Support Multi Process Tokenizer Manager (#6555)

Signed-off-by: ybyang <ybyang7@iflytek.com>
Signed-off-by: huanglong <huanglong@linux.alibaba.com>
Co-authored-by: lw9527 <952799980@qq.com>
Co-authored-by: huanglong <huanglong@linux.alibaba.com>
Co-authored-by: Huang Long <121648372+LLLL114@users.noreply.github.com>

python/sglang/srt/entrypoints/http_server.py

@@ -18,14 +18,18 @@ This file implements HTTP APIs for the inference engine via fastapi.
 """
 
 import asyncio
+import ctypes
 import dataclasses
 import json
 import logging
 import multiprocessing as multiprocessing
 import os
+import sys
+import tempfile
 import threading
 import time
 from http import HTTPStatus
+from multiprocessing import Lock, Manager, Value, shared_memory
 from typing import AsyncIterator, Callable, Dict, Optional
 
 # Fix a bug of Python threading
@@ -94,7 +98,7 @@ from sglang.srt.managers.template_manager import TemplateManager
 from sglang.srt.managers.tokenizer_manager import ServerStatus, TokenizerManager
 from sglang.srt.metrics.func_timer import enable_func_timer
 from sglang.srt.reasoning_parser import ReasoningParser
-from sglang.srt.server_args import ServerArgs
+from sglang.srt.server_args import PortArgs, ServerArgs
 from sglang.srt.utils import (
     add_api_key_middleware,
     add_prometheus_middleware,
@@ -129,8 +133,165 @@ def set_global_state(global_state: _GlobalState):
     _global_state = global_state
 
 
+def serialize_port_args(port_args: PortArgs) -> dict:
+    """Serialize PortArgs into a shareable dictionary"""
+    return {
+        "tokenizer_ipc_name": port_args.tokenizer_ipc_name,
+        "scheduler_input_ipc_name": port_args.scheduler_input_ipc_name,
+        "detokenizer_ipc_name": port_args.detokenizer_ipc_name,
+        "nccl_port": port_args.nccl_port,
+        "rpc_ipc_name": port_args.rpc_ipc_name,
+        "metrics_ipc_name": port_args.metrics_ipc_name,
+        "tokenizer_worker_ipc_name": port_args.tokenizer_worker_ipc_name,
+    }
+
+
+def deserialize_port_args(data: dict) -> PortArgs:
+    """Deserialize PortArgs from a shared dictionary"""
+    return PortArgs(**data)
+
+
+def serialize_server_args(server_args: ServerArgs) -> dict:
+    """Serialize ServerArgs into a shareable dictionary"""
+    return dataclasses.asdict(server_args)
+
+
+def deserialize_server_args(data: dict) -> ServerArgs:
+    """Deserialize ServerArgs from a shared dictionary"""
+    return ServerArgs(**data)
+
+
+def serialize_scheduler_info(scheduler_info: Dict) -> dict:
+    """Serialize scheduler_info into a shareable dictionary"""
+    return scheduler_info
+
+
+def deserialize_scheduler_info(data: dict) -> Dict:
+    """Deserialize scheduler_info from a shared dictionary"""
+    return data
+
+
+def write_to_shared_memory(data: dict, name: str) -> shared_memory.SharedMemory:
+    """Write data to shared memory"""
+    serialized = json.dumps(data).encode("utf-8")
+    size = len(serialized)
+    try:
+        # Try to open existing shared memory
+        shm = shared_memory.SharedMemory(name=name)
+        # If size is insufficient, close and recreate
+        if shm.size < size:
+            shm.close()
+            shm.unlink()
+            shm = shared_memory.SharedMemory(create=True, size=size, name=name)
+    except FileNotFoundError:
+        # If not present, create new shared memory
+        shm = shared_memory.SharedMemory(create=True, size=size, name=name)
+
+    shm.buf[:size] = serialized
+    return shm
+
+
+def read_from_shared_memory(name: str) -> dict:
+    """Read data from shared memory"""
+    try:
+        shm = shared_memory.SharedMemory(name=name)
+        data = json.loads(bytes(shm.buf).decode("utf-8"))
+        shm.close()
+        return data
+    except FileNotFoundError:
+        raise FileNotFoundError(f"Shared memory {name} not found")
+
+
+def get_main_process_id() -> int:
+    """Get the main process ID"""
+    return multiprocessing.current_process()._parent_pid
+
+
+def write_data_for_multi_tokenizer(
+    port_args: PortArgs, server_args: ServerArgs, scheduler_info: Dict
+):
+    """Write args information to share memory for multi-tokenizer"""
+    # get main process ID
+    main_pid = get_main_process_id()
+    current_pid = os.getpid()
+    logger.info(f"main process ID: {main_pid}, current process ID: {current_pid}")
+
+    # Write port_args to shared memory
+    port_args_shm = write_to_shared_memory(
+        serialize_port_args(port_args), f"port_args_{current_pid}"
+    )
+    # Write server_args to shared memory
+    server_args_shm = write_to_shared_memory(
+        serialize_server_args(server_args), f"server_args_{current_pid}"
+    )
+    # Write scheduler_info to shared memory
+    scheduler_info_shm = write_to_shared_memory(
+        serialize_scheduler_info(scheduler_info), f"scheduler_info_{current_pid}"
+    )
+
+    port_args_shm.close()
+    server_args_shm.close()
+    scheduler_info_shm.close()
+
+    return port_args_shm, server_args_shm, scheduler_info_shm
+
+
+def init_multi_tokenizer() -> ServerArgs:
+    """Read args information from shm and init tokenizer manager for current process"""
+    pid = os.getpid()
+    main_pid = get_main_process_id()
+    logger.info(f"current worker_id: {pid}, main processID: {main_pid}")
+
+    # Read port_args, server_args, and scheduler_info from shared memory
+    port_args_data = read_from_shared_memory(f"port_args_{main_pid}")
+    server_args_data = read_from_shared_memory(f"server_args_{main_pid}")
+    scheduler_info_data = read_from_shared_memory(f"scheduler_info_{main_pid}")
+    port_args = deserialize_port_args(port_args_data)
+    server_args = deserialize_server_args(server_args_data)
+    scheduler_info = deserialize_scheduler_info(scheduler_info_data)
+
+    port_args.tokenizer_ipc_name = (
+        f"ipc://{tempfile.NamedTemporaryFile(delete=False).name}"
+    )
+
+    # Launch tokenizer process
+    tokenizer_manager = TokenizerManager(server_args, port_args, False)
+    template_manager = TemplateManager()
+    template_manager.initialize_templates(
+        tokenizer_manager=tokenizer_manager,
+        model_path=server_args.model_path,
+        chat_template=server_args.chat_template,
+        completion_template=server_args.completion_template,
+    )
+    # register multi tokenizer
+    tokenizer_manager.register_to_main_tokenizer_manager()
+
+    tokenizer_manager.max_req_input_len = scheduler_info["max_req_input_len"]
+    set_global_state(
+        _GlobalState(
+            tokenizer_manager=tokenizer_manager,
+            template_manager=template_manager,
+            scheduler_info=scheduler_info,
+        )
+    )
+    return server_args
+
+
 @asynccontextmanager
 async def lifespan(fast_api_app: FastAPI):
+    server_args = getattr(fast_api_app, "server_args", None)
+    if server_args is None:
+        # for multi-tokenizer
+        fast_api_app.server_args = init_multi_tokenizer()
+        fast_api_app.warmup_thread = threading.Thread(
+            target=_wait_and_warmup,
+            args=(
+                fast_api_app.server_args,
+                None,  # pipe_finish_writer not needed in worker
+                None,  # launch_callback not needed in worker
+            ),
+        )
+
     # Initialize OpenAI serving handlers
     fast_api_app.state.openai_serving_completion = OpenAIServingCompletion(
         _global_state.tokenizer_manager, _global_state.template_manager
@@ -191,7 +352,15 @@ async def lifespan(fast_api_app: FastAPI):
     warmup_thread = getattr(fast_api_app, "warmup_thread", None)
     if warmup_thread is not None:
         warmup_thread.start()
+
+    try:
         yield
+    finally:
+        if server_args.tokenizer_worker_num > 1:
+            pid = os.getpid()
+            logger.info(f"uvicorn worker {pid} ending...")
+            warmup_thread.join()
+            logger.info(f"uvicorn {pid} ended")
 
 
 # Fast API
@@ -208,6 +377,30 @@ app.add_middleware(
 )
 
 
+# Function to setup all middlewares for multi-process compatibility
+def setup_middlewares():
+    """Setup all middlewares for both single and multi-process modes"""
+    worker_pid = os.getpid()
+
+    # Setup API key middleware
+    api_key = os.environ.get("SGLANG_API_KEY", "")
+    if api_key:
+        add_api_key_middleware(app, api_key)
+        logger.info(f"Worker {worker_pid} added API key middleware")
+
+    # Setup prometheus middleware
+    # Check if metrics are enabled via environment variable
+    enable_metrics = get_bool_env_var("SGLANG_ENABLE_METRICS", "false")
+    if enable_metrics:
+        add_prometheus_middleware(app)
+        enable_func_timer()
+        logger.info(f"Worker {worker_pid} added prometheus middleware")
+
+
+# Call setup function at module level for multi-process compatibility
+setup_middlewares()
+
+
 @app.exception_handler(HTTPException)
 async def validation_exception_handler(request: Request, exc: HTTPException):
     """Enrich HTTP exception with status code and other details"""
@@ -993,9 +1186,19 @@ def launch_server(
     1. The HTTP server, Engine, and TokenizerManager both run in the main process.
     2. Inter-process communication is done through IPC (each process uses a different port) via the ZMQ library.
     """
+    if server_args.tokenizer_worker_num > 1:
+        port_args = PortArgs.init_new(server_args)
+        port_args.tokenizer_worker_ipc_name = (
+            f"ipc://{tempfile.NamedTemporaryFile(delete=False).name}"
+        )
         tokenizer_manager, template_manager, scheduler_info = _launch_subprocesses(
-        server_args=server_args
+            server_args=server_args, port_args=port_args
         )
+    else:
+        tokenizer_manager, template_manager, scheduler_info = _launch_subprocesses(
+            server_args=server_args,
+        )
+
     set_global_state(
         _GlobalState(
             tokenizer_manager=tokenizer_manager,
@@ -1004,6 +1207,24 @@ def launch_server(
         )
     )
 
+    if server_args.tokenizer_worker_num > 1:
+        # Set environment variables for middlewares in main process
+        if server_args.api_key:
+            os.environ["SGLANG_API_KEY"] = server_args.api_key
+            logger.info("Main process set SGLANG_API_KEY")
+
+        if server_args.enable_metrics:
+            os.environ["SGLANG_ENABLE_METRICS"] = "true"
+            logger.info("Main process set SGLANG_ENABLE_METRICS=true")
+
+        port_args_shm, server_args_shm, scheduler_info_shm = (
+            write_data_for_multi_tokenizer(
+                port_args,
+                server_args,
+                scheduler_info,
+            )
+        )
+    else:
         # Add api key authorization
         if server_args.api_key:
             add_api_key_middleware(app, server_args.api_key)
@@ -1030,6 +1251,24 @@ def launch_server(
         set_uvicorn_logging_configs()
         app.server_args = server_args
         # Listen for HTTP requests
+        if server_args.tokenizer_worker_num > 1:
+            from uvicorn.config import LOGGING_CONFIG
+
+            LOGGING_CONFIG["loggers"]["sglang.srt.entrypoints.http_server"] = {
+                "handlers": ["default"],
+                "level": "INFO",
+                "propagate": False,
+            }
+            uvicorn.run(
+                "sglang.srt.entrypoints.http_server:app",
+                host=server_args.host,
+                port=server_args.port,
+                log_level=server_args.log_level_http or server_args.log_level,
+                timeout_keep_alive=5,
+                loop="uvloop",
+                workers=server_args.tokenizer_worker_num,
+            )
+        else:
             uvicorn.run(
                 app,
                 host=server_args.host,
@@ -1039,6 +1278,11 @@ def launch_server(
                 loop="uvloop",
             )
     finally:
+        if server_args.tokenizer_worker_num > 1:
+            port_args_shm.unlink()
+            server_args_shm.unlink()
+            scheduler_info_shm.unlink()
+        else:
             warmup_thread.join()
 
 

python/sglang/srt/managers/detokenizer_manager.py

@@ -31,10 +31,12 @@ from sglang.srt.managers.io_struct import (
     BatchMultimodalOut,
     BatchStrOut,
     BatchTokenIDOut,
+    MultiTokenizerRegisterReq,
 )
 from sglang.srt.server_args import PortArgs, ServerArgs
 from sglang.srt.utils import (
     configure_logger,
+    get_workerids_from_rids,
     get_zmq_socket,
     kill_itself_when_parent_died,
 )
@@ -81,7 +83,6 @@ class DetokenizerManager:
         self.send_to_tokenizer = get_zmq_socket(
             context, zmq.PUSH, port_args.tokenizer_ipc_name, False
         )
-
         if server_args.skip_tokenizer_init:
             self.tokenizer = None
         else:
@@ -94,21 +95,208 @@ class DetokenizerManager:
 
         self.decode_status = LimitedCapacityDict(capacity=DETOKENIZER_MAX_STATES)
         self.is_dummy = server_args.load_format == "dummy"
-
+        self.tokenizer_worker_num = server_args.tokenizer_worker_num
         self._request_dispatcher = TypeBasedDispatcher(
             [
                 (BatchEmbeddingOut, self.handle_batch_embedding_out),
                 (BatchTokenIDOut, self.handle_batch_token_id_out),
                 (BatchMultimodalDecodeReq, self.handle_multimodal_decode_req),
+                (MultiTokenizerRegisterReq, lambda x: None),
             ]
         )
 
     def event_loop(self):
         """The event loop that handles requests"""
+
         while True:
+            try:
                 recv_obj = self.recv_from_scheduler.recv_pyobj()
                 output = self._request_dispatcher(recv_obj)
+                if self.tokenizer_worker_num <= 1:
                     self.send_to_tokenizer.send_pyobj(output)
+                else:
+                    # Extract worker_id from rid
+                    if isinstance(recv_obj.rids, list):
+                        worker_ids = get_workerids_from_rids(recv_obj.rids)
+                    else:
+                        raise RuntimeError(
+                            f"tokenizer_worker_num > 1, recv_obj.rids must be list"
+                        )
+
+                    if not hasattr(self, "tokenizer_mapping"):
+                        self.tokenizer_mapping = {}
+
+                    # Create ZMQ context if needed
+                    if not hasattr(self, "_zmq_context"):
+                        self._zmq_context = zmq.Context()
+
+                    # Send data using the corresponding socket
+                    for i, worker_id in enumerate(worker_ids):
+                        if worker_id not in self.tokenizer_mapping:
+                            # register the worker if not already done
+                            if isinstance(recv_obj, MultiTokenizerRegisterReq):
+                                self.init_tokenizer_mapping(recv_obj, worker_id)
+                            else:
+                                logger.error(
+                                    f"Worker {worker_id} not registered and not found in tokenizer mapping . "
+                                    "Please ensure the worker is registered correctly."
+                                )
+                            continue
+                        else:
+                            if isinstance(recv_obj, MultiTokenizerRegisterReq):
+                                continue
+
+                        # Create a new output object based on the type
+                        if isinstance(output, BatchEmbeddingOut):
+                            new_output = BatchEmbeddingOut(
+                                rids=[output.rids[i]],
+                                finished_reasons=[output.finished_reasons[i]],
+                                embeddings=[output.embeddings[i]],
+                                prompt_tokens=[output.prompt_tokens[i]],
+                                cached_tokens=[output.cached_tokens[i]],
+                            )
+                        elif isinstance(output, BatchStrOut):
+                            new_output = BatchStrOut(
+                                rids=[output.rids[i]],
+                                finished_reasons=(
+                                    [output.finished_reasons[i]]
+                                    if len(output.finished_reasons) > i
+                                    else None
+                                ),
+                                output_strs=(
+                                    [output.output_strs[i]]
+                                    if len(output.output_strs) > i
+                                    else None
+                                ),
+                                output_ids=(
+                                    [output.output_ids[i]]
+                                    if output.output_ids and len(output.output_ids) > i
+                                    else None
+                                ),
+                                prompt_tokens=(
+                                    [output.prompt_tokens[i]]
+                                    if len(output.prompt_tokens) > i
+                                    else None
+                                ),
+                                completion_tokens=(
+                                    [output.completion_tokens[i]]
+                                    if len(output.completion_tokens) > i
+                                    else None
+                                ),
+                                cached_tokens=(
+                                    [output.cached_tokens[i]]
+                                    if len(output.cached_tokens) > i
+                                    else None
+                                ),
+                                spec_verify_ct=(
+                                    [output.spec_verify_ct[i]]
+                                    if len(output.spec_verify_ct) > i
+                                    else None
+                                ),
+                                input_token_logprobs_val=(
+                                    [output.input_token_logprobs_val[i]]
+                                    if output.input_token_logprobs_val
+                                    else None
+                                ),
+                                input_token_logprobs_idx=(
+                                    [output.input_token_logprobs_idx[i]]
+                                    if output.input_token_logprobs_idx
+                                    else None
+                                ),
+                                output_token_logprobs_val=(
+                                    [output.output_token_logprobs_val[i]]
+                                    if output.output_token_logprobs_val
+                                    else None
+                                ),
+                                output_token_logprobs_idx=(
+                                    [output.output_token_logprobs_idx[i]]
+                                    if output.output_token_logprobs_idx
+                                    else None
+                                ),
+                                input_top_logprobs_val=(
+                                    [output.input_top_logprobs_val[i]]
+                                    if output.input_top_logprobs_val
+                                    else None
+                                ),
+                                input_top_logprobs_idx=(
+                                    [output.input_top_logprobs_idx[i]]
+                                    if output.input_top_logprobs_idx
+                                    else None
+                                ),
+                                output_top_logprobs_val=(
+                                    [output.output_top_logprobs_val[i]]
+                                    if output.output_top_logprobs_val
+                                    else None
+                                ),
+                                output_top_logprobs_idx=(
+                                    [output.output_top_logprobs_idx[i]]
+                                    if output.output_top_logprobs_idx
+                                    else None
+                                ),
+                                input_token_ids_logprobs_val=(
+                                    [output.input_token_ids_logprobs_val[i]]
+                                    if output.input_token_ids_logprobs_val
+                                    else None
+                                ),
+                                input_token_ids_logprobs_idx=(
+                                    [output.input_token_ids_logprobs_idx[i]]
+                                    if output.input_token_ids_logprobs_idx
+                                    else None
+                                ),
+                                output_token_ids_logprobs_val=(
+                                    [output.output_token_ids_logprobs_val[i]]
+                                    if output.output_token_ids_logprobs_val
+                                    else None
+                                ),
+                                output_token_ids_logprobs_idx=(
+                                    [output.output_token_ids_logprobs_idx[i]]
+                                    if output.output_token_ids_logprobs_idx
+                                    else None
+                                ),
+                                output_hidden_states=(
+                                    [output.output_hidden_states[i]]
+                                    if output.output_hidden_states
+                                    else None
+                                ),
+                            )
+                        elif isinstance(output, BatchMultimodalOut):
+                            new_output = BatchMultimodalOut(
+                                rids=[output.rids[i]],
+                                finished_reasons=[output.finished_reasons[i]],
+                                prompt_tokens=[output.prompt_tokens[i]],
+                                completion_tokens=[output.completion_tokens[i]],
+                                cached_tokens=[output.cached_tokens[i]],
+                            )
+                        else:
+                            new_output = output
+
+                        try:
+                            self.tokenizer_mapping[worker_id].send_pyobj(new_output)
+                        except zmq.error.ZMQError as e:
+                            logger.info(
+                                f"ZMQ error when sending to worker {worker_id}: {e}"
+                            )
+            except Exception as e:
+                logger.error(f"Error in detokenizer event loop: {e}")
+                raise e
+
+    def init_tokenizer_mapping(
+        self, recv_obj: MultiTokenizerRegisterReq, worker_id: str
+    ):
+        """init tokenizer mapping from register request"""
+        ipc_name = recv_obj.ipc_name
+        worker_id_int = int(worker_id)
+
+        if worker_id_int not in self.tokenizer_mapping:
+            socket = get_zmq_socket(self._zmq_context, zmq.PUSH, ipc_name, False)
+            self.tokenizer_mapping[worker_id_int] = socket
+            logger.info(
+                f"Detokenizer Manager Created ZMQ socket for worker {worker_id} with ipc_name {ipc_name}"
+            )
+        else:
+            logger.info(
+                f"ZMQ socket for worker {worker_id} already exists, skipping creation"
+            )
 
     def trim_matched_stop(
         self, output: Union[str, List[int]], finished_reason: Dict, no_stop_trim: bool

python/sglang/srt/managers/io_struct.py

@@ -782,12 +782,13 @@ class BatchEmbeddingOut:
 
 @dataclass
 class FlushCacheReqInput:
-    pass
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
 class FlushCacheReqOutput:
     success: bool
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
@@ -798,6 +799,7 @@ class UpdateWeightFromDiskReqInput:
     load_format: Optional[str] = None
     # Whether to abort all requests before updating weights
     abort_all_requests: bool = False
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
@@ -806,6 +808,7 @@ class UpdateWeightFromDiskReqOutput:
     message: str
     # Number of paused requests during weight sync.
     num_paused_requests: Optional[int] = 0
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
@@ -819,12 +822,14 @@ class UpdateWeightsFromDistributedReqInput:
     flush_cache: bool = True
     # Whether to abort all requests before updating weights
     abort_all_requests: bool = False
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
 class UpdateWeightsFromDistributedReqOutput:
     success: bool
     message: str
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
@@ -842,12 +847,14 @@ class UpdateWeightsFromTensorReqInput:
     flush_cache: bool = True
     # Whether to abort all requests before updating weights
     abort_all_requests: bool = False
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
 class UpdateWeightsFromTensorReqOutput:
     success: bool
     message: str
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
@@ -864,23 +871,27 @@ class InitWeightsUpdateGroupReqInput:
     group_name: str = "weight_update_group"
     # The backend
     backend: str = "nccl"
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
 class InitWeightsUpdateGroupReqOutput:
     success: bool
     message: str
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
 class GetWeightsByNameReqInput:
     name: str
     truncate_size: int = 100
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
 class GetWeightsByNameReqOutput:
     parameter: list
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
@@ -888,11 +899,12 @@ class ReleaseMemoryOccupationReqInput:
     # Optional tags to identify the memory region, which is primarily used for RL
     # Currently we only support `weights` and `kv_cache`
     tags: Optional[List[str]] = None
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
 class ReleaseMemoryOccupationReqOutput:
-    pass
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
@@ -900,21 +912,23 @@ class ResumeMemoryOccupationReqInput:
     # Optional tags to identify the memory region, which is primarily used for RL
     # Currently we only support `weights` and `kv_cache`
     tags: Optional[List[str]] = None
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
 class ResumeMemoryOccupationReqOutput:
-    pass
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
 class SlowDownReqInput:
     forward_sleep_time: Optional[float]
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
 class SlowDownReqOutput:
-    pass
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
@@ -923,29 +937,37 @@ class AbortReq:
     rid: str = ""
     # Whether to abort all requests
     abort_all: bool = False
-    # The finished reason data
+
+    rids: Optional[Union[List[str], str]] = None
+
     finished_reason: Optional[Dict[str, Any]] = None
 
+    def __post_init__(self):
+        self.rids = self.rid
+
 
 @dataclass
 class GetInternalStateReq:
-    pass
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
 class GetInternalStateReqOutput:
     internal_state: Dict[Any, Any]
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
 class SetInternalStateReq:
     server_args: Dict[str, Any]
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
 class SetInternalStateReqOutput:
     updated: bool
     server_args: Dict[str, Any]
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
@@ -961,6 +983,7 @@ class ProfileReqInput:
     profile_by_stage: bool = False
     with_stack: Optional[bool] = None
     record_shapes: Optional[bool] = None
+    rids: Optional[Union[List[str], str]] = None
 
 
 class ProfileReqType(Enum):
@@ -979,12 +1002,14 @@ class ProfileReq:
     with_stack: Optional[bool] = None
     record_shapes: Optional[bool] = None
     profile_id: Optional[str] = None
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
 class ProfileReqOutput:
     success: bool
     message: str
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
@@ -993,27 +1018,32 @@ class ConfigureLoggingReq:
     log_requests_level: Optional[int] = None
     dump_requests_folder: Optional[str] = None
     dump_requests_threshold: Optional[int] = None
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
 class OpenSessionReqInput:
     capacity_of_str_len: int
     session_id: Optional[str] = None
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
 class CloseSessionReqInput:
     session_id: str
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
 class OpenSessionReqOutput:
     session_id: Optional[str]
     success: bool
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
 class HealthCheckOutput:
+    rids: Optional[Union[List[str], str]] = None
     pass
 
 
@@ -1025,7 +1055,7 @@ class ExpertDistributionReq(Enum):
 
 @dataclass
 class ExpertDistributionReqOutput:
-    pass
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
@@ -1050,18 +1080,21 @@ class ParseFunctionCallReq:
     tool_call_parser: Optional[str] = (
         None  # Specify the parser type, e.g. 'llama3', 'qwen25', or 'mistral'. If not specified, tries all.
     )
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
 class SeparateReasoningReqInput:
     text: str  # The text to parse.
     reasoning_parser: str  # Specify the parser type, e.g., "deepseek-r1".
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
 class VertexGenerateReqInput:
     instances: List[dict]
     parameters: Optional[dict] = None
+    rids: Optional[Union[List[str], str]] = None
 
 
 @dataclass
@@ -1086,6 +1119,7 @@ class LoadLoRAAdapterReqInput:
     pinned: bool = False
     # The unique identifier for the LoRA adapter, which automatically generated in the `TokenizerManager`.
     lora_id: Optional[str] = None
+    rids: Optional[Union[List[str], str]] = None
 
     def to_ref(self) -> LoRARef:
         return LoRARef(
@@ -1102,6 +1136,7 @@ class UnloadLoRAAdapterReqInput:
     lora_name: str
     # The unique identifier for the LoRA adapter, which automatically generated in the `TokenizerManager`.
     lora_id: Optional[str] = None
+    rids: Optional[Union[List[str], str]] = None
 
     def to_ref(self) -> LoRARef:
         return LoRARef(
@@ -1115,11 +1150,18 @@ class LoRAUpdateResult:
     success: bool
     error_message: Optional[str] = None
     loaded_adapters: Optional[Dict[str, LoRARef]] = None
+    rids: Optional[Union[List[str], str]] = None
 
 
 LoadLoRAAdapterReqOutput = UnloadLoRAAdapterReqOutput = LoRAUpdateResult
 
 
+@dataclass
+class MultiTokenizerRegisterReq:
+    rids: Optional[Union[List[str], str]] = None
+    ipc_name: Optional[str] = None
+
+
 class BlockReqType(Enum):
     BLOCK = 1
     UNBLOCK = 2

python/sglang/srt/managers/scheduler.py

@@ -79,6 +79,7 @@ from sglang.srt.managers.io_struct import (
     InitWeightsUpdateGroupReqInput,
     LoadLoRAAdapterReqInput,
     LoadLoRAAdapterReqOutput,
+    MultiTokenizerRegisterReq,
     OpenSessionReqInput,
     OpenSessionReqOutput,
     ProfileReq,
@@ -247,7 +248,6 @@ class Scheduler(
         # Init inter-process communication
         context = zmq.Context(2)
         self.idle_sleeper = None
-
         if self.pp_rank == 0 and self.attn_tp_rank == 0:
             self.recv_from_tokenizer = get_zmq_socket(
                 context, zmq.PULL, port_args.scheduler_input_ipc_name, False
@@ -522,6 +522,7 @@ class Scheduler(
                 (ExpertDistributionReq, self.expert_distribution_handle),
                 (LoadLoRAAdapterReqInput, self.load_lora_adapter),
                 (UnloadLoRAAdapterReqInput, self.unload_lora_adapter),
+                (MultiTokenizerRegisterReq, self.register_multi_tokenizer),
             ]
         )
 
@@ -1063,6 +1064,8 @@ class Scheduler(
                     if self.recv_from_rpc is not None:
                         self.recv_from_rpc.send_pyobj(output)
                 else:
+                    if recv_req.rids is not None:
+                        output.rids = recv_req.rids
                     self.send_to_tokenizer.send_pyobj(output)
 
     def handle_generate_request(
@@ -2400,6 +2403,10 @@ class Scheduler(
         result = self.tp_worker.unload_lora_adapter(recv_req)
         return result
 
+    def register_multi_tokenizer(self, recv_req: MultiTokenizerRegisterReq):
+        self.send_to_detokenizer.send_pyobj(recv_req)
+        return recv_req
+
     def slow_down(self, recv_req: SlowDownReqInput):
         t = recv_req.forward_sleep_time
         if t is not None and t <= 0:

python/sglang/srt/server_args.py

@@ -51,6 +51,7 @@ class ServerArgs:
     model_path: str
     tokenizer_path: Optional[str] = None
     tokenizer_mode: str = "auto"
+    tokenizer_worker_num: int = 1
     skip_tokenizer_init: bool = False
     load_format: str = "auto"
     model_loader_extra_config: str = "{}"
@@ -730,6 +731,12 @@ class ServerArgs:
             default=ServerArgs.tokenizer_path,
             help="The path of the tokenizer.",
         )
+        parser.add_argument(
+            "--tokenizer-worker-num",
+            type=int,
+            default=ServerArgs.tokenizer_worker_num,
+            help="The worker num of the tokenizer manager.",
+        )
         parser.add_argument(
             "--tokenizer-mode",
             type=str,
@@ -2081,6 +2088,9 @@ class ServerArgs:
             self.chunked_prefill_size % self.page_size == 0
         ), "chunked_prefill_size must be divisible by page_size"
 
+        # Check multi tokenizer
+        assert self.tokenizer_worker_num > 0, "Tokenizer worker num must >= 1"
+
     def check_lora_server_args(self):
         assert (
             self.max_loras_per_batch > 0
@@ -2246,6 +2256,9 @@ class PortArgs:
     # The ipc filename for Scheduler to send metrics
     metrics_ipc_name: str
 
+    # The ipc filename for Tokenizer and worker tokenizer
+    tokenizer_worker_ipc_name: Optional[str]
+
     @staticmethod
     def init_new(server_args, dp_rank: Optional[int] = None) -> "PortArgs":
         if server_args.nccl_port is None:
@@ -2269,6 +2282,7 @@ class PortArgs:
                 nccl_port=nccl_port,
                 rpc_ipc_name=f"ipc://{tempfile.NamedTemporaryFile(delete=False).name}",
                 metrics_ipc_name=f"ipc://{tempfile.NamedTemporaryFile(delete=False).name}",
+                tokenizer_worker_ipc_name=None,
             )
         else:
             # DP attention. Use TCP + port to handle both single-node and multi-node.
@@ -2302,6 +2316,7 @@ class PortArgs:
                 nccl_port=nccl_port,
                 rpc_ipc_name=f"tcp://{dist_init_host}:{rpc_port}",
                 metrics_ipc_name=f"tcp://{dist_init_host}:{metrics_ipc_name}",
+                tokenizer_worker_ipc_name=None,
             )
 
 

python/sglang/srt/utils.py

@@ -2754,6 +2754,20 @@ def lru_cache_frozenset(maxsize=128):
     return decorator
 
 
+def get_workerids_from_rids(rids):
+    if isinstance(rids, list):
+        worker_ids = [int(rid.split("_")[0]) for rid in rids]
+    elif isinstance(rids, str):
+        worker_ids = [int(rids.split("_")[0])]
+    else:
+        worker_ids = []
+    return worker_ids
+
+
+def get_origin_rid(rid):
+    return rid.split("_", 1)[1] if "_" in rid else rid
+
+
 def apply_module_patch(target_module, target_function, wrappers):
     original_module, original_function = parse_module_path(
         target_module, target_function, False

test/srt/run_suite.py

@@ -78,6 +78,7 @@ suites = {
         TestFile("test_mla_int8_deepseek_v3.py", 429),
         TestFile("test_mla_flashinfer.py", 302),
         TestFile("test_mla_fp8.py", 93),
+        TestFile("test_multi_tokenizer.py", 200),
         TestFile("test_no_chunked_prefill.py", 108),
         TestFile("test_no_overlap_scheduler.py", 234),
         TestFile("test_penalty.py", 41),

test/srt/test_multi_tokenizer.py

+import inspect
+import unittest
+from dataclasses import fields, is_dataclass
+from types import SimpleNamespace
+
+import sglang.srt.managers.io_struct as io_struct
+from sglang.srt.utils import kill_process_tree
+from sglang.test.run_eval import run_eval
+from sglang.test.test_utils import (
+    DEFAULT_MODEL_NAME_FOR_TEST,
+    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+    DEFAULT_URL_FOR_TEST,
+    CustomTestCase,
+    auto_config_device,
+    get_benchmark_args,
+    is_in_ci,
+    popen_launch_server,
+    run_benchmark,
+    write_github_step_summary,
+)
+
+
+class TestMultiTokenizer(CustomTestCase):
+    # from test_hicache.py
+    @classmethod
+    def setUpClass(cls):
+        cls.model = DEFAULT_MODEL_NAME_FOR_TEST
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        cls.process = popen_launch_server(
+            cls.model,
+            cls.base_url,
+            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+            other_args=[
+                "--tokenizer-worker-num",
+                8,
+                "--mem-fraction-static",
+                0.7,
+            ],
+        )
+
+    @classmethod
+    def tearDownClass(cls):
+        kill_process_tree(cls.process.pid)
+
+    def test_mmlu(self):
+        args = SimpleNamespace(
+            base_url=self.base_url,
+            model=self.model,
+            eval_name="mmlu",
+            num_examples=64,
+            num_threads=32,
+        )
+        metrics = run_eval(args)
+        self.assertGreaterEqual(metrics["score"], 0.65)
+
+    def test_all_io_struct(self):
+        print("check all req types in io_struct.py")
+        result = []
+        for name, obj in inspect.getmembers(io_struct):
+            if inspect.isclass(obj) and is_dataclass(obj):
+                field_names = [f.name for f in fields(obj)]
+                if "rids" in field_names or "rid" in field_names:
+                    continue
+                result.append(name)
+        print(f"WARNING:Some Request types in io_struct.py have no rids: {result}")
+        print(
+            "If a special request type can't work, check the rids field which is needed for multi-tokenizer."
+        )
+
+    def test_multi_tokenizer_ttft(self):
+        # from test_bench_serving.py run_bench_serving
+        args = get_benchmark_args(
+            base_url=self.base_url,
+            dataset_name="random",
+            dataset_path="",
+            tokenizer=None,
+            num_prompts=100,
+            random_input_len=4096,
+            random_output_len=2048,
+            sharegpt_context_len=None,
+            request_rate=1,
+            disable_stream=False,
+            disable_ignore_eos=False,
+            seed=0,
+            device=auto_config_device(),
+            lora_name=None,
+        )
+        res = run_benchmark(args)
+        if is_in_ci():
+            write_github_step_summary(
+                f"### test_multi_tokenizer_ttft\n"
+                f"median_e2e_latency_ms: {res['median_e2e_latency_ms']:.2f} ms\n"
+            )
+            self.assertLess(res["median_e2e_latency_ms"], 11000)
+            self.assertLess(res["median_ttft_ms"], 86)
+            self.assertLess(res["median_itl_ms"], 10)
+
+
+if __name__ == "__main__":
+    unittest.main()