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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) | |