polish code with new pre-commit rule (#2923)

fastdeploy/demo/offline_disaggregated_demo.py

@@ -14,55 +14,50 @@
 # limitations under the License.
 """
 
-import time
-import os
 import multiprocessing
+import os
+import time
 
 from fastdeploy.entrypoints.llm import LLM
-from fastdeploy.engine.sampling_params import SamplingParams
-
-
 
 model_name_or_path = "baidu/ERNIE-4.5-21B-A3B-Paddle"
 
 
-
 def start_decode(model_name_or_path):
-     os.environ["CUDA_VISIBLE_DEVICES"] = "1"
+    os.environ["CUDA_VISIBLE_DEVICES"] = "1"
     os.environ["FD_LOG_DIR"] = "log_decode"
     llm_decode = LLM(
-        model=model_name_or_path,  
-        tensor_parallel_size=1, 
+        model=model_name_or_path,
+        tensor_parallel_size=1,
         splitwise_role="decode",
         engine_worker_queue_port=6678,
         innode_prefill_ports=[6676],
-        cache_queue_port=55668
-        )
+        cache_queue_port=55668,
+    )
     return llm_decode
 
+
 def start_prefill(model_name_or_path):
     os.environ["CUDA_VISIBLE_DEVICES"] = "0"
     os.environ["FD_LOG_DIR"] = "log_prefill"
-    llm_prefill = LLM(
-        model=model_name_or_path, 
-        tensor_parallel_size=1, 
+    LLM(
+        model=model_name_or_path,
+        tensor_parallel_size=1,
         splitwise_role="prefill",
-        engine_worker_queue_port=6677, 
+        engine_worker_queue_port=6677,
         cache_queue_port=55667,
-        )
+    )
 
 
 def main():
-    prefill = multiprocessing.Process(
-        target=start_prefill,
-        args=(model_name_or_path,)).start()
+    prefill = multiprocessing.Process(target=start_prefill, args=(model_name_or_path,)).start()
     time.sleep(10)
     llm_decode = start_decode(model_name_or_path)
 
     output = llm_decode.generate(prompts=["who are you？", "what can you do？"], use_tqdm=True)
     print(output)
 
-    decode.join()
+    prefill.join()
 
 
 if __name__ == "__main__":