Fix batch size handling in prediction_loop for DataLoaderShard (huggi…

…ngface#34343)

* Fix batch size handling in prediction_loop for DataLoaderShard

Updated the prediction_loop method in the Trainer class to correctly handle batch size when using DataLoaderShard. This ensures that the batch size is retrieved from total_batch_size for distributed training scenarios, preventing TypeError related to NoneType during evaluation.

* Update src/transformers/trainer.py

Co-authored-by: Zach Mueller <[email protected]>

* Applied the fix to remove unused imports

---------

Co-authored-by: Zach Mueller <[email protected]>

src/transformers/trainer.py

@@ -4714,7 +4714,17 @@ def prediction_loop(
             elif args.bf16_full_eval:
                 model = model.to(dtype=torch.bfloat16, device=args.device)
 
-        batch_size = dataloader.batch_size
+        batch_size = (
+            dataloader.total_batch_size
+            if getattr(dataloader, "_is_accelerate_prepared", False)
+            else dataloader.batch_size
+        )
+
+        if batch_size is None:
+            raise ValueError(
+                "Batch size cannot be None. Ensure the dataloader has a valid batch_size or total_batch_size."
+            )
+
         num_examples = self.num_examples(dataloader)
         logger.info(f"\n***** Running {description} *****")
         logger.info(f"  Num examples = {num_examples}")