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# Copyright 2025 LMSYS and the LlamaFactory team.
# Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li
#
# This code is inspired by the LMSYS's FastChat library.
# https://github.com/lm-sys/FastChat/blob/v0.2.30/fastchat/train/train.py
#
# 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 math
from typing import TYPE_CHECKING
from ...extras import logging
from ...extras.constants import RopeScaling
if TYPE_CHECKING:
from transformers import PretrainedConfig
from ...hparams import ModelArguments
logger = logging.get_logger(__name__)
def configure_rope(config: "PretrainedConfig", model_args: "ModelArguments") -> None:
if model_args.rope_scaling is None:
return
if not hasattr(config, "rope_scaling"):
logger.warning_rank0("Current model does not support RoPE scaling.")
return
if hasattr(config, "max_position_embeddings"):
old_max_length = getattr(config, "max_position_embeddings", None)
else:
logger.warning_rank0("Cannot find the max position embeddings in the config.")
return
if model_args.model_max_length is not None: # training
if model_args.model_max_length <= old_max_length:
logger.warning_rank0("Input length is smaller than max length. Disabling rope scaling.")
return
if model_args.rope_scaling == RopeScaling.DYNAMIC:
logger.warning_rank0(
"Dynamic NTK scaling may not work well with fine-tuning. "
"See: https://github.com/huggingface/transformers/pull/24653"
)
rope_factor = float(math.ceil(model_args.model_max_length / old_max_length))
else: # inference
rope_factor = 2.0
rope_kwargs = {
"rope_type": getattr(model_args.rope_scaling, "value", model_args.rope_scaling), # handle enum
"factor": rope_factor,
}
setattr(config, "max_position_embeddings", old_max_length * rope_factor)
logger.info_rank0(f"Enlarge max model length from {old_max_length} to {old_max_length * rope_factor}.")
if model_args.rope_scaling in [RopeScaling.DYNAMIC, RopeScaling.YARN]:
rope_kwargs["original_max_position_embeddings"] = old_max_length
elif model_args.rope_scaling == RopeScaling.LLAMA3:
rope_kwargs["original_max_position_embeddings"] = old_max_length
rope_kwargs["low_freq_factor"] = 1.0
rope_kwargs["high_freq_factor"] = 4.0
setattr(config, "rope_scaling", rope_kwargs)
logger.info_rank0(
f"Using {rope_kwargs['rope_type']} scaling strategy and setting scaling factor to {rope_kwargs['factor']}."
)
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