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# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
#
# 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.
import os
from dataclasses import dataclass, field
from typing import Literal, Optional
from ..trainer.utils import OnPolicyConfig
@dataclass
class PPOConfig(OnPolicyConfig):
r"""
Configuration class for the [`PPOTrainer`].
This class includes only the parameters that are specific to PPO training. For a full list of training arguments,
please refer to the [`~transformers.TrainingArguments`] and [`OnPolicyConfig`] documentation. Note that default
values in this class may differ from those in [`~transformers.TrainingArguments`].
Using [`~transformers.HfArgumentParser`] we can turn this class into
[argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
command line.
Parameters:
exp_name (`str`, *optional*, defaults to `os.path.basename(__file__)[:-3]`):
Name of this experiment.
reward_model_path (`str`, *optional*, defaults to `"EleutherAI/pythia-160m"`):
Path to the reward model.
model_adapter_name (`str` or `None`, *optional*, defaults to `None`):
Name of the train target PEFT adapter, when using LoRA with multiple adapters.
ref_adapter_name (`str` or `None`, *optional*, defaults to `None`):
Name of the reference PEFT adapter, when using LoRA with multiple adapters.
num_ppo_epochs (`int`, *optional*, defaults to `4`):
Number of epochs to train.
whiten_rewards (`bool`, *optional*, defaults to `False`):
Whether to whiten the rewards.
kl_coef (`float`, *optional*, defaults to `0.05`):
KL coefficient.
kl_estimator (`Literal["k1", "k3"]`, *optional*, defaults to `"k1"`):
Which estimator for KL-Divergence to use from [Approximating KL Divergence](http://joschu.net/blog/kl-approx.html).
Defaults to "k1", a straightforward, unbiased estimator. Can be set to "k3", an unbiased estimator with
lower variance which "appears to be a strictly better estimator". Cannot be set to "k2", as it is used for
logging purposes.
cliprange (`float`, *optional*, defaults to `0.2`):
Clip range.
vf_coef (`float`, *optional*, defaults to `0.1`):
Value function coefficient.
cliprange_value (`float`, *optional*, defaults to `0.2`):
Clip range for the value function.
gamma (`float`, *optional*, defaults to `1.0`):
Discount factor.
lam (`float`, *optional*, defaults to `0.95`):
Lambda value for GAE.
ds3_gather_for_generation (`bool`, *optional*, defaults to `True`):
This setting applies to DeepSpeed ZeRO-3. If enabled, the policy model weights are gathered for generation,
improving generation speed. However, disabling this option allows training models that exceed the VRAM
capacity of a single GPU, albeit at the cost of slower generation.
"""
exp_name: str = field(
default=os.path.basename(__file__)[:-3],
metadata={"help": "Name of this experiment."},
)
reward_model_path: str = field(
default="EleutherAI/pythia-160m",
metadata={"help": "Path to the reward model."},
)
model_adapter_name: Optional[str] = field(
default=None,
metadata={"help": "Name of the train target PEFT adapter, when using LoRA with multiple adapters."},
)
ref_adapter_name: Optional[str] = field(
default=None,
metadata={"help": "Name of the reference PEFT adapter, when using LoRA with multiple adapters."},
)
num_ppo_epochs: int = field(
default=4,
metadata={"help": "Number of epochs to train."},
)
whiten_rewards: bool = field(
default=False,
metadata={"help": "Whether to whiten the rewards."},
)
kl_coef: float = field(
default=0.05,
metadata={"help": "KL coefficient."},
)
kl_estimator: Literal["k1", "k3"] = field(
default="k1",
metadata={
"help": "Which estimator for KL-Divergence to use from Approximating KL Divergence "
"(http://joschu.net/blog/kl-approx.html). Defaults to 'k1', a straightforward, unbiased estimator. Can be "
"set to 'k3', an unbiased estimator with lower variance which 'appears to be a strictly better "
"estimator'. Cannot be set to 'k2', as it is used for logging purposes."
},
)
cliprange: float = field(
default=0.2,
metadata={"help": "Clip range."},
)
vf_coef: float = field(
default=0.1,
metadata={"help": "Value function coefficient."},
)
cliprange_value: float = field(
default=0.2,
metadata={"help": "Clip range for the value function."},
)
gamma: float = field(
default=1.0,
metadata={"help": "Discount factor."},
)
lam: float = field(
default=0.95,
metadata={"help": "Lambda value for GAE."},
)
ds3_gather_for_generation: bool = field(
default=True,
metadata={
"help": "This setting applies to DeepSpeed ZeRO-3. If enabled, the policy model weights are gathered for "
"generation, improving generation speed. However, disabling this option allows training models that "
"exceed the VRAM capacity of a single GPU, albeit at the cost of slower generation."
},
)
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