[LLM] add port check and fix bugs (#10153)

* [LLM] add port check and fix bugs

* [LLM] fix stream output

* [LLM] delete unused param

* [LLM] fix start script

Author: ltd0924

llm/server/dockerfiles/Dockerfile_serving_cuda118_cudnn8

@@ -8,6 +8,7 @@ RUN python3 -m pip install --pre paddlepaddle-gpu -i https://www.paddlepaddle.or
     && python3 -m pip install --no-cache-dir --force-reinstall https://paddle-qa.bj.bcebos.com/paddlenlp/wheel/2f85a64edd4aa9911c94ccb5ce53e83ac41ce22b/paddlenlp-3.0.0b3.post20250123-py3-none-any.whl \
     && python3 -m pip install --no-cache-dir --force-reinstall https://paddlepaddle-inference-banchmark.bj.bcebos.com/paddlenlp_ops-0.0.0-py3-none-any.whl \
     && python3 -m pip install --no-cache-dir sentencepiece pycryptodome tritonclient[all]==2.41.1 \
+    && apt update && apt install net-tools \
     && apt-get clean && rm -rf /var/lib/apt/lists/*
 
 # clone paddle & paddlenlp 源码（代码版本应与上述安装版本对齐）

llm/server/dockerfiles/Dockerfile_serving_cuda124_cudnn9

@@ -8,6 +8,7 @@ RUN python3 -m pip install --pre paddlepaddle-gpu -i https://www.paddlepaddle.or
     && python3 -m pip install --no-cache-dir --force-reinstall https://paddle-qa.bj.bcebos.com/paddlenlp/wheel/2f85a64edd4aa9911c94ccb5ce53e83ac41ce22b/paddlenlp-3.0.0b3.post20250123-py3-none-any.whl \
     && python3 -m pip install --no-cache-dir --force-reinstall https://paddlepaddle-inference-banchmark.bj.bcebos.com/paddlenlp_ops-0.0.0-py3-none-any.whl \
     && python3 -m pip install --no-cache-dir sentencepiece pycryptodome tritonclient[all]==2.41.1 \
+    && apt update && apt install net-tools \
     && apt-get clean && rm -rf /var/lib/apt/lists/*
 
 # clone paddle & paddlenlp 源码（代码版本应与上述安装版本对齐）

llm/server/docs/deploy_usage_tutorial.md

@@ -416,6 +416,7 @@ docker build --network=host -f ./dockerfiles/Dockerfile_serving_cuda124_cudnn9 -
 | USE_CACHE_KV_INT8 | int | 是否将 INT8配置为 KV Cache 的类型 | 否 | 0 | c8量化模型需要配置为1 |
 | MODEL_DIR | str | 模型文件路径 | 否 | /models/ |  |
 | model_name | str | 模型名称 | 否 | 无 |  用于支持模型静态图下载，具体名称参考文档(#./static_models.md)|
+| OUTPUT_LOG_TO_CONSOLE | str | 是否定向输出到console 文件中 | 否 | 0 |  |
 
 ## 请求参数介绍
 

llm/server/server/scripts/start_server.sh

@@ -55,6 +55,21 @@ export SERVICE_GRPC_PORT=${GRPC_PORT:-${SERVICE_GRPC_PORT:-"8811"}}
 export INTER_PROC_PORT=${INTER_QUEUE_PORT:-${INTER_PROC_PORT:-"8813"}}
 export SERVICE_HTTP_PORT=${PUSH_MODE_HTTP_PORT:-${SERVICE_HTTP_PORT:-"9965"}}
 
+check_port_occupied() {
+    local port=$1
+    if netstat -tuln | grep -q ":${port}\b"; then
+        echo  "PORT: ${port} occupied! Please change the port!"
+        exit 1
+    fi
+}
+
+check_port_occupied ${HEALTH_HTTP_PORT}
+check_port_occupied ${METRICS_HTTP_PORT}
+check_port_occupied ${SERVICE_GRPC_PORT}
+check_port_occupied ${INTER_PROC_PORT}
+check_port_occupied ${SERVICE_HTTP_PORT}
+
+
 
 if [ ! -d "llm_model" ];then
     ln -s /opt/source/PaddleNLP/llm/server/server/llm_model llm_model
@@ -83,15 +98,20 @@ else
     sleep ${SERVER_WAITTING_TIME:-"25"}
 fi
 
-
-
-tritonserver --exit-timeout-secs 100000 --cuda-memory-pool-byte-size 0:0 --cuda-memory-pool-byte-size 1:0 \
+OUTPUT_LOG_TO_CONSOLE=${OUTPUT_LOG_TO_CONSOLE:-"0"}
+# Set the log redirection based on whether logs should be output to the console
+LOG_REDIRECT=""
+# If OUTPUT_LOG_TO_CONSOLE is set to "1", redirect logs to the console log file
+if [ "$OUTPUT_LOG_TO_CONSOLE" == "1" ]; then
+    LOG_REDIRECT="> log/console.log 2>&1"
+fi
+eval tritonserver --exit-timeout-secs 100000 --cuda-memory-pool-byte-size 0:0 --cuda-memory-pool-byte-size 1:0 \
                  --cuda-memory-pool-byte-size 2:0 --cuda-memory-pool-byte-size 3:0 --cuda-memory-pool-byte-size 4:0 \
                  --cuda-memory-pool-byte-size 5:0 --cuda-memory-pool-byte-size 6:0 --cuda-memory-pool-byte-size 7:0 \
                  --pinned-memory-pool-byte-size 0 --model-repository llm_model/ \
                  --allow-http false \
                  --grpc-port=${SERVICE_GRPC_PORT} \
                  --metrics-port=${METRICS_HTTP_PORT} \
-                 --log-file log/server.log --log-info true &
+                 --log-file log/server.log --log-info true $LOG_REDIRECT &
 
 echo "The logs for the model service, please check" ${PWD}"/log/server.log and "${PWD}"/log/workerlog.0"

llm/server/server/server/data/processor.py

@@ -186,7 +186,9 @@ def process_response(self, response_dict, **kwargs):
         response_dict["usage"] = {"completion_tokens" : response_dict["send_idx"] + 1}
 
         if is_end:
-            response_dict["tokens_all"] = self.clear_request_status(req_id)
+            self.clear_request_status(req_id)
+            token_ids = response_dict.get("tokens_all_ids", [])
+            response_dict["tokens_all"] = self.ids2tokens(token_ids, response_dict["req_id"])
         return response_dict
 
     def text2ids(self, text):

llm/server/server/server/engine/infer.py

@@ -612,7 +612,7 @@ def run(self):
                 engine_healthy_recorded_time_array,
             ) = self.initialize_engine_healthy_recorded_time_flag()
             engine_healthy_recorded_time_array[0] = time.time()
-            # infer_live_flag_shm = self.initialize_engine_live_flag()
+            infer_live_flag_shm = self.initialize_engine_live_flag()
         infer_seed_increment = paddle.full(shape=[self.args.max_batch_size, 1], fill_value=4, dtype="int64")
         # thread_executor = ThreadPoolExecutor(max_workers=1)
         real_bsz = None

llm/server/server/server/engine/token_processor.py

@@ -100,13 +100,12 @@ def process_sampling_results(self):
             except Exception as e:
                 model_server_logger.info("while get input_data error: {0} {1}".format(e, str(traceback.format_exc())))
 
-    def postprocess(self, batch_result, exist_finished_task=False):
+    def postprocess(self, batch_result):
         """
         single post-processing function
 
         Args:
             batch_result (list): batch results
-            exist_finished_task (bool): whether there is a finished task
         """
         result_dir = "./generate_token_results"
         if not os.path.exists(result_dir):
@@ -213,7 +212,6 @@ def _process_batch_output(self):
             accept_num = tokens[2 : batch + 2]
 
         batch_result = list()
-        exist_finished_task = False
         for i in range(batch):
             if self.resource_manager.stop_flags[i]:
                 continue
@@ -248,11 +246,10 @@ def _process_batch_output(self):
                         f"Speculate accept ratio: {1 - self.total_step * 1.0 / self.number_of_output_tokens}"
                         f" total step: {self.total_step}. total_output_token_num: {self.number_of_output_tokens}"
                     )
-                    exist_finished_task = True
                     break
             batch_result.append(result)
 
-        self.postprocess(batch_result, exist_finished_task)
+        self.postprocess(batch_result)
 
 
 class WarmUpTokenProcessor(TokenProcessor):
@@ -265,7 +262,7 @@ def __init__(self, cfg):
         self._is_running = True
         self._is_blocking = True
 
-    def postprocess(self, batch_result, exist_finished_task=False):
+    def postprocess(self, batch_result):
         pass
 
     def process_sampling_results(self):

llm/server/server/server/triton_server.py

@@ -111,12 +111,12 @@ def _cache_special_tokens(self, batch_result):
                             ["req_id"]] + batch_result[i]["token_scores"]
                     del self.score_buffer[batch_result[i]["req_id"]]
 
-    def postprocess(self, batch_result, exist_finished_task=False):
+    def postprocess(self, batch_result):
         """
         single postprocess for triton
         """
         try:
-            self._cache_special_tokens(batch_result)
+            # self._cache_special_tokens(batch_result)
             self.cached_generated_tokens.put(batch_result)
         except Exception as e:
             model_server_logger.info(