diff --git a/src/llmtuner/tuner/sft/trainer.py b/src/llmtuner/tuner/sft/trainer.py
index e07a1936..851767f8 100644
--- a/src/llmtuner/tuner/sft/trainer.py
+++ b/src/llmtuner/tuner/sft/trainer.py
@@ -36,31 +36,44 @@ class Seq2SeqPeftTrainer(PeftTrainer):
             inputs["labels"] = self._pad_tensors_to_target_len(inputs["labels"], inputs["input_ids"])
         if label_len > prompt_len:
             inputs["input_ids"] = self._pad_tensors_to_target_len(inputs["input_ids"], inputs["labels"])
+            if "attention_mask" in inputs:
+                inputs["attention_mask"] = self._pad_tensors_to_target_len(
+                    inputs["attention_mask"], inputs["labels"], pad_token_id=0
+                )
+            if "position_ids" in inputs:
+                inputs["position_ids"] = self._pad_tensors_to_target_len(
+                    inputs["position_ids"], inputs["labels"], pad_token_id=0
+                )
 
         loss, generated_tokens, labels = super().prediction_step(
             model, inputs, prediction_loss_only=prediction_loss_only, ignore_keys=ignore_keys
         )
-        generated_tokens = generated_tokens[:, max(prompt_len, label_len):] if generated_tokens is not None else None
+        generated_tokens = (
+            generated_tokens[:, max(prompt_len, label_len):] if generated_tokens is not None else None
+        )
 
         return (loss, generated_tokens, labels)
 
-    def _pad_tensors_to_target_len(self, src_tensor: torch.Tensor, tgt_tensor: torch.Tensor) -> torch.Tensor:
+    def _pad_tensors_to_target_len(
+        self,
+        src_tensor: torch.Tensor,
+        tgt_tensor: torch.Tensor,
+        pad_token_id: Optional[int] = None
+    ) -> torch.Tensor:
         r"""
         Pads the tensor to the same length as the target tensor.
 
         Should only be called when predict_with_generate=True.
         """
-        if self.tokenizer is not None and hasattr(self.tokenizer, "pad_token_id"):
-            assert self.tokenizer.padding_side == "left", "This method only accepts left-padded tensor."
-            # If PAD token is not defined at least EOS token has to be defined
-            pad_token_id = (
-                self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
-            )
-        else:
-            if self.model.config.pad_token_id is not None:
-                pad_token_id = self.model.config.pad_token_id
+        if pad_token_id is None:
+            if self.tokenizer is not None and hasattr(self.tokenizer, "pad_token_id"):
+                assert self.tokenizer.padding_side == "left", "This method only accepts left-padded tensor."
+                pad_token_id = self.tokenizer.pad_token_id
             else:
-                raise ValueError("Pad_token_id must be set in the configuration of the model, in order to pad tensors")
+                if self.model.config.pad_token_id is not None:
+                    pad_token_id = self.model.config.pad_token_id
+                else:
+                    raise ValueError("Pad_token_id must be set in the configuration of the model.")
 
         padded_tensor = pad_token_id * torch.ones_like(tgt_tensor)
         padded_tensor[:, -src_tensor.shape[-1]:] = src_tensor # adopt left-padding