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# Copyright 2024 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.logging import get_logger
if TYPE_CHECKING:
from transformers import PretrainedConfig
from ...hparams import ModelArguments
logger = get_logger(__name__)
def configure_rope(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
if model_args.rope_scaling is None:
return
if not hasattr(config, "rope_scaling"):
logger.warning("Current model does not support RoPE scaling.")
return
if model_args.model_max_length is not None:
if is_trainable and model_args.rope_scaling == "dynamic":
logger.warning(
"Dynamic NTK scaling may not work well with fine-tuning. "
"See: https://github.com/huggingface/transformers/pull/24653"
)
current_max_length = getattr(config, "max_position_embeddings", None)
if current_max_length and model_args.model_max_length > current_max_length:
logger.info(
"Enlarge max model length from {} to {}.".format(current_max_length, model_args.model_max_length)
)
setattr(config, "max_position_embeddings", model_args.model_max_length)
scaling_factor = float(math.ceil(model_args.model_max_length / current_max_length))
else:
logger.warning("Input length is smaller than max length. Consider increase input length.")
scaling_factor = 1.0
else:
scaling_factor = 2.0
setattr(config, "rope_scaling", {"type": model_args.rope_scaling, "factor": scaling_factor})
logger.info(
"Using {} scaling strategy and setting scaling factor to {}".format(model_args.rope_scaling, scaling_factor)
)
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