chore: import upstream snapshot with attribution

FlagEmbedding/finetune/embedder/decoder_only/base/trainer.py

@@ -0,0 +1,45 @@
+import os
+import torch
+import logging
+from typing import Optional
+
+from FlagEmbedding.abc.finetune.embedder import AbsEmbedderTrainer
+
+logger = logging.getLogger(__name__)
+
+
+class DecoderOnlyEmbedderTrainer(AbsEmbedderTrainer):
+    """
+    Trainer class for base encoder models.
+    """
+    def _save(self, output_dir: Optional[str] = None, state_dict=None):
+        """Save the model to directory.
+
+        Args:
+            output_dir (Optional[str], optional): Output directory to save the model. Defaults to ``None``.
+
+        Raises:
+            NotImplementedError
+        """
+        output_dir = output_dir if output_dir is not None else self.args.output_dir
+        os.makedirs(output_dir, exist_ok=True)
+        logger.info("Saving model checkpoint to %s", output_dir)
+        # Save a trained model and configuration using `save_pretrained()`.
+        # They can then be reloaded using `from_pretrained()`
+        if not hasattr(self.model, 'save'):
+            raise NotImplementedError(
+                f'MODEL {self.model.__class__.__name__} '
+                f'does not support save interface')
+        else:
+            self.model.save(output_dir)
+
+        if self.tokenizer is not None and self.is_world_process_zero():
+            self.tokenizer.save_pretrained(output_dir)
+
+        torch.save(self.args, os.path.join(output_dir, "training_args.bin"))
+
+        # save the checkpoint for sentence-transformers library
+        # if self.is_world_process_zero():
+        #     save_ckpt_for_sentence_transformers(output_dir,
+        #                                         pooling_mode=self.args.sentence_pooling_method,
+        #                                         normlized=self.args.normlized)