Initial commit

verl/workers/actor/base.py

@@ -0,0 +1,66 @@
+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+The base class for Actor
+"""
+from abc import ABC, abstractmethod
+from typing import Iterable, Dict
+
+from verl import DataProto
+import torch
+
+__all__ = ['BasePPOActor']
+
+
+class BasePPOActor(ABC):
+
+    def __init__(self, config):
+        """The base class for PPO actor
+
+        Args:
+            config (DictConfig): a config passed to the PPOActor. We expect the type to be
+                DictConfig (https://omegaconf.readthedocs.io/), but it can be any namedtuple in general.
+        """
+        super().__init__()
+        self.config = config
+
+    @abstractmethod
+    def compute_log_prob(self, data: DataProto) -> torch.Tensor:
+        """Compute logits given a batch of data.
+
+        Args:
+            data (DataProto): a batch of data represented by DataProto. It must contain key ```input_ids```,
+                ```attention_mask``` and ```position_ids```.
+
+        Returns:
+            DataProto: a DataProto containing the key ```log_probs```
+
+
+        """
+        pass
+
+    @abstractmethod
+    def update_policy(self, data: DataProto) -> Dict:
+        """Update the policy with an iterator of DataProto
+
+        Args:
+            data (DataProto): an iterator over the DataProto that returns by
+                ```make_minibatch_iterator```
+
+        Returns:
+            Dict: a dictionary contains anything. Typically, it contains the statistics during updating the model
+            such as ```loss```, ```grad_norm```, etc,.
+
+        """
+        pass