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