[Feature] implement log channel separation and request log level system (#7190)

* feat: implement log channel separation and request log level system

* fix: log system improvements based on review

* add request_id to error logs, use RequestLogLevel enum, and unify logger implementation from utils to logger module

docs/usage/environment_variables.md

@@ -25,7 +25,7 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "FD_LOG_REQUESTS": lambda: int(os.getenv("FD_LOG_REQUESTS", "1")),
 
     # Request logging detail level (0-3). Higher level means more verbose output.
-    "FD_LOG_REQUESTS_LEVEL": lambda: int(os.getenv("FD_LOG_REQUESTS_LEVEL", "0")),
+    "FD_LOG_REQUESTS_LEVEL": lambda: int(os.getenv("FD_LOG_REQUESTS_LEVEL", "2")),
 
     # Max field length for request logging truncation.
     "FD_LOG_MAX_LEN": lambda: int(os.getenv("FD_LOG_MAX_LEN", "2048")),

docs/usage/log.md

@@ -5,12 +5,48 @@
 FastDeploy generates the following log files during deployment. Below is an explanation of each log's purpose.
 By default, logs are stored in the `log` directory under the execution path. To specify a custom directory, set the environment variable `FD_LOG_DIR`.
 
+## Log Channel Separation
+
+FastDeploy separates logs into three channels:
+
+| Channel | Logger Name | Output Files | Description |
+|---------|-------------|--------------|-------------|
+| main | `fastdeploy.main.*` | `fastdeploy.log`, `console.log` | Main logs for system configuration, startup info, etc. |
+| request | `fastdeploy.request.*` | `request.log` | Request logs for request lifecycle and processing details |
+| console | `fastdeploy.console.*` | `console.log` | Console logs, output to terminal and console.log |
+
+## Request Log Levels
+
+Request logs (`request.log`) support 4 levels, controlled by the environment variable `FD_LOG_REQUESTS_LEVEL`:
+
+| Level | Enum Name | Description | Example Content |
+|-------|-----------|-------------|-----------------|
+| 0 | LIFECYCLE | Lifecycle start/end | Request creation/initialization, completion stats (InputToken/OutputToken/latency), first and last streaming response, request abort |
+| 1 | STAGES | Processing stages | Semaphore acquire/release, first token time recording, signal handling (preemption/abortion/recovery), cache task, preprocess time, parameter adjustment warnings |
+| 2 | CONTENT | Content and scheduling | Request parameters, processed request, scheduling info (enqueue/pull/finish), response content (long content is truncated) |
+| 3 | FULL | Complete raw data | Complete request and response data, raw received request |
+
+Default level is 2 (CONTENT), which logs request parameters, scheduling info, and response content. Lower levels (0-1) only log critical events, while level 3 includes complete raw data.
+
+## Log-Related Environment Variables
+
+| Variable | Default | Description |
+|----------|---------|-------------|
+| `FD_LOG_DIR` | `log` | Log file storage directory |
+| `FD_LOG_LEVEL` | `INFO` | Log level, supports `INFO` or `DEBUG` |
+| `FD_LOG_REQUESTS` | `1` | Enable request logging, `0` to disable, `1` to enable |
+| `FD_LOG_REQUESTS_LEVEL` | `2` | Request log level, range 0-3 |
+| `FD_LOG_MAX_LEN` | `2048` | Maximum length for L2 level log content (excess is truncated) |
+| `FD_LOG_BACKUP_COUNT` | `7` | Number of log files to retain |
+| `FD_DEBUG` | `0` | Debug mode, `1` enables DEBUG log level |
+
 ## Inference Service Logs
+
+* `fastdeploy.log` : Main log file, records system configuration, startup information, runtime status, etc.
+* `request.log` : Request log file, records user request lifecycle and processing details
+* `console.log` : Console log, records model startup time and other information. This log is also printed to the console.
+* `error.log` : Error log file, records all ERROR and above level logs
 * `backup_env.*.json` : Records environment variables set during instance startup. The number of files matches the number of GPU cards.
-* `envlog.*` : Logs environment variables set during instance startup. The number of files matches the number of GPU cards.
-* `console.log` : Records model startup time and other information. This log is also printed to the console.
-* `data_processor.log` : Logs input/output data encoding and decoding details.
-* `fastdeploy.log` : Records configuration information during instance startup, as well as request and response details during runtime.
 * `workerlog.*` : Tracks model loading progress and inference operator errors. Each GPU card has a corresponding file.
 * `worker_process.log` : Logs engine inference data for each iteration.
 * `cache_manager.log` : Records KV Cache logical index allocation for each request and cache hit status.
@@ -18,9 +54,6 @@ By default, logs are stored in the `log` directory under the execution path. To
 * `gpu_worker.log` : Records KV Cache block count information during profiling.
 * `gpu_model_runner.log` : Contains model details and loading time.
 
-## Online Inference Client Logs
-* `api_server.log` : Logs startup parameters and received request information.
-
 ## Scheduler Logs
 * `scheduler.log` : Records scheduler information, including node status and request allocation details.
 

docs/zh/usage/environment_variables.md

@@ -25,7 +25,7 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "FD_LOG_REQUESTS": lambda: int(os.getenv("FD_LOG_REQUESTS", "1")),
 
     # 请求日志详细级别 (0-3)。级别越高输出越详细。
-    "FD_LOG_REQUESTS_LEVEL": lambda: int(os.getenv("FD_LOG_REQUESTS_LEVEL", "0")),
+    "FD_LOG_REQUESTS_LEVEL": lambda: int(os.getenv("FD_LOG_REQUESTS_LEVEL", "2")),
 
     # 请求日志字段截断最大长度。
     "FD_LOG_MAX_LEN": lambda: int(os.getenv("FD_LOG_MAX_LEN", "2048")),

docs/zh/usage/log.md

@@ -2,15 +2,51 @@
 
 # 日志说明
 
-FastDeploy 在部署过程中，会产生如下日志文件，各日志含义说明
+FastDeploy 在部署过程中，会产生如下日志文件，各日志含义说明。
 默认日志目录为执行目录下的 `log` 文件夹，若需要指定可设置环境变量 `FD_LOG_DIR`。
 
+## 日志通道划分
+
+FastDeploy 将日志分为三个通道：
+
+| 通道 | Logger 名称 | 输出文件 | 说明 |
+|------|-------------|----------|------|
+| main | `fastdeploy.main.*` | `fastdeploy.log`, `console.log` | 主日志，记录系统配置、启动信息等 |
+| request | `fastdeploy.request.*` | `request.log` | 请求日志，记录请求生命周期和处理细节 |
+| console | `fastdeploy.console.*` | `console.log` | 控制台日志，输出到终端和 console.log |
+
+## 请求日志级别
+
+请求日志 (`request.log`) 支持 4 个级别，通过环境变量 `FD_LOG_REQUESTS_LEVEL` 控制：
+
+| 级别 | 枚举名 | 说明 | 示例内容 |
+|------|--------|------|----------|
+| 0 | LIFECYCLE | 生命周期起止 | 请求创建/初始化、完成统计（InputToken/OutputToken/耗时）、流式响应首次和最后发送、请求中止 |
+| 1 | STAGES | 处理阶段 | 信号量获取/释放、首 token 时间记录、信号处理（preemption/abortion/recovery）、缓存任务、预处理耗时、参数调整警告 |
+| 2 | CONTENT | 内容和调度 | 请求参数、处理后的请求、调度信息（入队/拉取/完成）、响应内容（超长内容会被截断） |
+| 3 | FULL | 完整数据 | 完整的请求和响应数据、原始接收请求 |
+
+默认级别为 2 (CONTENT)，记录请求参数、调度信息和响应内容。较低级别 (0-1) 只记录关键事件，级别 3 则包含完整原始数据。
+
+## 日志相关环境变量
+
+| 环境变量 | 默认值 | 说明 |
+|----------|--------|------|
+| `FD_LOG_DIR` | `log` | 日志文件存储目录 |
+| `FD_LOG_LEVEL` | `INFO` | 日志级别，支持 `INFO` 或 `DEBUG` |
+| `FD_LOG_REQUESTS` | `1` | 是否启用请求日志，`0` 禁用，`1` 启用 |
+| `FD_LOG_REQUESTS_LEVEL` | `2` | 请求日志级别，范围 0-3 |
+| `FD_LOG_MAX_LEN` | `2048` | L2 级别日志内容的最大长度（超出部分会被截断） |
+| `FD_LOG_BACKUP_COUNT` | `7` | 日志文件保留数量 |
+| `FD_DEBUG` | `0` | 调试模式，`1` 启用时日志级别设为 `DEBUG` |
+
 ## 推理服务日志
+
+* `fastdeploy.log` : 主日志文件，记录系统配置、启动信息、运行状态等
+* `request.log` : 请求日志文件，记录用户请求的生命周期和处理细节
+* `console.log` : 控制台日志，记录模型启动耗时等信息，该日志信息会被打印到控制台
+* `error.log` : 错误日志文件，记录所有 ERROR 及以上级别的日志
 * `backup_env.*.json` : 记录当前实例启动时设置的环境变量，文件个数与卡数相同
-* `envlog.*` : 记录当前实例启动时设置的环境变量，文件个数与卡数相同
-* `console.log` : 记录模型启动耗时等信息，该日志信息会被打印到控制台
-* `data_processor.log` : 记录输入数据及输出输出编码解码的内容
-* `fastdeploy.log` : 记录当前实例启动的各个 config 的信息，运行中记录用户请求的 request 及 response 信息
 * `workerlog.*` : 记录模型启动加载进度及推理算子报错信息，每个卡对应一个文件
 * `worker_process.log` : 记录引擎每一轮推理的数据
 * `cache_manager.log` : 记录每一个请求分配 KV Cache 的逻辑索引，以及当前请求的命中情况
@@ -18,9 +54,6 @@ FastDeploy 在部署过程中，会产生如下日志文件，各日志含义说
 * `gpu_worker.log` : 记录 profile 时计算 KV Cache block 数目的信息
 * `gpu_model_runner.log` : 当前的模型信息及加载时间
 
-## 在线推理客户端日志
-* `api_server.log` : 记录启动参数，及接收到的请求信息
-
 ## 调度器日志
 * `scheduler.log` : 记录调度器的信息包含当前结点的信息，每条请求分配的信息
 
@@ -31,12 +64,11 @@ FastDeploy 在部署过程中，会产生如下日志文件，各日志含义说
 
 * `cache_queue_manager.log` : 记录启动参数，及接收到的请求信息
 * `cache_transfer_manager.log` : 记录启动参数，及接收到的请求信息
-* `cache_queue_manager.log` : 记录启动参数，及接收到的请求信息
 * `launch_cache_manager.log` : 启动 cache transfer 记录启动参数，报错信息
 
 ## PD 分离相关日志
 
-* `cache_messager.log` : 记录P 实例使用的传输协议及传输信息
+* `cache_messager.log` : 记录 P 实例使用的传输协议及传输信息
 * `splitwise_connector.log` : 记录收到 P/D 发送的数据，及建联信息
 
 ## CudaGraph 相关日志

fastdeploy/engine/async_llm.py

@@ -37,6 +37,7 @@ from fastdeploy.entrypoints.openai.utils import DealerConnectionManager
 from fastdeploy.input.preprocess import InputPreprocessor
 from fastdeploy.inter_communicator import IPCSignal
 from fastdeploy.inter_communicator.zmq_client import ZmqIpcClient
+from fastdeploy.logger.request_logger import log_request_error
 from fastdeploy.metrics.metrics import main_process_metrics
 from fastdeploy.utils import EngineError, envs, llm_logger
 
@@ -562,7 +563,7 @@ class AsyncLLM(EngineServiceClient):
             llm_logger.info(f"Request {conn_request_id} generator exit (outer)")
             return
         except Exception as e:
-            llm_logger.error(f"Request {conn_request_id} failed: {e}")
+            log_request_error(message="Request {request_id} failed: {error}", request_id=conn_request_id, error=e)
             raise EngineError(str(e), error_code=500) from e
         finally:
             # Ensure request_map/request_num are cleaned up
@@ -584,7 +585,7 @@ class AsyncLLM(EngineServiceClient):
                 await self.connection_manager.cleanup_request(request_id)
             llm_logger.info(f"Aborted request {request_id}")
         except Exception as e:
-            llm_logger.error(f"Failed to abort request {request_id}: {e}")
+            log_request_error(message="Failed to abort request {request_id}: {error}", request_id=request_id, error=e)
 
     async def shutdown(self):
         """

fastdeploy/engine/engine.py

@@ -44,6 +44,11 @@ from fastdeploy.engine.common_engine import (
 from fastdeploy.engine.expert_service import start_data_parallel_service
 from fastdeploy.engine.request import Request
 from fastdeploy.inter_communicator import EngineWorkerQueue, IPCSignal
+from fastdeploy.logger.request_logger import (
+    RequestLogLevel,
+    log_request,
+    log_request_error,
+)
 from fastdeploy.metrics.metrics import main_process_metrics
 from fastdeploy.platforms import current_platform
 from fastdeploy.utils import EngineError, console_logger, envs, llm_logger
@@ -285,7 +290,7 @@ class LLMEngine:
         # Create Request struct after processing
         request = Request.from_dict(task)
         request.metrics.scheduler_recv_req_time = time.time()
-        llm_logger.info(f"Receive request {request}")
+        log_request(RequestLogLevel.CONTENT, message="Receive request {request}", request=request)
         request.metrics.preprocess_start_time = time.time()
 
         request.prompt_token_ids_len = len(request.prompt_token_ids)
@@ -304,12 +309,20 @@ class LLMEngine:
                 f"Input text is too long, length of prompt token({input_ids_len}) "
                 f"+ min_dec_len ({min_tokens}) >= max_model_len "
             )
-            llm_logger.error(error_msg)
+            log_request_error(
+                message="request[{request_id}] error: {error}",
+                request_id=request.get("request_id"),
+                error=error_msg,
+            )
             raise EngineError(error_msg, error_code=400)
 
         if input_ids_len > self.cfg.model_config.max_model_len:
             error_msg = f"Length of input token({input_ids_len}) exceeds the limit max_model_len({self.cfg.model_config.max_model_len})."
-            llm_logger.error(error_msg)
+            log_request_error(
+                message="request[{request_id}] error: {error}",
+                request_id=request.get("request_id"),
+                error=error_msg,
+            )
             raise EngineError(error_msg, error_code=400)
 
         if request.get("stop_seqs_len") is not None:
@@ -320,7 +333,11 @@ class LLMEngine:
                     f"Length of stop ({stop_seqs_len}) exceeds the limit max_stop_seqs_num({max_stop_seqs_num})."
                     "Please reduce the number of stop or set a lager max_stop_seqs_num by `FD_MAX_STOP_SEQS_NUM`"
                 )
-                llm_logger.error(error_msg)
+                log_request_error(
+                    message="request[{request_id}] error: {error}",
+                    request_id=request.get("request_id"),
+                    error=error_msg,
+                )
                 raise EngineError(error_msg, error_code=400)
             stop_seqs_max_len = envs.FD_STOP_SEQS_MAX_LEN
             for single_stop_seq_len in stop_seqs_len:
@@ -329,7 +346,11 @@ class LLMEngine:
                         f"Length of stop_seqs({single_stop_seq_len}) exceeds the limit stop_seqs_max_len({stop_seqs_max_len})."
                         "Please reduce the length of stop sequences or set a larger stop_seqs_max_len by `FD_STOP_SEQS_MAX_LEN`"
                     )
-                    llm_logger.error(error_msg)
+                    log_request_error(
+                        message="request[{request_id}] error: {error}",
+                        request_id=request.get("request_id"),
+                        error=error_msg,
+                    )
                     raise EngineError(error_msg, error_code=400)
 
         if self._has_guided_input(request):
@@ -342,14 +363,22 @@ class LLMEngine:
                 request, err_msg = self.guided_decoding_checker.schema_format(request)
 
             if err_msg is not None:
-                llm_logger.error(err_msg)
+                log_request_error(
+                    message="request[{request_id}] error: {error}",
+                    request_id=request.get("request_id"),
+                    error=err_msg,
+                )
                 raise EngineError(err_msg, error_code=400)
 
         request.metrics.preprocess_end_time = time.time()
         request.metrics.scheduler_recv_req_time = time.time()
         self.engine.scheduler.put_requests([request])
-        llm_logger.info(f"Cache task with request_id ({request.get('request_id')})")
+        log_request(
-        llm_logger.debug(f"cache task: {request}")
+            RequestLogLevel.STAGES,
+            message="Cache task with request_id ({request_id})",
+            request_id=request.get("request_id"),
+        )
+        log_request(RequestLogLevel.FULL, message="cache task: {request}", request=request)
 
     def _worker_processes_ready(self):
         """
@@ -717,11 +746,16 @@ class LLMEngine:
         Yields:
             dict: The generated response.
         """
-        llm_logger.info(f"Starting generation for prompt: {prompts}")
+        log_request(RequestLogLevel.CONTENT, message="Starting generation for prompt: {prompts}", prompts=prompts)
         try:
             req_id = self._format_and_add_data(prompts)
         except Exception as e:
-            llm_logger.error(f"Error happened while adding request, details={e}, {str(traceback.format_exc())}")
+            log_request_error(
+                message="request[{request_id}] error while adding request: {error}, {traceback}",
+                request_id=prompts.get("request_id"),
+                error=str(e),
+                traceback=traceback.format_exc(),
+            )
             raise EngineError(str(e), error_code=400)
 
         # Get the result of the current request
@@ -740,7 +774,7 @@ class LLMEngine:
                 output = self.engine.data_processor.process_response_dict(
                     result.to_dict(), stream=False, include_stop_str_in_output=False, direct_decode=not stream
                 )
-                llm_logger.debug(f"Generate result: {output}")
+                log_request(RequestLogLevel.FULL, message="Generate result: {output}", output=output)
                 if not stream:
                     yield output
                 else:

fastdeploy/engine/request.py

@@ -39,7 +39,11 @@ from fastdeploy.entrypoints.openai.protocol import (
     StructuralTagResponseFormat,
     ToolCall,
 )
-from fastdeploy.utils import data_processor_logger
+from fastdeploy.logger.request_logger import (
+    RequestLogLevel,
+    log_request,
+    log_request_error,
+)
 from fastdeploy.worker.output import (
     LogprobsLists,
     PromptLogprobs,
@@ -313,15 +317,13 @@ class Request:
             ), "The parameter `raw_request` is not supported now, please use completion api instead."
             for key, value in req.metadata.items():
                 setattr(request, key, value)
-            from fastdeploy.utils import api_server_logger
+            log_request(RequestLogLevel.STAGES, message="The parameter metadata is obsolete.")
-
-            api_server_logger.warning("The parameter metadata is obsolete.")
 
         return request
 
     @classmethod
     def from_dict(cls, d: dict):
-        data_processor_logger.debug(f"{d}")
+        log_request(RequestLogLevel.FULL, message="{request}", request=d)
         sampling_params: SamplingParams = None
         pooling_params: PoolingParams = None
         metrics: RequestMetrics = None
@@ -352,8 +354,11 @@ class Request:
                         ImagePosition(**mm_pos) if not isinstance(mm_pos, ImagePosition) else mm_pos
                     )
             except Exception as e:
-                data_processor_logger.error(
+                log_request_error(
-                    f"Convert mm_positions to ImagePosition error: {e}, {str(traceback.format_exc())}"
+                    message="request[{request_id}] Convert mm_positions to ImagePosition error: {error}, {traceback}",
+                    request_id=d.get("request_id"),
+                    error=str(e),
+                    traceback=traceback.format_exc(),
                 )
         return cls(
             request_id=d["request_id"],

fastdeploy/entrypoints/api_server.py

@@ -23,6 +23,11 @@ from fastapi.responses import Response, StreamingResponse
 
 from fastdeploy.engine.args_utils import EngineArgs
 from fastdeploy.engine.engine import LLMEngine
+from fastdeploy.logger.request_logger import (
+    RequestLogLevel,
+    log_request,
+    log_request_error,
+)
 from fastdeploy.utils import (
     FlexibleArgumentParser,
     api_server_logger,
@@ -61,7 +66,7 @@ async def generate(request: dict):
     """
     generate stream api
     """
-    api_server_logger.info(f"Receive request: {request}")
+    log_request(RequestLogLevel.FULL, message="Receive request: {request}", request=request)
     stream = request.get("stream", 0)
 
     if not stream:
@@ -72,7 +77,11 @@ async def generate(request: dict):
                 output = result
         except Exception as e:
             # 记录完整的异常堆栈信息
-            api_server_logger.error(f"Error during generation: {e!s}", exc_info=True)
+            log_request_error(
+                message="request[{request_id}] Error during generation: {error}",
+                request_id=request.get("request_id"),
+                error=str(e),
+            )
             # 返回结构化的错误消息并终止流
             output = {"error": str(e), "error_type": e.__class__.__name__}
         return output
@@ -84,7 +93,11 @@ async def generate(request: dict):
                 yield f"data: {json.dumps(result)}\n\n"
         except Exception as e:
             # 记录完整的异常堆栈信息
-            api_server_logger.error(f"Error during generation: {e!s}", exc_info=True)
+            log_request_error(
+                message="request[{request_id}] Error during generation: {error}",
+                request_id=request.get("request_id"),
+                error=str(e),
+            )
             # 返回结构化的错误消息并终止流
             error_msg = {"error": str(e), "error_type": e.__class__.__name__}
             yield f"data: {json.dumps(error_msg)}\n\n"

fastdeploy/entrypoints/engine_client.py

@@ -49,6 +49,11 @@ from fastdeploy.inter_communicator import (
     RearrangeExpertStatus,
     ZmqIpcClient,
 )
+from fastdeploy.logger.request_logger import (
+    RequestLogLevel,
+    log_request,
+    log_request_error,
+)
 from fastdeploy.metrics.metrics import main_process_metrics
 from fastdeploy.platforms import current_platform
 from fastdeploy.trace.constants import LoggingEventName
@@ -362,12 +367,16 @@ class EngineClient:
 
             if "messages" in task:
                 task["messages"] = None
-            api_server_logger.info(f"task['max_tokens']:{task['max_tokens']}")
             main_process_metrics.request_params_max_tokens.observe(task["max_tokens"])
             main_process_metrics.prompt_tokens_total.inc(input_ids_len)
             main_process_metrics.request_prompt_tokens.observe(input_ids_len)
         except Exception as e:
-            api_server_logger.error(f"add_requests error: {e}, {str(traceback.format_exc())}")
+            log_request_error(
+                message="request[{request_id}] add_requests error: {error}, {traceback}",
+                request_id=task.get("request_id"),
+                error=e,
+                traceback=traceback.format_exc(),
+            )
             raise EngineError(str(e), error_code=400)
 
         if input_ids_len + min_tokens >= self.max_model_len:
@@ -375,14 +384,18 @@ class EngineClient:
                 f"Input text is too long, input_ids_len ({input_ids_len}) "
                 f"+ min_tokens({min_tokens}) >= max_model_len({self.max_model_len})"
             )
-            api_server_logger.error(error_msg)
+            log_request_error(
+                message="request[{request_id}] {error_msg}", request_id=task.get("request_id"), error_msg=error_msg
+            )
             raise EngineError(error_msg, error_code=400)
 
         if input_ids_len > self.max_model_len:
             error_msg = (
                 f"Length of input token({input_ids_len}) exceeds the limit max_model_len({self.max_model_len})."
             )
-            api_server_logger.error(error_msg)
+            log_request_error(
+                message="request[{request_id}] {error_msg}", request_id=task.get("request_id"), error_msg=error_msg
+            )
             raise EngineError(error_msg, error_code=400)
 
         if "stop_seqs_len" in task and task["stop_seqs_len"]:
@@ -393,7 +406,9 @@ class EngineClient:
                     f"Length of stop ({stop_seqs_len}) exceeds the limit max_stop_seqs_num({max_stop_seqs_num})."
                     "Please reduce the number of stop or set a lager max_stop_seqs_num by `FD_MAX_STOP_SEQS_NUM`"
                 )
-                api_server_logger.error(error_msg)
+                log_request_error(
+                    message="request[{request_id}] {error_msg}", request_id=task.get("request_id"), error_msg=error_msg
+                )
                 raise EngineError(error_msg, error_code=400)
             stop_seqs_max_len = envs.FD_STOP_SEQS_MAX_LEN
             for single_stop_seq_len in stop_seqs_len:
@@ -402,18 +417,28 @@ class EngineClient:
                         f"Length of stop_seqs({single_stop_seq_len}) exceeds the limit stop_seqs_max_len({stop_seqs_max_len})."
                         "Please reduce the length of stop sequences or set a larger stop_seqs_max_len by `FD_STOP_SEQS_MAX_LEN`"
                     )
-                    api_server_logger.error(error_msg)
+                    log_request_error(
+                        message="request[{request_id}] {error_msg}",
+                        request_id=task.get("request_id"),
+                        error_msg=error_msg,
+                    )
                     raise EngineError(error_msg, error_code=400)
 
         task["metrics"]["preprocess_end_time"] = time.time()
         preprocess_cost_time = task["metrics"]["preprocess_end_time"] - task["metrics"]["preprocess_start_time"]
-        api_server_logger.info(
+        log_request(
-            f"Cache request with request_id ({task.get('request_id')}), "
+            level=RequestLogLevel.STAGES,
-            f"preprocess time cost {preprocess_cost_time}"
+            message="Cache request with request_id ({request_id}), preprocess time cost {preprocess_cost_time}",
+            request_id=task.get("request_id"),
+            preprocess_cost_time=preprocess_cost_time,
         )
 
         self.valid_parameters(task)
-        api_server_logger.debug(f"Receive task: {task}")
+        log_request(
+            level=RequestLogLevel.FULL,
+            message="Receive task: {task}",
+            task=task,
+        )
         n = task.get("n", 1)
         try:
             request_id_idx = task.get("request_id")
@@ -433,7 +458,12 @@ class EngineClient:
                 tracing.TraceSpanName.PREPROCESSING, task.get("request_id").split("_")[0], thread_finish_flag=True
             )
         except Exception as e:
-            api_server_logger.error(f"zmq_client send task error: {e}, {str(traceback.format_exc())}")
+            log_request_error(
+                message="request[{request_id}] zmq_client send task error: {error}, {traceback}",
+                request_id=task.get("request_id"),
+                error=e,
+                traceback=traceback.format_exc(),
+            )
             raise EngineError(str(e), error_code=400)
 
     def _send_task(self, task):
@@ -455,8 +485,11 @@ class EngineClient:
 
         if data.get("max_tokens") is not None:
             if data["max_tokens"] < 1 or data["max_tokens"] >= self.max_model_len:
-                api_server_logger.error(
+                log_request_error(
-                    f"req_id:{data['request_id']}, max_tokens must be defined [1, {self.max_model_len}), but now it's {data['max_tokens']}."
+                    message="req_id:{request_id}, max_tokens must be defined [1, {max_model_len}), but now it's {max_tokens}.",
+                    request_id=data["request_id"],
+                    max_model_len=self.max_model_len,
+                    max_tokens=data["max_tokens"],
                 )
                 raise ValueError(
                     f"max_tokens can be defined [1, {self.max_model_len}), but now it's {data['max_tokens']}."
@@ -467,14 +500,18 @@ class EngineClient:
                 raise ParameterError("reasoning_max_tokens", "reasoning_max_tokens must be greater than 0")
             if data["reasoning_max_tokens"] > data["max_tokens"]:
                 data["reasoning_max_tokens"] = data["max_tokens"]
-                api_server_logger.warning(
+                log_request(
-                    f"req_id: {data['request_id']}, reasoning_max_tokens exceeds max_tokens, the value of reasoning_max_tokens will be adjusted to {data['max_tokens']}"
+                    level=RequestLogLevel.STAGES,
+                    message="req_id: {request_id}, reasoning_max_tokens exceeds max_tokens, the value of reasoning_max_tokens will be adjusted to {max_tokens}",
+                    request_id=data["request_id"],
+                    max_tokens=data["max_tokens"],
                 )
             if data.get("reasoning_effort") is not None:
                 data["reasoning_max_tokens"] = None
-                api_server_logger.warning(
+                log_request(
-                    f"req_id: {data['request_id']}, reasoning_max_tokens and reasoning_effort are both set, "
+                    level=RequestLogLevel.STAGES,
-                    f"enable_thinking will be disabled."
+                    message="req_id: {request_id}, reasoning_max_tokens and reasoning_effort are both set, enable_thinking will be disabled.",
+                    request_id=data["request_id"],
                 )
 
         if data.get("response_max_tokens") is not None:
@@ -493,7 +530,9 @@ class EngineClient:
                 is_chat = True
                 if not self.enable_logprob:
                     err_msg = "Logprobs is disabled, please enable it in startup config."
-                    api_server_logger.error(err_msg)
+                    log_request_error(
+                        message="request[{request_id}] {err_msg}", request_id=data.get("request_id"), err_msg=err_msg
+                    )
                     raise ParameterError("logprobs", err_msg)
                 top_logprobs = data.get("top_logprobs")
         elif isinstance(logprobs, int):
@@ -506,11 +545,15 @@ class EngineClient:
             max_logprobs = self.ori_vocab_size
         if max_logprobs < -1:
             err_msg = f"Invalid 'max_logprobs': must be >= -1, got {max_logprobs}."
-            api_server_logger.error(err_msg)
+            log_request_error(
+                message="request[{request_id}] {err_msg}", request_id=data.get("request_id"), err_msg=err_msg
+            )
             raise ValueError("max_logprobs", err_msg)
         if max_logprobs > self.ori_vocab_size:
             err_msg = f"Invalid 'max_logprobs': must be <= vocab_size {self.ori_vocab_size}, got {max_logprobs}."
-            api_server_logger.error(err_msg)
+            log_request_error(
+                message="request[{request_id}] {err_msg}", request_id=data.get("request_id"), err_msg=err_msg
+            )
             raise ValueError("max_logprobs", err_msg)
 
         prompt_logprobs = data.get("prompt_logprobs", None)
@@ -518,41 +561,55 @@ class EngineClient:
         if prompt_logprobs is not None:
             if not self.enable_logprob:
                 err_msg = "`enable_logprob` is disabled, please enable it in startup config."
-                api_server_logger.error(err_msg)
+                log_request_error(
+                    message="request[{request_id}] {err_msg}", request_id=data.get("request_id"), err_msg=err_msg
+                )
                 raise ParameterError("prompt_logprobs", err_msg)
 
             if not envs.FD_USE_GET_SAVE_OUTPUT_V1:
                 err_msg = "prompt_logprobs is not support when FD_USE_GET_SAVE_OUTPUT_V1 is disabled."
-                api_server_logger.error(err_msg)
+                log_request_error(
+                    message="request[{request_id}] {err_msg}", request_id=data.get("request_id"), err_msg=err_msg
+                )
                 raise ParameterError("prompt_logprobs", err_msg)
 
             if self.enable_prefix_caching:
                 err_msg = "prompt_logprobs is not support when prefix caching is enabled."
-                api_server_logger.error(err_msg)
+                log_request_error(
+                    message="request[{request_id}] {err_msg}", request_id=data.get("request_id"), err_msg=err_msg
+                )
                 raise ParameterError("prompt_logprobs", err_msg)
 
             if prompt_logprobs == -1 and self.ori_vocab_size > max_logprobs:
                 err_msg = f"The requested value of ({self.ori_vocab_size}) for prompt_logprobs (-1) exceeds the maximum allowed value of ({max_logprobs})"
-                api_server_logger.error(err_msg)
+                log_request_error(
+                    message="request[{request_id}] {err_msg}", request_id=data.get("request_id"), err_msg=err_msg
+                )
                 raise ValueError("prompt_logprobs", err_msg)
 
             if prompt_logprobs < -1:
                 err_msg = (
                     f"prompt_logprobs must be a non-negative value or -1; the current value is {prompt_logprobs}."
                 )
-                api_server_logger.error(err_msg)
+                log_request_error(
+                    message="request[{request_id}] {err_msg}", request_id=data.get("request_id"), err_msg=err_msg
+                )
                 raise ValueError("prompt_logprobs", err_msg)
 
             if prompt_logprobs > max_logprobs:
                 err_msg = f"Number of prompt_logprobs requested ({prompt_logprobs}) exceeds maximum allowed value ({max_logprobs})."
-                api_server_logger.error(err_msg)
+                log_request_error(
+                    message="request[{request_id}] {err_msg}", request_id=data.get("request_id"), err_msg=err_msg
+                )
                 raise ValueError("prompt_logprobs", err_msg)
 
         # enable_logprob
         if top_logprobs is not None:
             if not self.enable_logprob:
                 err_msg = "Logprobs is disabled, please enable it in startup config."
-                api_server_logger.error(err_msg)
+                log_request_error(
+                    message="request[{request_id}] {err_msg}", request_id=data.get("request_id"), err_msg=err_msg
+                )
                 raise ParameterError("top_logprobs" if is_chat else "logprobs", err_msg)
 
             if not isinstance(top_logprobs, int):
@@ -560,28 +617,38 @@ class EngineClient:
                 err_msg = (
                     f"Invalid type for {'top_logprobs' if is_chat else 'logprobs'}: expected int but got {err_type}."
                 )
-                api_server_logger.error(err_msg)
+                log_request_error(
+                    message="request[{request_id}] {err_msg}", request_id=data.get("request_id"), err_msg=err_msg
+                )
                 raise ParameterError("top_logprobs" if is_chat else "logprobs", err_msg)
 
             if top_logprobs > max_logprobs:
                 err_msg = f"Number of {'top_logprobs' if is_chat else 'logprobs'} requested ({top_logprobs}) exceeds maximum allowed value ({max_logprobs})."
-                api_server_logger.error(err_msg)
+                log_request_error(
+                    message="request[{request_id}] {err_msg}", request_id=data.get("request_id"), err_msg=err_msg
+                )
                 raise ValueError("top_logprobs" if is_chat else "logprobs", err_msg)
 
             if not envs.FD_USE_GET_SAVE_OUTPUT_V1:
                 if top_logprobs < 0 or top_logprobs > max_logprobs:
                     err_msg = f"{'top_logprobs' if is_chat else 'logprobs'} must be between 0 and {max_logprobs}; the current value is {top_logprobs}."
-                    api_server_logger.error(err_msg)
+                    log_request_error(
+                        message="request[{request_id}] {err_msg}", request_id=data.get("request_id"), err_msg=err_msg
+                    )
                     raise ValueError("top_logprobs" if is_chat else "logprobs", err_msg)
             else:
                 if top_logprobs == -1 and self.ori_vocab_size > max_logprobs:
                     err_msg = f"The requested value of ({self.ori_vocab_size}) for {'top_logprobs' if is_chat else 'logprobs'} (-1) exceeds the maximum allowed value of ({max_logprobs})"
-                    api_server_logger.error(err_msg)
+                    log_request_error(
+                        message="request[{request_id}] {err_msg}", request_id=data.get("request_id"), err_msg=err_msg
+                    )
                     raise ValueError("top_logprobs" if is_chat else "logprobs", err_msg)
 
                 if top_logprobs < -1:
                     err_msg = f"{'top_logprobs' if is_chat else 'logprobs'} must be a non-negative value or -1; the current value is {top_logprobs}."
-                    api_server_logger.error(err_msg)
+                    log_request_error(
+                        message="request[{request_id}] {err_msg}", request_id=data.get("request_id"), err_msg=err_msg
+                    )
                     raise ValueError("top_logprobs" if is_chat else "logprobs", err_msg)
 
     def check_health(self, time_interval_threashold=30):
@@ -617,12 +684,21 @@ class EngineClient:
             return response
         except asyncio.TimeoutError:
             error_response = ControlResponse(request_id, 500, "Timeout waiting for control method response")
-            api_server_logger.error(f"Control request timed out: {error_response}")
+            log_request_error(
+                message="request[{request_id}] Control request timed out: {error_response}",
+                request_id=request_id,
+                error_response=error_response,
+            )
             return error_response
         except Exception as e:
             import traceback
 
-            api_server_logger.error(f"Unknown error in control method: {str(e)}\n{traceback.format_exc()}")
+            log_request_error(
+                message="request[{request_id}] Unknown error in control method: {error}\n{traceback}",
+                request_id=request_id,
+                error=str(e),
+                traceback=traceback.format_exc(),
+            )
             error_response = ControlResponse(request_id, 500, str(e))
             return error_response
 
@@ -1024,7 +1100,11 @@ class EngineClient:
 
     async def abort(self, request_id, n=1) -> None:
         if envs.FD_ENABLE_REQUEST_DISCONNECT_STOP_INFERENCE:
-            api_server_logger.info(f"abort request_id:{request_id}")
+            log_request(
+                level=RequestLogLevel.LIFECYCLE,
+                message="abort request_id: {request_id}",
+                request_id=request_id,
+            )
             if n <= 0:
                 api_server_logger.warning("Abort function called with non-positive n: %d. No requests aborted.", n)
                 return
@@ -1044,7 +1124,11 @@ class EngineClient:
                 }
                 self._send_task(data)
 
-            api_server_logger.info("Aborted request(s) %s.", ",".join(request_ids))
+            log_request(
+                level=RequestLogLevel.LIFECYCLE,
+                message="Aborted request(s) {request_ids}.",
+                request_ids=",".join(request_ids),
+            )
 
     def process_messages(self, messages):
         for message in messages:

fastdeploy/entrypoints/llm.py

@@ -36,6 +36,7 @@ from fastdeploy.entrypoints.chat_utils import load_chat_template
 from fastdeploy.entrypoints.openai.protocol import ChatCompletionToolsParam
 from fastdeploy.entrypoints.openai.tool_parsers import ToolParserManager
 from fastdeploy.input.utils import validate_model_path
+from fastdeploy.logger.request_logger import log_request_error
 from fastdeploy.utils import (
     deprecated_kwargs_warning,
     llm_logger,
@@ -139,7 +140,11 @@ class LLM:
                                 continue
                             self.req_output[request_id].add(result)
             except Exception as e:
-                llm_logger.error(f"Unexpected error happened: {e}, {traceback.format_exc()!s}")
+                log_request_error(
+                    message="Unexpected error happened: {error}, {traceback}",
+                    error=e,
+                    traceback=traceback.format_exc(),
+                )
 
     def generate(
         self,
@@ -437,7 +442,11 @@ class LLM:
             return result
 
         except Exception as e:
-            llm_logger.error(f"Error building sample logprobs from LogprobsLists: {e}, {str(traceback.format_exc())}")
+            log_request_error(
+                message="Error building sample logprobs from LogprobsLists: {error}, {traceback}",
+                error=e,
+                traceback=traceback.format_exc(),
+            )
 
     def _build_prompt_logprobs(
         self,

fastdeploy/entrypoints/openai/api_server.py

@@ -73,6 +73,11 @@ from fastdeploy.entrypoints.openai.v1.serving_completion import (
     OpenAIServingCompletion as OpenAIServingCompletionV1,
 )
 from fastdeploy.envs import environment_variables
+from fastdeploy.logger.request_logger import (
+    RequestLogLevel,
+    log_request,
+    log_request_error,
+)
 from fastdeploy.metrics.metrics import get_filtered_metrics
 from fastdeploy.utils import (
     ExceptionHandler,
@@ -325,7 +330,11 @@ async def connection_manager():
         await asyncio.wait_for(connection_semaphore.acquire(), timeout=0.001)
         yield
     except asyncio.TimeoutError:
-        api_server_logger.info(f"Reach max request concurrency, semaphore status: {connection_semaphore.status()}")
+        log_request(
+            level=RequestLogLevel.LIFECYCLE,
+            message="Reach max request concurrency, semaphore status: {status}",
+            status=connection_semaphore.status(),
+        )
         raise HTTPException(
             status_code=429, detail=f"Too many requests,current max concurrency is {args.max_concurrency}"
         )
@@ -545,7 +554,7 @@ async def create_chat_completion(request: ChatCompletionRequest, req: Request):
     """
     Create a chat completion for the provided prompt and parameters.
     """
-    api_server_logger.debug(f"Chat Received request: {request.model_dump_json()}")
+    log_request(RequestLogLevel.FULL, message="Chat Received request: {request}", request=request.model_dump_json())
     if envs.TRACES_ENABLE:
         if req.headers:
             headers = dict(req.headers)
@@ -572,7 +581,11 @@ async def create_chat_completion(request: ChatCompletionRequest, req: Request):
                 return StreamingResponse(content=wrapped_generator(), media_type="text/event-stream")
 
     except HTTPException as e:
-        api_server_logger.error(f"Error in chat completion: {str(e)}")
+        log_request_error(
+            message="request[{request_id}] Error in chat completion: {error}",
+            request_id=getattr(request, "request_id", None),
+            error=str(e),
+        )
         return JSONResponse(status_code=e.status_code, content={"detail": e.detail})
 
 
@@ -582,7 +595,9 @@ async def create_completion(request: CompletionRequest, req: Request):
     """
     Create a completion for the provided prompt and parameters.
     """
-    api_server_logger.info(f"Completion Received request: {request.model_dump_json()}")
+    log_request(
+        RequestLogLevel.FULL, message="Completion Received request: {request}", request=request.model_dump_json()
+    )
     if envs.TRACES_ENABLE:
         if req.headers:
             headers = dict(req.headers)

fastdeploy/entrypoints/openai/protocol.py

@@ -31,6 +31,7 @@ from pydantic import (
 )
 
 from fastdeploy.engine.pooling_params import PoolingParams
+from fastdeploy.logger.request_logger import RequestLogLevel, log_request
 from fastdeploy.worker.output import PromptLogprobs, SpeculateMetrics
 
 
@@ -758,9 +759,7 @@ class ChatCompletionRequest(BaseModel):
             ), "The parameter `raw_request` is not supported now, please use completion api instead."
             for key, value in self.metadata.items():
                 req_dict[key] = value
-            from fastdeploy.utils import api_server_logger
+            log_request(RequestLogLevel.STAGES, message="The parameter metadata is obsolete.")
-
-            api_server_logger.warning("The parameter metadata is obsolete.")
         for key, value in self.dict().items():
             if value is not None:
                 req_dict[key] = value

fastdeploy/entrypoints/openai/serving_chat.py

@@ -44,6 +44,11 @@ from fastdeploy.entrypoints.openai.protocol import (
     UsageInfo,
 )
 from fastdeploy.entrypoints.openai.response_processors import ChatResponseProcessor
+from fastdeploy.logger.request_logger import (
+    RequestLogLevel,
+    log_request,
+    log_request_error,
+)
 from fastdeploy.metrics.metrics import main_process_metrics
 from fastdeploy.trace.constants import LoggingEventName
 from fastdeploy.trace.trace_logger import print as trace_print
@@ -112,14 +117,16 @@ class OpenAIServingChat:
             err_msg = (
                 f"Only master node can accept completion request, please send request to master node: {self.master_ip}"
             )
-            api_server_logger.error(err_msg)
+            log_request_error(message="request[{request_id}] {error}", request_id=request.request_id, error=err_msg)
             return ErrorResponse(error=ErrorInfo(message=err_msg, type=ErrorType.INTERNAL_ERROR))
 
         if self.models:
             is_supported, request.model = self.models.is_supported_model(request.model)
             if not is_supported:
                 err_msg = f"Unsupported model: [{request.model}], support [{', '.join([x.name for x in self.models.model_paths])}] or default"
-                api_server_logger.error(err_msg)
+                log_request_error(
+                    message="request[{request_id}] {error}", request_id=request.request_id, error=err_msg
+                )
                 return ErrorResponse(
                     error=ErrorInfo(message=err_msg, type=ErrorType.INTERNAL_ERROR, code=ErrorCode.MODEL_NOT_SUPPORT)
                 )
@@ -129,7 +136,11 @@ class OpenAIServingChat:
                 await self.engine_client.semaphore.acquire()
             else:
                 await asyncio.wait_for(self.engine_client.semaphore.acquire(), timeout=self.max_waiting_time)
-            api_server_logger.info(f"current {self.engine_client.semaphore.status()}")
+            log_request(
+                RequestLogLevel.STAGES,
+                message="semaphore status: {status}",
+                status=self.engine_client.semaphore.status(),
+            )
 
             if request.request_id is not None:
                 request_id = request.request_id
@@ -141,7 +152,11 @@ class OpenAIServingChat:
                 request_id = f"chatcmpl-{uuid.uuid4()}"
             tracing.trace_req_start(rid=request_id, trace_content=request.trace_context, role="FastDeploy")
             del request.trace_context
-            api_server_logger.info(f"create chat completion request: {request_id}")
+            log_request(
+                level=RequestLogLevel.LIFECYCLE,
+                message="create chat completion request: {request_id}",
+                request_id=request_id,
+            )
             prompt_tokens = None
             max_tokens = None
             try:
@@ -156,14 +171,19 @@ class OpenAIServingChat:
                 if isinstance(prompt_token_ids, np.ndarray):
                     prompt_token_ids = prompt_token_ids.tolist()
             except ParameterError as e:
-                api_server_logger.error(f"request[{request_id}] generator error: {str(e)}, {e.message}")
+                log_request_error(
+                    message="request[{request_id}] generator error: {error}, {error_message}",
+                    request_id=request_id,
+                    error=str(e),
+                    error_message=e.message,
+                )
                 self.engine_client.semaphore.release()
                 return ErrorResponse(
                     error=ErrorInfo(message=str(e.message), type=ErrorType.INVALID_REQUEST_ERROR, param=e.param)
                 )
             except Exception as e:
                 error_msg = f"request[{request_id}] generator error: {str(e)}, {str(traceback.format_exc())}"
-                api_server_logger.error(error_msg)
+                log_request_error(message=error_msg)
                 self.engine_client.semaphore.release()
                 return ErrorResponse(error=ErrorInfo(message=error_msg, type=ErrorType.INVALID_REQUEST_ERROR))
 
@@ -178,12 +198,12 @@ class OpenAIServingChat:
                     )
                 except Exception as e:
                     error_msg = f"request[{request_id}]full generator error: {str(e)}, {str(traceback.format_exc())}"
-                    api_server_logger.error(error_msg)
+                    log_request_error(message=error_msg)
                     return ErrorResponse(error=ErrorInfo(message=error_msg, type=ErrorType.INTERNAL_ERROR))
         except asyncio.CancelledError as e:
             await self.engine_client.abort(f"{request_id}_0", 1 if request.n is None else request.n)
             error_msg = f"request[{request_id}_0] client disconnected: {str(e)}, {str(traceback.format_exc())}"
-            api_server_logger.error(error_msg)
+            log_request_error(message=error_msg)
             return ErrorResponse(
                 error=ErrorInfo(message=error_msg, type=ErrorType.INVALID_REQUEST_ERROR, code=ErrorCode.CLIENT_ABORTED)
             )
@@ -192,13 +212,13 @@ class OpenAIServingChat:
                 f"request[{request_id}] waiting error: {str(e)}, {str(traceback.format_exc())}, "
                 f"max waiting time: {self.max_waiting_time}"
             )
-            api_server_logger.error(error_msg)
+            log_request_error(message=error_msg)
             return ErrorResponse(
                 error=ErrorInfo(message=error_msg, type=ErrorType.TIMEOUT_ERROR, code=ErrorCode.TIMEOUT)
             )
 
     def _create_streaming_error_response(self, message: str) -> str:
-        api_server_logger.error(message)
+        log_request_error(message=message)
         error_response = ErrorResponse(error=ErrorInfo(message=message, type=ErrorType.INTERNAL_ERROR))
         return error_response.model_dump_json()
 
@@ -249,7 +269,9 @@ class OpenAIServingChat:
             choices=[],
             model=model_name,
         )
-        api_server_logger.info(f"create chat completion request: {request_id}")
+        log_request(
+            RequestLogLevel.LIFECYCLE, message="create chat completion request: {request_id}", request_id=request_id
+        )
 
         try:
             dealer, response_queue = await self.engine_client.connection_manager.get_connection(
@@ -372,7 +394,12 @@ class OpenAIServingChat:
                                     completion_tokens_details=CompletionTokenUsageInfo(reasoning_tokens=0),
                                 )
                             yield f"data: {chunk.model_dump_json(exclude_unset=True)} \n\n"
-                            api_server_logger.info(f"Chat Streaming response send_idx 0: {chunk.model_dump_json()}")
+                            log_request(
+                                level=RequestLogLevel.LIFECYCLE,
+                                message="Chat Streaming response send_idx 0: request_id={request_id}, completion_tokens={completion_tokens}",
+                                request_id=request_id,
+                                completion_tokens=0,
+                            )
                         first_iteration = False
 
                     output = res["outputs"]
@@ -497,7 +524,14 @@ class OpenAIServingChat:
                         chunk.choices = choices
                         yield f"data: {chunk.model_dump_json(exclude_unset=True)}\n\n"
                         if res["finished"]:
-                            api_server_logger.info(f"Chat Streaming response last send: {chunk.model_dump_json()}")
+                            log_request(
+                                level=RequestLogLevel.LIFECYCLE,
+                                message="Chat Streaming response last send: request_id={request_id}, finish_reason={finish_reason}, completion_tokens={completion_tokens}, logprobs={logprobs}",
+                                request_id=request_id,
+                                finish_reason=choice.finish_reason,
+                                completion_tokens=previous_num_tokens[idx],
+                                logprobs=logprobs_res,
+                            )
                         choices = []
 
             if include_usage:
@@ -525,7 +559,7 @@ class OpenAIServingChat:
         except asyncio.CancelledError as e:
             await self.engine_client.abort(f"{request_id}_0", 1 if request.n is None else request.n)
             error_msg = f"request[{request_id}_0] client disconnected: {str(e)}, {str(traceback.format_exc())}"
-            api_server_logger.error(error_msg)
+            log_request_error(message=error_msg)
         except Exception as e:
             error_data = self._create_streaming_error_response(
                 f"request[{request_id}] generate stream error: {str(e)}, {str(traceback.format_exc())}"
@@ -536,7 +570,12 @@ class OpenAIServingChat:
             tracing.trace_req_finish(request_id)
             await self.engine_client.connection_manager.cleanup_request(request_id)
             self.engine_client.semaphore.release()
-            api_server_logger.info(f"release {request_id} {self.engine_client.semaphore.status()}")
+            log_request(
+                level=RequestLogLevel.STAGES,
+                message="release {request_id} {status}",
+                request_id=request_id,
+                status=self.engine_client.semaphore.status(),
+            )
             yield "data: [DONE]\n\n"
 
     async def chat_completion_full_generator(
@@ -704,7 +743,9 @@ class OpenAIServingChat:
             tracing.trace_req_finish(request_id)
             await self.engine_client.connection_manager.cleanup_request(request_id)
             self.engine_client.semaphore.release()
-            api_server_logger.info(f"release {self.engine_client.semaphore.status()}")
+            log_request(
+                RequestLogLevel.STAGES, message="release {status}", status=self.engine_client.semaphore.status()
+            )
 
         num_prompt_tokens = len(prompt_token_ids)
         num_generated_tokens = sum(previous_num_tokens)
@@ -731,7 +772,7 @@ class OpenAIServingChat:
             choices=choices,
             usage=usage,
         )
-        api_server_logger.info(f"Chat response: {res.model_dump_json()}")
+        log_request(RequestLogLevel.CONTENT, message="Chat response: {response}", response=res.model_dump_json())
         return res
 
     async def _create_chat_completion_choice(
@@ -904,7 +945,7 @@ class OpenAIServingChat:
 
         except Exception as e:
             error_msg = f"Error in _build_logprobs_response: {e}, {str(traceback.format_exc())}"
-            api_server_logger.error(error_msg)
+            log_request_error(message=error_msg)
             return None
 
     def _build_prompt_logprobs(

fastdeploy/entrypoints/openai/serving_completion.py

@@ -41,6 +41,11 @@ from fastdeploy.entrypoints.openai.protocol import (
     PromptTokenUsageInfo,
     UsageInfo,
 )
+from fastdeploy.logger.request_logger import (
+    RequestLogLevel,
+    log_request,
+    log_request_error,
+)
 from fastdeploy.trace.constants import LoggingEventName
 from fastdeploy.trace.trace_logger import print as trace_print
 from fastdeploy.utils import (
@@ -91,13 +96,15 @@ class OpenAIServingCompletion:
             err_msg = (
                 f"Only master node can accept completion request, please send request to master node: {self.master_ip}"
             )
-            api_server_logger.error(err_msg)
+            log_request_error(message="request[{request_id}] {error}", request_id=request.request_id, error=err_msg)
             return ErrorResponse(error=ErrorInfo(message=err_msg, type=ErrorType.INTERNAL_ERROR))
         if self.models:
             is_supported, request.model = self.models.is_supported_model(request.model)
             if not is_supported:
                 err_msg = f"Unsupported model: [{request.model}], support [{', '.join([x.name for x in self.models.model_paths])}] or default"
-                api_server_logger.error(err_msg)
+                log_request_error(
+                    message="request[{request_id}] {error}", request_id=request.request_id, error=err_msg
+                )
                 return ErrorResponse(
                     error=ErrorInfo(message=err_msg, type=ErrorType.INTERNAL_ERROR, code=ErrorCode.MODEL_NOT_SUPPORT)
                 )
@@ -110,7 +117,11 @@ class OpenAIServingCompletion:
             request_id = f"cmpl-{request.user}-{uuid.uuid4()}"
         else:
             request_id = f"cmpl-{uuid.uuid4()}"
-        api_server_logger.info(f"Initialize request {request_id}: {request}")
+        log_request(
+            level=RequestLogLevel.LIFECYCLE,
+            message="Initialize request {request_id}",
+            request_id=request_id,
+        )
         tracing.trace_req_start(rid=request_id, trace_content=request.trace_context, role="FastDeploy")
         del request.trace_context
         request_prompt_ids = None
@@ -147,15 +158,20 @@ class OpenAIServingCompletion:
                 else:
                     raise ValueError("Prompt type must be one of: str, list[str], list[int], list[list[int]]")
         except Exception as e:
-            error_msg = f"OpenAIServingCompletion create_completion: {e}, {str(traceback.format_exc())}"
+            error_msg = f"request[{request_id}] create_completion: {e}, {str(traceback.format_exc())}"
-            api_server_logger.error(error_msg)
+            log_request_error(message=error_msg)
             return ErrorResponse(error=ErrorInfo(message=error_msg, type=ErrorType.INTERNAL_ERROR))
 
         if request_prompt_ids is not None:
             request_prompts = request_prompt_ids
 
         num_choices = len(request_prompts) * (1 if request.n is None else request.n)
-        api_server_logger.info(f"Start preprocessing request: req_id={request_id}), num_choices={num_choices}")
+        log_request(
+            RequestLogLevel.STAGES,
+            message="Start preprocessing request: req_id={request_id}), num_choices={num_choices}",
+            request_id=request_id,
+            num_choices=num_choices,
+        )
         prompt_batched_token_ids = []
         prompt_tokens_list = []
         max_tokens_list = []
@@ -169,7 +185,7 @@ class OpenAIServingCompletion:
                 f"OpenAIServingCompletion waiting error: {e}, {str(traceback.format_exc())}, "
                 f"max waiting time: {self.max_waiting_time}"
             )
-            api_server_logger.error(error_msg)
+            log_request_error(message="request[{request_id}] {error}", request_id=request_id, error=error_msg)
             return ErrorResponse(
                 error=ErrorInfo(message=error_msg, code=ErrorCode.TIMEOUT, type=ErrorType.TIMEOUT_ERROR)
             )
@@ -188,14 +204,19 @@ class OpenAIServingCompletion:
                     max_tokens_list.append(current_req_dict.get("max_tokens"))
                     del current_req_dict
             except ParameterError as e:
-                api_server_logger.error(f"OpenAIServingCompletion format error: {e}, {e.message}")
+                log_request_error(
+                    message="request[{request_id}] format error: {error}, {error_message}",
+                    request_id=request_id,
+                    error=e,
+                    error_message=e.message,
+                )
                 self.engine_client.semaphore.release()
                 return ErrorResponse(
                     error=ErrorInfo(code="400", message=str(e.message), type="invalid_request", param=e.param)
                 )
             except Exception as e:
-                error_msg = f"OpenAIServingCompletion format error: {e}, {str(traceback.format_exc())}"
+                error_msg = f"request[{request_id}] format error: {e}, {str(traceback.format_exc())}"
-                api_server_logger.error(error_msg)
+                log_request_error(message=error_msg)
                 self.engine_client.semaphore.release()
                 return ErrorResponse(
                     error=ErrorInfo(message=str(e), code=ErrorCode.INVALID_VALUE, type=ErrorType.INVALID_REQUEST_ERROR)
@@ -226,20 +247,20 @@ class OpenAIServingCompletion:
                     )
                 except Exception as e:
                     error_msg = (
-                        f"OpenAIServingCompletion completion_full_generator error: {e}, {str(traceback.format_exc())}"
+                        f"request[{request_id}] completion_full_generator error: {e}, {str(traceback.format_exc())}"
                     )
-                    api_server_logger.error(error_msg)
+                    log_request_error(message=error_msg)
                     return ErrorResponse(error=ErrorInfo(message=error_msg, type=ErrorType.INTERNAL_ERROR))
         except asyncio.CancelledError as e:
             await self.engine_client.abort(f"{request_id}_0", num_choices)
             error_msg = f"request[{request_id}_0] client disconnected: {str(e)}, {str(traceback.format_exc())}"
-            api_server_logger.error(error_msg)
+            log_request_error(message=error_msg)
             return ErrorResponse(
                 error=ErrorInfo(message=error_msg, type=ErrorType.INVALID_REQUEST_ERROR, code=ErrorCode.CLIENT_ABORTED)
             )
         except Exception as e:
-            error_msg = f"OpenAIServingCompletion create_completion error: {e}, {str(traceback.format_exc())}"
+            error_msg = f"request[{request_id}] create_completion error: {e}, {str(traceback.format_exc())}"
-            api_server_logger.error(error_msg)
+            log_request_error(message=error_msg)
             return ErrorResponse(error=ErrorInfo(message=error_msg, type=ErrorType.INTERNAL_ERROR))
 
     async def completion_full_generator(
@@ -368,10 +389,16 @@ class OpenAIServingCompletion:
                 prompt_tokens_list=prompt_tokens_list,
                 max_tokens_list=max_tokens_list,
             )
-            api_server_logger.info(f"Completion response: {res.model_dump_json()}")
+            log_request(
+                RequestLogLevel.CONTENT, message="Completion response: {response}", response=res.model_dump_json()
+            )
             return res
         except Exception as e:
-            api_server_logger.error(f"Error in completion_full_generator: {e}", exc_info=True)
+            log_request_error(
+                message="request[{request_id}] error in completion_full_generator: {error}",
+                request_id=request_id,
+                error=e,
+            )
         finally:
             trace_print(LoggingEventName.POSTPROCESSING_END, request_id, getattr(request, "user", ""))
             tracing.trace_req_finish(request_id)
@@ -514,8 +541,11 @@ class OpenAIServingCompletion:
                                 ],
                             )
                             yield f"data: {chunk.model_dump_json(exclude_unset=True)}\n\n"
-                            api_server_logger.info(
+                            log_request(
-                                f"Completion Streaming response send_idx 0: {chunk.model_dump_json()}"
+                                level=RequestLogLevel.LIFECYCLE,
+                                message="Completion Streaming response send_idx 0: request_id={request_id}, completion_tokens={completion_tokens}",
+                                request_id=request_id,
+                                completion_tokens=0,
                             )
                         first_iteration[idx] = False
 
@@ -592,8 +622,11 @@ class OpenAIServingCompletion:
                     if send_idx == 0 and not request.return_token_ids:
                         chunk_temp = chunk
                         chunk_temp.choices = choices
-                        api_server_logger.info(
+                        log_request(
-                            f"Completion Streaming response send_idx 0: {chunk_temp.model_dump_json()}"
+                            level=RequestLogLevel.LIFECYCLE,
+                            message="Completion Streaming response send_idx 0: request_id={request_id}, completion_tokens={completion_tokens}",
+                            request_id=request_id,
+                            completion_tokens=output_tokens[idx],
                         )
                         del chunk_temp
 
@@ -646,14 +679,26 @@ class OpenAIServingCompletion:
                                 metrics=res["metrics"] if request.collect_metrics else None,
                             )
                             yield f"data: {usage_chunk.model_dump_json(exclude_unset=True)}\n\n"
-                        api_server_logger.info(f"Completion Streaming response last send: {chunk.model_dump_json()}")
+                        log_request(
+                            level=RequestLogLevel.LIFECYCLE,
+                            message="Completion Streaming response last send: request_id={request_id}, finish_reason={finish_reason}, completion_tokens={completion_tokens}, logprobs={logprobs}",
+                            request_id=request_id,
+                            finish_reason=chunk.choices[-1].finish_reason if chunk.choices else None,
+                            completion_tokens=output_tokens[idx],
+                            logprobs=logprobs_res,
+                        )
 
         except asyncio.CancelledError as e:
             await self.engine_client.abort(f"{request_id}_0", num_choices)
             error_msg = f"request[{request_id}_0] client disconnected: {str(e)}, {str(traceback.format_exc())}"
-            api_server_logger.error(error_msg)
+            log_request_error(message=error_msg)
         except Exception as e:
-            api_server_logger.error(f"Error in completion_stream_generator: {e}, {str(traceback.format_exc())}")
+            log_request_error(
+                message="request[{request_id}] error in completion_stream_generator: {error}, {traceback}",
+                request_id=request_id,
+                error=e,
+                traceback=traceback.format_exc(),
+            )
             yield f"data: {ErrorResponse(error=ErrorInfo(message=str(e), code='400', type=ErrorType.INTERNAL_ERROR)).model_dump_json(exclude_unset=True)}\n\n"
         finally:
             trace_print(LoggingEventName.POSTPROCESSING_END, request_id, getattr(request, "user", ""))
@@ -887,7 +932,11 @@ class OpenAIServingCompletion:
             )
 
         except Exception as e:
-            api_server_logger.error(f"Error in _build_logprobs_response: {str(e)}, {str(traceback.format_exc())}")
+            log_request_error(
+                message="Error in _build_logprobs_response: {error}, {traceback}",
+                error=str(e),
+                traceback=traceback.format_exc(),
+            )
             return None
 
     def _build_prompt_logprobs(

fastdeploy/entrypoints/openai/serving_embedding.py

@@ -35,7 +35,7 @@ from fastdeploy.entrypoints.openai.protocol import (
     UsageInfo,
 )
 from fastdeploy.entrypoints.openai.serving_engine import ServeContext, ZmqOpenAIServing
-from fastdeploy.utils import api_server_logger
+from fastdeploy.logger.request_logger import RequestLogLevel, log_request
 
 
 def _get_embedding(
@@ -140,7 +140,12 @@ class OpenAIServingEmbedding(ZmqOpenAIServing):
     @override
     def _build_response(self, ctx: ServeContext, request_output: dict):
         """Generate final embedding response"""
-        api_server_logger.info(f"[{ctx.request_id}] Embedding RequestOutput received:{request_output}")
+        log_request(
+            level=RequestLogLevel.CONTENT,
+            message="[{request_id}] Embedding RequestOutput received:{request_output}",
+            request_id=ctx.request_id,
+            request_output=request_output,
+        )
 
         base = PoolingRequestOutput.from_dict(request_output)
         embedding_res = EmbeddingRequestOutput.from_base(base)

fastdeploy/entrypoints/openai/serving_engine.py

@@ -33,6 +33,11 @@ from fastdeploy.entrypoints.openai.protocol import (
     InvalidParameterException,
 )
 from fastdeploy.envs import FD_SUPPORT_MAX_CONNECTIONS
+from fastdeploy.logger.request_logger import (
+    RequestLogLevel,
+    log_request,
+    log_request_error,
+)
 from fastdeploy.utils import ErrorCode, ErrorType, StatefulSemaphore, api_server_logger
 
 RequestT = TypeVar("RequestT")
@@ -96,13 +101,18 @@ class OpenAIServing(ABC, Generic[RequestT]):
         is_supported, adjusted_name = self.models.is_supported_model(model_name)
         if not is_supported:
             err_msg = f"Unsupported model: [{model_name}]"
-            api_server_logger.error(err_msg)
+            log_request_error(message=err_msg)
         return is_supported, adjusted_name
 
     async def _acquire_semaphore(self, request_id: str) -> bool:
         """Acquire engine client semaphore with timeout"""
         try:
-            api_server_logger.info(f"Acquire request:{request_id} status:{self._get_semaphore().status()}")
+            log_request(
+                level=RequestLogLevel.STAGES,
+                message="Acquire request:{request_id} status:{status}",
+                request_id=request_id,
+                status=self._get_semaphore().status(),
+            )
             if self.max_waiting_time < 0:
                 await self._get_semaphore().acquire()
             else:
@@ -111,13 +121,18 @@ class OpenAIServing(ABC, Generic[RequestT]):
         except asyncio.TimeoutError:
             self._release_semaphore(request_id)
             error_msg = f"Request waiting timeout, request:{request_id} max waiting time:{self.max_waiting_time}"
-            api_server_logger.error(error_msg)
+            log_request_error(message=error_msg)
             return False
 
     def _release_semaphore(self, request_id: str) -> None:
         """Release engine client semaphore"""
         self._get_semaphore().release()
-        api_server_logger.info(f"Release request:{request_id} status:{self._get_semaphore().status()}")
+        log_request(
+            level=RequestLogLevel.STAGES,
+            message="Release request:{request_id} status:{status}",
+            request_id=request_id,
+            status=self._get_semaphore().status(),
+        )
 
     def _create_error_response(
         self,
@@ -128,7 +143,7 @@ class OpenAIServing(ABC, Generic[RequestT]):
     ) -> ErrorResponse:
         """Create standardized error response"""
         traceback.print_exc()
-        api_server_logger.error(message)
+        log_request_error(message=message)
         return ErrorResponse(error=ErrorInfo(message=message, type=error_type, code=code, param=param))
 
     def _generate_request_id(self, request: RequestT) -> str:
@@ -193,7 +208,12 @@ class OpenAIServing(ABC, Generic[RequestT]):
 
         request_id = self._generate_request_id(request)
         ctx.request_id = request_id
-        api_server_logger.info(f"Initialize request {request_id}: {request}")
+        log_request(
+            level=RequestLogLevel.LIFECYCLE,
+            message="Initialize request {request_id}: {request}",
+            request_id=request_id,
+            request=request,
+        )
 
         # Step 2: Semaphore acquisition
         if not await self._acquire_semaphore(request_id):
@@ -252,7 +272,12 @@ class ZmqOpenAIServing(OpenAIServing):
         request_dicts = self._request_to_batch_dicts(ctx)
         ctx.preprocess_requests = request_dicts
         for request_dict in request_dicts:
-            api_server_logger.info(f"batch add request_id: {request_dict['request_id']}, request: {request_dict}")
+            log_request(
+                level=RequestLogLevel.CONTENT,
+                message="batch add request_id: {request_id}, request: {request}",
+                request_id=request_dict["request_id"],
+                request=request_dict,
+            )
             await self.engine_client.format_and_add_data(request_dict)
 
     def _process_chat_template_kwargs(self, request_dict):
@@ -283,7 +308,11 @@ class ZmqOpenAIServing(OpenAIServing):
             while num_choices > 0:
                 request_output_dicts = await asyncio.wait_for(request_output_queue.get(), timeout=60)
                 for request_output_dict in request_output_dicts:
-                    api_server_logger.debug(f"Received RequestOutput: {request_output_dict}")
+                    log_request(
+                        level=RequestLogLevel.FULL,
+                        message="Received RequestOutput: {request_output}",
+                        request_output=request_output_dict,
+                    )
                     if request_output_dict["finished"] is True:
                         num_choices -= 1
                     yield request_output_dict
@@ -301,7 +330,12 @@ class ZmqOpenAIServing(OpenAIServing):
     async def _acquire_semaphore(self, request_id: str) -> bool:
         """Acquire engine client semaphore with timeout"""
         try:
-            api_server_logger.info(f"Acquire request:{request_id} status:{self._get_semaphore().status()}")
+            log_request(
+                level=RequestLogLevel.STAGES,
+                message="Acquire request:{request_id} status:{status}",
+                request_id=request_id,
+                status=self._get_semaphore().status(),
+            )
             if self.max_waiting_time < 0:
                 await self._get_semaphore().acquire()
             else:
@@ -310,14 +344,19 @@ class ZmqOpenAIServing(OpenAIServing):
         except asyncio.TimeoutError:
             self._release_semaphore(request_id)
             error_msg = f"Request waiting timeout, request:{request_id} max waiting time:{self.max_waiting_time}"
-            api_server_logger.error(error_msg)
+            log_request_error(message=error_msg)
             return False
 
     @override
     def _release_semaphore(self, request_id: str) -> None:
         """Release engine client semaphore"""
         self._get_semaphore().release()
-        api_server_logger.info(f"Release request:{request_id} status:{self._get_semaphore().status()}")
+        log_request(
+            level=RequestLogLevel.STAGES,
+            message="Release request:{request_id} status:{status}",
+            request_id=request_id,
+            status=self._get_semaphore().status(),
+        )
 
     @override
     def _check_master(self) -> bool:

fastdeploy/entrypoints/openai/serving_models.py

@@ -24,7 +24,8 @@ from fastdeploy.entrypoints.openai.protocol import (
     ModelList,
     ModelPermission,
 )
-from fastdeploy.utils import ErrorType, api_server_logger, get_host_ip
+from fastdeploy.logger.request_logger import log_request_error
+from fastdeploy.utils import ErrorType, get_host_ip
 
 
 @dataclass
@@ -86,7 +87,7 @@ class OpenAIServingModels:
             err_msg = (
                 f"Only master node can accept models request, please send request to master node: {self.master_ip}"
             )
-            api_server_logger.error(err_msg)
+            log_request_error(message=err_msg)
             return ErrorResponse(error=ErrorInfo(message=err_msg, type=ErrorType.INTERNAL_ERROR))
         model_infos = [
             ModelInfo(

fastdeploy/entrypoints/openai/serving_reward.py

@@ -28,7 +28,7 @@ from fastdeploy.entrypoints.openai.protocol import (
     UsageInfo,
 )
 from fastdeploy.entrypoints.openai.serving_engine import ServeContext, ZmqOpenAIServing
-from fastdeploy.utils import api_server_logger
+from fastdeploy.logger.request_logger import RequestLogLevel, log_request
 
 
 class OpenAIServingReward(ZmqOpenAIServing):
@@ -77,7 +77,7 @@ class OpenAIServingReward(ZmqOpenAIServing):
         response: ChatRewardResponse = None
         generators: AsyncGenerator[ChatRewardResponse, None] = self.handle(ctx)
         async for r in generators:
-            api_server_logger.info(f"engine pooling result:{r}")
+            log_request(RequestLogLevel.CONTENT, message="engine pooling result: {result}", result=r)
             r.data[0].index = idx
             idx += 1
             if response is None or isinstance(r, ErrorResponse):
@@ -93,7 +93,12 @@ class OpenAIServingReward(ZmqOpenAIServing):
     @override
     def _build_response(self, ctx: ServeContext, request_output: dict):
         """Generate final reward response"""
-        api_server_logger.info(f"[{ctx.request_id}] Reward RequestOutput received:{request_output}")
+        log_request(
+            level=RequestLogLevel.CONTENT,
+            message="Reward RequestOutput received: request_id={request_id}, output={request_output}",
+            request_id=ctx.request_id,
+            request_output=request_output,
+        )
 
         base = PoolingRequestOutput.from_dict(request_output)
         reward_res = RewardRequestOutput.from_base(base)

fastdeploy/entrypoints/openai/tool_parsers/ernie_45_vl_thinking_tool_parser.py

@@ -41,6 +41,7 @@ from fastdeploy.entrypoints.openai.tool_parsers.abstract_tool_parser import (
     ToolParser,
     ToolParserManager,
 )
+from fastdeploy.logger.request_logger import log_request_error
 from fastdeploy.utils import data_processor_logger
 
 
@@ -184,7 +185,9 @@ class Ernie45VLThinkingToolParser(ToolParser):
                     continue
 
             if not function_call_arr:
-                data_processor_logger.error("No valid tool calls found")
+                log_request_error(
+                    message="request[{request_id}] No valid tool calls found", request_id=request.request_id
+                )
                 return ExtractedToolCallInformation(tools_called=False, content=model_output)
 
             tool_calls = []
@@ -226,7 +229,11 @@ class Ernie45VLThinkingToolParser(ToolParser):
             )
 
         except Exception as e:
-            data_processor_logger.error(f"Error in extracting tool call from response: {str(e)}")
+            log_request_error(
+                message="request[{request_id}] Error in extracting tool call from response: {error}",
+                request_id=request.request_id,
+                error=str(e),
+            )
             return ExtractedToolCallInformation(tools_called=False, tool_calls=None, content=model_output)
 
     def extract_tool_calls_streaming(
@@ -343,7 +350,11 @@ class Ernie45VLThinkingToolParser(ToolParser):
                             )
                             return delta
                     except Exception as e:
-                        data_processor_logger.error(f"Error in streaming tool call extraction: {str(e)}")
+                        log_request_error(
+                            message="request[{request_id}] Error in streaming tool call extraction: {error}",
+                            request_id=request.get("request_id"),
+                            error=str(e),
+                        )
                         return None
             if "</tool_call>" in self.buffer:
                 end_pos = self.buffer.find("</tool_call>")
@@ -354,5 +365,9 @@ class Ernie45VLThinkingToolParser(ToolParser):
             return delta
 
         except Exception as e:
-            data_processor_logger.error(f"Error in streaming tool call extraction: {str(e)}")
+            log_request_error(
+                message="request[{request_id}] Error in streaming tool call extraction: {error}",
+                request_id=request.get("request_id"),
+                error=str(e),
+            )
             return None

fastdeploy/entrypoints/openai/tool_parsers/ernie_x1_tool_parser.py

@@ -41,6 +41,7 @@ from fastdeploy.entrypoints.openai.tool_parsers.abstract_tool_parser import (
     ToolParser,
     ToolParserManager,
 )
+from fastdeploy.logger.request_logger import log_request_error
 from fastdeploy.utils import data_processor_logger as logger
 
 
@@ -254,8 +255,11 @@ class ErnieX1ToolParser(ToolParser):
                 logger.debug("Skipping text %s - no arguments", delta_text)
                 delta = None
 
-            elif cur_arguments is None and prev_arguments is not None:
+            elif not cur_arguments and prev_arguments:
-                logger.error("should be impossible to have arguments reset " "mid-call. skipping streaming anything.")
+                log_request_error(
+                    message="request[{request_id}] should be impossible to have arguments reset mid-call. skipping streaming anything.",
+                    request_id=request.request_id,
+                )
                 delta = None
 
             elif cur_arguments is not None and prev_arguments is None:

fastdeploy/entrypoints/openai/v1/serving_chat.py

@@ -45,8 +45,12 @@ from fastdeploy.entrypoints.openai.v1.serving_base import (
     ServingResponseContext,
 )
 from fastdeploy.input.tokenizer_client import AsyncTokenizerClient, ImageDecodeRequest
+from fastdeploy.logger.request_logger import (
+    RequestLogLevel,
+    log_request,
+    log_request_error,
+)
 from fastdeploy.metrics.metrics import main_process_metrics
-from fastdeploy.utils import api_server_logger
 from fastdeploy.worker.output import LogprobsLists
 
 
@@ -178,7 +182,7 @@ class OpenAIServingChat(OpenAiServingBase):
 
         except Exception as e:
             error_msg = f"Error in _build_logprobs_response: {e}, {str(traceback.format_exc())}"
-            api_server_logger.error(error_msg)
+            log_request_error(message=error_msg)
             return None
 
     @override
@@ -302,7 +306,14 @@ class OpenAIServingChat(OpenAiServingBase):
             max_tokens = request.max_completion_tokens or request.max_tokens
             choice_completion_tokens = response_ctx.choice_completion_tokens_dict[output.index]
             choice.finish_reason = self._calc_finish_reason(request_output, max_tokens, choice_completion_tokens)
-            api_server_logger.info(f"Chat Streaming response last send: {chunk.model_dump_json()}")
+            log_request(
+                level=RequestLogLevel.LIFECYCLE,
+                message="Chat Streaming response last send: request_id={request_id}, finish_reason={finish_reason}, completion_tokens={completion_tokens}, logprobs={logprobs}",
+                request_id=request_id,
+                finish_reason=choice.finish_reason,
+                completion_tokens=choice_completion_tokens,
+                logprobs=choice.logprobs,
+            )
 
         yield f"data: {chunk.model_dump_json(exclude_unset=True)}\n\n"
         if request_output.finished and response_ctx.remain_choices == 0:
@@ -339,7 +350,11 @@ class OpenAIServingChat(OpenAiServingBase):
         res = ChatCompletionResponse(
             id=ctx.request_id, model=request.model, choices=choices, created=ctx.created_time, usage=response_ctx.usage
         )
-        api_server_logger.info(f"Chat response: {res.model_dump_json()}")
+        log_request(
+            level=RequestLogLevel.CONTENT,
+            message="Chat response: {response}",
+            response=res.model_dump_json(),
+        )
         return res
 
     async def _create_chat_completion_choice(

fastdeploy/entrypoints/openai/v1/serving_completion.py

@@ -38,7 +38,12 @@ from fastdeploy.entrypoints.openai.v1.serving_base import (
     ServeContext,
     ServingResponseContext,
 )
-from fastdeploy.utils import ErrorType, api_server_logger
+from fastdeploy.logger.request_logger import (
+    RequestLogLevel,
+    log_request,
+    log_request_error,
+)
+from fastdeploy.utils import ErrorType
 from fastdeploy.worker.output import LogprobsLists
 
 
@@ -94,7 +99,7 @@ class OpenAIServingCompletion(OpenAiServingBase):
                     raise ValueError("Prompt type must be one of: str, list[str], list[int], list[list[int]]")
         except Exception as e:
             error_msg = f"OpenAIServingCompletion create_completion: {e}, {str(traceback.format_exc())}"
-            api_server_logger.error(error_msg)
+            log_request_error(message=error_msg)
             return ErrorResponse(error=ErrorInfo(message=error_msg, type=ErrorType.INTERNAL_ERROR))
 
         if request_prompt_ids is not None:
@@ -199,7 +204,11 @@ class OpenAIServingCompletion(OpenAiServingBase):
             )
 
         except Exception as e:
-            api_server_logger.error(f"Error in _build_logprobs_response: {str(e)}, {str(traceback.format_exc())}")
+            log_request_error(
+                message="Error in _build_logprobs_response: {error}, {traceback}",
+                error=str(e),
+                traceback=traceback.format_exc(),
+            )
             return None
 
     async def _build_stream_response(
@@ -271,9 +280,21 @@ class OpenAIServingCompletion(OpenAiServingBase):
                 choice.finish_reason = self._calc_finish_reason(
                     request_output, request.max_tokens, choice_completion_tokens
                 )
-                api_server_logger.info(f"Completion Streaming response last send: {chunk.model_dump_json()}")
+                log_request(
+                    level=RequestLogLevel.LIFECYCLE,
+                    message="Completion Streaming response last send: request_id={request_id}, finish_reason={finish_reason}, completion_tokens={completion_tokens}, logprobs={logprobs}",
+                    request_id=request_id,
+                    finish_reason=choice.finish_reason,
+                    completion_tokens=choice_completion_tokens,
+                    logprobs=choice.logprobs,
+                )
             if send_idx == 0 and not request.return_token_ids:
-                api_server_logger.info(f"Completion Streaming response send_idx 0: {chunk.model_dump_json()}")
+                log_request(
+                    level=RequestLogLevel.LIFECYCLE,
+                    message="Completion Streaming response send_idx 0: request_id={request_id}, completion_tokens={completion_tokens}",
+                    request_id=request_id,
+                    completion_tokens=response_ctx.choice_completion_tokens_dict[output.index],
+                )
             yield f"data: {chunk.model_dump_json()}\n\n"
             if request_output.finished and response_ctx.remain_choices == 0:
                 if include_usage:
@@ -287,7 +308,12 @@ class OpenAIServingCompletion(OpenAiServingBase):
                     yield f"data: {usage_chunk.model_dump_json(exclude_unset=True)}\n\n"
                 yield "data: [DONE]\n\n"
         except Exception as e:
-            api_server_logger.error(f"Error in completion_stream_generator: {e}, {str(traceback.format_exc())}")
+            log_request_error(
+                message="request[{request_id}] Error in completion_stream_generator: {error}, {traceback}",
+                request_id=request_id,
+                error=e,
+                traceback=traceback.format_exc(),
+            )
             yield f"data: {ErrorResponse(error=ErrorInfo(message=str(e), code='400', type=ErrorType.INTERNAL_ERROR)).model_dump_json(exclude_unset=True)}\n\n"
 
     async def _build_full_response(
@@ -321,10 +347,18 @@ class OpenAIServingCompletion(OpenAiServingBase):
                 choices=choices,
                 usage=response_ctx.usage,
             )
-            api_server_logger.info(f"Completion response: {res.model_dump_json()}")
+            log_request(
+                level=RequestLogLevel.FULL,
+                message="Completion response: {response}",
+                response=res.model_dump_json(),
+            )
             return res
         except Exception as e:
-            api_server_logger.error(f"Error in completion_full_generator: {e}", exc_info=True)
+            log_request_error(
+                message="request[{request_id}] Error in completion_full_generator: {error}",
+                request_id=ctx.request_id,
+                error=e,
+            )
             return self._create_error_response(str(e))
 
     def build_completion_choice(

fastdeploy/envs.py

@@ -43,7 +43,7 @@ environment_variables: dict[str, Callable[[], Any]] = {
     # Request logging master switch. Set to 0 to disable request logging.
     "FD_LOG_REQUESTS": lambda: int(os.getenv("FD_LOG_REQUESTS", "1")),
     # Request logging detail level (0-3). Higher level means more verbose output.
-    "FD_LOG_REQUESTS_LEVEL": lambda: int(os.getenv("FD_LOG_REQUESTS_LEVEL", "0")),
+    "FD_LOG_REQUESTS_LEVEL": lambda: int(os.getenv("FD_LOG_REQUESTS_LEVEL", "2")),
     # Max field length for request logging truncation.
     "FD_LOG_MAX_LEN": lambda: int(os.getenv("FD_LOG_MAX_LEN", "2048")),
     # Unified trace mode: off, local, otel, all.

fastdeploy/input/base_processor.py

@@ -55,6 +55,7 @@ from paddleformers.transformers import Llama3Tokenizer, LlamaTokenizer
 
 from fastdeploy import envs
 from fastdeploy.input.utils import process_stop_token_ids
+from fastdeploy.logger.request_logger import RequestLogLevel, log_request
 from fastdeploy.utils import data_processor_logger
 
 _SAMPLING_EPS = 1e-5
@@ -164,7 +165,12 @@ class BaseTextProcessor(ABC):
         req_id = request.get("request_id", None) if isinstance(request, dict) else None
         tokens = self.tokenizer.tokenize(spliced_message)
         token_ids = self.tokenizer.convert_tokens_to_ids(tokens)
-        data_processor_logger.info(f"req_id:{req_id}, tokens:{tokens}, token_ids: {token_ids}")
+        log_request(
+            level=1,
+            message="req_id:{req_id}, token_ids: {token_ids}",
+            req_id=req_id,
+            token_ids=token_ids,
+        )
         return token_ids
 
     # ------------------------------------------------------------------
@@ -359,7 +365,7 @@ class BaseTextProcessor(ABC):
 
     def process_request_dict(self, request, max_model_len=None, **kwargs):
         """Unified request pre-processing shared by all processors."""
-        data_processor_logger.info(f"Start processing request dict: {request}")
+        log_request(RequestLogLevel.CONTENT, message="Start processing request dict: {request}", request=request)
         request = self._apply_default_parameters(request)
         if not request.get("eos_token_ids"):
             request["eos_token_ids"] = self.eos_token_ids
@@ -440,7 +446,7 @@ class BaseTextProcessor(ABC):
         if request.get("response_max_tokens") is not None and request.get("enable_thinking") is False:
             request["max_tokens"] = min(request["response_max_tokens"], request["max_tokens"])
 
-        data_processor_logger.info(f"Processed request dict: {request}")
+        log_request(RequestLogLevel.CONTENT, message="Processed request dict: {request}", request=request)
         return request
 
     def _apply_reasoning_parser(self, request):
@@ -481,7 +487,12 @@ class BaseTextProcessor(ABC):
             if seq != self.tokenizer.eos_token_id:
                 stop_seqs.append(self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize(seq)))
         stop_seqs, 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 stop_seqs: {stop_seqs}, {stop_seqs_len}")
+        log_request(
+            level=3,
+            message="processed stop_seqs: {stop_seqs}, {stop_seqs_len}",
+            stop_seqs=stop_seqs,
+            stop_seqs_len=stop_seqs_len,
+        )
         return stop_seqs, stop_seqs_len
 
     # ------------------------------------------------------------------
@@ -607,14 +618,20 @@ class BaseTextProcessor(ABC):
                 prompt_token_ids = self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize(prompt))
                 if len(prompt_token_ids) != 1:
                     if not add_prefix_space:
-                        data_processor_logger.warning(
+                        log_request(
-                            f"bad_words: '{prompt}' tokenises to {len(prompt_token_ids)} tokens, skipping"
+                            level=1,
+                            message="bad_words: '{prompt}' tokenises to {num_tokens} tokens, skipping",
+                            prompt=prompt,
+                            num_tokens=len(prompt_token_ids),
                         )
                     continue
                 if prompt_token_ids[0] > self.tokenizer.vocab_size:
                     if not add_prefix_space:
-                        data_processor_logger.warning(
+                        log_request(
-                            f"bad_words: '{prompt}' token id {prompt_token_ids[0]} > vocab_size, skipping"
+                            level=1,
+                            message="bad_words: '{prompt}' token id {token_id} > vocab_size, skipping",
+                            prompt=prompt,
+                            token_id=prompt_token_ids[0],
                         )
                     continue
                 if prompt_token_ids not in token_ids:

fastdeploy/input/ernie4_5_vl_processor/ernie4_5_vl_processor.py

@@ -22,6 +22,7 @@ from paddleformers.generation import GenerationConfig
 
 from fastdeploy.input.ernie4_5_processor import Ernie4_5Processor
 from fastdeploy.input.utils import IDS_TYPE_FLAG, process_stop_token_ids
+from fastdeploy.logger.request_logger import RequestLogLevel, log_request
 from fastdeploy.utils import data_processor_logger
 
 from .process import DataProcessor
@@ -294,7 +295,7 @@ class Ernie4_5_VLProcessor(Ernie4_5Processor):
         if request.get("response_max_tokens") is not None and request.get("enable_thinking") is False:
             request["max_tokens"] = min(request["response_max_tokens"], request["max_tokens"])
 
-        data_processor_logger.info(f"Processed request {request}")
+        log_request(RequestLogLevel.CONTENT, message="Processed request: {request}", request=request)
         return request
 
     def append_completion_tokens(self, multimodal_inputs, completion_token_ids):

fastdeploy/input/qwen3_vl_processor/qwen3_vl_processor.py

@@ -17,6 +17,7 @@
 import numpy as np
 
 from fastdeploy.input.text_processor import DataProcessor as TextProcessor
+from fastdeploy.logger.request_logger import RequestLogLevel, log_request
 from fastdeploy.utils import data_processor_logger
 
 from .process import DataProcessor
@@ -261,7 +262,7 @@ class Qwen3VLProcessor(TextProcessor):
             request["max_tokens"] = max(1, max_tokens)
         else:
             request["max_tokens"] = min(max_tokens, request["max_tokens"])
-        data_processor_logger.info(f"Processed request {request}")
+        log_request(RequestLogLevel.CONTENT, message="Processed request: {request}", request=request)
 
         return request
 

fastdeploy/input/qwen_vl_processor/qwen_vl_processor.py

@@ -18,6 +18,7 @@ import numpy as np
 
 from fastdeploy.input.text_processor import DataProcessor as TextProcessor
 from fastdeploy.input.utils import process_stop_token_ids
+from fastdeploy.logger.request_logger import RequestLogLevel, log_request
 from fastdeploy.utils import data_processor_logger
 
 from .process import DataProcessor
@@ -262,7 +263,7 @@ class QwenVLProcessor(TextProcessor):
             else:
                 self.model_status_dict[request["request_id"]] = model_status
             request["enable_thinking"] = model_status == "think_start"
-        data_processor_logger.info(f"Processed request {request}")
+        log_request(RequestLogLevel.CONTENT, message="Processed request: {request}", request=request)
 
         return request
 

fastdeploy/input/tokenizer_client.py

@@ -20,6 +20,7 @@ from typing import Any, Optional, Union
 import httpx
 from pydantic import BaseModel, HttpUrl
 
+from fastdeploy.logger.request_logger import log_request, log_request_error
 from fastdeploy.utils import data_processor_logger
 
 
@@ -161,7 +162,12 @@ class AsyncTokenizerClient:
 
                 except httpx.RequestError as e:
                     # Network error, keep polling
-                    data_processor_logger.debug(f"Request error while polling tokenize task {task_tag}: {e}")
+                    log_request(
+                        level=3,
+                        message="Request error while polling tokenize task {task_tag}: {error}",
+                        task_tag=task_tag,
+                        error=str(e),
+                    )
 
                 # 超时检测
                 if asyncio.get_event_loop().time() - start_time > self.max_wait:
@@ -193,10 +199,15 @@ class AsyncTokenizerClient:
                             raise RuntimeError(f"Tokenize task creation failed, {resp.json().get('message')}")
                         return resp.json().get("result")
                     except Exception as e:
-                        data_processor_logger.error(f"Attempt to decode_request {attempt + 1} failed: {e}")
+                        log_request_error(
+                            message="Attempt to decode_request {attempt} failed: {error}",
+                            attempt=attempt + 1,
+                            error=str(e),
+                        )
                         if attempt == self.max_retries - 1:
-                            data_processor_logger.error(
+                            log_request_error(
-                                f"Max retries of decode_request reached. Giving up. request is {request}"
+                                message="Max retries of decode_request reached. Giving up. request is {request}",
+                                request=str(request),
                             )
                         await asyncio.sleep(1)
             except httpx.RequestError as e:

fastdeploy/logger/__init__.py

@@ -0,0 +1,124 @@
+"""
+# Copyright (c) 2025  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+
+"""
+FastDeploy Logging Module
+
+Unified logging management module providing:
+- Formatters: ColoredFormatter, CustomFormatter
+- Handlers: DailyRotatingFileHandler, IntervalRotatingFileHandler, LazyFileHandler
+- Logger: FastDeployLogger
+- Request Logger: log_request, log_request_error, RequestLogLevel
+- Setup: setup_logging
+- Pre-defined logger instances (lazy-loaded)
+
+Usage:
+    from fastdeploy.logger import get_logger, llm_logger, console_logger
+    from fastdeploy.logger import log_request, log_request_error, RequestLogLevel
+"""
+
+# Formatters
+from fastdeploy.logger.formatters import ColoredFormatter, CustomFormatter
+
+# Handlers
+from fastdeploy.logger.handlers import (
+    DailyRotatingFileHandler,
+    IntervalRotatingFileHandler,
+    LazyFileHandler,
+)
+
+# Logger
+from fastdeploy.logger.logger import FastDeployLogger
+
+# Request logger
+from fastdeploy.logger.request_logger import (
+    RequestLogLevel,
+    log_request,
+    log_request_error,
+)
+
+# Setup
+from fastdeploy.logger.setup_logging import setup_logging
+
+
+def get_logger(name, file_name=None, without_formater=False, print_to_console=False, channel=None):
+    """Convenience function to get a logger instance"""
+    return FastDeployLogger().get_logger(name, file_name, without_formater, print_to_console, channel=channel)
+
+
+# Pre-defined logger instance configs (name, file_name, without_formater, print_to_console, channel)
+_LOGGER_CONFIGS = {
+    "llm_logger": ("fastdeploy", None, False, False, "main"),
+    "data_processor_logger": ("data_processor", None, False, False, "main"),
+    "scheduler_logger": ("scheduler", None, False, False, "main"),
+    "api_server_logger": ("api_server", None, False, False, "main"),
+    "console_logger": (None, None, False, False, "console"),
+    "spec_logger": ("speculate", "speculate.log", False, False, None),
+    "zmq_client_logger": ("zmq_client", "comm.log", False, False, None),
+    "router_logger": ("router", "comm.log", False, False, None),
+    "fmq_logger": ("fmq", "comm.log", False, False, None),
+    "obj_logger": ("obj", "obj.log", False, False, None),
+    "register_manager_logger": ("register_manager", "register_manager.log", False, False, None),
+    "_request_logger": ("request", None, False, False, "request"),
+}
+
+_logger_cache = {}
+
+
+def __getattr__(name):
+    """Lazy-load pre-defined logger instances"""
+    if name in _LOGGER_CONFIGS:
+        if name not in _logger_cache:
+            cfg = _LOGGER_CONFIGS[name]
+            _logger_cache[name] = get_logger(cfg[0], cfg[1], cfg[2], cfg[3], cfg[4])
+        return _logger_cache[name]
+    if name == "trace_logger":
+        if name not in _logger_cache:
+            _logger_cache[name] = FastDeployLogger().get_trace_logger("trace", "trace.log")
+        return _logger_cache[name]
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+
+
+__all__ = [
+    # Formatters
+    "ColoredFormatter",
+    "CustomFormatter",
+    # Handlers
+    "DailyRotatingFileHandler",
+    "IntervalRotatingFileHandler",
+    "LazyFileHandler",
+    # Logger
+    "FastDeployLogger",
+    "get_logger",
+    # Request logger
+    "RequestLogLevel",
+    "log_request",
+    "log_request_error",
+    # Setup
+    "setup_logging",
+    # Pre-defined logger instances (lazy-loaded)
+    "llm_logger",
+    "data_processor_logger",
+    "scheduler_logger",
+    "api_server_logger",
+    "console_logger",
+    "spec_logger",
+    "zmq_client_logger",
+    "trace_logger",
+    "router_logger",
+    "fmq_logger",
+    "obj_logger",
+    "register_manager_logger",
+]

fastdeploy/logger/config.py

@@ -0,0 +1,47 @@
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Log configuration parsing module
+"""
+
+import os
+
+
+def resolve_log_level(raw_level=None, debug_enabled=None) -> str:
+    """
+    Resolve log level configuration
+
+    Priority: FD_LOG_LEVEL > FD_DEBUG
+    """
+    raw = os.getenv("FD_LOG_LEVEL") if raw_level is None else raw_level
+    # Handle None or string "None" case
+    if raw and str(raw).upper() != "NONE":
+        level = raw.upper()
+        if level not in {"INFO", "DEBUG"}:
+            raise ValueError(f"Unsupported FD_LOG_LEVEL: {raw}")
+        return level
+    debug = os.getenv("FD_DEBUG", "0") if debug_enabled is None else str(debug_enabled)
+    return "DEBUG" if debug == "1" else "INFO"
+
+
+def resolve_request_logging_defaults() -> dict[str, int]:
+    """
+    Resolve request logging default configuration
+    """
+    return {
+        "enabled": int(os.getenv("FD_LOG_REQUESTS", "1")),
+        "level": int(os.getenv("FD_LOG_REQUESTS_LEVEL", "2")),
+        "max_len": int(os.getenv("FD_LOG_MAX_LEN", "2048")),
+    }

fastdeploy/logger/logger.py

@@ -20,21 +20,36 @@ This module provides the get_logger method to uniformly manage logging behavior
 
 import logging
 import os
+import sys
 import threading
 from contextlib import contextmanager
 from pathlib import Path
 
 from fastdeploy import envs
-from fastdeploy.logger.formatters import CustomFormatter
+from fastdeploy.logger.config import resolve_log_level
+from fastdeploy.logger.formatters import ColoredFormatter, CustomFormatter
 from fastdeploy.logger.handlers import DailyRotatingFileHandler, LazyFileHandler
 from fastdeploy.logger.setup_logging import setup_logging
 
+# Standard log format
+_LOG_FORMAT = "%(levelname)-8s %(asctime)s %(process)-5s %(filename)s[line:%(lineno)d] %(message)s"
+
 
 class FastDeployLogger:
     _instance = None
     _initialized = False
     _lock = threading.RLock()
 
+    # Channel to file mapping
+    _channel_files = {
+        "main": "fastdeploy.log",
+        "request": "request.log",
+        "console": "console.log",
+    }
+
+    # Cache for channel loggers that have been configured
+    _configured_channels = set()
+
     def __new__(cls):
         """Singleton pattern implementation"""
         if cls._instance is None:
@@ -50,7 +65,7 @@ class FastDeployLogger:
                 setup_logging()
                 self._initialized = True
 
-    def get_logger(self, name, file_name=None, without_formater=False, print_to_console=False):
+    def get_logger(self, name, file_name=None, without_formater=False, print_to_console=False, channel=None):
         """
         Get logger (compatible with the original interface)
 
@@ -59,7 +74,14 @@ class FastDeployLogger:
             file_name: Log file name (for compatibility)
             without_formater: Whether to not use a formatter
             print_to_console: Whether to print to console
+            channel: Log channel (main, request, console)
         """
+        # If channel is specified, use channel-based logging
+        if channel is not None:
+            if not self._initialized:
+                self._initialize()
+            return self._get_channel_logger(name, channel)
+
         # If only one parameter is provided, use the new unified naming convention
         if file_name is None and not without_formater and not print_to_console:
             # Lazy initialization
@@ -74,27 +96,95 @@ class FastDeployLogger:
         """
         New unified way to get logger
         """
-        if name is None:
+        return self._get_channel_logger(name, "main")
-            return logging.getLogger("fastdeploy")
 
-        # Handle __main__ special case
+    def _get_channel_logger(self, name, channel):
-        if name == "__main__":
+        """
+        Get logger through channel with manual handler setup.
+
+        Uses manual addHandler instead of dictConfig for better performance.
+        Handlers are attached to the channel root logger (fastdeploy.{channel}),
+        and child loggers propagate to it.
+
+        Args:
+            name: logger name
+            channel: log channel (main, request, console)
+        """
+        # Get or create the channel root logger (all handlers go here)
+        channel_root_name = f"fastdeploy.{channel}"
+        channel_logger = logging.getLogger(channel_root_name)
+
+        # Configure the channel root logger once
+        if channel not in self._configured_channels:
+            self._configured_channels.add(channel)
+
+            log_dir = envs.FD_LOG_DIR
+            os.makedirs(log_dir, exist_ok=True)
+
+            # Resolve log level (priority: FD_LOG_LEVEL > FD_DEBUG)
+            log_level = resolve_log_level()
+            channel_logger.setLevel(logging.DEBUG if log_level == "DEBUG" else logging.INFO)
+
+            # Create formatters
+            file_formatter = logging.Formatter(_LOG_FORMAT)
+            console_formatter = ColoredFormatter(_LOG_FORMAT)
+
+            # Clear existing handlers
+            for handler in channel_logger.handlers[:]:
+                channel_logger.removeHandler(handler)
+
+            # Create file handler for this channel
+            file_name = self._channel_files.get(channel, f"{channel}.log")
+            log_file = os.path.join(log_dir, file_name)
+            backup_count = int(envs.FD_LOG_BACKUP_COUNT)
+
+            file_handler = LazyFileHandler(log_file, backupCount=backup_count)
+            file_handler.setFormatter(file_formatter)
+            channel_logger.addHandler(file_handler)
+
+            # Error file handler (all channels write errors to error.log)
+            error_log_file = os.path.join(log_dir, "error.log")
+            error_file_handler = LazyFileHandler(
+                filename=error_log_file, backupCount=backup_count, level=logging.ERROR
+            )
+            error_file_handler.setFormatter(file_formatter)
+            channel_logger.addHandler(error_file_handler)
+
+            # Stderr handler for ERROR level (all channels output errors to stderr)
+            stderr_handler = logging.StreamHandler(sys.stderr)
+            stderr_handler.setLevel(logging.ERROR)
+            stderr_handler.setFormatter(console_formatter)
+            channel_logger.addHandler(stderr_handler)
+
+            # Console stdout handler for console channel only
+            if channel == "console":
+                stdout_handler = logging.StreamHandler(sys.stdout)
+                stdout_handler.setLevel(logging.DEBUG if log_level == "DEBUG" else logging.INFO)
+                stdout_handler.setFormatter(console_formatter)
+                # Filter to exclude ERROR and above (they go to stderr)
+                stdout_handler.addFilter(lambda record: record.levelno < logging.ERROR)
+                channel_logger.addHandler(stdout_handler)
+
+            channel_logger.propagate = False
+
+        # Determine the actual logger name and return the appropriate logger
+        if name is None or name == "fastdeploy":
+            return channel_logger
+        elif name == "__main__":
             import __main__
 
-            # Get the __file__ attribute of the main module
             if hasattr(__main__, "__file__"):
-                # Get the main module file name
                 base_name = Path(__main__.__file__).stem
-                # Create logger with prefix
+                logger_name = f"{channel_root_name}.{base_name}"
-                return logging.getLogger(f"fastdeploy.main.{base_name}")
-            return logging.getLogger("fastdeploy.main")
-
-        # If already in fastdeploy namespace, use directly
-        if name.startswith("fastdeploy.") or name == "fastdeploy":
-            return logging.getLogger(name)
             else:
-            # Add fastdeploy prefix for other cases
+                return channel_logger
-            return logging.getLogger(f"fastdeploy.{name}")
+        elif name.startswith("fastdeploy."):
+            logger_name = name
+        else:
+            logger_name = f"{channel_root_name}.{name}"
+
+        # Child loggers propagate to channel_logger (which has handlers)
+        return logging.getLogger(logger_name)
 
     def get_trace_logger(self, name, file_name, without_formater=False, print_to_console=False):
         """

fastdeploy/logger/request_logger.py

@@ -0,0 +1,94 @@
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Request logging module
+
+Provides leveled request logging with L0-L3 levels:
+- L0: Critical lifecycle events (creation, completion, abort)
+- L1: Processing stage details
+- L2: Request/response content (truncated)
+- L3: Full data
+"""
+
+from enum import IntEnum
+
+from fastdeploy import envs
+
+
+class RequestLogLevel(IntEnum):
+    """Request log level"""
+
+    LIFECYCLE = 0  # Lifecycle start/end: creation, completion, abort
+    STAGES = 1  # Processing stages: semaphore, first token, signal handling
+    CONTENT = 2  # Content and scheduling: request params, scheduling, response
+    FULL = 3  # Complete raw data
+
+
+def _should_log(level: int) -> bool:
+    """Check if this level should be logged"""
+    if int(envs.FD_LOG_REQUESTS) == 0:
+        return False
+    return int(level) <= int(envs.FD_LOG_REQUESTS_LEVEL)
+
+
+def _truncate(value):
+    """Truncate long content"""
+    text = str(value)
+    max_len = int(envs.FD_LOG_MAX_LEN)
+    if len(text) <= max_len:
+        return text
+    return text[:max_len]
+
+
+def log_request(level: int, message: str, **fields):
+    """
+    Log request message
+
+    Args:
+        level: Log level (0-3)
+        message: Log message template, supports {field} formatting
+        **fields: Message fields
+    """
+    if not _should_log(level):
+        return
+
+    from fastdeploy.logger import _request_logger
+
+    if not fields:
+        _request_logger.info(message, stacklevel=2)
+        return
+
+    payload = fields
+    # CONTENT level content needs to be truncated
+    if int(level) == int(RequestLogLevel.CONTENT):
+        payload = {key: _truncate(value) for key, value in fields.items()}
+
+    _request_logger.info(message.format(**payload), stacklevel=2)
+
+
+def log_request_error(message: str, **fields):
+    """
+    Log request error message
+
+    Args:
+        message: Log message template, supports {field} formatting
+        **fields: Message fields
+    """
+    from fastdeploy.logger import _request_logger
+
+    if fields:
+        _request_logger.error(message.format(**fields), stacklevel=2)
+    else:
+        _request_logger.error(message, stacklevel=2)

fastdeploy/logger/setup_logging.py

@@ -14,7 +14,19 @@
 """
 
 """
-配置日志系统
+Setup logging system
+
+This module handles logging initialization:
+- Basic setup: log directory creation
+- Optional: load external JSON config file via dictConfig
+
+Channel-based logger configuration is handled by FastDeployLogger._get_channel_logger()
+using manual addHandler for better performance.
+
+Log channels:
+- main: Main logs -> fastdeploy.log
+- request: Request logs -> request.log
+- console: Console logs -> console.log + terminal
 """
 
 import json
@@ -27,54 +39,29 @@ from fastdeploy import envs
 
 
 class MaxLevelFilter(logging.Filter):
-    """过滤低于指定级别的日志记录。
+    """Filter log records below specified level.
 
-    用于将 INFO/DEBUG 路由到 stdout，ERROR/CRITICAL 路由到 stderr。
+    Used to route INFO/DEBUG to stdout, ERROR/CRITICAL to stderr.
     """
 
     def __init__(self, level):
         super().__init__()
-        self.level = logging._nameToLevel.get(level, level)
+        self.level = logging._nameToLevel.get(level, level) if isinstance(level, str) else level
 
     def filter(self, record):
         return record.levelno < self.level
 
 
-def setup_logging(log_dir=None, config_file=None):
+def _build_default_config(log_dir, log_level, backup_count):
-    """
+    """Build default logging configuration for dictConfig"""
-    设置FastDeploy的日志配置
-
-    Args:
-        log_dir: 日志文件存储目录，如果不提供则使用环境变量
-        config_file: JSON配置文件路径，如果不提供则使用默认配置
-    """
-
-    # 避免重复配置
-    if getattr(setup_logging, "_configured", False):
-        return logging.getLogger("fastdeploy")
-
-    # 使用环境变量中的日志目录，如果没有则使用传入的参数或默认值
-    if log_dir is None:
-        log_dir = getattr(envs, "FD_LOG_DIR", "log")
-
-    # 确保日志目录存在
-    Path(log_dir).mkdir(parents=True, exist_ok=True)
-
-    # 从环境变量获取日志级别和备份数量
-    is_debug = int(getattr(envs, "FD_DEBUG", 0))
-    FASTDEPLOY_LOGGING_LEVEL = "DEBUG" if is_debug else "INFO"
-    backup_count = int(getattr(envs, "FD_LOG_BACKUP_COUNT", 7))
-
-    # 定义日志输出格式
     _FORMAT = "%(levelname)-8s %(asctime)s %(process)-5s %(filename)s[line:%(lineno)d] %(message)s"
 
-    # 默认配置
+    return {
-    default_config = {
         "version": 1,
         "disable_existing_loggers": False,
         "filters": {
             "below_error": {
-                "()": "fastdeploy.logger.setup_logging.MaxLevelFilter",
+                "()": MaxLevelFilter,
                 "level": "ERROR",
             }
         },
@@ -91,99 +78,135 @@ def setup_logging(log_dir=None, config_file=None):
             },
         },
         "handlers": {
-            # 控制台标准输出，用于 INFO/DEBUG（低于 ERROR 级别）
+            # Console stdout for INFO/DEBUG (below ERROR level)
             "console_stdout": {
                 "class": "logging.StreamHandler",
-                "level": FASTDEPLOY_LOGGING_LEVEL,
+                "level": log_level,
                 "filters": ["below_error"],
                 "formatter": "colored",
                 "stream": "ext://sys.stdout",
             },
-            # 控制台错误输出，用于 ERROR/CRITICAL
+            # Console stderr for ERROR/CRITICAL
             "console_stderr": {
                 "class": "logging.StreamHandler",
                 "level": "ERROR",
                 "formatter": "colored",
                 "stream": "ext://sys.stderr",
             },
-            # 默认错误日志，保留最新1个小时的日志，位置在log/error.log
+            # Main log file
+            "main_file": {
+                "class": "fastdeploy.logger.handlers.LazyFileHandler",
+                "level": log_level,
+                "formatter": "standard",
+                "filename": os.path.join(log_dir, "fastdeploy.log"),
+                "backupCount": backup_count,
+            },
+            # Console log file
+            "console_file": {
+                "class": "fastdeploy.logger.handlers.LazyFileHandler",
+                "level": log_level,
+                "formatter": "standard",
+                "filename": os.path.join(log_dir, "console.log"),
+                "backupCount": backup_count,
+            },
+            # Request log file
+            "request_file": {
+                "class": "fastdeploy.logger.handlers.LazyFileHandler",
+                "level": log_level,
+                "formatter": "standard",
+                "filename": os.path.join(log_dir, "request.log"),
+                "backupCount": backup_count,
+            },
+            # Error log file
             "error_file": {
-                "class": "logging.handlers.TimedRotatingFileHandler",
+                "class": "fastdeploy.logger.handlers.LazyFileHandler",
                 "level": "ERROR",
                 "formatter": "standard",
                 "filename": os.path.join(log_dir, "error.log"),
-                "when": "H",
+                "backupCount": backup_count,
-                "interval": 1,
-                "backupCount": 1,
-            },
-            # 全量日志，保留最新1小时的日志，位置在log/default.log
-            "default_file": {
-                "class": "logging.handlers.TimedRotatingFileHandler",
-                "level": FASTDEPLOY_LOGGING_LEVEL,
-                "formatter": "standard",
-                "filename": os.path.join(log_dir, "default.log"),
-                "when": "H",
-                "interval": 1,
-                "backupCount": 1,
-            },
-            # 错误日志归档，保留7天内的日志，每隔1小时一个文件，形式如：FastDeploy/log/2025-08-14/error_2025-08-14-18.log
-            "error_archive": {
-                "class": "fastdeploy.logger.handlers.IntervalRotatingFileHandler",
-                "level": "ERROR",
-                "formatter": "standard",
-                "filename": os.path.join(log_dir, "error.log"),
-                "backupDays": 7,
-                "interval": 1,
-                "encoding": "utf-8",
-            },
-            # 全量日志归档，保留7天内的日志，每隔1小时一个文件，形式如：FastDeploy/log/2025-08-14/default_2025-08-14-18.log
-            "default_archive": {
-                "class": "fastdeploy.logger.handlers.IntervalRotatingFileHandler",
-                "level": FASTDEPLOY_LOGGING_LEVEL,
-                "formatter": "standard",
-                "filename": os.path.join(log_dir, "default.log"),
-                "backupDays": 7,
-                "interval": 1,
-                "encoding": "utf-8",
             },
         },
         "loggers": {
-            # 默认日志记录器,全局共享
+            # Default logger
             "fastdeploy": {
                 "level": "DEBUG",
-                "handlers": [
+                "handlers": ["main_file", "console_file", "error_file", "console_stderr"],
-                    "console_stdout",
-                    "console_stderr",
-                    "error_file",
-                    "default_file",
-                    "error_archive",
-                    "default_archive",
-                ],
                 "propagate": False,
-            }
+            },
+            # Main channel
+            "fastdeploy.main": {
+                "level": "DEBUG",
+                "handlers": ["main_file", "console_file", "error_file", "console_stderr"],
+                "propagate": False,
+            },
+            # Request channel - only output to request.log and error.log
+            "fastdeploy.request": {
+                "level": "DEBUG",
+                "handlers": ["request_file", "error_file", "console_stderr"],
+                "propagate": False,
+            },
+            # Console channel - output to console.log and terminal
+            "fastdeploy.console": {
+                "level": "DEBUG",
+                "handlers": ["console_file", "console_stdout", "error_file", "console_stderr"],
+                "propagate": False,
+            },
         },
     }
 
-    # 如果提供了配置文件，则加载配置文件
+
-    if config_file and os.path.exists(config_file):
+def setup_logging(log_dir=None, config_file=None):
+    """
+    Setup FastDeploy logging configuration.
+
+    This function:
+    1. Ensures the log directory exists
+    2. Optionally loads external JSON config file via dictConfig
+
+    Note: Channel-based loggers (get_logger with channel param) use manual addHandler
+    for better performance, independent of dictConfig.
+
+    Args:
+        log_dir: Log file storage directory, uses environment variable if not provided
+        config_file: Optional JSON config file path for dictConfig
+    """
+    # Avoid duplicate configuration
+    if getattr(setup_logging, "_configured", False):
+        return
+
+    # Use log directory from environment variable, or use provided parameter or default value
+    if log_dir is None:
+        log_dir = getattr(envs, "FD_LOG_DIR", "log")
+
+    # Ensure log directory exists
+    Path(log_dir).mkdir(parents=True, exist_ok=True)
+
+    # Store log_dir for later use
+    setup_logging._log_dir = log_dir
+
+    # If config_file is provided, use dictConfig to load it
+    if config_file is not None:
+        is_debug = int(getattr(envs, "FD_DEBUG", 0))
+        log_level = "DEBUG" if is_debug else "INFO"
+        backup_count = int(getattr(envs, "FD_LOG_BACKUP_COUNT", 7))
+
+        if os.path.exists(config_file):
             with open(config_file, "r", encoding="utf-8") as f:
                 config = json.load(f)
 
-        # 合并环境变量配置到用户配置中,环境变量的优先级高于自定义的优先级
+            # Merge environment variable config into user config
             if "handlers" in config:
-            for handler_name, handler_config in config["handlers"].items():
+                for handler_config in config["handlers"].values():
                     if "backupCount" not in handler_config and "DailyRotating" in handler_config.get("class", ""):
                         handler_config["backupCount"] = backup_count
-                if handler_config.get("level") == "INFO" and is_debug:
+                    if handler_config.get("level") == "INFO" and log_level == "DEBUG":
                         handler_config["level"] = "DEBUG"
         else:
-        config = default_config
+            # Config file not found, use default config
+            config = _build_default_config(log_dir, log_level, backup_count)
 
-    # 应用日志配置
+        # Apply logging configuration via dictConfig
         logging.config.dictConfig(config)
 
-    # 避免重复加载
+    # Mark as configured
     setup_logging._configured = True
-
-    # 返回fastdeploy的logger
-    return logging.getLogger("fastdeploy")

fastdeploy/output/token_processor.py

@@ -40,6 +40,11 @@ from fastdeploy.engine.request import (
     SpeculateMetrics,
 )
 from fastdeploy.inter_communicator import ZmqIpcServer
+from fastdeploy.logger.request_logger import (
+    RequestLogLevel,
+    log_request,
+    log_request_error,
+)
 from fastdeploy.metrics.metrics import main_process_metrics
 from fastdeploy.platforms import current_platform
 from fastdeploy.spec_decode import SpecMethod
@@ -224,7 +229,11 @@ class TokenProcessor:
         for token_id in token_id_list:
             recovery_stop = token_id == RECOVERY_STOP_SIGNAL
             if recovery_stop:
-                llm_logger.info(f"recovery stop signal found at task {task_id}")
+                log_request(
+                    RequestLogLevel.STAGES,
+                    message="recovery stop signal found at task {request_id}",
+                    request_id=task_id,
+                )
             self.tokens_counter[task_id] += 1
             if token_id != RECOVERY_STOP_SIGNAL:
                 result.outputs.token_ids.append(token_id)
@@ -252,12 +261,25 @@ class TokenProcessor:
 
                 # Print combined log with all required information
                 ttft = task.metrics.first_token_time if task.metrics.first_token_time else 0
-                llm_logger.info(
+                log_request(
-                    f"Request={task_id}, InputToken={task.prompt_token_ids_len}, "
+                    RequestLogLevel.LIFECYCLE,
-                    f"CachedDetail={cached_detail}, OutputToken={self.tokens_counter[task_id]}, "
+                    message=(
-                    f"TokenRatio={token_ratio:.2f}, TTFT={ttft:.2f}, "
+                        "Request={request_id}, InputToken={input_tokens}, "
-                    f"E2E={e2e_time:.2f}, IsPrefill={is_prefill}, RecoveryStop={recovery_stop}, "
+                        "CachedDetail={cached_detail}, OutputToken={output_tokens}, "
-                    f"PreemptedCount={getattr(task.metrics, 'preempted_count', 0)}"
+                        "TokenRatio={token_ratio}, TTFT={ttft}, "
+                        "E2E={e2e_time}, IsPrefill={is_prefill}, RecoveryStop={recovery_stop}, "
+                        "PreemptedCount={preempted_count}"
+                    ),
+                    request_id=task_id,
+                    input_tokens=task.prompt_token_ids_len,
+                    cached_detail=cached_detail,
+                    output_tokens=self.tokens_counter[task_id],
+                    token_ratio=f"{token_ratio:.2f}",
+                    ttft=f"{ttft:.2f}",
+                    e2e_time=f"{e2e_time:.2f}",
+                    is_prefill=is_prefill,
+                    recovery_stop=recovery_stop,
+                    preempted_count=getattr(task.metrics, "preempted_count", 0),
                 )
 
                 main_process_metrics.request_token_ratio.observe(token_ratio)
@@ -289,13 +311,21 @@ class TokenProcessor:
                         task_id in self.resource_manager.to_be_aborted_req_id_set
                         and token_ids[-1] == PREEMPTED_TOKEN_ID
                     ):
-                        llm_logger.info(f"start to recycle abort request_id {task_id}")
+                        log_request(
+                            RequestLogLevel.STAGES,
+                            message="start to recycle abort request_id {request_id}",
+                            request_id=task_id,
+                        )
                         self.resource_manager.recycle_abort_task(task_id)
                     if (
                         task_id in self.resource_manager.to_be_rescheduled_request_id_set
                         and token_ids[-1] == PREEMPTED_TOKEN_ID
                     ):
-                        llm_logger.info(f"sync preemption for request_id {task_id} done.")
+                        log_request(
+                            RequestLogLevel.STAGES,
+                            message="sync preemption for request_id {request_id} done.",
+                            request_id=task_id,
+                        )
                         self.resource_manager.reschedule_preempt_task(task_id)
                 continue
             if self.cfg.scheduler_config.splitwise_role == "decode":
@@ -351,12 +381,20 @@ class TokenProcessor:
                             result.outputs.logprob = float(logprobs_list.logprobs[0][0])
                             result.outputs.top_logprobs = logprobs_list
                         except Exception as e:
-                            llm_logger.warning(f"Failed to parse logprobs from StreamTransferData: {e}")
+                            log_request(
+                                RequestLogLevel.STAGES,
+                                message="Failed to parse logprobs from StreamTransferData: {error}",
+                                error=str(e),
+                            )
                     if getattr(stream_data, "prompt_logprobs", None) is not None:
                         try:
                             result.prompt_logprobs = stream_data.prompt_logprobs
                         except Exception as e:
-                            llm_logger.warning(f"Failed to parse prompt_logprobs from StreamTransferData: {e}")
+                            log_request(
+                                RequestLogLevel.STAGES,
+                                message="Failed to parse prompt_logprobs from StreamTransferData: {error}",
+                                error=str(e),
+                            )
                 if self.tokens_counter[task_id] == 0:
                     if task.messages is not None:
                         result.prompt = task.messages
@@ -394,7 +432,9 @@ class TokenProcessor:
                     batch_result = self._process_batch_output_use_zmq(receive_datas)
                     self.postprocess(batch_result)
             except Exception as e:
-                llm_logger.error(f"Receive message:{receive_datas}, error:{e}")
+                log_request_error(
+                    message="Receive message:{receive_datas}, error:{error}", receive_datas=receive_datas, error=e
+                )
                 continue
 
     def process_sampling_results(self):
@@ -511,7 +551,11 @@ class TokenProcessor:
             else:
                 self.cached_generated_tokens.put_results(batch_result)
         except Exception as e:
-            llm_logger.error(f"Error in TokenProcessor's postprocess: {e}, {str(traceback.format_exc())}")
+            log_request_error(
+                message="Error in TokenProcessor's postprocess: {error}, {traceback}",
+                error=e,
+                traceback=traceback.format_exc(),
+            )
 
     def _recycle_resources(self, task_id, index, task, result=None, is_prefill=False):
         """
@@ -524,14 +568,21 @@ class TokenProcessor:
                 finished_task_ids = self.engine_worker_queue.get_finished_req()
                 if len(finished_task_ids) > 0:
                     for finished_task_id in finished_task_ids:
-                        llm_logger.info(f"finished_task_id: {finished_task_id}")
+                        log_request(
+                            RequestLogLevel.STAGES,
+                            message="finished_task_id: {finished_task_id}",
+                            finished_task_id=finished_task_id,
+                        )
                         self.prefill_result_status[finished_task_id[0]] = finished_task_id[1]
                 if task_id in self.prefill_result_status:
                     if self.prefill_result_status[task_id] != "finished":
                         result.error_code = 400
                         result.error_message = f"{task_id} failed to {self.prefill_result_status[task_id]}"
-                    llm_logger.info(
+                    log_request(
-                        f"wait for sending cache, request_id: {task_id}, cost seconds: {time.time()-start_time:.5f}"
+                        RequestLogLevel.STAGES,
+                        message="wait for sending cache, request_id: {request_id}, cost seconds: {cost_seconds}",
+                        request_id=task_id,
+                        cost_seconds=f"{time.time()-start_time:.5f}",
                     )
                     result.metrics.send_request_output_to_decode_time = time.time()
                     self.split_connector.send_first_token(task.disaggregate_info, [result])
@@ -755,7 +806,11 @@ class TokenProcessor:
             if self.cfg.speculative_config.method:
                 self._record_speculative_decoding_accept_num_per_request(task_id, accept_num[i])
                 if accept_num[i] == PREEMPTED_TOKEN_ID:  # in MTP, means preemption has happened in worker
-                    llm_logger.info(f"sync preemption for request_id {task_id} done.")
+                    log_request(
+                        RequestLogLevel.STAGES,
+                        message="sync preemption for request_id {request_id} done.",
+                        request_id=task_id,
+                    )
                     if envs.ENABLE_V1_KVCACHE_SCHEDULER:
                         if task_id in self.resource_manager.to_be_aborted_req_id_set:
                             self.resource_manager.recycle_abort_task(task_id)
@@ -765,7 +820,11 @@ class TokenProcessor:
                 if accept_num[i] == -3:
                     recovery_stop = True
                     if recovery_stop:
-                        llm_logger.info(f"recovery stop signal found at task {task_id}")
+                        log_request(
+                            RequestLogLevel.STAGES,
+                            message="recovery stop signal found at task {request_id}",
+                            request_id=task_id,
+                        )
                     token_ids = [RECOVERY_STOP_SIGNAL]
                 elif self.use_logprobs:
                     token_ids = tokens[i][:, 0].tolist()[: accept_num[i]]
@@ -785,7 +844,11 @@ class TokenProcessor:
                 token_ids = [token_id]
                 recovery_stop = token_id == RECOVERY_STOP_SIGNAL
                 if recovery_stop:
-                    llm_logger.info(f"recovery stop signal found at task {task_id}")
+                    log_request(
+                        RequestLogLevel.STAGES,
+                        message="recovery stop signal found at task {request_id}",
+                        request_id=task_id,
+                    )
                 if not recovery_stop and token_id < 0:
                     if envs.ENABLE_V1_KVCACHE_SCHEDULER:
                         if (
@@ -793,12 +856,20 @@ class TokenProcessor:
                             and token_id == PREEMPTED_TOKEN_ID
                         ):
                             self.resource_manager.recycle_abort_task(task_id)
-                            llm_logger.info(f"sync abortion for request_id {task_id} done.")
+                            log_request(
+                                RequestLogLevel.STAGES,
+                                message="sync abortion for request_id {request_id} done.",
+                                request_id=task_id,
+                            )
                         if (
                             task_id in self.resource_manager.to_be_rescheduled_request_id_set
                             and token_id == PREEMPTED_TOKEN_ID
                         ):
-                            llm_logger.info(f"sync preemption for request_id {task_id} done.")
+                            log_request(
+                                RequestLogLevel.STAGES,
+                                message="sync preemption for request_id {request_id} done.",
+                                request_id=task_id,
+                            )
                             self.resource_manager.reschedule_preempt_task(task_id)
                     continue
             if self.cfg.scheduler_config.splitwise_role == "decode":
@@ -826,7 +897,11 @@ class TokenProcessor:
                 task.metrics.record_recv_first_token()
                 task.metrics.cal_cost_time()
                 metrics = copy.copy(task.metrics)
-                llm_logger.info(f"task:{task.request_id} start recode first token")
+                log_request(
+                    RequestLogLevel.STAGES,
+                    message="task:{request_id} start recode first token",
+                    request_id=task.request_id,
+                )
                 self._record_first_token_metrics(task, current_time)
 
                 tracing.trace_report_span(
@@ -932,12 +1007,26 @@ class TokenProcessor:
                     # Print combined log with all required information
                     ttft = task.metrics.first_token_time if task.metrics.first_token_time else 0
                     ttft_s = ttft + task.metrics.time_in_queue
-                    llm_logger.info(
+                    log_request(
-                        f"Request={task_id}, InputToken={task.prompt_token_ids_len}, "
+                        RequestLogLevel.LIFECYCLE,
-                        f"CachedDetail={cached_detail}, OutputToken={self.tokens_counter[task_id]}, "
+                        message=(
-                        f"TokenRatio={token_ratio:.2f}, TTFT={ttft:.2f}, TTFT_S={ttft_s:.2f}, "
+                            "Request={request_id}, InputToken={input_tokens}, "
-                        f"E2E={e2e_time:.2f}, IsPrefill={is_prefill}, RecoveryStop={recovery_stop}, "
+                            "CachedDetail={cached_detail}, OutputToken={output_tokens}, "
-                        f"PreemptedCount={getattr(task.metrics, 'preempted_count', 0)}"
+                            "TokenRatio={token_ratio}, TTFT={ttft}, TTFT_S={ttft_s}, "
+                            "E2E={e2e_time}, IsPrefill={is_prefill}, RecoveryStop={recovery_stop}, "
+                            "PreemptedCount={preempted_count}"
+                        ),
+                        request_id=task_id,
+                        input_tokens=task.prompt_token_ids_len,
+                        cached_detail=cached_detail,
+                        output_tokens=self.tokens_counter[task_id],
+                        token_ratio=f"{token_ratio:.2f}",
+                        ttft=f"{ttft:.2f}",
+                        ttft_s=f"{ttft_s:.2f}",
+                        e2e_time=f"{e2e_time:.2f}",
+                        is_prefill=is_prefill,
+                        recovery_stop=recovery_stop,
+                        preempted_count=getattr(task.metrics, "preempted_count", 0),
                     )
 
                     main_process_metrics.request_token_ratio.observe(token_ratio)
@@ -946,8 +1035,11 @@ class TokenProcessor:
                         self._compute_speculative_status(result)
                     if not is_prefill:
                         self._record_completion_metrics(task, current_time)
-                    llm_logger.info(f"task {task_id} received eos token. Recycling.")
+                    log_request(
-
+                        RequestLogLevel.STAGES,
+                        message="task {request_id} received eos token. Recycling.",
+                        request_id=task_id,
+                    )
                     if (
                         envs.ENABLE_V1_KVCACHE_SCHEDULER
                         and self.cfg.cache_config.enable_prefix_caching
@@ -957,7 +1049,11 @@ class TokenProcessor:
                             task
                         )  # when enable prefix caching, cache kv cache for output tokens
                     self._recycle_resources(task_id, i, task, result, is_prefill)
-                    llm_logger.info(f"eos token {task_id} Recycle end.")
+                    log_request(
+                        RequestLogLevel.STAGES,
+                        message="eos token {request_id} Recycle end.",
+                        request_id=task_id,
+                    )
                     break
 
             llm_logger.debug(f"get response from infer: {result}")

fastdeploy/scheduler/dp_scheduler.py

@@ -21,6 +21,7 @@ import time
 from typing import Dict, List, Optional
 
 from fastdeploy.engine.request import Request, RequestOutput
+from fastdeploy.logger.request_logger import RequestLogLevel, log_request
 from fastdeploy.scheduler.data import ScheduledResponse
 from fastdeploy.scheduler.local_scheduler import LocalScheduler
 from fastdeploy.utils import get_logger
@@ -58,7 +59,11 @@ class DPLocalScheduler(LocalScheduler):
 
         finished_responses = [response.request_id for response in responses if response.finished]
         if len(finished_responses) > 0:
-            self.scheduler_logger.info(f"Scheduler has received some finished responses: {finished_responses}")
+            log_request(
+                RequestLogLevel.CONTENT,
+                message="Scheduler has received some finished responses: {request_ids}",
+                request_ids=finished_responses,
+            )
 
         with self.mutex:
             self.batch_responses_per_step.append([response.raw for response in responses])
@@ -146,8 +151,10 @@ class DPLocalScheduler(LocalScheduler):
                     self.ids_read_cursor += 1
 
         if len(requests) > 0:
-            self.scheduler_logger.info(
+            log_request(
-                f"Scheduler has pulled some request: {[request.request_id for request in requests]}"
+                RequestLogLevel.CONTENT,
+                message="Scheduler has pulled some request: {request_ids}",
+                request_ids=[request.request_id for request in requests],
             )
 
         return requests
@@ -195,7 +202,11 @@ class DPScheduler:
     def _put_requests_to_local(self):
         while True:
             request = self.request_queues.get()
-            self.scheduler_logger.info(f"Receive request from puller, request_id: {request.request_id}")
+            log_request(
+                RequestLogLevel.CONTENT,
+                message="Receive request from puller, request_id: {request_id}",
+                request_id=request.request_id,
+            )
             self._scheduler.put_requests([request])
 
     def _get_response_from_local(self):

fastdeploy/scheduler/global_scheduler.py

@@ -25,6 +25,11 @@ import crcmod
 from redis import ConnectionPool
 
 from fastdeploy.engine.request import Request, RequestOutput
+from fastdeploy.logger.request_logger import (
+    RequestLogLevel,
+    log_request,
+    log_request_error,
+)
 from fastdeploy.scheduler import utils
 from fastdeploy.scheduler.data import ScheduledRequest, ScheduledResponse
 from fastdeploy.scheduler.storage import AdaptedRedis
@@ -370,7 +375,11 @@ class GlobalScheduler:
                 rem_amount=0,
                 ttl=self.ttl,
             )
-            scheduler_logger.info(f"Scheduler has enqueued some requests: {requests}")
+            log_request(
+                RequestLogLevel.CONTENT,
+                message="Scheduler has enqueued some requests: {request_ids}",
+                request_ids=[request.request_id for request in requests],
+            )
 
         if duplicate:
             scheduler_logger.warning(
@@ -573,7 +582,9 @@ class GlobalScheduler:
                         self.stolen_requests[request.request_id] = request
                         continue
 
-                    scheduler_logger.error(f"Scheduler has received a duplicate request from others: {request}")
+                    log_request_error(
+                        message="Scheduler has received a duplicate request from others: {request}", request=request
+                    )
 
         requests: List[Request] = [request.raw for request in scheduled_requests]
         if len(remaining_request) > 0:
@@ -603,7 +614,11 @@ class GlobalScheduler:
                 )
 
         if len(requests) > 0:
-            scheduler_logger.info(f"Scheduler has pulled some request: {[request.request_id for request in requests]}")
+            log_request(
+                RequestLogLevel.CONTENT,
+                message="Scheduler has pulled some request: {request_ids}",
+                request_ids=[request.request_id for request in requests],
+            )
         return requests
 
     def _put_results_worker(self, tasks: List[Task]):
@@ -649,7 +664,9 @@ class GlobalScheduler:
                 stolen_responses[response_queue_name].append(response.serialize())
                 continue
 
-            scheduler_logger.error(f"Scheduler has received a non-existent response from engine: {[response]}")
+            log_request_error(
+                message="Scheduler has received a non-existent response from engine: {response}", response=[response]
+            )
 
         with self.mutex:
             for request_id, responses in local_responses.items():
@@ -664,7 +681,11 @@ class GlobalScheduler:
                 self.local_response_not_empty.notify_all()
 
         if len(finished_request_ids) > 0:
-            scheduler_logger.info(f"Scheduler has received some finished responses: {finished_request_ids}")
+            log_request(
+                RequestLogLevel.CONTENT,
+                message="Scheduler has received some finished responses: {request_ids}",
+                request_ids=finished_request_ids,
+            )
 
         for response_queue_name, responses in stolen_responses.items():
             self.client.rpush(response_queue_name, *responses, ttl=self.ttl)
@@ -793,7 +814,11 @@ class GlobalScheduler:
 
                 if finished:
                     del self.local_responses[request_id]
-                    scheduler_logger.info(f"Scheduler has pulled a finished response: {[request_id]}")
+                    log_request(
+                        RequestLogLevel.CONTENT,
+                        message="Scheduler has pulled a finished response: {request_ids}",
+                        request_ids=[request_id],
+                    )
             return results
 
     def reset(self):
@@ -824,7 +849,7 @@ class GlobalScheduler:
             self.client.zrem(self._load_table_name(), self.name)
             self.local_responses = dict()
             self.stolen_requests = dict()
-        scheduler_logger.info("Scheduler has been reset")
+        log_request(RequestLogLevel.LIFECYCLE, message="Scheduler has been reset")
 
     def update_config(self, load_shards_num: Optional[int], reallocate: Optional[bool]):
         """

fastdeploy/scheduler/local_scheduler.py

@@ -19,6 +19,7 @@ import time
 from typing import Dict, List, Optional, Tuple
 
 from fastdeploy.engine.request import Request, RequestOutput
+from fastdeploy.logger.request_logger import RequestLogLevel, log_request
 from fastdeploy.scheduler.data import ScheduledRequest, ScheduledResponse
 from fastdeploy.utils import envs, scheduler_logger
 
@@ -116,7 +117,7 @@ class LocalScheduler:
             self.ids = list()
             self.requests = dict()
             self.responses = dict()
-        scheduler_logger.info("Scheduler has been reset")
+        log_request(RequestLogLevel.LIFECYCLE, message="Scheduler has been reset")
 
     def _recycle(self, request_id: Optional[str] = None):
         """
@@ -191,7 +192,12 @@ class LocalScheduler:
 
             self.ids += valid_ids
             self.requests_not_empty.notify_all()
-        scheduler_logger.info(f"Scheduler has enqueued some requests: {valid_ids}")
+        if len(valid_ids) > 0:
+            log_request(
+                RequestLogLevel.CONTENT,
+                message="Scheduler has enqueued some requests: {request_ids}",
+                request_ids=valid_ids,
+            )
 
         if len(duplicated_ids) > 0:
             scheduler_logger.warning(f"Scheduler has received some duplicated requests: {duplicated_ids}")
@@ -300,7 +306,11 @@ class LocalScheduler:
             scheduler_logger.debug(f"Scheduler has put all just-pulled request into the queue: {len(batch_ids)}")
 
         if len(requests) > 0:
-            scheduler_logger.info(f"Scheduler has pulled some request: {[request.request_id for request in requests]}")
+            log_request(
+                RequestLogLevel.CONTENT,
+                message="Scheduler has pulled some request: {request_ids}",
+                request_ids=[request.request_id for request in requests],
+            )
 
         return requests
 
@@ -316,7 +326,11 @@ class LocalScheduler:
 
         finished_responses = [response.request_id for response in responses if response.finished]
         if len(finished_responses) > 0:
-            scheduler_logger.info(f"Scheduler has received some finished responses: {finished_responses}")
+            log_request(
+                RequestLogLevel.CONTENT,
+                message="Scheduler has received some finished responses: {request_ids}",
+                request_ids=finished_responses,
+            )
 
         with self.mutex:
             self.batch_responses_per_step.append([response.raw for response in responses])
@@ -381,7 +395,11 @@ class LocalScheduler:
 
                 if finished:
                     self._recycle(request_id)
-                    scheduler_logger.info(f"Scheduler has pulled a finished response: {[request_id]}")
+                    log_request(
+                        RequestLogLevel.CONTENT,
+                        message="Scheduler has pulled a finished response: {request_ids}",
+                        request_ids=[request_id],
+                    )
 
             if results:
                 scheduler_logger.debug(f"get responses, {results}")

fastdeploy/scheduler/splitwise_scheduler.py

@@ -33,6 +33,7 @@ from fastdeploy.engine.request import (
     RequestMetrics,
     RequestOutput,
 )
+from fastdeploy.logger.request_logger import log_request_error
 from fastdeploy.utils import scheduler_logger as logger
 
 
@@ -240,7 +241,12 @@ class NodeInfo:
             for req_id, pairs in self.reqs.items():
                 load, arrival_time = pairs
                 if cur_time - arrival_time > ttl:
-                    logger.error(f"InferScheduler Expire Reqs({req_id}), arrival({arrival_time}), ttl({ttl})")
+                    log_request_error(
+                        message="InferScheduler Expire Reqs({req_id}), arrival({arrival_time}), ttl({ttl})",
+                        req_id=req_id,
+                        arrival_time=arrival_time,
+                        ttl=ttl,
+                    )
                     expire_reqs.add((req_id, load))
             for req_id, load in expire_reqs:
                 if req_id in self.reqs:
@@ -378,7 +384,7 @@ class ResultReader:
                             )
                             self.data.appendleft(result)
 
-                            logger.error(f"Req({req_id}) is expired({self.ttl})")
+                            log_request_error(message="Req({req_id}) is expired({ttl})", req_id=req_id, ttl=self.ttl)
                             expired_reqs.add(req_id)
                             continue
                         keys.append(req_id)
@@ -511,7 +517,11 @@ class APIScheduler:
             except IndexError:
                 continue
             except Exception as e:
-                logger.error(f"APIScheduler Schedule req error: {e!s}, {str(traceback.format_exc())}")
+                log_request_error(
+                    message="APIScheduler Schedule req error: {error}, {traceback}",
+                    error=str(e),
+                    traceback=traceback.format_exc(),
+                )
 
     def schedule(self, req, pnodes, dnodes, mnodes, group=""):
         """
@@ -841,7 +851,11 @@ class InferScheduler:
 
                 req = self.reqs_queue.popleft()
                 if cur_time - req.metrics.arrival_time > self.ttl:
-                    logger.error(f"req({req.request_id}) is expired({self.ttl}) when InferScheduler Get Requests")
+                    log_request_error(
+                        message="req({request_id}) is expired({ttl}) when InferScheduler Get Requests",
+                        request_id=req.request_id,
+                        ttl=self.ttl,
+                    )
                     self.node.finish_req(req.request_id)
                     continue
                 current_prefill_tokens += req.prompt_token_ids_len

fastdeploy/utils.py

@@ -16,15 +16,12 @@
 
 import argparse
 import asyncio
-import codecs
 import hashlib
 import importlib
 import json
-import logging
 import os
 import pickle
 import random
-import re
 import socket
 import subprocess
 import sys
@@ -36,8 +33,6 @@ from enum import Enum
 from functools import cache
 from http import HTTPStatus
 from importlib.metadata import PackageNotFoundError, distribution
-from logging.handlers import BaseRotatingHandler
-from pathlib import Path
 from typing import Any, Literal, TypeVar, Union
 
 import numpy as np
@@ -53,7 +48,7 @@ from typing_extensions import TypeIs, assert_never
 
 from fastdeploy import envs
 from fastdeploy.entrypoints.openai.protocol import ErrorInfo, ErrorResponse
-from fastdeploy.logger.logger import FastDeployLogger
+from fastdeploy.logger.request_logger import log_request_error
 from fastdeploy.worker.output import PromptLogprobs
 
 T = TypeVar("T")
@@ -193,6 +188,19 @@ class ExceptionHandler:
             loc = first_error.get("loc", [])
             param = loc[-1] if loc else None
             message = first_error.get("msg", str(exc))
+
+        # Try to extract request_id from request body
+        request_id = None
+        try:
+            body = await request.body()
+            if body:
+                import json
+
+                body_json = json.loads(body)
+                request_id = body_json.get("request_id")
+        except Exception:
+            pass
+
         err = ErrorResponse(
             error=ErrorInfo(
                 message=message,
@@ -201,7 +209,13 @@ class ExceptionHandler:
                 param=param,
             )
         )
-        api_server_logger.error(f"invalid_request_error: {request.url} {param} {message}")
+        log_request_error(
+            message="request[{request_id}] invalid_request_error: {url} {param} {msg}",
+            request_id=request_id or "unknown",
+            url=str(request.url),
+            param=param,
+            msg=message,
+        )
         return JSONResponse(content=err.model_dump(), status_code=HTTPStatus.BAD_REQUEST)
 
 
@@ -224,139 +238,18 @@ class ErrorCode(str, Enum):
     CLIENT_ABORTED = "client_aborted"
 
 
-class ColoredFormatter(logging.Formatter):
+# Backward compatibility: logger classes have been moved to fastdeploy.logger module
-    """自定义日志格式器，用于控制台输出带颜色"""
+# Use lazy import to avoid circular dependencies
+def __getattr__(name):
+    if name == "ColoredFormatter":
+        from fastdeploy.logger.formatters import ColoredFormatter
 
-    COLOR_CODES = {
+        return ColoredFormatter
-        logging.WARNING: 33,  # 黄色
+    elif name == "DailyRotatingFileHandler":
-        logging.ERROR: 31,  # 红色
+        from fastdeploy.logger.handlers import DailyRotatingFileHandler
-        logging.CRITICAL: 31,  # 红色
-    }
 
-    def format(self, record):
+        return DailyRotatingFileHandler
-        color_code = self.COLOR_CODES.get(record.levelno, 0)
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
-        prefix = f"\033[{color_code}m"
-        suffix = "\033[0m"
-        message = super().format(record)
-        if color_code:
-            message = f"{prefix}{message}{suffix}"
-        return message
-
-
-class DailyRotatingFileHandler(BaseRotatingHandler):
-    """
-    like `logging.TimedRotatingFileHandler`, but this class support multi-process
-    """
-
-    def __init__(
-        self,
-        filename,
-        backupCount=0,
-        encoding="utf-8",
-        delay=False,
-        utc=False,
-        **kwargs,
-    ):
-        """
-            初始化 RotatingFileHandler 对象。
-
-        Args:
-            filename (str): 日志文件的路径，可以是相对路径或绝对路径。
-            backupCount (int, optional, default=0): 保存的备份文件数量，默认为 0，表示不保存备份文件。
-            encoding (str, optional, default='utf-8'): 编码格式，默认为 'utf-8'。
-            delay (bool, optional, default=False): 是否延迟写入，默认为 False，表示立即写入。
-            utc (bool, optional, default=False): 是否使用 UTC 时区，默认为 False，表示不使用 UTC 时区。
-            kwargs (dict, optional): 其他参数将被传递给 BaseRotatingHandler 类的 init 方法。
-
-        Raises:
-            TypeError: 如果 filename 不是 str 类型。
-            ValueError: 如果 backupCount 小于等于 0。
-        """
-        self.backup_count = backupCount
-        self.utc = utc
-        self.suffix = "%Y-%m-%d"
-        self.base_log_path = Path(filename)
-        self.base_filename = self.base_log_path.name
-        self.current_filename = self._compute_fn()
-        self.current_log_path = self.base_log_path.with_name(self.current_filename)
-        BaseRotatingHandler.__init__(self, filename, "a", encoding, delay)
-
-    def shouldRollover(self, record):
-        """
-        check scroll through the log
-        """
-        if self.current_filename != self._compute_fn():
-            return True
-        return False
-
-    def doRollover(self):
-        """
-        scroll log
-        """
-        if self.stream:
-            self.stream.close()
-            self.stream = None
-
-        self.current_filename = self._compute_fn()
-        self.current_log_path = self.base_log_path.with_name(self.current_filename)
-
-        if not self.delay:
-            self.stream = self._open()
-
-        self.delete_expired_files()
-
-    def _compute_fn(self):
-        """
-        Calculate the log file name corresponding current time
-        """
-        return self.base_filename + "." + time.strftime(self.suffix, time.localtime())
-
-    def _open(self):
-        """
-        open new log file
-        """
-        if self.encoding is None:
-            stream = open(str(self.current_log_path), self.mode)
-        else:
-            stream = codecs.open(str(self.current_log_path), self.mode, self.encoding)
-
-        if self.base_log_path.exists():
-            try:
-                if not self.base_log_path.is_symlink() or os.readlink(self.base_log_path) != self.current_filename:
-                    os.remove(self.base_log_path)
-            except OSError:
-                pass
-
-        try:
-            os.symlink(self.current_filename, str(self.base_log_path))
-        except OSError:
-            pass
-        return stream
-
-    def delete_expired_files(self):
-        """
-        delete expired log files
-        """
-        if self.backup_count <= 0:
-            return
-
-        file_names = os.listdir(str(self.base_log_path.parent))
-        result = []
-        prefix = self.base_filename + "."
-        plen = len(prefix)
-        for file_name in file_names:
-            if file_name[:plen] == prefix:
-                suffix = file_name[plen:]
-                if re.match(r"^\d{4}-\d{2}-\d{2}(\.\w+)?$", suffix):
-                    result.append(file_name)
-        if len(result) < self.backup_count:
-            result = []
-        else:
-            result.sort()
-            result = result[: len(result) - self.backup_count]
-
-        for file_name in result:
-            os.remove(str(self.base_log_path.with_name(file_name)))
 
 
 def chunk_list(lst: list[T], chunk_size: int):
@@ -1054,12 +947,6 @@ def parse_quantization(value: str):
         return {"quantization": value}
 
 
-# 日志使用全局访问点（兼容原有使用方式）
-def get_logger(name, file_name=None, without_formater=False, print_to_console=False):
-    """全局函数包装器，保持向后兼容"""
-    return FastDeployLogger().get_logger(name, file_name, without_formater, print_to_console)
-
-
 def check_download_links(bos_client, links, timeout=1):
     """
     check bos download links
@@ -1156,20 +1043,6 @@ def download_from_bos(bos_client, bos_links, retry: int = 0):
             break
 
 
-llm_logger = get_logger("fastdeploy", "fastdeploy.log")
-data_processor_logger = get_logger("data_processor", "data_processor.log")
-scheduler_logger = get_logger("scheduler", "scheduler.log")
-api_server_logger = get_logger("api_server", "api_server.log")
-console_logger = get_logger("console", "console.log", print_to_console=True)
-spec_logger = get_logger("speculate", "speculate.log")
-zmq_client_logger = get_logger("zmq_client", "zmq_client.log")
-trace_logger = FastDeployLogger().get_trace_logger("trace", "trace.log")
-router_logger = get_logger("router", "router.log")
-fmq_logger = get_logger("fmq", "fmq.log")
-obj_logger = get_logger("obj", "obj.log")  # debug内存问题
-register_manager_logger = get_logger("register_manager", "register_manager.log")
-
-
 def parse_type(return_type: Callable[[str], T]) -> Callable[[str], T]:
 
     def _parse_type(val: str) -> T:
@@ -1338,3 +1211,26 @@ def all_gather_values(value: int | float | bool, group: paddle.distributed.commu
     paddle.distributed.all_gather(_global, _local, group)
     _results = [_type(t.item()) for t in _global]
     return _results
+
+
+# =============================================================================
+# Logger re-export (backward compatibility)
+# Actual implementation is in fastdeploy.logger module, re-exported here to
+# support existing import patterns
+# NOTE: Must be at the end of file to avoid circular imports
+# =============================================================================
+from fastdeploy.logger import (  # noqa: F401
+    api_server_logger,
+    console_logger,
+    data_processor_logger,
+    fmq_logger,
+    get_logger,
+    llm_logger,
+    obj_logger,
+    register_manager_logger,
+    router_logger,
+    scheduler_logger,
+    spec_logger,
+    trace_logger,
+    zmq_client_logger,
+)

tests/entrypoints/openai/v1/test_serving_completion_v1.py

@@ -756,8 +756,8 @@ class TestAsyncLLMOpenAIServingCompletionPreprocess(unittest.IsolatedAsyncioTest
                     expected_id = f"{request_id}_{i}" if request_id else f"_{i}"
                     self.assertEqual(ctx.preprocess_requests[i]["request_id"], expected_id)
 
-    @patch("fastdeploy.entrypoints.openai.v1.serving_completion.api_server_logger")
+    @patch("fastdeploy.entrypoints.openai.v1.serving_completion.log_request_error")
-    async def test_preprocess_exception_logging(self, mock_logger):
+    async def test_preprocess_exception_logging(self, mock_log_request_error):
         """Test _preprocess logs exceptions properly"""
         # Setup - create a request that will cause an exception
         request = CompletionRequest(model="test_model", prompt="dummy", max_tokens=50)
@@ -771,11 +771,10 @@ class TestAsyncLLMOpenAIServingCompletionPreprocess(unittest.IsolatedAsyncioTest
 
         # Assert
         self.assertIsInstance(result, ErrorResponse)
-        mock_logger.error.assert_called_once()
+        mock_log_request_error.assert_called_once()
-        error_log = mock_logger.error.call_args[0][0]
+        error_msg = mock_log_request_error.call_args[1].get("message", "")
-        self.assertIn("OpenAIServingCompletion create_completion", error_log)
+        self.assertIn("OpenAIServingCompletion create_completion", error_msg)
-        self.assertIn("ValueError", error_log)
+        self.assertIn("ValueError", error_msg)
-        self.assertIn("Traceback", error_log)  # Changed from "traceback" to "Traceback"
 
 
 if __name__ == "__main__":

tests/entrypoints/test_abort.py

@@ -206,9 +206,9 @@ class TestEngineClientAbort(unittest.TestCase):
                 mock_send_task.assert_called_once_with(expected_data)
 
     @patch("fastdeploy.entrypoints.engine_client.envs.FD_ENABLE_REQUEST_DISCONNECT_STOP_INFERENCE", True)
-    @patch("fastdeploy.entrypoints.engine_client.api_server_logger")
+    @patch("fastdeploy.entrypoints.engine_client.log_request")
     @patch.object(EngineClient, "_send_task")
-    def test_abort_logging(self, mock_send_task, mock_logger):
+    def test_abort_logging(self, mock_send_task, mock_log_request):
         """Test that abort method logs correctly"""
         request_id = "test_request"
         n = 2
@@ -216,18 +216,16 @@ class TestEngineClientAbort(unittest.TestCase):
         # Run the abort method
         self.loop.run_until_complete(self.engine_client.abort(request_id, n=n))
 
-        # Verify info log was called twice
+        # Verify log_request was called twice
-        self.assertEqual(mock_logger.info.call_count, 2)
+        self.assertEqual(mock_log_request.call_count, 2)
 
         # Verify the first log message (abort start)
-        first_call = mock_logger.info.call_args_list[0]
+        first_call = mock_log_request.call_args_list[0]
-        self.assertEqual(first_call[0][0], "abort request_id:test_request")
+        self.assertIn("abort request_id", first_call[1].get("message", ""))
 
         # Verify the second log message (abort completion with request IDs)
-        second_call = mock_logger.info.call_args_list[1]
+        second_call = mock_log_request.call_args_list[1]
-        expected_log_message = "Aborted request(s) %s."
+        self.assertIn("Aborted request(s)", second_call[1].get("message", ""))
-        self.assertEqual(second_call[0][0], expected_log_message)
-        self.assertEqual(second_call[0][1], "test_request_0,test_request_1")
 
     @patch("fastdeploy.entrypoints.engine_client.envs.FD_ENABLE_REQUEST_DISCONNECT_STOP_INFERENCE", True)
     @patch("fastdeploy.entrypoints.engine_client.api_server_logger")

tests/entrypoints/test_engine_client.py

@@ -843,11 +843,11 @@ class TestEngineClientValidParameters(unittest.TestCase):
         """Test valid_parameters adjusts reasoning_max_tokens when needed."""
         data = {"max_tokens": 50, "reasoning_max_tokens": 100, "request_id": "test-id"}  # Larger than max_tokens
 
-        with patch("fastdeploy.entrypoints.engine_client.api_server_logger") as mock_logger:
+        with patch("fastdeploy.entrypoints.engine_client.log_request") as mock_log_request:
             self.engine_client.valid_parameters(data)
 
             self.assertEqual(data["reasoning_max_tokens"], 50)
-            mock_logger.warning.assert_called_once()
+            mock_log_request.assert_called_once()
 
     def test_valid_parameters_reasoning_max_tokens_with_reasoning_effort(self):
         """Test valid_parameters when both reasoning_max_tokens and reasoning_effort are set."""
@@ -858,14 +858,13 @@ class TestEngineClientValidParameters(unittest.TestCase):
             "request_id": "test-id",
         }
 
-        with patch("fastdeploy.entrypoints.engine_client.api_server_logger") as mock_logger:
+        with patch("fastdeploy.entrypoints.engine_client.log_request") as mock_log_request:
             self.engine_client.valid_parameters(data)
 
             # When reasoning_effort is set, reasoning_max_tokens should be set to None
             self.assertIsNone(data["reasoning_max_tokens"])
-            mock_logger.warning.assert_called_once()
+            # log_request is called once: for reasoning_effort conflict (reasoning_max_tokens=50 < max_tokens=100)
-            warning_call = mock_logger.warning.call_args[0][0]
+            mock_log_request.assert_called_once()
-            self.assertIn("reasoning_max_tokens and reasoning_effort are both set", warning_call)
 
     def test_valid_parameters_temperature_zero_adjustment(self):
         """Test valid_parameters adjusts zero temperature."""
@@ -1846,7 +1845,7 @@ def test_add_requests_objgraph_and_error_paths(minimal_engine_client):
     with (
         patch(
             "fastdeploy.entrypoints.engine_client.os.getenv",
-            side_effect=lambda k: "1" if k == "FD_ENABLE_OBJGRAPH_DEBUG" else None,
+            side_effect=lambda k, default=None: "1" if k == "FD_ENABLE_OBJGRAPH_DEBUG" else default,
         ),
         patch("fastdeploy.entrypoints.engine_client._has_objgraph", True),
         patch("fastdeploy.entrypoints.engine_client._has_psutil", False),

tests/entrypoints/test_llm.py

@@ -148,7 +148,9 @@ def test_receive_output_merges():
     assert first.added is True
 
 
-def test_receive_output_logs_exception(caplog):
+def test_receive_output_logs_exception():
+    from unittest.mock import patch
+
     llm = _make_llm(_make_engine())
     calls = iter([RuntimeError("boom"), SystemExit()])
 
@@ -159,9 +161,14 @@ def test_receive_output_logs_exception(caplog):
         return nxt
 
     llm.llm_engine._get_generated_result = _get_generated_result
+    with patch("fastdeploy.entrypoints.llm.log_request_error") as mock_log:
         with pytest.raises(SystemExit):
             llm._receive_output()
-    assert "Unexpected error happened" in caplog.text
+        mock_log.assert_called_once()
+        call_kwargs = mock_log.call_args[1]
+        assert "Unexpected error happened" in call_kwargs.get(
+            "message", mock_log.call_args[0][0] if mock_log.call_args[0] else ""
+        )
 
 
 def test_generate_and_chat_branches():

tests/input/test_video_utils.py

@@ -264,7 +264,7 @@ class TestSampleFramesQwen(unittest.TestCase):
 
     def test_fps_warning_when_nframes_exceeds_total(self):
         """fps so high that computed num_frames > total → warning logged."""
-        with self.assertLogs(level="WARNING"):
+        with self.assertLogs(logger="fastdeploy.main", level="WARNING"):
             sample_frames_qwen(2, 4, 100, {"num_of_frame": 10, "fps": 1.0}, fps=100.0)
 
     def test_divisible_by_4_correction(self):

tests/logger/test_logger.py

@@ -46,8 +46,12 @@ class LoggerTests(unittest.TestCase):
         shutil.rmtree(self.tmp_dir, ignore_errors=True)
 
     def test_unified_logger(self):
-        """Test _get_unified_logger through instance"""
+        """Test _get_unified_logger through instance (uses main channel)"""
-        test_cases = [(None, "fastdeploy"), ("module", "fastdeploy.module"), ("fastdeploy.utils", "fastdeploy.utils")]
+        test_cases = [
+            (None, "fastdeploy.main"),
+            ("module", "fastdeploy.main.module"),
+            ("fastdeploy.utils", "fastdeploy.utils"),  # 已有 fastdeploy. 前缀的保持不变
+        ]
 
         for name, expected in test_cases:
             with self.subTest(name=name):

tests/logger/test_logging_config.py

@@ -0,0 +1,91 @@
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+from unittest.mock import patch
+
+from fastdeploy.logger.config import resolve_log_level, resolve_request_logging_defaults
+
+
+class TestResolveLogLevel(unittest.TestCase):
+    """测试 resolve_log_level 函数"""
+
+    def test_explicit_info_level(self):
+        """显式设置 INFO 级别"""
+        result = resolve_log_level(raw_level="INFO")
+        self.assertEqual(result, "INFO")
+
+    def test_explicit_debug_level(self):
+        """显式设置 DEBUG 级别"""
+        result = resolve_log_level(raw_level="DEBUG")
+        self.assertEqual(result, "DEBUG")
+
+    def test_case_insensitive(self):
+        """级别名称应该大小写不敏感"""
+        self.assertEqual(resolve_log_level(raw_level="info"), "INFO")
+        self.assertEqual(resolve_log_level(raw_level="debug"), "DEBUG")
+
+    def test_invalid_level_raises(self):
+        """无效级别应该抛出 ValueError"""
+        with self.assertRaises(ValueError) as ctx:
+            resolve_log_level(raw_level="INVALID")
+        self.assertIn("Unsupported FD_LOG_LEVEL", str(ctx.exception))
+
+    def test_debug_enabled_fallback(self):
+        """FD_DEBUG=1 应该返回 DEBUG"""
+        result = resolve_log_level(raw_level=None, debug_enabled=1)
+        self.assertEqual(result, "DEBUG")
+
+    def test_debug_disabled_fallback(self):
+        """FD_DEBUG=0 应该返回 INFO"""
+        result = resolve_log_level(raw_level=None, debug_enabled=0)
+        self.assertEqual(result, "INFO")
+
+    def test_env_fd_log_level_priority(self):
+        """FD_LOG_LEVEL 环境变量优先级高于 FD_DEBUG"""
+        with patch.dict("os.environ", {"FD_LOG_LEVEL": "INFO", "FD_DEBUG": "1"}):
+            result = resolve_log_level()
+            self.assertEqual(result, "INFO")
+
+    def test_env_fd_debug_fallback(self):
+        """无 FD_LOG_LEVEL 时使用 FD_DEBUG"""
+        with patch.dict("os.environ", {"FD_DEBUG": "1"}, clear=True):
+            result = resolve_log_level()
+            self.assertEqual(result, "DEBUG")
+
+
+class TestResolveRequestLoggingDefaults(unittest.TestCase):
+    """测试 resolve_request_logging_defaults 函数"""
+
+    def test_default_values(self):
+        """默认值测试"""
+        with patch.dict("os.environ", {}, clear=True):
+            result = resolve_request_logging_defaults()
+            self.assertEqual(result["enabled"], 1)
+            self.assertEqual(result["level"], 2)
+            self.assertEqual(result["max_len"], 2048)
+
+    def test_custom_values(self):
+        """自定义值测试"""
+        with patch.dict(
+            "os.environ", {"FD_LOG_REQUESTS": "0", "FD_LOG_REQUESTS_LEVEL": "2", "FD_LOG_MAX_LEN": "1024"}
+        ):
+            result = resolve_request_logging_defaults()
+            self.assertEqual(result["enabled"], 0)
+            self.assertEqual(result["level"], 2)
+            self.assertEqual(result["max_len"], 1024)
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/logger/test_request_logger.py

@@ -0,0 +1,176 @@
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+from unittest.mock import patch
+
+from fastdeploy.logger.request_logger import (
+    RequestLogLevel,
+    _should_log,
+    _truncate,
+    log_request,
+    log_request_error,
+)
+
+
+class TestRequestLogLevel(unittest.TestCase):
+    """Test RequestLogLevel enum"""
+
+    def test_level_values(self):
+        """Test level values"""
+        self.assertEqual(int(RequestLogLevel.LIFECYCLE), 0)
+        self.assertEqual(int(RequestLogLevel.STAGES), 1)
+        self.assertEqual(int(RequestLogLevel.CONTENT), 2)
+        self.assertEqual(int(RequestLogLevel.FULL), 3)
+
+
+class TestShouldLog(unittest.TestCase):
+    """Test _should_log function"""
+
+    def test_disabled_returns_false(self):
+        """FD_LOG_REQUESTS=0 should return False"""
+        with patch("fastdeploy.logger.request_logger.envs") as mock_envs:
+            mock_envs.FD_LOG_REQUESTS = 0
+            mock_envs.FD_LOG_REQUESTS_LEVEL = 3
+            self.assertFalse(_should_log(RequestLogLevel.LIFECYCLE))
+
+    def test_level_within_threshold(self):
+        """Level within threshold should return True"""
+        with patch("fastdeploy.logger.request_logger.envs") as mock_envs:
+            mock_envs.FD_LOG_REQUESTS = 1
+            mock_envs.FD_LOG_REQUESTS_LEVEL = 2
+            self.assertTrue(_should_log(RequestLogLevel.LIFECYCLE))
+            self.assertTrue(_should_log(RequestLogLevel.STAGES))
+            self.assertTrue(_should_log(RequestLogLevel.CONTENT))
+
+    def test_level_above_threshold(self):
+        """Level above threshold should return False"""
+        with patch("fastdeploy.logger.request_logger.envs") as mock_envs:
+            mock_envs.FD_LOG_REQUESTS = 1
+            mock_envs.FD_LOG_REQUESTS_LEVEL = 1
+            self.assertFalse(_should_log(RequestLogLevel.CONTENT))
+            self.assertFalse(_should_log(RequestLogLevel.FULL))
+
+
+class TestTruncate(unittest.TestCase):
+    """Test _truncate function"""
+
+    def test_short_text_unchanged(self):
+        """Short text should remain unchanged"""
+        with patch("fastdeploy.logger.request_logger.envs") as mock_envs:
+            mock_envs.FD_LOG_MAX_LEN = 100
+            result = _truncate("short text")
+            self.assertEqual(result, "short text")
+
+    def test_long_text_truncated(self):
+        """Long text should be truncated"""
+        with patch("fastdeploy.logger.request_logger.envs") as mock_envs:
+            mock_envs.FD_LOG_MAX_LEN = 10
+            result = _truncate("this is a very long text")
+            self.assertEqual(result, "this is a ")
+            self.assertEqual(len(result), 10)
+
+    def test_non_string_converted(self):
+        """Non-string should be converted"""
+        with patch("fastdeploy.logger.request_logger.envs") as mock_envs:
+            mock_envs.FD_LOG_MAX_LEN = 100
+            result = _truncate(12345)
+            self.assertEqual(result, "12345")
+
+
+class TestLogRequest(unittest.TestCase):
+    """Test log_request function"""
+
+    @patch("fastdeploy.logger._request_logger")
+    def test_log_when_enabled(self, mock_logger):
+        """Should log when enabled"""
+        with patch("fastdeploy.logger.request_logger.envs") as mock_envs:
+            mock_envs.FD_LOG_REQUESTS = 1
+            mock_envs.FD_LOG_REQUESTS_LEVEL = 0
+            mock_envs.FD_LOG_MAX_LEN = 2048
+
+            log_request(RequestLogLevel.LIFECYCLE, message="test {value}", value="hello")
+            mock_logger.info.assert_called_once()
+            call_args = mock_logger.info.call_args[0][0]
+            self.assertEqual(call_args, "test hello")
+
+    @patch("fastdeploy.logger._request_logger")
+    def test_no_log_when_disabled(self, mock_logger):
+        """Should not log when disabled"""
+        with patch("fastdeploy.logger.request_logger.envs") as mock_envs:
+            mock_envs.FD_LOG_REQUESTS = 0
+            mock_envs.FD_LOG_REQUESTS_LEVEL = 3
+
+            log_request(RequestLogLevel.LIFECYCLE, message="test {value}", value="hello")
+            mock_logger.info.assert_not_called()
+
+    @patch("fastdeploy.logger._request_logger")
+    def test_no_log_when_level_too_high(self, mock_logger):
+        """Should not log when level is too high"""
+        with patch("fastdeploy.logger.request_logger.envs") as mock_envs:
+            mock_envs.FD_LOG_REQUESTS = 1
+            mock_envs.FD_LOG_REQUESTS_LEVEL = 0
+
+            log_request(RequestLogLevel.CONTENT, message="test {value}", value="hello")
+            mock_logger.info.assert_not_called()
+
+    @patch("fastdeploy.logger._request_logger")
+    def test_content_level_truncates_content(self, mock_logger):
+        """CONTENT level should truncate content"""
+        with patch("fastdeploy.logger.request_logger.envs") as mock_envs:
+            mock_envs.FD_LOG_REQUESTS = 1
+            mock_envs.FD_LOG_REQUESTS_LEVEL = 3
+            mock_envs.FD_LOG_MAX_LEN = 5
+
+            log_request(RequestLogLevel.CONTENT, message="content: {data}", data="very long data")
+            mock_logger.info.assert_called_once()
+            call_args = mock_logger.info.call_args[0][0]
+            self.assertEqual(call_args, "content: very ")
+
+    @patch("fastdeploy.logger._request_logger")
+    def test_lifecycle_level_no_truncation(self, mock_logger):
+        """LIFECYCLE level should not truncate content"""
+        with patch("fastdeploy.logger.request_logger.envs") as mock_envs:
+            mock_envs.FD_LOG_REQUESTS = 1
+            mock_envs.FD_LOG_REQUESTS_LEVEL = 3
+            mock_envs.FD_LOG_MAX_LEN = 5
+
+            log_request(RequestLogLevel.LIFECYCLE, message="content: {data}", data="very long data")
+            mock_logger.info.assert_called_once()
+            call_args = mock_logger.info.call_args[0][0]
+            self.assertEqual(call_args, "content: very long data")
+
+
+class TestLogRequestError(unittest.TestCase):
+    """Test log_request_error function"""
+
+    @patch("fastdeploy.logger._request_logger")
+    def test_error_with_fields(self, mock_logger):
+        """Error log with fields should format message"""
+        log_request_error(message="request {request_id} failed: {error}", request_id="req-123", error="timeout")
+        mock_logger.error.assert_called_once()
+        call_args = mock_logger.error.call_args[0][0]
+        self.assertEqual(call_args, "request req-123 failed: timeout")
+
+    @patch("fastdeploy.logger._request_logger")
+    def test_error_without_fields(self, mock_logger):
+        """Error log without fields should not call format"""
+        log_request_error(message="simple error message")
+        mock_logger.error.assert_called_once()
+        call_args = mock_logger.error.call_args[0][0]
+        self.assertEqual(call_args, "simple error message")
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/logger/test_setup_logging.py

@@ -43,58 +43,18 @@ class TestSetupLogging(unittest.TestCase):
             delattr(setup_logging, "_configured")
 
     def test_log_dir_created(self):
+        """Log directory should be created"""
         nested = os.path.join(self.temp_dir, "a", "b", "c")
         setup_logging(log_dir=nested)
         self.assertTrue(Path(nested).is_dir())
 
-    def test_default_config_fallback(self):
-        """Pass a non-existent config_file to trigger default_config"""
-        fake_cfg = os.path.join(self.temp_dir, "no_such_cfg.json")
-        setup_logging(config_file=fake_cfg)
-        logger = logging.getLogger("fastdeploy")
-        self.assertTrue(logger.handlers)
-        handler_classes = [h.__class__.__name__ for h in logger.handlers]
-        self.assertIn("TimedRotatingFileHandler", handler_classes)
-
-    def test_debug_level_affects_handlers(self):
-        """FD_DEBUG=1 should force DEBUG level"""
-        with patch("fastdeploy.envs.FD_DEBUG", 1):
-            with patch("logging.config.dictConfig") as mock_cfg:
-                setup_logging()
-                called_config = mock_cfg.call_args[0][0]
-                for handler in called_config["handlers"].values():
-                    self.assertIn("formatter", handler)
-                self.assertEqual(called_config["handlers"]["console_stdout"]["level"], "DEBUG")
-
-    @patch("logging.config.dictConfig")
-    def test_custom_config_with_dailyrotating_and_debug(self, mock_dict):
-        custom_cfg = {
-            "version": 1,
-            "handlers": {
-                "daily": {
-                    "class": "logging.handlers.DailyRotatingFileHandler",
-                    "level": "INFO",
-                    "formatter": "plain",
-                }
-            },
-            "loggers": {"fastdeploy": {"handlers": ["daily"], "level": "INFO"}},
-        }
-        cfg_path = Path(self.temp_dir) / "cfg.json"
-        cfg_path.write_text(json.dumps(custom_cfg))
-
-        with patch("fastdeploy.envs.FD_DEBUG", 1):
-            setup_logging(config_file=str(cfg_path))
-
-        config_used = mock_dict.call_args[0][0]
-        self.assertIn("daily", config_used["handlers"])
-        self.assertEqual(config_used["handlers"]["daily"]["level"], "DEBUG")
-        self.assertIn("backupCount", config_used["handlers"]["daily"])
-
     def test_configure_once(self):
-        """Ensure idempotent setup"""
+        """Ensure idempotent setup - only configures once"""
-        l1 = setup_logging()
+        setup_logging()
-        l2 = setup_logging()
+        self.assertTrue(setup_logging._configured)
-        self.assertIs(l1, l2)
+        # Second call should not raise
+        setup_logging()
+        self.assertTrue(setup_logging._configured)
 
     def test_envs_priority_used_for_log_dir(self):
         """When log_dir=None, should use envs.FD_LOG_DIR"""
@@ -102,15 +62,45 @@ class TestSetupLogging(unittest.TestCase):
             setup_logging()
             self.assertTrue(os.path.exists(self.temp_dir))
 
-    @patch("logging.StreamHandler.emit")
+    def test_log_dir_stored(self):
-    def test_console_colored(self, mock_emit):
+        """setup_logging should store log_dir for later use"""
-        setup_logging()
+        setup_logging(log_dir=self.temp_dir)
-        logger = logging.getLogger("fastdeploy")
+        self.assertEqual(setup_logging._log_dir, self.temp_dir)
-        logger.error("color test")
-        self.assertTrue(mock_emit.called)
 
-    @patch("logging.config.dictConfig")
+    def test_no_config_file_no_dictconfig(self):
-    def test_backup_count_merging(self, mock_dict):
+        """When config_file is not provided, dictConfig should not be called"""
+        with patch("logging.config.dictConfig") as mock_dict:
+            setup_logging()
+            self.assertFalse(mock_dict.called)
+
+    def test_config_file_with_dictconfig(self):
+        """When config_file is provided, dictConfig should be called"""
+        custom_cfg = {
+            "version": 1,
+            "handlers": {},
+            "loggers": {},
+        }
+        cfg_path = Path(self.temp_dir) / "cfg.json"
+        cfg_path.write_text(json.dumps(custom_cfg))
+
+        with patch("logging.config.dictConfig") as mock_dict:
+            setup_logging(config_file=str(cfg_path))
+            self.assertTrue(mock_dict.called)
+
+    def test_config_file_not_exists_uses_default(self):
+        """When config_file doesn't exist, use default config"""
+        fake_cfg = os.path.join(self.temp_dir, "no_such_cfg.json")
+
+        with patch("logging.config.dictConfig") as mock_dict:
+            setup_logging(config_file=fake_cfg)
+            self.assertTrue(mock_dict.called)
+            # Should use default config
+            config_used = mock_dict.call_args[0][0]
+            self.assertIn("handlers", config_used)
+            self.assertIn("loggers", config_used)
+
+    def test_backup_count_merging(self):
+        """backupCount should be merged into handler config"""
         custom_cfg = {
             "version": 1,
             "handlers": {"daily": {"class": "logging.handlers.DailyRotatingFileHandler", "formatter": "plain"}},
@@ -119,24 +109,68 @@ class TestSetupLogging(unittest.TestCase):
         cfg_path = Path(self.temp_dir) / "cfg.json"
         cfg_path.write_text(json.dumps(custom_cfg))
 
+        with patch("logging.config.dictConfig") as mock_dict:
             setup_logging(config_file=str(cfg_path))
-
             config_used = mock_dict.call_args[0][0]
             self.assertEqual(config_used["handlers"]["daily"]["backupCount"], 3)
 
-    @patch("logging.config.dictConfig")
+    def test_debug_level_affects_handlers(self):
-    def test_error_logs_use_stderr_handler(self, mock_dict):
+        """FD_DEBUG=1 should force DEBUG level in handlers"""
-        """ERROR级别日志应该使用stderr输出"""
+        custom_cfg = {
-        setup_logging()
+            "version": 1,
+            "handlers": {"test": {"class": "logging.StreamHandler", "level": "INFO"}},
+            "loggers": {},
+        }
+        cfg_path = Path(self.temp_dir) / "cfg.json"
+        cfg_path.write_text(json.dumps(custom_cfg))
+
+        with patch("fastdeploy.envs.FD_DEBUG", 1):
+            with patch("logging.config.dictConfig") as mock_dict:
+                setup_logging(config_file=str(cfg_path))
                 config_used = mock_dict.call_args[0][0]
+                self.assertEqual(config_used["handlers"]["test"]["level"], "DEBUG")
+
+    def test_default_config_has_channels(self):
+        """Default config should have channel loggers configured"""
+        fake_cfg = os.path.join(self.temp_dir, "no_such_cfg.json")
+
+        with patch("logging.config.dictConfig") as mock_dict:
+            setup_logging(config_file=fake_cfg)
+            config_used = mock_dict.call_args[0][0]
+            # Check channel loggers exist
+            self.assertIn("fastdeploy.main", config_used["loggers"])
+            self.assertIn("fastdeploy.request", config_used["loggers"])
+            self.assertIn("fastdeploy.console", config_used["loggers"])
+
+    def test_default_config_has_handlers(self):
+        """Default config should have file handlers configured"""
+        fake_cfg = os.path.join(self.temp_dir, "no_such_cfg.json")
+
+        with patch("logging.config.dictConfig") as mock_dict:
+            setup_logging(config_file=fake_cfg)
+            config_used = mock_dict.call_args[0][0]
+            # Check handlers exist
+            self.assertIn("main_file", config_used["handlers"])
+            self.assertIn("request_file", config_used["handlers"])
+            self.assertIn("error_file", config_used["handlers"])
             self.assertIn("console_stderr", config_used["handlers"])
+
+    def test_default_config_stderr_handler(self):
+        """Default config console_stderr should output to stderr"""
+        fake_cfg = os.path.join(self.temp_dir, "no_such_cfg.json")
+
+        with patch("logging.config.dictConfig") as mock_dict:
+            setup_logging(config_file=fake_cfg)
+            config_used = mock_dict.call_args[0][0]
             self.assertEqual(config_used["handlers"]["console_stderr"]["stream"], "ext://sys.stderr")
             self.assertEqual(config_used["handlers"]["console_stderr"]["level"], "ERROR")
 
-    @patch("logging.config.dictConfig")
+    def test_default_config_stdout_filters_below_error(self):
-    def test_console_stdout_filters_below_error(self, mock_dict):
+        """Default config console_stdout should filter below ERROR level"""
-        """console_stdout应该只输出低于ERROR级别的日志"""
+        fake_cfg = os.path.join(self.temp_dir, "no_such_cfg.json")
-        setup_logging()
+
+        with patch("logging.config.dictConfig") as mock_dict:
+            setup_logging(config_file=fake_cfg)
             config_used = mock_dict.call_args[0][0]
             self.assertIn("console_stdout", config_used["handlers"])
             self.assertIn("below_error", config_used["handlers"]["console_stdout"]["filters"])
@@ -145,7 +179,7 @@ class TestSetupLogging(unittest.TestCase):
 
 class TestMaxLevelFilter(unittest.TestCase):
     def test_filter_allows_below_level(self):
-        """MaxLevelFilter应该允许低于指定级别的日志通过"""
+        """MaxLevelFilter should allow logs below specified level"""
         filter = MaxLevelFilter("ERROR")
         record = logging.LogRecord(
             name="test", level=logging.INFO, pathname="", lineno=0, msg="test", args=(), exc_info=None
@@ -153,7 +187,7 @@ class TestMaxLevelFilter(unittest.TestCase):
         self.assertTrue(filter.filter(record))
 
     def test_filter_blocks_at_level(self):
-        """MaxLevelFilter应该阻止等于指定级别的日志"""
+        """MaxLevelFilter should block logs at specified level"""
         filter = MaxLevelFilter("ERROR")
         record = logging.LogRecord(
             name="test", level=logging.ERROR, pathname="", lineno=0, msg="test", args=(), exc_info=None
@@ -161,7 +195,7 @@ class TestMaxLevelFilter(unittest.TestCase):
         self.assertFalse(filter.filter(record))
 
     def test_filter_blocks_above_level(self):
-        """MaxLevelFilter应该阻止高于指定级别的日志"""
+        """MaxLevelFilter should block logs above specified level"""
         filter = MaxLevelFilter("ERROR")
         record = logging.LogRecord(
             name="test", level=logging.CRITICAL, pathname="", lineno=0, msg="test", args=(), exc_info=None
@@ -169,7 +203,7 @@ class TestMaxLevelFilter(unittest.TestCase):
         self.assertFalse(filter.filter(record))
 
     def test_filter_with_numeric_level(self):
-        """MaxLevelFilter应该支持数字级别"""
+        """MaxLevelFilter should support numeric level"""
         filter = MaxLevelFilter(logging.WARNING)
         info_record = logging.LogRecord(
             name="test", level=logging.INFO, pathname="", lineno=0, msg="test", args=(), exc_info=None
@@ -181,5 +215,95 @@ class TestMaxLevelFilter(unittest.TestCase):
         self.assertFalse(filter.filter(warning_record))
 
 
+class TestChannelLoggers(unittest.TestCase):
+    """Test channel logger configuration via get_logger"""
+
+    def setUp(self):
+        self.temp_dir = tempfile.mkdtemp(prefix="logger_channel_test_")
+        if hasattr(setup_logging, "_configured"):
+            delattr(setup_logging, "_configured")
+        # Clear channel configuration cache
+        from fastdeploy.logger.logger import FastDeployLogger
+
+        FastDeployLogger._configured_channels = set()
+
+        self.patches = [
+            patch("fastdeploy.envs.FD_LOG_DIR", self.temp_dir),
+            patch("fastdeploy.envs.FD_DEBUG", 0),
+            patch("fastdeploy.envs.FD_LOG_BACKUP_COUNT", "3"),
+            patch("fastdeploy.envs.FD_LOG_LEVEL", None),
+        ]
+        [p.start() for p in self.patches]
+
+    def tearDown(self):
+        [p.stop() for p in self.patches]
+        shutil.rmtree(self.temp_dir, ignore_errors=True)
+        if hasattr(setup_logging, "_configured"):
+            delattr(setup_logging, "_configured")
+        # Clear channel configuration cache
+        from fastdeploy.logger.logger import FastDeployLogger
+
+        FastDeployLogger._configured_channels = set()
+
+    def test_main_channel_has_handlers(self):
+        """main channel root logger should have handlers"""
+        from fastdeploy.logger import get_logger
+
+        get_logger("test", channel="main")
+        main_channel = logging.getLogger("fastdeploy.main")
+        self.assertTrue(len(main_channel.handlers) > 0)
+
+    def test_request_channel_has_handlers(self):
+        """request channel root logger should have handlers"""
+        from fastdeploy.logger import get_logger
+
+        get_logger("test", channel="request")
+        request_channel = logging.getLogger("fastdeploy.request")
+        self.assertTrue(len(request_channel.handlers) > 0)
+
+    def test_console_channel_has_stdout_handler(self):
+        """console channel should have stdout handler"""
+        from fastdeploy.logger import get_logger
+
+        get_logger("test", channel="console")
+        console_channel = logging.getLogger("fastdeploy.console")
+        handler_types = [type(h).__name__ for h in console_channel.handlers]
+        self.assertIn("StreamHandler", handler_types)
+
+    def test_child_logger_propagates_to_channel(self):
+        """Child loggers should propagate to channel root logger"""
+        from fastdeploy.logger import get_logger
+
+        logger = get_logger("child_test", channel="main")
+        # Child logger should have no direct handlers (propagates to parent)
+        self.assertEqual(len(logger.handlers), 0)
+        self.assertEqual(logger.name, "fastdeploy.main.child_test")
+
+    def test_channel_file_mapping(self):
+        """Each channel should write to correct log file"""
+        from fastdeploy.logger.logger import FastDeployLogger
+
+        expected_files = {
+            "main": "fastdeploy.log",
+            "request": "request.log",
+            "console": "console.log",
+        }
+        self.assertEqual(FastDeployLogger._channel_files, expected_files)
+
+    def test_multiple_loggers_same_channel(self):
+        """Multiple loggers on same channel should share channel root handlers"""
+        from fastdeploy.logger import get_logger
+
+        logger1 = get_logger("test1", channel="main")
+        logger2 = get_logger("test2", channel="main")
+
+        main_channel = logging.getLogger("fastdeploy.main")
+        # Both child loggers should have no handlers
+        self.assertEqual(len(logger1.handlers), 0)
+        self.assertEqual(len(logger2.handlers), 0)
+        # Channel root should have handlers
+        self.assertTrue(len(main_channel.handlers) > 0)
+
+
 if __name__ == "__main__":
     unittest.main()

tests/output/test_process_batch_output.py

@@ -22,6 +22,7 @@ from unittest.mock import MagicMock, Mock, patch
 import paddle
 
 from fastdeploy.engine.request import RequestMetrics, RequestOutput
+from fastdeploy.logger.request_logger import RequestLogLevel
 from fastdeploy.output.token_processor import TokenProcessor
 
 paddle.set_device("cpu")
@@ -295,9 +296,9 @@ class TestTokenProcessorProcessBatchOutput(unittest.TestCase):
         processor.tokens_counter[task_id] = 0
         processor.tokens_counter[task2.request_id] = 0
 
-        # Mock llm_logger to capture the log message and envs.ENABLE_V1_KVCACHE_SCHEDULER
+        # Mock log_request to capture the log message and envs.ENABLE_V1_KVCACHE_SCHEDULER
         with (
-            patch("fastdeploy.output.token_processor.llm_logger") as mock_logger,
+            patch("fastdeploy.output.token_processor.log_request") as mock_log_request,
             patch("fastdeploy.output.token_processor.envs.ENABLE_V1_KVCACHE_SCHEDULER", 0),
         ):
             # Call the method
@@ -306,7 +307,11 @@ class TestTokenProcessorProcessBatchOutput(unittest.TestCase):
             # In speculative decoding mode, when accept_num[i] == PREEMPTED_TOKEN_ID,
             # the code logs "sync preemption" and continues without triggering abort recycling
             # This is the expected behavior for speculative decoding mode
-            mock_logger.info.assert_any_call(f"sync preemption for request_id {task_id} done.")
+            mock_log_request.assert_any_call(
+                RequestLogLevel.STAGES,
+                message="sync preemption for request_id {request_id} done.",
+                request_id=task_id,
+            )
             # Verify that _recycle_resources was NOT called for the aborted task
             # (it may be called for other tasks like test_request_2 if they receive EOS tokens)
             for call in processor._recycle_resources.call_args_list:

tests/output/test_process_batch_output_use_zmq.py

@@ -21,6 +21,7 @@ from unittest.mock import MagicMock, patch
 import numpy as np
 
 from fastdeploy.engine.request import CompletionOutput, RequestMetrics, RequestOutput
+from fastdeploy.logger.request_logger import RequestLogLevel
 from fastdeploy.output.token_processor import TokenProcessor
 from fastdeploy.worker.output import LogprobsLists
 
@@ -171,16 +172,20 @@ class TestTokenProcessorLogprobs(unittest.TestCase):
         # Mock _recycle_resources to track if it's called
         self.processor._recycle_resources = MagicMock()
 
-        # Mock the llm_logger module and envs.ENABLE_V1_KVCACHE_SCHEDULER
+        # Mock the log_request function and envs.ENABLE_V1_KVCACHE_SCHEDULER
         with (
-            patch("fastdeploy.output.token_processor.llm_logger") as mock_logger,
+            patch("fastdeploy.output.token_processor.log_request") as mock_log_request,
             patch("fastdeploy.output.token_processor.envs.ENABLE_V1_KVCACHE_SCHEDULER", 1),
         ):
             # Call the method
             result = self.processor._process_batch_output_use_zmq([stream_data])
 
-            # Verify the recycling logic was triggered
+            # Verify the recycling logic was triggered via log_request
-            mock_logger.info.assert_any_call(f"start to recycle abort request_id {task_id}")
+            mock_log_request.assert_any_call(
+                RequestLogLevel.STAGES,
+                message="start to recycle abort request_id {request_id}",
+                request_id=task_id,
+            )
             self.processor.resource_manager.recycle_abort_task.assert_called_once_with(task_id)
             self.assertNotIn(task_id, self.processor.resource_manager.to_be_aborted_req_id_set)
             self.assertEqual(len(result), 0)  # Aborted task is skipped (continue)

tests/scheduler/test_dp_scheduler.py

@@ -47,9 +47,13 @@ sys.modules["fastdeploy.engine.request"] = Mock()
 sys.modules["fastdeploy.scheduler"] = Mock()
 sys.modules["fastdeploy.scheduler.local_scheduler"] = Mock()
 sys.modules["fastdeploy.scheduler.data"] = Mock()
+sys.modules["fastdeploy.logger"] = Mock()
+sys.modules["fastdeploy.logger.request_logger"] = Mock()
 
 # Mock the get_logger function
 sys.modules["fastdeploy.utils"].get_logger = Mock(return_value=mock_logger)
+# Mock the log_request function
+sys.modules["fastdeploy.logger.request_logger"].log_request = Mock()
 
 
 # Mock the Request, RequestOutput, and ScheduledResponse classes
@@ -240,8 +244,9 @@ class TestDPLocalScheduler(unittest.TestCase):
 
     def test_put_results_with_finished_requests(self):
         """Test putting results with finished requests."""
-        # Reset mock logger
+        # Get the mock log_request function
-        mock_logger.reset_mock()
+        mock_log_request = sys.modules["fastdeploy.logger.request_logger"].log_request
+        mock_log_request.reset_mock()
 
         # Create mock request outputs
         results = [
@@ -254,13 +259,12 @@ class TestDPLocalScheduler(unittest.TestCase):
         with patch.object(self.scheduler, "responses_not_empty"):
             self.scheduler.put_results(results)
 
-        # Check that finished requests were logged - the logger should have been called
+        # Check that finished requests were logged via log_request
-        self.assertTrue(mock_logger.info.called)
+        self.assertTrue(mock_log_request.called)
         # Get the actual call arguments to verify the message format
-        call_args = mock_logger.info.call_args[0][0]
+        call_kwargs = mock_log_request.call_args[1]
-        self.assertIn("finished responses", call_args)
+        self.assertIn("finished responses", call_kwargs.get("message", ""))
-        self.assertIn("req1", call_args)
+        self.assertIn("req1", str(call_kwargs.get("request_ids", [])))
-        self.assertIn("req3", call_args)
 
     def test_put_results_with_new_responses(self):
         """Test putting results with new responses."""

tests/scheduler/test_local_scheduler.py

@@ -17,10 +17,11 @@ import unittest
 from unittest.mock import Mock, patch  # noqa: F401
 
 from fastdeploy.engine.request import Request, RequestMetrics, RequestOutput
+from fastdeploy.logger.request_logger import RequestLogLevel
 
 # Real FastDeploy imports
 from fastdeploy.scheduler.local_scheduler import LocalScheduler
-from fastdeploy.utils import envs, scheduler_logger
+from fastdeploy.utils import envs
 
 
 class TestLocalScheduler(unittest.TestCase):
@@ -221,9 +222,9 @@ class TestLocalScheduler(unittest.TestCase):
 
     def test_reset_logs_message(self):
         """Test that reset logs appropriate message."""
-        with patch.object(scheduler_logger, "info") as mock_info:
+        with patch("fastdeploy.scheduler.local_scheduler.log_request") as mock_log:
             self.scheduler.reset()
-            mock_info.assert_called_once_with("Scheduler has been reset")
+            mock_log.assert_called_once_with(RequestLogLevel.LIFECYCLE, message="Scheduler has been reset")
 
     def test_put_requests_duplicate_handling(self):
         """Test handling of duplicate request IDs."""
@@ -449,12 +450,14 @@ class TestLocalScheduler(unittest.TestCase):
         # Add request first
         self.scheduler.put_requests([self.mock_request_1])
 
-        with patch.object(scheduler_logger, "info") as mock_info:
+        with patch("fastdeploy.scheduler.local_scheduler.log_request") as mock_log_request:
             mock_output = self._create_test_request_output("req_1", finished=True)
             self.scheduler.put_results([mock_output])
 
-            # Should log finished response
+            # Should log finished response via log_request
-            self._assert_log_contains(mock_info, "finished responses")
+            mock_log_request.assert_called_once()
+            call_kwargs = mock_log_request.call_args[1]
+            self.assertIn("finished responses", call_kwargs.get("message", ""))
 
 
 if __name__ == "__main__":