Repository: huggingface/transformers
Pull request: Add new dim to `num_items_in_batch` if necessary

# What does this PR do?

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https://github.com/huggingface/transformers/pull/36861 introduced an issue with multi-gpu runs. See [this comment](https://github.com/huggingface/transformers/pull/36861#issuecomment-2747698449) for a script that enables to reproduce it.

`num_items_in_batch` can be a scalar and, in that case, Torch DataParallel will raise an error as a scalar cannot be split in chunks. The fix consists in adding a dimension to the this tensor. Note that this is only needed for DataParallel as DistributedDataParallel was not affected by this issue.

Should fix https://github.com/huggingface/trl/issues/2338 and https://github.com/huggingface/transformers/issues/36492 too.


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Changed files:
filename: src/transformers/trainer.py
patch: @@ -5268,6 +5268,10 @@ def get_batch_samples(self, epoch_iterator, num_batches, device):
             if torch.is_tensor(num_items_in_batch):
                 num_items_in_batch = num_items_in_batch.to(device)
 
+                if self.args.n_gpu > 1 and num_items_in_batch.dim() == 0:
+                    # In the DataParallel case, convert the scalar tensor into a 1-dim tensor
+                    num_items_in_batch = num_items_in_batch.unsqueeze(0)
+
         return batch_samples, num_items_in_batch
 
     def set_initial_training_values(