Repository: huggingface/transformers
Pull request: Only count num items in batch when needed

# What does this PR do?

Similar to https://github.com/huggingface/transformers/pull/36861 this PR reduces the overhead from calculating `num_items_in_batch` when it's not needed, e.g in cases like image classification where models don't take loss kwargs.

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      Pull Request section?
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      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

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Changed files:
filename: src/transformers/trainer.py
patch: @@ -5232,13 +5232,29 @@ def _fsdp_qlora_plugin_updates(self):
     def get_batch_samples(self, epoch_iterator, num_batches, device):
         batch_samples = []
         num_items_in_batch = None
+
         for _ in range(num_batches):
             try:
-                batch_samples += [next(epoch_iterator)]
+                batch_samples.append(next(epoch_iterator))
             except StopIteration:
                 break
 
-        if len(batch_samples) > 0 and "labels" in batch_samples[0]:
+        count_num_items_in_batch = (
+            len(batch_samples) > 0
+            and "labels" in batch_samples[0]
+            and (
+                # num_items_in_batch is passed to model forward
+                # https://github.com/huggingface/transformers/blob/v4.49.0/src/transformers/trainer.py#L3757
+                self.model_accepts_loss_kwargs
+                # num_items_in_batch is passed to compute_loss_func
+                # https://github.com/huggingface/transformers/blob/v4.49.0/src/transformers/trainer.py#L3773
+                or self.compute_loss_func is not None
+                # num_items_in_batch is also verified if (self.model_accepts_loss_kwargs or self.compute_loss_func)
+                # https://github.com/huggingface/transformers/blob/v4.49.0/src/transformers/trainer.py#L3790
+            )
+        )
+
+        if count_num_items_in_batch:
             # For now we don't support object detection
             try:
                 num_items_in_batch = sum([(batch["labels"].ne(-100)).sum() for batch in batch_samples])