| from typing import Any, Dict, List |
|
|
| from swift.arguments import SamplingArguments |
| from swift.infer_engine import TransformersEngine |
| from swift.ray.base 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 |
|
|
