wehub-resource-sync
60 lines
1.9 KiB
Python
60 lines
1.9 KiB
Python
from typing import Any, Dict, List
|
|
|
|
from swift.arguments import SamplingArguments
|
|
from swift.infer_engine import TransformersEngine
|
|
from swift.ray_utils import RayHelper
|
|
from swift.rewards import orms, prms
|
|
from swift.utils import get_logger
|
|
|
|
logger = get_logger()
|
|
|
|
|
|
class Sampler:
|
|
|
|
def __init__(self, input_args: SamplingArguments):
|
|
self.args = input_args
|
|
self.template = None
|
|
self.processor = None
|
|
self.prm_model = None
|
|
self.orm_model = None
|
|
self._prepare_model_tokenizer()
|
|
self._prepare_template()
|
|
self._prepare_prm()
|
|
self._prepare_orm()
|
|
|
|
def _prepare_model_tokenizer(self):
|
|
args = self.args
|
|
_, self.processor = args.get_model_processor(load_model=False)
|
|
|
|
@RayHelper.function(group='prm')
|
|
def _prepare_prm(self):
|
|
if self.args.prm_model is None:
|
|
self.prm_model = None
|
|
logger.warning('prm_model is None.')
|
|
elif self.args.prm_model in prms:
|
|
self.prm_model = prms[self.args.prm_model]()
|
|
else:
|
|
self.prm_model = TransformersEngine(self.args.prm_model, max_batch_size=64)
|
|
|
|
@RayHelper.function(group='orm')
|
|
def _prepare_orm(self):
|
|
if self.args.orm_model is None:
|
|
self.orm_model = None
|
|
logger.warning('orm_model is None.')
|
|
elif self.args.orm_model in orms:
|
|
self.orm_model = orms[self.args.orm_model]()
|
|
else:
|
|
self.orm_model = TransformersEngine(self.args.orm_model, max_batch_size=64)
|
|
|
|
def _prepare_template(self) -> None:
|
|
template = self.args.get_template(self.processor)
|
|
self.template = template
|
|
self.template.set_mode('train')
|
|
|
|
def truncate_input(self, slices: List[Dict[str, Any]]):
|
|
"""Truncate the input rows to avoid hitting the max length of the policy model"""
|
|
return slices
|
|
|
|
def do_sample(self, data):
|
|
raise NotImplementedError
|