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# flake8: noqa: E501
import os.path as osp
import random
from typing import Dict, List, Optional
import mmengine
from datasets import Dataset
from mmengine.config import ConfigDict
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.registry import ICL_PROMPT_TEMPLATES
from opencompass.utils import build_dataset_from_cfg, build_model_from_cfg
from opencompass.utils.logging import get_logger
from opencompass.utils.text_postprocessors import first_number_postprocess
from opencompass.utils.types import get_type_from_cfg
def extract_dicts(data):
max_round_num = max(len(sublist) for sublist in data)
predictions = [[] for _ in range(max_round_num)]
for sublist in data:
for i, d in enumerate(sublist):
predictions[i].append(d.get('assistant'))
for j in range(i + 1, max_round_num):
predictions[j].append(None)
return predictions
def order_preds_and_record_references(predictions,
references,
infer_order,
seed=2680):
"""Order predictions based on args and recording regrading references.
Args:
predictions (List): List of multi model predictions.
references (List): List of reference based on each problem.
infer_order (str, optional): The mode of inference order.
seed (int, optional): Random seed.
"""
random.seed(seed)
list_of_preds = [[] for _ in range(len(predictions))]
for i in range(len(predictions[0]['model_preds'])):
preds = [[pred['model_preds'][i], pred['model_name']]
for pred in predictions]
if infer_order == 'random':
random.shuffle(preds)
for j in range(len(preds)):
list_of_preds[j].append(preds[j][0])
references[i][f'answer{j+1}'] = preds[j][1]
if infer_order == 'double':
assert len(predictions) == 2
list_of_preds = [
a + b for a, b in zip(list_of_preds, reversed(list_of_preds))
]
reversed_references = []
for item in references:
reversed_item = item.copy()
reversed_item['answer1'], reversed_item['answer2'] = reversed_item[
'answer2'], reversed_item['answer1']
reversed_references.append(reversed_item)
references += reversed_references
return list_of_preds, references
class LMEvaluator:
"""Evaluate output with language model.
Args:
prompt_template (ConfigDict): Prompt template configuration. Used to
prompt the language model for scores. User can use two reserved
keywords, ``{prediction}`` and ``{reference}``, referring to
the prediction and optionally the reference answer.
judge_cfg (ConfigDict): The config of language model as a judge.
output_path (str): The path to prediction output.
dataset_cfg (ConfigDict, optional): The config of the dataset to be
evaluated.
postprocessor (ConfigDict): The model prediction's postprocessor
config.
"""
def __init__(
self,
prompt_template: ConfigDict,
judge_cfg: ConfigDict,
output_path: str,
infer_order: Optional[str] = 'random',
dataset_cfg: Optional[ConfigDict] = None,
postprocessor: ConfigDict = dict(type=first_number_postprocess)
) -> None:
assert infer_order in ['random', 'double']
self.output_path = output_path
out_dir, out_name = osp.split(output_path)
if not out_dir:
out_dir = './'
self.prompt_tmpl = ICL_PROMPT_TEMPLATES.build(prompt_template)
max_out_len = judge_cfg.get('max_out_len', None)
batch_size = judge_cfg.get('batch_size', None)
model = build_model_from_cfg(model_cfg=judge_cfg)
self.inferencer = GenInferencer(model,
max_out_len=max_out_len,
batch_size=batch_size,
output_json_filepath=out_dir,
output_json_filename=out_name)
self.postprocessor = get_type_from_cfg(postprocessor)
self.logger = get_logger()
self.dataset_cfg = dataset_cfg
self.infer_order = infer_order
def score(self, predictions, references: Optional[List] = None) -> Dict:
dup_indices = []
if type(predictions) == list:
"""Apply to multi-model comparison."""
references = [{} for _ in range(len(predictions[0]['model_preds']))
] if references is None else references
predictions, references = order_preds_and_record_references(
predictions, references, self.infer_order)
# calculate dupicated predictions numbers
total_predictions_num = len(predictions[0])
# since there is impossible that two models response same pattern in multi-round chat, so we just check dup for single chat
if isinstance(predictions[0][0], str):
for i in range(len(predictions[0])):
check = [sub[i] for sub in predictions]
if len(set(check)) == 1:
dup_indices.append(i)
elif type(predictions) == dict:
"""Apply to single-model scoring."""
references = [{} for _ in range(len(predictions[0]['model_preds']))
] if references is None else references
predictions = [predictions['model_preds']]
if len(dup_indices) != 0:
# remove dupicated predictions
for index in sorted(dup_indices, reverse=True):
for sublist in predictions:
del sublist[index]
del references[index]
pred_dict = {}
if isinstance(
predictions[0][0], str
): #single chat for format like [['xxx', 'xxxx'], ['xxx', 'xxxx']]
for i in range(len(predictions)):
key = 'prediction' if i == 0 else f'prediction{i + 1}'
pred_dict[key] = predictions[i]
elif isinstance(
predictions[0][0], list
): #multi round for format like [[[{'round':1, 'user':'', 'assistant':''}, {'round':2, 'user':'', 'assistant':''}], [{'round':1, 'user':'', 'assistant':''}, {'round':2, 'user':'', 'assistant':''}]]]
for i in range(len(predictions)):
multiround_predictions = extract_dicts(predictions[i])
for j in range(len(multiround_predictions)):
key = 'prediction' if i == 0 else f'prediction{i}'
key += '_r' + str(j + 1)
pred_dict[key] = multiround_predictions[j]
if self.dataset_cfg:
dataset = build_dataset_from_cfg(self.dataset_cfg)
if self.infer_order == 'double':
new_ds = {
k: dataset.test[k] * 2
for k in dataset.test.column_names
}
dataset.reader.dataset['test'] = Dataset.from_dict(new_ds)
if len(dup_indices) != 0:
remaining_indices = [
idx for idx in range(len(dataset.test))
if idx not in dup_indices
]
dataset.reader.dataset['test'] = dataset.test.select(
remaining_indices)
print(
f'Among total {total_predictions_num} predictions, there are {len(dup_indices)} predictions totally same, which are removed!'
)
for k, v in pred_dict.items():
dataset.reader.dataset['test'] = dataset.test.add_column(k, v)
dataset.reader.input_columns.append(k)
if references:
dataset.reader.input_columns.append('reference')
dataset.reader.dataset['test'] = dataset.test.add_column(
'reference', references)
else:
# build a default dataset just for comparison
from opencompass.datasets.lmeval import LMEvalDataset
input_columns = list(pred_dict.keys())
if references:
input_columns.append('reference')
dataset = LMEvalDataset(reader_cfg=dict(
input_columns=input_columns,
output_column=None,
train_split='test'),
reference=references,
**pred_dict)
dataset.reader.output_column = 'reference'
retriever = ZeroRetriever(dataset)
self.inferencer.inference(retriever=retriever,
prompt_template=self.prompt_tmpl)
output = mmengine.load(self.output_path)
return self.postprocess(output)
def postprocess(self, output: Dict) -> Dict:
"""Postprocess output by adding necessary statistics or data into
it."""
return output
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