🔮 Fix unused precomputed ref log probs in DPO (#2431)

trl/trainer/dpo_trainer.py

@@ -130,6 +130,9 @@ def torch_call(self, examples: list[Union[list[int], Any, dict[str, Any]]]) -> d
             pixel_values = [torch.tensor(example["pixel_values"]) for example in examples]
         if "pixel_attention_mask" in examples[0]:
             pixel_attention_mask = [torch.tensor(example["pixel_attention_mask"]) for example in examples]
+        if "ref_chosen_logps" in examples[0] and "ref_rejected_logps" in examples[0]:
+            ref_chosen_logps = torch.tensor([example["ref_chosen_logps"] for example in examples])
+            ref_rejected_logps = torch.tensor([example["ref_rejected_logps"] for example in examples])
 
         # Pad
         output = {}
@@ -145,6 +148,9 @@ def torch_call(self, examples: list[Union[list[int], Any, dict[str, Any]]]) -> d
             output["pixel_attention_mask"] = pad(pixel_attention_mask, padding_value=0)
         if "image_sizes" in examples[0]:
             output["image_sizes"] = torch.tensor([example["image_sizes"] for example in examples])
+        if "ref_chosen_logps" in examples[0] and "ref_rejected_logps" in examples[0]:
+            output["ref_chosen_logps"] = ref_chosen_logps
+            output["ref_rejected_logps"] = ref_rejected_logps
 
         return output
 
@@ -162,7 +168,7 @@ class DPOTrainer(Trainer):
         args (`DPOConfig`):
             The DPO config arguments to use for training.
         data_collator (`transformers.DataCollator`):
-            The data collator to use for training. If None is specified, the default data collator (`DPODataCollatorWithPadding`) will be used
+            The data collator to use for training. If None is specified, the default data collator (`PreferenceCollator`) will be used
             which will pad the sequences to the maximum length of the sequences in the batch, given a dataset of paired sequences.
         train_dataset (`datasets.Dataset`):
             The dataset to use for training.
@@ -672,9 +678,16 @@ def _set_signature_columns_if_needed(self):
         # If `self.args.remove_unused_columns` is True, non-signature columns are removed.
         # By default, this method sets `self._signature_columns` to the model's expected inputs.
         # In DPOTrainer, we preprocess data, so using the model's signature columns doesn't work.
-        # Instead, we set them to the columns expected by `DPODataCollatorWithPadding`, hence the override.
+        # Instead, we set them to the columns expected by `PreferenceCollator`, hence the override.
         if self._signature_columns is None:
-            self._signature_columns = ["prompt_input_ids", "chosen_input_ids", "rejected_input_ids", "image_sizes"]
+            self._signature_columns = [
+                "prompt_input_ids",
+                "chosen_input_ids",
+                "rejected_input_ids",
+                "image_sizes",
+                "ref_chosen_logps",
+                "ref_rejected_logps",
+            ]
 
     def get_train_dataloader(self) -> DataLoader:
         """