fix RM accuracy

src/utils/pairwise.py

@@ -13,8 +13,7 @@ logger = get_logger(__name__)
 
 def compute_accuracy(eval_preds: Sequence[Union[np.ndarray, Tuple[np.ndarray]]]) -> Dict[str, float]:
     preds, _ = eval_preds
-    preds = np.array(preds)
-    return {"accuracy": (preds[:, 0] > preds[:, 1]).sum() / len(preds)}
+    return {"accuracy": (preds[0] > preds[1]).sum() / len(preds[0])}
 
 
 class PairwiseDataCollatorWithPadding(DynamicDataCollatorWithPadding):
@@ -49,9 +48,13 @@ class PairwisePeftTrainer(PeftTrainer):
         We use score on the EOS token to represent reward of the whole sentence.
 
         Subclass and override to inject custom behavior. It should not be directly used by external scripts.
+
+        Note that the first element will be removed from the output tuple.
+
+        See: https://github.com/huggingface/transformers/blob/v4.30.2/src/transformers/trainer.py#L3509
         """
         batch_size = inputs["input_ids"].size(0) // 2
         _, _, values = model(**inputs)
         r_accept, r_reject = values[:, -1].split(batch_size, dim=0)
         loss = -torch.log(torch.sigmoid(r_accept - r_reject)).mean()
-        return (loss, torch.stack((r_accept, r_reject), dim=-1)) if return_outputs else loss
+        return (loss, [loss, r_accept, r_reject]) if return_outputs else loss