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#
# 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 Any
import torch
from ...protocol import DataProto
from .config import ActorConfig
__all__ = ["BasePPOActor"]
class BasePPOActor(ABC):
def __init__(self, config: ActorConfig):
"""The base class for PPO actor
Args:
config (ActorConfig): a config passed to the PPOActor.
"""
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[str, Any]:
"""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
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