format style

Former-commit-id: 53b683531b83cd1d19de97c6565f16c1eca6f5e1

src/llmtuner/train/dpo/collator.py

@@ -1,6 +1,7 @@
-import torch
 from dataclasses import dataclass
 from typing import Any, Dict, List, Sequence, Tuple
+
+import torch
 from transformers import DataCollatorForSeq2Seq
 
 
@@ -20,7 +21,7 @@ class DPODataCollatorWithPadding(DataCollatorForSeq2Seq):
             padded_tensor = self.label_pad_token_id * torch.ones_like(feature)
             padded_tensor[start:end] = feature[start:end]
             padded_labels.append(padded_tensor)
-        return torch.stack(padded_labels, dim=0).contiguous() # in contiguous memory
+        return torch.stack(padded_labels, dim=0).contiguous()  # in contiguous memory
 
     def __call__(self, features: Sequence[Dict[str, Any]]) -> Dict[str, torch.Tensor]:
         r"""
@@ -34,10 +35,12 @@ class DPODataCollatorWithPadding(DataCollatorForSeq2Seq):
         for key in ("chosen_ids", "rejected_ids"):
             for feature in features:
                 prompt_len, answer_len = len(feature["prompt_ids"]), len(feature[key])
-                concatenated_features.append({
-                    "input_ids": feature["prompt_ids"] + feature[key],
-                    "attention_mask": [1] * (prompt_len + answer_len)
-                })
+                concatenated_features.append(
+                    {
+                        "input_ids": feature["prompt_ids"] + feature[key],
+                        "attention_mask": [1] * (prompt_len + answer_len),
+                    }
+                )
                 label_positions.append((prompt_len, answer_len))
 
         batch = self.tokenizer.pad(