diff --git a/colossalai/inference/tensor_parallel/engine.py b/colossalai/inference/tensor_parallel/engine.py
index 283f719e5..3be213274 100644
--- a/colossalai/inference/tensor_parallel/engine.py
+++ b/colossalai/inference/tensor_parallel/engine.py
@@ -218,7 +218,7 @@ class TPInferEngine:
         ), "Discrepancy between the tp size of TPInferEngine and the tp size of shard config"
         model_name = model.__class__.__name__
         assert model_name in self.supported_models, f"Unsupported model cls {model_name} for TP inference."
-        
+
         model = model.model if self.shard_config.inference_gptq else model
         policy = get_autopolicy(model, shard_config=self.shard_config)
 
@@ -311,7 +311,7 @@ class TPInferEngine:
                 seq_start_indexes[i] = start_index
                 start_index += curr_seq_len
                 max_len_in_batch = curr_seq_len if curr_seq_len > max_len_in_batch else max_len_in_batch
-        
+
         block_loc = torch.empty((batch_size, self.max_input_len + self.max_output_len), dtype=torch.long, device="cuda")
         batch_infer_state = BatchInferState(batch_size, max_len_in_batch)
         batch_infer_state.seq_len = seq_lengths.to("cuda")
diff --git a/tests/test_infer/test_pipeline_infer.py b/tests/test_infer/test_pipeline_infer.py
index 1cf38c1ec..3544153da 100644
--- a/tests/test_infer/test_pipeline_infer.py
+++ b/tests/test_infer/test_pipeline_infer.py
@@ -24,6 +24,7 @@ for k, v in inputs.items():
         new_shape[0] = 16
         inputs[k] = v.to("cuda").repeat(*new_shape)
 
+
 def pipeline_inference_test(tp_size, pp_size, max_output_len, micro_batch_size):
     model = transformers.LlamaForCausalLM(
         transformers.LlamaConfig(
@@ -58,7 +59,6 @@ def run_pipeline_inference_test(tp_size, pp_size, max_output_len, micro_batch_si
 @parameterize("pp_size", [2])
 @parameterize("max_output_len", [4])
 @parameterize("micro_batch_size", [1])
-
 @clear_cache_before_run()
 def run_tp_pipeline_inference_test(tp_size, pp_size, max_output_len, micro_batch_size):
     pipeline_inference_test(tp_size, pp_size, max_output_len, micro_batch_size)
@@ -76,7 +76,6 @@ def check_tp_pipeline_inference(rank, world_size, port):
 
 
 @pytest.mark.skipif(not CUDA_SUPPORT, reason="kv-cache manager engine requires cuda version to be higher than 11.5")
-
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 @clear_cache_before_run()