feat: processors 支持批量 tokenize，优化性能并缓存 chat template

pipeline/processors/dpo.py

@@ -74,6 +74,33 @@ class DPOProcessor(BaseProcessor):
             "rejected_mask": rejected_m,
         }
 
+    def process_batch(self, input_dicts: List[Dict[str, Any]]) -> List[Dict[str, Tensor]]:
+        query_batch = self.tokenizer.encode([item["query"] for item in input_dicts])
+        chosen_batch = self.tokenizer.encode([item["chosen"] for item in input_dicts])
+        rejected_batch = self.tokenizer.encode(
+            [item["rejected"] for item in input_dicts]
+        )
+        results = []
+        for query_tokens, chosen_tokens, rejected_tokens in zip(
+            query_batch, chosen_batch, rejected_batch
+        ):
+            prompt = self.strategy.assemble_prompt(query_tokens)
+            chosen_t, chosen_m = encode_with_mask(
+                prompt, self.strategy.assemble_response(chosen_tokens)
+            )
+            rejected_t, rejected_m = encode_with_mask(
+                prompt, self.strategy.assemble_response(rejected_tokens)
+            )
+            results.append(
+                {
+                    "chosen": chosen_t,
+                    "chosen_mask": chosen_m,
+                    "rejected": rejected_t,
+                    "rejected_mask": rejected_m,
+                }
+            )
+        return results
+
     @property
     def output_keys(self) -> List[str]:
         return ["chosen", "chosen_mask", "rejected", "rejected_mask"]