diff --git a/llm/server/server/data/processor.py b/llm/server/server/data/processor.py
index 83b365bb5..efa798fde 100644
--- a/llm/server/server/data/processor.py
+++ b/llm/server/server/data/processor.py
@@ -143,6 +143,9 @@ class DataProcessor(BaseDataProcessor):
             request["eos_token_ids"] = []
         request["eos_token_ids"].extend(get_eos_token_id(self.tokenizer, self.config.generation_config))
 
+        if "stop_seqs" not in request or (isinstance(request["stop_seqs"], (list, tuple)) and len(request["stop_seqs"]) == 0):
+            self.update_stop_seq(request)
+
         if "input_ids" not in request or \
             (isinstance(request["input_ids"], (list, tuple)) and len(request["input_ids"]) == 0):
             if "text" in request:
@@ -334,3 +337,19 @@ class DataProcessor(BaseDataProcessor):
         if isinstance(self.tokenizer, (LlamaTokenizer, Llama3Tokenizer)) and not self.tokenizer.pad_token_id:
             return self.tokenizer.eos_token
         return self.tokenizer.pad_token_id
+
+    def update_stop_seq(self, request):
+        """
+        Update stop sequences from request.
+        """
+        stop_seqs =  [[2], [100273]]
+        for seq in request.get("stop_sequences", []):
+            if seq != self._get_eos_token_id():
+                stop_seqs.append(self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize(seq)))
+        request["stop_seqs"], request["stop_seqs_len"] = self.pad_batch_data(
+            stop_seqs,
+            pad_id=-1,
+            return_seq_len=True,
+            return_array=False
+        )
+        data_processor_logger.debug(f"processed request: {request['stop_seqs'], request['stop_seqs_len']}")
diff --git a/llm/server/server/engine/infer.py b/llm/server/server/engine/infer.py
index b6e15e17a..3e448b4d3 100644
--- a/llm/server/server/engine/infer.py
+++ b/llm/server/server/engine/infer.py
@@ -54,6 +54,9 @@ class ModelRunner:
 
         self.reduce_dialogue_repetition = int(os.environ.get("REDUCE_DIALOGUE_REPETITION", 0))
 
+        self.max_stop_seqs_num = int(os.getenv("MAX_STOP_SEQS_NUM", 5))
+        self.stop_seqs_max_len = int(os.getenv("STOP_SEQS_MAX_LEN", 8))
+
         self.nranks = dist.get_world_size()
         self.init_dist_env()
         self.rank = fleet.worker_index()
@@ -248,6 +251,13 @@ class ModelRunner:
         self.share_inputs['free_list_len'] = paddle.full(
                             shape=[1], fill_value=self.free_list_len, dtype="int32")
 
+        self.share_inputs['stop_seqs_len'] = paddle.full(shape=[max_stop_seqs_num,],
+                                            fill_value=0,
+                                            dtype="int32")
+        self.share_inputs['stop_seqs'] = paddle.full(shape=[max_stop_seqs_num, stop_seqs_max_len],
+                                                fill_value=-1,
+                                                dtype="int64")
+
         if self.reduce_dialogue_repetition:
             self.share_inputs["first_token_ids"] = paddle.full(
                 shape=[self.args.max_batch_size, 1], fill_value=-1, dtype="int64")
@@ -300,6 +310,14 @@ class ModelRunner:
             self.share_inputs["block_tables"][idx:idx + 1, :encoder_block_num] = np.array(
                                             task['block_tables'], dtype="int32")
 
+            if "stop_seqs_len" in task:
+                stop_seqs_num = len(task["stop_seqs_len"])
+                for i in range(stop_seqs_num, max_stop_seqs_num):
+                    task["stop_seqs_len"].append(0)
+                share_inputs['stop_seqs_len'][:] = np.array(
+                                                        task["stop_seqs_len"], dtype="int32")
+                share_inputs['stop_seqs'][:stop_seqs_num, :len(task['stop_seqs'][0])] = np.array(
+                                                        task["stop_seqs"], dtype="int64")
     def step_cuda(self, seq_lens_this_time):
         """
         step cuda
@@ -486,6 +504,11 @@ class InferenceEngine(object):
         config.switch_ir_optim(False)
         config.enable_use_gpu(100, device_id)
 
+        pir_flag = int(os.environ.get("FLAGS_enable_pir_api", 0))
+        if pir_flag == 1:
+            config.enable_new_executor()
+            config.enable_new_ir()
+
         # distributed config
         if self.mp_degree > 1:
             trainer_endpoints = fleet.worker_endpoints()