lm_eval/api/filter.py (20 lines of code) (raw):
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import Callable, Iterable, List, Union
from lm_eval.api.instance import Instance
class Filter(ABC):
"""
Filter classes operate on a per-task level.
They take all model outputs (`instance.resps` for all `task.instances`)
across all instances of a task, and perform operations.
In a single run, one can configure any number of separate filters or lists of filters.
"""
def __init__(self, **kwargs) -> None:
"""
Can define custom behavior here, if an individual instantiation of a Filter class should have state.
"""
@abstractmethod
def apply(self, resps: Union[List, Iterable], docs: List[dict]) -> Iterable:
"""
Defines the operation to perform on a list of the `inst.resps` properties of `Instance` objects.
Should return the list of (filtered) response lists *in the same order as they were input*, e.g.
if pass in [<inst.resps for instance 0>, <inst.resps for instance 1>] should return
[<filtered resps for instance 0>, <filtered resps for instance 1>]
"""
return resps
@dataclass
class FilterEnsemble:
"""
FilterEnsemble creates a pipeline applying multiple filters.
Its intended usage is to stack multiple post-processing steps in order.
`task.apply_filters` should use a list of FilterEnsemble classes that it stores, to apply each
pipeline separately.
"""
name: str
filters: List[Callable[[], Filter]]
def apply(self, instances: List[Instance]) -> None:
resps, docs = zip(*((inst.resps, inst.doc) for inst in instances))
resps, docs = list(resps), list(docs)
for f in self.filters:
# apply filters in sequence
resps = f().apply(resps, docs)
# add the end results after filtering to filtered_requests of their respective source instances.
# has key `self.name`: each FilterEnsemble applied in a given run should use a different name.
for inst, resp in zip(instances, resps):
inst.filtered_resps[self.name] = resp