference results list.
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**kwargs: kwargs.
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Returns: reviews list.
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"""
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reviews_list = []
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review_file_name = self.dataset_name + '_' + subset_name + '.jsonl'
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review_file_path = os.path.join(self.outputs_structure.reviews_dir, self.model_name, review_file_name)
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os.makedirs(os.path.dirname(review_file_path), exist_ok=True)
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# Load existing reviews if using cache
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existing_reviews = {}
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if self.use_cache and os.path.exists(review_file_path):
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with open(review_file_path, 'r') as f:
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for line in f:
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review = json.loads(line.strip())
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existing_reviews[review['index']] = review
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logger.info(f'Reusing review result from {review_file_path}, got {len(existing_reviews)} reviews.')
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def process_single_review(answer_d):
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# Check if review already exists in cache
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if self.use_cache and answer_d['index'] in existing_reviews:
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return existing_reviews[answer_d['index']]
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review_id, reviewer_spec = self._generate_review_id(answer_d)
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# Get review
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review_d = self._get_review(answer_d=answer_d, review_id=review_id, reviewer_spec=reviewer_spec)
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logger.debug(review_d)
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return review_d
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with ThreadPoolExecutor(max_workers=self.task_cfg.judge_worker_num) as executor:
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# Submit all tasks and get futures
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futures = [executor.submit(process_single_review, answer_d) for answer_d in answers_list]
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# Process completed futures with progress bar
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for future in tqdm(as_completed(futures), total=len(futures), desc=f'Reviewing({subset_name}): '):
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review_d = future.result()
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reviews_list.append(review_d)
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# Dump new reviews only if not using cache or review is new
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if not self.use_cache or review_d['index'] not in existing_reviews:
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dump_jsonl_data(review_d, review_file_path, dump_mode=DumpMode.APPEND)
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return reviews_list
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def compute_metrics(self, reviews_list: List[dict]) -> List[dict]:
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"""
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To compute metrics from reviews_list for each subset.
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It is required to rewrite this method to support your own evaluator.
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Args:
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reviews_list: reviews list.
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Returns:
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The metric result. Depends on the metric function in data_adapter.
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"""
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# Get max choices
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choices_lengths = [
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len(review_d[AnswerKeys.CHOICES]) for review_d in reviews_list if review_d.get(ReviewKeys.REVIEWED)
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]
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if choices_lengths:
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max_choices = max(choices_lengths)
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else:
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max_choices = 0
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# Get review result
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review_res_list = []
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for review_d in reviews_list:
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if not review_d[ReviewKeys.REVIEWED]:
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logger.warning(f'Review not finished for answer_id: {review_d[AnswerKeys.ANSWER_ID]}, skipping ...')
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continue
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if len(review_d[AnswerKeys.CHOICES]) == 0:
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logger.warning(f'No choices found for answer_id: {review_d[AnswerKeys.ANSWER_ID]}, skipping ...')
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continue
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elif len(review_d[AnswerKeys.CHOICES]) == 1 and max_choices == 1:
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review_res = review_d[AnswerKeys.CHOICES][0][ReviewKeys.REVIEW][ReviewKeys.RESULT]
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else:
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review_res = [choice[ReviewKeys.REVIEW][ReviewKeys.RESULT] for choice in review_d[AnswerKeys.CHOICES]]
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if len(review_d[AnswerKeys.CHOICES]) < max_choices:
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logger.warning(
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f'Less choices found for answer_id: {review_d[AnswerKeys.ANSWER_ID]}, '
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f'max_choices is {max_choices}, but only {len(review_d[AnswerKeys.CHOICES])} choices found')
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review_res_list.append(review_res)
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metric_score: List[dict] = self.data_adapter.compute_metric(
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review_res_list=review_res_list, reviews_list=reviews_list)
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return metric_score
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def dump_report(self, reviews_score_all: List[dict]):
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"""
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Get report for total reviews of specific dataset.
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It is required to rewrite this method to support your own evaluator.
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Args:
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reviews_score_all: reviews score list. Generated by func self.data_adapter.compute_metric().
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Returns: None
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"""
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report_path = os.path.join(self.outputs_structure.reports_dir, self.model_name)
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os.makedirs(report_path, exist_ok=True)
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# Get report map
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report_map: Report = self.data_adapter.gen_report(
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subset_score_map=reviews_score_all, model_name=self.model_name)
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# Make table
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try:
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report_table = gen_table(report_list=[report_map], add_overall_metric=True)
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logger.info(f'\n{self.dataset_name_or_path} report table:'
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f'\n{report_table} \n')
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except Exception:
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logger.error('Failed to generate report table.')
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# Make report analysis
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if self.task_cfg.analysis_report:
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logger.info('Generating report analysis, please wait ...')
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analysis = report_map.generate_analysis(self.task_cfg.judge_model_args)
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logger.info('Report analysis:\n%s', analysis)
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else:
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logger.info('Skipping report analysis (`analysis_report=False`).')
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# Dump report
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report_file = os.path.join(report_path, f'{self.dataset_name}.json')
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report_map.to_json(report_file)
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logger.info(f'Dump report to: {report_file} \n')
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# Post process report
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try:
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|
self.data_adapter.post_process_report(report_map, report_path=report_path)
|
|
except Exception as e:
|
|
logger.error(f'Failed to post process report: {e}')
|
|
|
|
return report_map
|
|
|
|
def eval(self, **kwargs) -> dict:
|
|
"""
|
|
Evaluate the model on the specific benchmark. Streaming & parallel mode is supported.
|
|
It is required to rewrite this method to support your own evaluator.
|
|
|
|
The evaluation process is as follows:
|
|
1. Get the input samples from the dataset (benchmarks on the ModelScope or HuggingFace).
|
|
2. Get the input prompts from dataset with specific data adapter.
|
|
3. Get answers with model inference.
|
|
4. Get reviews with metric function (or reviewers).
|
|
5. Generate report from review results.
|
|
|
|
Args:
|
|
infer_cfg: The config for model inference.
|
|
|
|
Returns:
|
|
Dict of results. Depends on the stage of evaluation.
|
|
|
|
stage == 'all': return the report_map
|
|
stage == 'infer': return the answers_map
|
|
stage == 'review': return the reviews_map
|
|
"""
|
|
|
|
logger.info(f'Start evaluating on dataset {self.dataset_name_or_path}')
|
|
|
|
reviews_score_all = {} # {subset_name: (score, num)}
|
|
stage_answers_dict = {}
|
|
stage_reviews_dict = {}
|
|
|
|
prompts = self.load_dataset()
|
|
for subset_name, prompts_list in prompts.items():
|
|
|
|
answers_list: list = self.get_answers(
|
|
subset_name=subset_name, prompts_list=prompts_list, infer_cfg=self.task_cfg.generation_config, **kwargs)
|
|
if self.stage == EvalStage.INFER:
|
|
stage_answers_dict[subset_name] = answers_list
|
|
continue
|
|
|
|
reviews_list: list = self.get_reviews(subset_name=subset_name, answers_list=answers_list, **kwargs)
|
|
|
|
metric_res = self.compute_metrics(reviews_list=reviews_list)
|
|
reviews_score_all[subset_name] = metric_res
|
|
stage_reviews_dict[subset_name] = reviews_list
|
|
|
|
if self.stage == EvalStage.INFER:
|
|
return stage_answers_dict
|
|
|
|
if self.stage == EvalStage.REVIEW:
|
|
return stage_reviews_dict
|
|
|
|
# Generate report
|
|
report_map = self.dump_report(reviews_score_all)
|
|
|
|
logger.info(f'Evaluation finished on {self.dataset_name_or_path}')
|
|
|
|
return report_map
|