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# Copyright 2024 the LlamaFactory team.
#
# 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 types import MethodType
from typing import TYPE_CHECKING, Optional
from transformers import Trainer
from typing_extensions import override
from ...extras.logging import get_logger
from ..callbacks import PissaConvertCallback, SaveProcessorCallback
from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
if TYPE_CHECKING:
import torch
from transformers import ProcessorMixin
from ...hparams import FinetuningArguments
logger = get_logger(__name__)
class CustomTrainer(Trainer):
r"""
Inherits Trainer for custom optimizer.
"""
def __init__(
self, finetuning_args: "FinetuningArguments", processor: Optional["ProcessorMixin"], **kwargs
) -> None:
super().__init__(**kwargs)
self.finetuning_args = finetuning_args
if processor is not None:
self.add_callback(SaveProcessorCallback(processor))
if finetuning_args.pissa_convert:
self.add_callback(PissaConvertCallback)
if finetuning_args.use_badam:
from badam import BAdamCallback, clip_grad_norm_old_version
self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
self.add_callback(BAdamCallback)
@override
def create_optimizer(self) -> "torch.optim.Optimizer":
if self.optimizer is None:
self.optimizer = create_custom_optimizer(self.model, self.args, self.finetuning_args)
return super().create_optimizer()
@override
def create_scheduler(
self, num_training_steps: int, optimizer: Optional["torch.optim.Optimizer"] = None
) -> "torch.optim.lr_scheduler.LRScheduler":
create_custom_scheduler(self.args, num_training_steps, optimizer)
return super().create_scheduler(num_training_steps, optimizer)
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