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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.
import os
from types import MethodType
from typing import TYPE_CHECKING, Dict, Optional
from transformers import Trainer
from ...extras.logging import get_logger
from ..trainer_utils import convert_pissa_adapter, create_custom_optimzer, 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
self.processor = processor
if finetuning_args.pissa_convert:
self.save_model(os.path.join(self.args.output_dir, "pissa_init"))
if finetuning_args.use_badam:
from badam import clip_grad_norm_for_sparse_tensor
self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_for_sparse_tensor, self.accelerator)
def create_optimizer(self) -> "torch.optim.Optimizer":
if self.optimizer is None:
self.optimizer = create_custom_optimzer(self.model, self.args, self.finetuning_args)
return super().create_optimizer()
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)
def _save(self, output_dir: Optional[str] = None, state_dict: Optional[Dict[str, "torch.Tensor"]] = None) -> None:
super()._save(output_dir, state_dict)
output_dir = output_dir if output_dir is not None else self.args.output_dir
if self.finetuning_args.pissa_convert:
convert_pissa_adapter(output_dir, state_dict, self.accelerator, self.model, self.args)
if self.processor is not None:
getattr(self.processor, "image_processor").save_pretrained(output_dir)
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