Repository: huggingface/transformers
Pull request: Making compute_loss_func always take priority in Trainer

# What does this PR do?

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Fixes #40601 

Currently, Trainer only uses compute_loss_func when labels are provided. This leads to unintuitive behavior:

1. User-defined losses are silently ignored if labels=None
2. Advanced training setups (unsupervised, contrastive, RLHF) cannot rely on custom losses consistently
3. Debugging is difficult since the function is skipped without notice

With this PR, now:
1. `compute_loss_func` always takes priority, regardless of whether labels are passed
2. If labels=None, the function is still called with labels=None
3. Adds a temporary warning to highlight the change 

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
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      to it if that's the case.  #40601 
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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).
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Changed files:
filename: src/transformers/trainer.py
patch: @@ -4115,16 +4115,27 @@ def compute_loss(
         if self.args.past_index >= 0:
             self._past = outputs[self.args.past_index]
 
-        if labels is not None:
+        # User-defined compute_loss function
+        if self.compute_loss_func is not None:
+            if labels is None:
+                logger.warning(
+                    "Trainer: `compute_loss_func` is defined but `labels=None`. "
+                    "Your custom loss function will still be called with labels=None. "
+                )
+            loss = self.compute_loss_func(
+                outputs,
+                labels,
+                num_items_in_batch=num_items_in_batch,
+            )
+        # Default HF loss handling (label smoothing) if no custom loss function
+        elif labels is not None:
             unwrapped_model = self.accelerator.unwrap_model(model)
-            if _is_peft_model(unwrapped_model):
-                model_name = unwrapped_model.base_model.model._get_name()
-            else:
-                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)
-            elif model_name in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES.values():
+            model_name = (
+                unwrapped_model.base_model.model._get_name()
+                if _is_peft_model(unwrapped_model)
+                else unwrapped_model._get_name()
+            )
+            if model_name in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES.values():
                 loss = self.label_smoother(outputs, labels, shift_labels=True)
             else:
                 loss = self.label_smoother(outputs, labels)