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* Update CascadeEvaluator * Update CascadeEvaluator * Update CascadeEvaluator * Update Config * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update
221 lines
8.2 KiB
Python
221 lines
8.2 KiB
Python
import os
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import os.path as osp
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from copy import deepcopy
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from typing import Dict, List, Optional
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import mmengine
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from datasets import Dataset
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from mmengine.config import ConfigDict
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from opencompass.openicl.icl_evaluator import BaseEvaluator
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from opencompass.openicl.icl_inferencer import GenInferencer
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from opencompass.openicl.icl_retriever import ZeroRetriever
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from opencompass.registry import (DICT_POSTPROCESSORS, ICL_PROMPT_TEMPLATES,
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TEXT_POSTPROCESSORS)
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from opencompass.utils import build_dataset_from_cfg, build_model_from_cfg
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from opencompass.utils.logging import get_logger
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logger = get_logger(__name__)
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class GenericLLMEvaluator(BaseEvaluator):
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"""Generic LLM evaluator.
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Arguments:
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prompt_template (ConfigDict): The prompt template for evaluation.
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judge_cfg (ConfigDict): The config for Judge LLM.
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dataset_cfg (ConfigDict): The config for dataset.
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pred_postprocessor (ConfigDict): The config for postprocessor.
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used for the prediction results.
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dict_postprocessor (ConfigDict): The config for postprocessor,
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used for evaluation results dict.
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"""
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def __init__(
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self,
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prompt_template: ConfigDict,
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judge_cfg: ConfigDict,
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dataset_cfg: Optional[ConfigDict] = None,
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pred_postprocessor: Optional[ConfigDict] = None,
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dict_postprocessor: Optional[ConfigDict] = None,
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keep_predictions: bool = False,
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) -> None:
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super().__init__(pred_postprocessor=pred_postprocessor)
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# If judge_cfg is not provided, fall back to the default configuration
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if not judge_cfg:
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self.judge_cfg = self.default_judge_cfg
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else:
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self.judge_cfg = judge_cfg
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self.output_path = ''
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self.prompt_template = ICL_PROMPT_TEMPLATES.build(prompt_template)
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# Build Dataset
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self.dataset_cfg = dataset_cfg
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assert dataset_cfg is not None, 'dataset_cfg is None'
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self.dict_postprocessor = dict_postprocessor
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self.pred_postprocessor = pred_postprocessor
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def build_inferencer(self):
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"""Build LLM Inference."""
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self.output_path = f'{self._out_dir}_replica{self.dataset_replica_idx}.json' # noqa
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logger.info(f'LLM judge details will be saved at:{self.output_path}')
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out_dir, out_name = osp.split(self.output_path)
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logger.info(
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f'Set self.output_path to {self.output_path} for current task')
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assert self.output_path is not None, 'output_path is None'
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# Build LLM Inference
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max_out_len = self.judge_cfg.get('max_out_len', None)
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batch_size = self.judge_cfg.get('batch_size', None)
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model = build_model_from_cfg(model_cfg=self.judge_cfg)
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self.inferencer = GenInferencer(
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model,
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max_out_len=max_out_len,
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batch_size=batch_size,
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output_json_filepath=out_dir,
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output_json_filename=out_name,
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)
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def score(
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self,
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predictions,
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references: Optional[List] = None,
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test_set: Optional[Dataset] = None,
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) -> Dict:
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"""Apply to single-model scoring.
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Args:
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predictions: List of model predictions
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references: List of reference answers
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test_set: Optional Dataset containing additional
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context for evaluation
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"""
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assert len(predictions) == len(
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references), 'predictions and references must have the same length'
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# -------------- Build Inferencer ----------------
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self.build_inferencer()
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# ---------------- Process Predictions ------------------
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predictions = self.pred_postprocess(predictions)
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# For Single Round Dialogue
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prediction_dict = {'prediction': predictions, 'obj_gold': references}
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# ---------------- Build Dataset for LLM Judge -----------------
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if self.dataset_cfg:
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dataset = build_dataset_from_cfg(self.dataset_cfg)
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for k, v in prediction_dict.items():
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dataset.reader.dataset['test'] = dataset.test.add_column(k, v)
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dataset.reader.input_columns.append(k)
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if references:
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dataset.reader.input_columns.append('reference')
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dataset.reader.dataset['test'] = dataset.test.add_column(
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'reference', references)
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else:
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# Handle test_set in the else branch
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from opencompass.datasets.lmeval import LMEvalDataset
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if test_set is not None:
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# If test_set is provided, use it as the base
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# Ensure necessary columns exist
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if 'prediction' not in test_set.column_names:
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test_set = test_set.add_column('prediction', predictions)
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if 'reference' not in test_set.column_names:
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test_set = test_set.add_column('reference', references)
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# Prepare input_columns and data dictionary
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input_columns = test_set.column_names
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data_dict = {
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column: test_set[column]
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for column in test_set.column_names
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}
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else:
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# Original default dataset building logic
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input_columns = list(prediction_dict.keys())
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if references:
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input_columns.append('reference')
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data_dict = prediction_dict.copy()
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if references:
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data_dict['reference'] = references
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# Create LMEvalDataset
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dataset = LMEvalDataset(
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reader_cfg=dict(
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input_columns=input_columns,
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output_column=None,
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train_split='test',
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),
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**data_dict,
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)
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dataset.reader.output_column = 'reference'
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retriever = ZeroRetriever(dataset)
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# ----------------- LLM Judge ----------------
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self.inferencer.inference(retriever=retriever,
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prompt_template=self.prompt_template)
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output = mmengine.load(self.output_path)
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return self.output_postprocess(output, dataset)
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def pred_postprocess(self, predictions: List) -> Dict:
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if self.pred_postprocessor is None:
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return predictions
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else:
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kwargs = self.pred_postprocessor
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proc = TEXT_POSTPROCESSORS.get(kwargs.pop('type'))
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return [proc(pred, **kwargs) for pred in predictions]
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def output_postprocess(self, output: Dict, dataset=None) -> Dict:
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"""Postprocess output by adding necessary statistics or data into
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it."""
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import inspect
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if self.dict_postprocessor is None:
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return output
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else:
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kwargs = deepcopy(self.dict_postprocessor)
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proc = DICT_POSTPROCESSORS.get(kwargs.pop('type'))
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sig = inspect.signature(proc)
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if 'dataset' in sig.parameters:
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return proc(output,
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self.output_path,
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dataset=dataset,
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**kwargs)
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else:
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return proc(output, self.output_path, **kwargs)
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@property
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def default_judge_cfg(self):
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from opencompass.models import OpenAISDK
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logger.info('Please set your judge model in `OC_JUDGE_MODEL`, \
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`OC_JUDGE_API_KEY`, `OC_JUDGE_API_BASE` environment variables.')
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DEFAULT_JUDGE_CFG = dict(
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type=OpenAISDK,
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path=os.environ['OC_JUDGE_MODEL'],
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key=os.environ['OC_JUDGE_API_KEY'],
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openai_api_base=[
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os.environ.get('OC_JUDGE_API_BASE',
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'https://api.openai.com/v1/')
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],
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meta_template=dict(round=[
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dict(role='HUMAN', api_role='HUMAN'),
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dict(role='BOT', api_role='BOT', generate=True),
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], ),
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query_per_second=16,
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batch_size=1024,
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temperature=0.001,
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tokenizer_path='gpt-4o-2024-05-13',
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verbose=True,
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max_out_len=16384,
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max_seq_len=49152,
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)
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return DEFAULT_JUDGE_CFG
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