make it optional

src/transformers/trainer.py

@@ -362,7 +362,7 @@ class Trainer:
             be able to choose different architectures according to hyper parameters (such as layer count, sizes of
             inner layers, dropout probabilities etc).
         compute_loss_func (`Callable`, *optional*):
-            A function that accepts the raw model outputs, labels, and the number of items in the entire accumulated
+            A function that accepts the raw model outputs, labels, and optionally the number of items in the entire accumulated
             batch (batch_size * gradient_accumulation_steps) and returns the loss. For example, see the default [loss function](https://github.com/huggingface/transformers/blob/052e652d6d53c2b26ffde87e039b723949a53493/src/transformers/trainer.py#L3618) used by [`Trainer`].
         compute_metrics (`Callable[[EvalPrediction], Dict]`, *optional*):
             The function that will be used to compute metrics at evaluation. Must take a [`EvalPrediction`] and return
@@ -3778,7 +3778,10 @@ class Trainer:
                 model_name = unwrapped_model._get_name()
             # User-defined compute_loss function
             if self.compute_loss_func is not None:
-                loss = self.compute_loss_func(outputs, labels, num_items_in_batch=num_items_in_batch)
+                compute_loss_kwargs = {}
+                if "num_items_in_batch" in inspect.signature(self.compute_loss_func).parameters.keys():
+                    compute_loss_kwargs["num_items_in_batch"] = num_items_in_batch
+                loss = self.compute_loss_func(outputs, labels, **compute_loss_kwargs)
             elif model_name in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES.values():
                 loss = self.label_smoother(outputs, labels, shift_labels=True)
             else: