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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 Optional
from transformers import TrainingArguments
@dataclass
class PRMConfig(TrainingArguments):
r"""
Configuration class for the [`PRMTrainer`].
This class includes only the parameters that are specific to PRM 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:
max_length (`int` or `None`, *optional*, defaults to `1024`):
Maximum length of the sequences (prompt + completion) used for truncation.
max_prompt_length (`int` or `None`, *optional*, defaults to `512`):
Maximum length of the prompt used for truncation.
max_completion_length (`int` or `None`, *optional*, defaults to `None`):
Maximum length of the completion used for truncation. The completion is the concatenation of the steps.
disable_dropout (`bool`, *optional*, defaults to `True`):
Whether to disable dropout in the model.
step_separator (`str`, *optional*, defaults to `"\n"`):
Separator used to separate each step of the reasoning process.
train_on_last_step_only (`bool`, *optional*, defaults to `False`):
Whether to train only on the last step.
dataset_num_proc (`int`, *optional*, defaults to `None`):
Number of processes to use for processing the dataset.
"""
# Parameters whose default values are overridden from TrainingArguments
learning_rate: float = field(
default=1e-5,
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: bool = field(
default=True,
metadata={
"help": (
"Whether to use bf16 (mixed) precision instead of 32-bit. Requires Ampere or higher NVIDIA "
"architecture or using CPU (use_cpu) or Ascend NPU. This is an experimental API and it may change."
)
},
)
average_tokens_across_devices: bool = field(
default=True,
metadata={
"help": "Whether or not to average tokens across devices. If enabled, will use all_reduce to synchronize "
"num_tokens_in_batch for precise loss calculation. Reference: https://github.com/huggingface/transformers/issues/34242 "
},
)
max_length: Optional[int] = field(
default=1024,
metadata={"help": "Maximum length of the sequences (prompt + completion) used for truncation."},
)
max_prompt_length: Optional[int] = field(
default=512,
metadata={"help": "Maximum length of the prompt used for truncation."},
)
max_completion_length: Optional[int] = field(
default=None,
metadata={
"help": "Maximum length of the completion used for truncation. The completion is the concatenation of the "
"steps."
},
)
disable_dropout: bool = field(
default=True,
metadata={"help": "Whether to disable dropout in the model and reference model."},
)
step_separator: str = field(
default="\n",
metadata={"help": "Separator used to separate each step of the reasoning process."},
)
train_on_last_step_only: bool = field(
default=False,
metadata={"help": "Whether to train only on the last step."},
)
dataset_num_proc: Optional[int] = field(
default=None,
metadata={"help": "Number of processes to use for processing the dataset."},
)
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