chore: import upstream snapshot with attribution

2026-07-13 13:34:58 +08:00
commit a203934033
1368 changed files with 175001 additions and 0 deletions
@@ -0,0 +1,59 @@
+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