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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.
from dataclasses import dataclass, field
from typing import Any, Optional
from transformers import TrainingArguments
@dataclass
class OnlineDPOConfig(TrainingArguments):
r"""
Configuration class for the [`OnlineDPOTrainer`].
This class includes only the parameters that are specific to Online DPO training. For a full list of training
arguments, please refer to the [`~transformers.TrainingArguments`] 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:
reward_model_path (`str` or `None`, *optional*, defaults to `None`):
Path to the reward model. Either `judge` or `reward_model_path` must be set, but not both.
judge (`str` or `None`, *optional*, defaults to `None`):
Name of the judge to use. Either `judge` or `reward_model_path` must be set, but not both.
max_new_tokens (`int`, *optional*, defaults to `64`):
Maximum number of tokens to generate per completion.
max_length (`int`, *optional*, defaults to `256`):
Maximum total length of the sequence (prompt + completion) used to compute log probabilities. If the
sequence exceeds this limit, the leftmost tokens will be truncated to preserve as much of the completion as
possible.
temperature (`float`, *optional*, defaults to `0.9`):
Temperature for sampling. The higher the temperature, the more random the completions.
missing_eos_penalty (`float` or `None`, *optional*, defaults to `None`):
Penalty applied to the score when the model fails to generate an EOS token. This is useful to encourage to
generate completions shorter than the maximum length (`max_new_tokens`). The penalty must be a positive
value.
beta (`float` or `list[float]`, *optional*, defaults to `0.1`):
Parameter controlling the deviation from the reference model. Higher β means less deviation from the
reference model. For the IPO loss (`loss_type="ipo"`), β is the regularization parameter denoted by τ in
the [paper](https://huggingface.co/papers/2310.12036). If a list of floats is provided then the β is
selected for each new epoch and the last β is used for the rest of the epochs.
loss_type (`str`, *optional*, defaults to `"sigmoid"`):
Type of loss to use. Possible values are:
- `"sigmoid"`: sigmoid loss from the original [DPO](https://huggingface.co/papers/2305.18290) paper.
- `"ipo"`: IPO loss from the [IPO](https://huggingface.co/papers/2310.12036) paper.
dataset_num_proc (`int` or `None`, *optional*, defaults to `None`):
Number of processes to use for processing the dataset.
disable_dropout (`bool`, *optional*, defaults to `True`):
Whether to disable dropout in the model and reference model.
use_vllm (`bool`, *optional*, defaults to `False`):
Whether to use vLLM for generating completions. Requires vLLM to be installed (`pip install vllm`).
vllm_model_impl (`str`, *optional*, defaults to `"vllm"`):
Model implementation to use for vLLM. Must be one of `"transformers"` or `"vllm"`. `"transformers"`: Use
the `transformers` backend for model implementation. `"vllm"`: Use the `vllm` library for model
implementation.
gpu_memory_utilization (`float`, *optional*, defaults to `0.55`):
The vLLM memory utilization. The default value is 0.55.
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.
model_init_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`):
Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the model from a
string.
"""
# Parameters whose default values are overridden from TrainingArguments
learning_rate: float = field(
default=5e-7,
metadata={"help": "The initial learning rate for AdamW."},
)
logging_steps: float = field(
default=10,
metadata={
"help": "Log every X updates steps. Should be an integer or a float in range `[0,1)`. If smaller than 1, "
"will be interpreted as ratio of total training steps."
},
)
bf16: Optional[bool] = field(
default=None,
metadata={
"help": "Whether to use bf16 (mixed) precision instead of 32-bit. Requires Ampere or higher NVIDIA "
"architecture or Intel XPU or using CPU (use_cpu) or Ascend NPU. If not set, it defaults to `True` if "
"`fp16` is not set."
},
)
reward_model_path: Optional[str] = field(
default=None,
metadata={
"help": "Path to the reward model. Either `judge` or `reward_model_path` must be set, but not both."
},
)
judge: Optional[str] = field(
default=None,
metadata={
"help": "Name of the judge to use. Either `judge` or `reward_model_path` must be set, but not both."
},
)
max_new_tokens: int = field(
default=64,
metadata={"help": "Maximum number of tokens to generate per completion."},
)
max_length: int = field(
default=512,
metadata={
"help": "Maximum total length of the sequence (prompt + completion) used to compute log probabilities. If "
"the sequence exceeds this limit, the leftmost tokens will be truncated to preserve as much of the "
"completion as possible."
},
)
temperature: float = field(
default=0.9,
metadata={"help": "Temperature for sampling. The higher the temperature, the more random the completions."},
)
missing_eos_penalty: Optional[float] = field(
default=None,
metadata={
"help": "Penalty applied to the score when the model fails to generate an EOS token. This is useful to "
"encourage to generate completions shorter than the maximum length (`max_new_tokens`). The penalty must be "
"a positive value."
},
)
beta: list[float] = field(
default_factory=lambda: [0.1],
metadata={
"help": "Parameter controlling the deviation from the reference model. Higher β means less deviation from "
"the reference model. For the IPO loss (`loss_type='ipo'`), β is the regularization parameter denoted by "
"τ in the [paper](https://huggingface.co/papers/2310.12036). If a list of floats is provided then the β "
"is selected for each new epoch and the last β is used for the rest of the epochs."
},
)
loss_type: str = field(
default="sigmoid",
metadata={
"help": "Type of loss to use.",
"choices": ["sigmoid", "ipo"],
},
)
dataset_num_proc: Optional[int] = field(
default=None,
metadata={"help": "Number of processes to use for processing the dataset."},
)
disable_dropout: bool = field(
default=True,
metadata={"help": "Whether to disable dropout in the model."},
)
use_vllm: bool = field(
default=False,
metadata={
"help": "Whether to use vLLM for generating completions. Requires vLLM to be installed "
"(`pip install vllm`)."
},
)
vllm_model_impl: str = field(
default="vllm",
metadata={
"help": "Model implementation to use for vLLM. Must be one of `transformers` or `vllm`. `transformers`: "
"Use the `transformers` backend for model implementation. `vllm`: Use the `vllm` library for "
"model implementation."
},
)
gpu_memory_utilization: Optional[float] = field(
default=0.55,
metadata={
"help": "The vLLM memory utilization. The default value is 0.55.",
},
)
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."
},
)
model_init_kwargs: Optional[dict[str, Any]] = field(
default=None,
metadata={
"help": "Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the model "
"from a string."
},
)
def __post_init__(self):
self.bf16 = not (self.fp16) if self.bf16 is None else self.bf16
super().__post_init__()
if hasattr(self.beta, "__len__") and len(self.beta) == 1:
self.beta = self.beta[0]
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