rename package

Former-commit-id: a07ff0c083558cfe6f474d13027642d3052fee08

src/llamafactory/train/pt/trainer.py

@@ -0,0 +1,51 @@
+from types import MethodType
+from typing import TYPE_CHECKING, Dict, Optional
+
+from transformers import Trainer
+
+from ...extras.logging import get_logger
+from ..utils import 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.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)
+        if self.processor is not None:
+            output_dir = output_dir if output_dir is not None else self.args.output_dir
+            getattr(self.processor, "image_processor").save_pretrained(output_dir)