Revert "Rename TokenizerManager to StdOrchestrator" (#3828)

docs/backend/function_calling.ipynb

@@ -426,7 +426,7 @@
     "from sglang.srt.managers.io_struct import Tool, Function\n",
     "\n",
     "llm = sgl.Engine(model_path=\"meta-llama/Meta-Llama-3.1-8B-Instruct\")\n",
-    "tokenizer = llm.orchestrator.tokenizer\n",
+    "tokenizer = llm.tokenizer_manager.tokenizer\n",
     "input_ids = tokenizer.apply_chat_template(\n",
     "    messages, tokenize=True, add_generation_prompt=True, tools=tools\n",
     ")\n",

python/sglang/srt/entrypoints/engine.py

@@ -48,8 +48,8 @@ from sglang.srt.managers.io_struct import (
     UpdateWeightsFromTensorReqInput,
 )
 from sglang.srt.managers.scheduler import run_scheduler_process
+from sglang.srt.managers.tokenizer_manager import TokenizerManager
 from sglang.srt.openai_api.adapter import load_chat_template_for_openai_api
-from sglang.srt.orchestration.std.orchestrator import StdOrchestrator
 from sglang.srt.server_args import PortArgs, ServerArgs
 from sglang.srt.torch_memory_saver_adapter import TorchMemorySaverAdapter
 from sglang.srt.utils import (
@@ -74,12 +74,12 @@ class Engine:
     The entry point to the inference engine.
 
     - The engine consists of three components:
-        1. StdOrchestrator: Tokenizes the requests and sends them to the scheduler.
+        1. TokenizerManager: Tokenizes the requests and sends them to the scheduler.
         2. Scheduler (subprocess): Receives requests from the Tokenizer Manager, schedules batches, forwards them, and sends the output tokens to the Detokenizer Manager.
         3. DetokenizerManager (subprocess): Detokenizes the output tokens and sends the result back to the Tokenizer Manager.
 
     Note:
-    1. The HTTP server, Engine, and StdOrchestrator both run in the main process.
+    1. The HTTP server, Engine, and TokenizerManager both run in the main process.
     2. Inter-process communication is done through ICP (each process uses a different port) via the ZMQ library.
     """
 
@@ -102,8 +102,10 @@ class Engine:
         atexit.register(self.shutdown)
 
         # Launch subprocesses
-        orchestrator, scheduler_info = _launch_subprocesses(server_args=server_args)
-        self.orchestrator = orchestrator
+        tokenizer_manager, scheduler_info = _launch_subprocesses(
+            server_args=server_args
+        )
+        self.tokenizer_manager = tokenizer_manager
         self.scheduler_info = scheduler_info
 
     def generate(
@@ -145,7 +147,7 @@ class Engine:
             stream=stream,
         )
         loop = asyncio.get_event_loop()
-        generator = self.orchestrator.generate_request(obj, None)
+        generator = self.tokenizer_manager.generate_request(obj, None)
 
         if stream:
 
@@ -195,7 +197,7 @@ class Engine:
             stream=stream,
             custom_logit_processor=custom_logit_processor,
         )
-        generator = self.orchestrator.generate_request(obj, None)
+        generator = self.tokenizer_manager.generate_request(obj, None)
 
         if stream is True:
             return generator
@@ -213,7 +215,7 @@ class Engine:
 
         obj = EmbeddingReqInput(text=prompt)
         loop = asyncio.get_event_loop()
-        generator = self.orchestrator.generate_request(obj, None)
+        generator = self.tokenizer_manager.generate_request(obj, None)
         ret = loop.run_until_complete(generator.__anext__())
         return ret
 
@@ -222,14 +224,14 @@ class Engine:
         kill_process_tree(os.getpid(), include_parent=False)
 
     def start_profile(self):
-        self.orchestrator.start_profile()
+        self.tokenizer_manager.start_profile()
 
     def stop_profile(self):
-        self.orchestrator.stop_profile()
+        self.tokenizer_manager.stop_profile()
 
     def get_server_info(self):
         return {
-            **dataclasses.asdict(self.orchestrator.server_args),  # server args
+            **dataclasses.asdict(self.tokenizer_manager.server_args),  # server args
             **self.scheduler_info,
             "version": __version__,
         }
@@ -254,7 +256,7 @@ class Engine:
         )
         loop = asyncio.get_event_loop()
         return loop.run_until_complete(
-            self.orchestrator.init_weights_update_group(obj, None)
+            self.tokenizer_manager.init_weights_update_group(obj, None)
         )
 
     def update_weights_from_distributed(self, name: str, dtype, shape):
@@ -266,7 +268,7 @@ class Engine:
         )
         loop = asyncio.get_event_loop()
         return loop.run_until_complete(
-            self.orchestrator.update_weights_from_distributed(obj, None)
+            self.tokenizer_manager.update_weights_from_distributed(obj, None)
         )
 
     def update_weights_from_tensor(self, named_tensors: List[Tuple[str, torch.Tensor]]):
@@ -276,21 +278,23 @@ class Engine:
         )
         loop = asyncio.get_event_loop()
         return loop.run_until_complete(
-            self.orchestrator.update_weights_from_tensor(obj, None)
+            self.tokenizer_manager.update_weights_from_tensor(obj, None)
         )
 
     def get_weights_by_name(self, name: str, truncate_size: int = 100):
         """Get weights by parameter name."""
         obj = GetWeightsByNameReqInput(name=name, truncate_size=truncate_size)
         loop = asyncio.get_event_loop()
-        return loop.run_until_complete(self.orchestrator.get_weights_by_name(obj, None))
+        return loop.run_until_complete(
+            self.tokenizer_manager.get_weights_by_name(obj, None)
+        )
 
     def release_memory_occupation(self):
         """Release GPU occupation temporarily."""
         obj = ReleaseMemoryOccupationReqInput()
         loop = asyncio.get_event_loop()
         return loop.run_until_complete(
-            self.orchestrator.release_memory_occupation(obj, None)
+            self.tokenizer_manager.release_memory_occupation(obj, None)
         )
 
     def resume_memory_occupation(self):
@@ -298,7 +302,7 @@ class Engine:
         obj = ResumeMemoryOccupationReqInput()
         loop = asyncio.get_event_loop()
         return loop.run_until_complete(
-            self.orchestrator.resume_memory_occupation(obj, None)
+            self.tokenizer_manager.resume_memory_occupation(obj, None)
         )
 
 
@@ -347,9 +351,9 @@ def _set_envs_and_config(server_args: ServerArgs):
     mp.set_start_method("spawn", force=True)
 
 
-def _launch_subprocesses(server_args: ServerArgs) -> Tuple[StdOrchestrator, Dict]:
+def _launch_subprocesses(server_args: ServerArgs) -> Tuple[TokenizerManager, Dict]:
     """
-    Launch the StdOrchestrator in the main process, the Scheduler in a subprocess, and the DetokenizerManager in another subprocess.
+    Launch the TokenizerManager in the main process, the Scheduler in a subprocess, and the DetokenizerManager in another subprocess.
     """
     # Configure global environment
     configure_logger(server_args)
@@ -432,10 +436,10 @@ def _launch_subprocesses(server_args: ServerArgs) -> Tuple[StdOrchestrator, Dict
     detoken_proc.start()
 
     # Launch tokenizer process
-    orchestrator = StdOrchestrator(server_args, port_args)
+    tokenizer_manager = TokenizerManager(server_args, port_args)
     if server_args.chat_template:
         load_chat_template_for_openai_api(
-            orchestrator, server_args.chat_template, server_args.model_path
+            tokenizer_manager, server_args.chat_template, server_args.model_path
         )
 
     # Wait for the model to finish loading
@@ -459,5 +463,5 @@ def _launch_subprocesses(server_args: ServerArgs) -> Tuple[StdOrchestrator, Dict
 
     # Assume all schedulers have the same scheduler_info
     scheduler_info = scheduler_infos[0]
-    orchestrator.configure_max_req_input_len(scheduler_info["max_req_input_len"])
-    return orchestrator, scheduler_info
+    tokenizer_manager.configure_max_req_input_len(scheduler_info["max_req_input_len"])
+    return tokenizer_manager, scheduler_info

python/sglang/srt/entrypoints/http_server.py

@@ -54,6 +54,7 @@ from sglang.srt.managers.io_struct import (
     UpdateWeightFromDiskReqInput,
     UpdateWeightsFromDistributedReqInput,
 )
+from sglang.srt.managers.tokenizer_manager import TokenizerManager
 from sglang.srt.metrics.func_timer import enable_func_timer
 from sglang.srt.openai_api.adapter import (
     v1_batches,
@@ -68,7 +69,6 @@ from sglang.srt.openai_api.adapter import (
     v1_retrieve_file_content,
 )
 from sglang.srt.openai_api.protocol import ModelCard, ModelList
-from sglang.srt.orchestration.std.orchestrator import StdOrchestrator
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils import (
     add_api_key_middleware,
@@ -97,7 +97,7 @@ app.add_middleware(
 # Store global states
 @dataclasses.dataclass
 class _GlobalState:
-    orchestrator: StdOrchestrator
+    tokenizer_manager: TokenizerManager
     scheduler_info: Dict
 
 
@@ -124,7 +124,7 @@ async def health_generate(request: Request) -> Response:
 
     sampling_params = {"max_new_tokens": 1, "temperature": 0.7}
 
-    if _global_state.orchestrator.is_generation:
+    if _global_state.tokenizer_manager.is_generation:
         gri = GenerateReqInput(
             input_ids=[0], sampling_params=sampling_params, log_metrics=False
         )
@@ -134,7 +134,7 @@ async def health_generate(request: Request) -> Response:
         )
 
     try:
-        async for _ in _global_state.orchestrator.generate_request(gri, request):
+        async for _ in _global_state.tokenizer_manager.generate_request(gri, request):
             break
         return Response(status_code=200)
     except Exception as e:
@@ -146,9 +146,9 @@ async def health_generate(request: Request) -> Response:
 async def get_model_info():
     """Get the model information."""
     result = {
-        "model_path": _global_state.orchestrator.model_path,
-        "tokenizer_path": _global_state.orchestrator.server_args.tokenizer_path,
-        "is_generation": _global_state.orchestrator.is_generation,
+        "model_path": _global_state.tokenizer_manager.model_path,
+        "tokenizer_path": _global_state.tokenizer_manager.server_args.tokenizer_path,
+        "is_generation": _global_state.tokenizer_manager.is_generation,
     }
     return result
 
@@ -156,7 +156,7 @@ async def get_model_info():
 @app.get("/get_server_info")
 async def get_server_info():
     return {
-        **dataclasses.asdict(_global_state.orchestrator.server_args),
+        **dataclasses.asdict(_global_state.tokenizer_manager.server_args),
         **_global_state.scheduler_info,
         "version": __version__,
     }
@@ -170,7 +170,7 @@ async def generate_request(obj: GenerateReqInput, request: Request):
 
         async def stream_results() -> AsyncIterator[bytes]:
             try:
-                async for out in _global_state.orchestrator.generate_request(
+                async for out in _global_state.tokenizer_manager.generate_request(
                     obj, request
                 ):
                     yield b"data: " + orjson.dumps(
@@ -186,11 +186,11 @@ async def generate_request(obj: GenerateReqInput, request: Request):
         return StreamingResponse(
             stream_results(),
             media_type="text/event-stream",
-            background=_global_state.orchestrator.create_abort_task(obj),
+            background=_global_state.tokenizer_manager.create_abort_task(obj),
         )
     else:
         try:
-            ret = await _global_state.orchestrator.generate_request(
+            ret = await _global_state.tokenizer_manager.generate_request(
                 obj, request
             ).__anext__()
             return ret
@@ -203,7 +203,7 @@ async def generate_request(obj: GenerateReqInput, request: Request):
 async def encode_request(obj: EmbeddingReqInput, request: Request):
     """Handle an embedding request."""
     try:
-        ret = await _global_state.orchestrator.generate_request(
+        ret = await _global_state.tokenizer_manager.generate_request(
             obj, request
         ).__anext__()
         return ret
@@ -215,7 +215,7 @@ async def encode_request(obj: EmbeddingReqInput, request: Request):
 async def classify_request(obj: EmbeddingReqInput, request: Request):
     """Handle a reward model request. Now the arguments and return values are the same as embedding models."""
     try:
-        ret = await _global_state.orchestrator.generate_request(
+        ret = await _global_state.tokenizer_manager.generate_request(
             obj, request
         ).__anext__()
         return ret
@@ -226,7 +226,7 @@ async def classify_request(obj: EmbeddingReqInput, request: Request):
 @app.post("/flush_cache")
 async def flush_cache():
     """Flush the radix cache."""
-    _global_state.orchestrator.flush_cache()
+    _global_state.tokenizer_manager.flush_cache()
     return Response(
         content="Cache flushed.\nPlease check backend logs for more details. "
         "(When there are running or waiting requests, the operation will not be performed.)\n",
@@ -237,7 +237,7 @@ async def flush_cache():
 @app.api_route("/start_profile", methods=["GET", "POST"])
 async def start_profile_async():
     """Start profiling."""
-    _global_state.orchestrator.start_profile()
+    _global_state.tokenizer_manager.start_profile()
     return Response(
         content="Start profiling.\n",
         status_code=200,
@@ -247,7 +247,7 @@ async def start_profile_async():
 @app.api_route("/stop_profile", methods=["GET", "POST"])
 async def stop_profile_async():
     """Stop profiling."""
-    _global_state.orchestrator.stop_profile()
+    _global_state.tokenizer_manager.stop_profile()
     return Response(
         content="Stop profiling. This will take some time.\n",
         status_code=200,
@@ -257,7 +257,7 @@ async def stop_profile_async():
 @app.post("/update_weights_from_disk")
 async def update_weights_from_disk(obj: UpdateWeightFromDiskReqInput, request: Request):
     """Update the weights from disk in-place without re-launching the server."""
-    success, message = await _global_state.orchestrator.update_weights_from_disk(
+    success, message = await _global_state.tokenizer_manager.update_weights_from_disk(
         obj, request
     )
     content = {"success": success, "message": message}
@@ -278,7 +278,7 @@ async def init_weights_update_group(
     obj: InitWeightsUpdateGroupReqInput, request: Request
 ):
     """Initialize the parameter update group."""
-    success, message = await _global_state.orchestrator.init_weights_update_group(
+    success, message = await _global_state.tokenizer_manager.init_weights_update_group(
         obj, request
     )
     content = {"success": success, "message": message}
@@ -293,8 +293,10 @@ async def update_weights_from_distributed(
     obj: UpdateWeightsFromDistributedReqInput, request: Request
 ):
     """Update model parameter from distributed online."""
-    success, message = await _global_state.orchestrator.update_weights_from_distributed(
-        obj, request
+    success, message = (
+        await _global_state.tokenizer_manager.update_weights_from_distributed(
+            obj, request
+        )
     )
     content = {"success": success, "message": message}
     if success:
@@ -307,7 +309,7 @@ async def update_weights_from_distributed(
 async def get_weights_by_name(obj: GetWeightsByNameReqInput, request: Request):
     """Get model parameter by name."""
     try:
-        ret = await _global_state.orchestrator.get_weights_by_name(obj, request)
+        ret = await _global_state.tokenizer_manager.get_weights_by_name(obj, request)
         if ret is None:
             return _create_error_response("Get parameter by name failed")
         else:
@@ -322,7 +324,7 @@ async def release_memory_occupation(
 ):
     """Release GPU occupation temporarily"""
     try:
-        await _global_state.orchestrator.release_memory_occupation(obj, request)
+        await _global_state.tokenizer_manager.release_memory_occupation(obj, request)
     except Exception as e:
         return _create_error_response(e)
 
@@ -333,7 +335,7 @@ async def resume_memory_occupation(
 ):
     """Resume GPU occupation"""
     try:
-        await _global_state.orchestrator.resume_memory_occupation(obj, request)
+        await _global_state.tokenizer_manager.resume_memory_occupation(obj, request)
     except Exception as e:
         return _create_error_response(e)
 
@@ -342,7 +344,7 @@ async def resume_memory_occupation(
 async def open_session(obj: OpenSessionReqInput, request: Request):
     """Open a session, and return its unique session id."""
     try:
-        session_id = await _global_state.orchestrator.open_session(obj, request)
+        session_id = await _global_state.tokenizer_manager.open_session(obj, request)
         if session_id is None:
             raise Exception(
                 "Failed to open the session. Check if a session with the same id is still open."
@@ -356,7 +358,7 @@ async def open_session(obj: OpenSessionReqInput, request: Request):
 async def close_session(obj: CloseSessionReqInput, request: Request):
     """Close the session"""
     try:
-        await _global_state.orchestrator.close_session(obj, request)
+        await _global_state.tokenizer_manager.close_session(obj, request)
         return Response(status_code=200)
     except Exception as e:
         return _create_error_response(e)
@@ -365,7 +367,7 @@ async def close_session(obj: CloseSessionReqInput, request: Request):
 @app.api_route("/configure_logging", methods=["GET", "POST"])
 async def configure_logging(obj: ConfigureLoggingReq, request: Request):
     """Close the session"""
-    _global_state.orchestrator.configure_logging(obj)
+    _global_state.tokenizer_manager.configure_logging(obj)
     return Response(status_code=200)
 
 
@@ -396,24 +398,24 @@ async def function_call_request(obj: FunctionCallReqInput, request: Request):
 
 @app.post("/v1/completions")
 async def openai_v1_completions(raw_request: Request):
-    return await v1_completions(_global_state.orchestrator, raw_request)
+    return await v1_completions(_global_state.tokenizer_manager, raw_request)
 
 
 @app.post("/v1/chat/completions")
 async def openai_v1_chat_completions(raw_request: Request):
-    return await v1_chat_completions(_global_state.orchestrator, raw_request)
+    return await v1_chat_completions(_global_state.tokenizer_manager, raw_request)
 
 
 @app.post("/v1/embeddings", response_class=ORJSONResponse)
 async def openai_v1_embeddings(raw_request: Request):
-    response = await v1_embeddings(_global_state.orchestrator, raw_request)
+    response = await v1_embeddings(_global_state.tokenizer_manager, raw_request)
     return response
 
 
 @app.get("/v1/models", response_class=ORJSONResponse)
 def available_models():
     """Show available models."""
-    served_model_names = [_global_state.orchestrator.served_model_name]
+    served_model_names = [_global_state.tokenizer_manager.served_model_name]
     model_cards = []
     for served_model_name in served_model_names:
         model_cards.append(ModelCard(id=served_model_name, root=served_model_name))
@@ -423,7 +425,7 @@ def available_models():
 @app.post("/v1/files")
 async def openai_v1_files(file: UploadFile = File(...), purpose: str = Form("batch")):
     return await v1_files_create(
-        file, purpose, _global_state.orchestrator.server_args.file_storage_pth
+        file, purpose, _global_state.tokenizer_manager.server_args.file_storage_pth
     )
 
 
@@ -435,13 +437,13 @@ async def delete_file(file_id: str):
 
 @app.post("/v1/batches")
 async def openai_v1_batches(raw_request: Request):
-    return await v1_batches(_global_state.orchestrator, raw_request)
+    return await v1_batches(_global_state.tokenizer_manager, raw_request)
 
 
 @app.post("/v1/batches/{batch_id}/cancel")
 async def cancel_batches(batch_id: str):
     # https://platform.openai.com/docs/api-reference/batch/cancel
-    return await v1_cancel_batch(_global_state.orchestrator, batch_id)
+    return await v1_cancel_batch(_global_state.tokenizer_manager, batch_id)
 
 
 @app.get("/v1/batches/{batch_id}")
@@ -490,18 +492,18 @@ def launch_server(
 
     - HTTP server: A FastAPI server that routes requests to the engine.
     - The engine consists of three components:
-        1. StdOrchestrator: Tokenizes the requests and sends them to the scheduler.
+        1. TokenizerManager: Tokenizes the requests and sends them to the scheduler.
         2. Scheduler (subprocess): Receives requests from the Tokenizer Manager, schedules batches, forwards them, and sends the output tokens to the Detokenizer Manager.
         3. DetokenizerManager (subprocess): Detokenizes the output tokens and sends the result back to the Tokenizer Manager.
 
     Note:
-    1. The HTTP server, Engine, and StdOrchestrator both run in the main process.
+    1. The HTTP server, Engine, and TokenizerManager both run in the main process.
     2. Inter-process communication is done through ICP (each process uses a different port) via the ZMQ library.
     """
-    orchestrator, scheduler_info = _launch_subprocesses(server_args=server_args)
+    tokenizer_manager, scheduler_info = _launch_subprocesses(server_args=server_args)
     set_global_state(
         _GlobalState(
-            orchestrator=orchestrator,
+            tokenizer_manager=tokenizer_manager,
             scheduler_info=scheduler_info,
         )
     )
@@ -521,7 +523,7 @@ def launch_server(
         args=(
             server_args,
             pipe_finish_writer,
-            _global_state.orchestrator.image_token_id,
+            _global_state.tokenizer_manager.image_token_id,
         ),
     )
     t.start()

python/sglang/srt/managers/image_processor.py

@@ -241,7 +241,7 @@ class LlavaImageProcessor(BaseImageProcessor):
 
                 return pixel_values, image_hash, image.size
         except Exception:
-            logger.error("Exception in StdOrchestrator:\n" + get_exception_traceback())
+            logger.error("Exception in TokenizerManager:\n" + get_exception_traceback())
 
     async def _process_single_image(
         self, image_data: Union[bytes, str], aspect_ratio: str, grid_pinpoints: str
@@ -491,7 +491,7 @@ class Qwen2VLImageProcessor(BaseImageProcessor):
 
                 return pixel_values, image_hash, image.size, image_grid_thws
         except Exception:
-            logger.error("Exception in StdOrchestrator:\n" + get_exception_traceback())
+            logger.error("Exception in TokenizerManager:\n" + get_exception_traceback())
 
     async def _process_single_image(self, image_data: Union[bytes, str]):
         if self.executor is not None:

python/sglang/srt/managers/io_struct.py

@@ -13,7 +13,7 @@
 # ==============================================================================
 """
 The definition of objects transfered between different
-processes (StdOrchestrator, DetokenizerManager, Controller).
+processes (TokenizerManager, DetokenizerManager, Controller).
 """
 
 import uuid

python/sglang/srt/managers/scheduler.py

@@ -174,7 +174,7 @@ class Scheduler:
             )
 
             if server_args.skip_tokenizer_init:
-                # Directly send to the StdOrchestrator
+                # Directly send to the TokenizerManager
                 self.send_to_detokenizer = get_zmq_socket(
                     context, zmq.PUSH, port_args.tokenizer_ipc_name, False
                 )

python/sglang/srt/orchestration/std/orchestrator.py → python/sglang/srt/managers/tokenizer_manager.py

@@ -11,6 +11,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
+"""TokenizerManager is a process that tokenizes the text."""
 
 import asyncio
 import logging
@@ -65,8 +66,8 @@ asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())
 logger = logging.getLogger(__name__)
 
 
-class StdOrchestrator:
-    """StdOrchestrator is the primary entrypoint of orchestration.std package"""
+class TokenizerManager:
+    """TokenizerManager is a process that tokenizes the text."""
 
     def __init__(
         self,
@@ -438,20 +439,20 @@ async def print_exception_wrapper(func):
         await func()
     except Exception:
         traceback = get_exception_traceback()
-        logger.error(f"StdOrchestrator hit an exception: {traceback}")
+        logger.error(f"TokenizerManager hit an exception: {traceback}")
         kill_process_tree(os.getpid(), include_parent=True)
         sys.exit(1)
 
 
 class SignalHandler:
-    def __init__(self, orchestrator):
-        self.orchestrator = orchestrator
+    def __init__(self, tokenizer_manager):
+        self.tokenizer_manager = tokenizer_manager
 
     def signal_handler(self, signum=None, frame=None):
         logger.warning(
             f"SIGTERM received. {signum=} {frame=}. Draining requests and shutting down..."
         )
-        self.orchestrator.gracefully_exit = True
+        self.tokenizer_manager.gracefully_exit = True
 
 
 T = TypeVar("T")

python/sglang/srt/openai_api/adapter.py

@@ -117,7 +117,7 @@ def create_streaming_error_response(
     return json_str
 
 
-def load_chat_template_for_openai_api(orchestrator, chat_template_arg, model_path):
+def load_chat_template_for_openai_api(tokenizer_manager, chat_template_arg, model_path):
     global chat_template_name
 
     logger.info(
@@ -133,7 +133,9 @@ def load_chat_template_for_openai_api(orchestrator, chat_template_arg, model_pat
         if chat_template_arg.endswith(".jinja"):
             with open(chat_template_arg, "r") as f:
                 chat_template = "".join(f.readlines()).strip("\n")
-            orchestrator.tokenizer.chat_template = chat_template.replace("\\n", "\n")
+            tokenizer_manager.tokenizer.chat_template = chat_template.replace(
+                "\\n", "\n"
+            )
             chat_template_name = None
         else:
             assert chat_template_arg.endswith(
@@ -229,7 +231,7 @@ async def v1_delete_file(file_id: str):
     return FileDeleteResponse(id=file_id, deleted=True)
 
 
-async def v1_batches(orchestrator, raw_request: Request):
+async def v1_batches(tokenizer_manager, raw_request: Request):
     try:
         body = await raw_request.json()
 
@@ -250,7 +252,7 @@ async def v1_batches(orchestrator, raw_request: Request):
         batch_storage[batch_id] = batch_response
 
         # Start processing the batch asynchronously
-        asyncio.create_task(process_batch(orchestrator, batch_id, batch_request))
+        asyncio.create_task(process_batch(tokenizer_manager, batch_id, batch_request))
 
         # Return the initial batch_response
         return batch_response
@@ -261,7 +263,7 @@ async def v1_batches(orchestrator, raw_request: Request):
         return {"error": str(e)}
 
 
-async def process_batch(orchestrator, batch_id: str, batch_request: BatchRequest):
+async def process_batch(tokenizer_manager, batch_id: str, batch_request: BatchRequest):
     try:
         # Update the batch status to "in_progress"
         batch_storage[batch_id].status = "in_progress"
@@ -304,7 +306,7 @@ async def process_batch(orchestrator, batch_id: str, batch_request: BatchRequest
 
         if end_point == "/v1/chat/completions":
             adapted_request, request = v1_chat_generate_request(
-                all_requests, orchestrator, request_ids=request_ids
+                all_requests, tokenizer_manager, request_ids=request_ids
             )
         elif end_point == "/v1/completions":
             adapted_request, request = v1_generate_request(
@@ -312,7 +314,7 @@ async def process_batch(orchestrator, batch_id: str, batch_request: BatchRequest
             )
 
         try:
-            ret = await orchestrator.generate_request(adapted_request).__anext__()
+            ret = await tokenizer_manager.generate_request(adapted_request).__anext__()
             if not isinstance(ret, list):
                 ret = [ret]
             if end_point == "/v1/chat/completions":
@@ -320,12 +322,12 @@ async def process_batch(orchestrator, batch_id: str, batch_request: BatchRequest
                     request,
                     ret,
                     to_file=True,
-                    cache_report=orchestrator.server_args.enable_cache_report,
-                    tool_call_parser=orchestrator.server_args.tool_call_parser,
+                    cache_report=tokenizer_manager.server_args.enable_cache_report,
+                    tool_call_parser=tokenizer_manager.server_args.tool_call_parser,
                 )
             else:
                 responses = v1_generate_response(
-                    request, ret, orchestrator, to_file=True
+                    request, ret, tokenizer_manager, to_file=True
                 )
 
         except Exception as e:
@@ -397,7 +399,7 @@ async def v1_retrieve_batch(batch_id: str):
     return batch_response
 
 
-async def v1_cancel_batch(orchestrator, batch_id: str):
+async def v1_cancel_batch(tokenizer_manager, batch_id: str):
     # Retrieve the batch job from the in-memory storage
     batch_response = batch_storage.get(batch_id)
     if batch_response is None:
@@ -408,7 +410,7 @@ async def v1_cancel_batch(orchestrator, batch_id: str):
         # Start cancelling the batch asynchronously
         asyncio.create_task(
             cancel_batch(
-                orchestrator=orchestrator,
+                tokenizer_manager=tokenizer_manager,
                 batch_id=batch_id,
                 input_file_id=batch_response.input_file_id,
             )
@@ -425,7 +427,7 @@ async def v1_cancel_batch(orchestrator, batch_id: str):
         )
 
 
-async def cancel_batch(orchestrator, batch_id: str, input_file_id: str):
+async def cancel_batch(tokenizer_manager, batch_id: str, input_file_id: str):
     try:
         # Update the batch status to "cancelling"
         batch_storage[batch_id].status = "cancelling"
@@ -449,7 +451,7 @@ async def cancel_batch(orchestrator, batch_id: str, input_file_id: str):
 
         # Cancel requests by request_ids
         for rid in request_ids:
-            orchestrator.abort_request(rid=rid)
+            tokenizer_manager.abort_request(rid=rid)
 
         retrieve_batch = batch_storage[batch_id]
         retrieve_batch.status = "cancelled"
@@ -577,7 +579,7 @@ def v1_generate_request(
     return adapted_request, all_requests if len(all_requests) > 1 else all_requests[0]
 
 
-def v1_generate_response(request, ret, orchestrator, to_file=False):
+def v1_generate_response(request, ret, tokenizer_manager, to_file=False):
     choices = []
     echo = False
 
@@ -589,13 +591,15 @@ def v1_generate_response(request, ret, orchestrator, to_file=False):
         elif isinstance(request.prompt, list) and isinstance(request.prompt[0], list):
             # for the case of multiple token ids prompts
             prompts = [
-                orchestrator.tokenizer.decode(prompt, skip_special_tokens=True)
+                tokenizer_manager.tokenizer.decode(prompt, skip_special_tokens=True)
                 for prompt in request.prompt
             ]
         elif isinstance(request.prompt, list) and isinstance(request.prompt[0], int):
             # for the case of single token ids prompt
             prompts = [
-                orchestrator.tokenizer.decode(request.prompt, skip_special_tokens=True)
+                tokenizer_manager.tokenizer.decode(
+                    request.prompt, skip_special_tokens=True
+                )
             ]
         else:
             # for the case of single str prompt
@@ -705,7 +709,7 @@ def v1_generate_response(request, ret, orchestrator, to_file=False):
     return response
 
 
-async def v1_completions(orchestrator, raw_request: Request):
+async def v1_completions(tokenizer_manager, raw_request: Request):
     request_json = await raw_request.json()
     all_requests = [CompletionRequest(**request_json)]
     adapted_request, request = v1_generate_request(all_requests)
@@ -718,7 +722,7 @@ async def v1_completions(orchestrator, raw_request: Request):
             prompt_tokens = {}
             completion_tokens = {}
             try:
-                async for content in orchestrator.generate_request(
+                async for content in tokenizer_manager.generate_request(
                     adapted_request, raw_request
                 ):
                     index = content.get("index", 0)
@@ -741,14 +745,14 @@ async def v1_completions(orchestrator, raw_request: Request):
                                     prompts = request.prompt[index // request.n]
                                 elif isinstance(request.prompt[0], int):
                                     # for the case of single token ids prompt
-                                    prompts = orchestrator.tokenizer.decode(
+                                    prompts = tokenizer_manager.tokenizer.decode(
                                         request.prompt, skip_special_tokens=True
                                     )
                                 elif isinstance(request.prompt[0], list) and isinstance(
                                     request.prompt[0][0], int
                                 ):
                                     # for the case of multiple token ids prompts
-                                    prompts = orchestrator.tokenizer.decode(
+                                    prompts = tokenizer_manager.tokenizer.decode(
                                         request.prompt[index // request.n],
                                         skip_special_tokens=True,
                                     )
@@ -843,12 +847,12 @@ async def v1_completions(orchestrator, raw_request: Request):
         return StreamingResponse(
             generate_stream_resp(),
             media_type="text/event-stream",
-            background=orchestrator.create_abort_task(adapted_request),
+            background=tokenizer_manager.create_abort_task(adapted_request),
         )
 
     # Non-streaming response.
     try:
-        ret = await orchestrator.generate_request(
+        ret = await tokenizer_manager.generate_request(
             adapted_request, raw_request
         ).__anext__()
     except ValueError as e:
@@ -857,13 +861,13 @@ async def v1_completions(orchestrator, raw_request: Request):
     if not isinstance(ret, list):
         ret = [ret]
 
-    response = v1_generate_response(request, ret, orchestrator)
+    response = v1_generate_response(request, ret, tokenizer_manager)
     return response
 
 
 def v1_chat_generate_request(
     all_requests: List[ChatCompletionRequest],
-    orchestrator,
+    tokenizer_manager,
     request_ids: List[str] = None,
 ):
     input_ids = []
@@ -918,7 +922,7 @@ def v1_chat_generate_request(
                     assistant_prefix = None
 
                 try:
-                    prompt_ids = orchestrator.tokenizer.apply_chat_template(
+                    prompt_ids = tokenizer_manager.tokenizer.apply_chat_template(
                         openai_compatible_messages,
                         tokenize=True,
                         add_generation_prompt=True,
@@ -929,7 +933,7 @@ def v1_chat_generate_request(
                     #  has a different tools input format that is not compatiable
                     #  with openAI's apply_chat_template tool_call format, like Mistral.
                     tools = [t if "function" in t else {"function": t} for t in tools]
-                    prompt_ids = orchestrator.tokenizer.apply_chat_template(
+                    prompt_ids = tokenizer_manager.tokenizer.apply_chat_template(
                         openai_compatible_messages,
                         tokenize=True,
                         add_generation_prompt=True,
@@ -937,8 +941,11 @@ def v1_chat_generate_request(
                     )
 
                 if assistant_prefix:
-                    encoded = orchestrator.tokenizer.encode(assistant_prefix)
-                    if encoded and encoded[0] == orchestrator.tokenizer.bos_token_id:
+                    encoded = tokenizer_manager.tokenizer.encode(assistant_prefix)
+                    if (
+                        encoded
+                        and encoded[0] == tokenizer_manager.tokenizer.bos_token_id
+                    ):
                         encoded = encoded[1:]
                     prompt_ids += encoded
                 stop = request.stop
@@ -955,7 +962,7 @@ def v1_chat_generate_request(
                         stop.append(request.stop)
                     else:
                         stop.extend(request.stop)
-                prompt_ids = orchestrator.tokenizer.encode(prompt)
+                prompt_ids = tokenizer_manager.tokenizer.encode(prompt)
         else:
             # Use the raw prompt and stop strings if the messages is already a string.
             prompt_ids = request.messages
@@ -1194,10 +1201,10 @@ def v1_chat_generate_response(
         return response
 
 
-async def v1_chat_completions(orchestrator, raw_request: Request):
+async def v1_chat_completions(tokenizer_manager, raw_request: Request):
     request_json = await raw_request.json()
     all_requests = [ChatCompletionRequest(**request_json)]
-    adapted_request, request = v1_chat_generate_request(all_requests, orchestrator)
+    adapted_request, request = v1_chat_generate_request(all_requests, tokenizer_manager)
 
     if adapted_request.stream:
         parser_dict = {}
@@ -1209,7 +1216,7 @@ async def v1_chat_completions(orchestrator, raw_request: Request):
             prompt_tokens = {}
             completion_tokens = {}
             try:
-                async for content in orchestrator.generate_request(
+                async for content in tokenizer_manager.generate_request(
                     adapted_request, raw_request
                 ):
                     index = content.get("index", 0)
@@ -1299,7 +1306,7 @@ async def v1_chat_completions(orchestrator, raw_request: Request):
                         if index not in parser_dict:
                             parser_dict[index] = FunctionCallParser(
                                 tools=request.tools,
-                                tool_call_parser=orchestrator.server_args.tool_call_parser,
+                                tool_call_parser=tokenizer_manager.server_args.tool_call_parser,
                             )
                         parser = parser_dict[index]
 
@@ -1431,12 +1438,12 @@ async def v1_chat_completions(orchestrator, raw_request: Request):
         return StreamingResponse(
             generate_stream_resp(),
             media_type="text/event-stream",
-            background=orchestrator.create_abort_task(adapted_request),
+            background=tokenizer_manager.create_abort_task(adapted_request),
         )
 
     # Non-streaming response.
     try:
-        ret = await orchestrator.generate_request(
+        ret = await tokenizer_manager.generate_request(
             adapted_request, raw_request
         ).__anext__()
     except ValueError as e:
@@ -1447,14 +1454,14 @@ async def v1_chat_completions(orchestrator, raw_request: Request):
     response = v1_chat_generate_response(
         request,
         ret,
-        cache_report=orchestrator.server_args.enable_cache_report,
-        tool_call_parser=orchestrator.server_args.tool_call_parser,
+        cache_report=tokenizer_manager.server_args.enable_cache_report,
+        tool_call_parser=tokenizer_manager.server_args.tool_call_parser,
     )
 
     return response
 
 
-def v1_embedding_request(all_requests, orchestrator):
+def v1_embedding_request(all_requests, tokenizer_manager):
     prompts = []
     sampling_params_list = []
     first_prompt_type = type(all_requests[0].input)
@@ -1509,13 +1516,13 @@ def v1_embedding_response(ret, model_path, to_file=False):
     )
 
 
-async def v1_embeddings(orchestrator, raw_request: Request):
+async def v1_embeddings(tokenizer_manager, raw_request: Request):
     request_json = await raw_request.json()
     all_requests = [EmbeddingRequest(**request_json)]
-    adapted_request, request = v1_embedding_request(all_requests, orchestrator)
+    adapted_request, request = v1_embedding_request(all_requests, tokenizer_manager)
 
     try:
-        ret = await orchestrator.generate_request(
+        ret = await tokenizer_manager.generate_request(
             adapted_request, raw_request
         ).__anext__()
     except ValueError as e:
@@ -1524,7 +1531,7 @@ async def v1_embeddings(orchestrator, raw_request: Request):
     if not isinstance(ret, list):
         ret = [ret]
 
-    response = v1_embedding_response(ret, orchestrator.model_path)
+    response = v1_embedding_response(ret, tokenizer_manager.model_path)
 
     return response
 

python/sglang/srt/server_args.py

@@ -1035,7 +1035,7 @@ class PortArgs:
             if dp_rank is None:
                 scheduler_input_port = (
                     port_base + 2
-                )  # StdOrchestrator to DataParallelController
+                )  # TokenizerManager to DataParallelController
             else:
                 scheduler_input_port = port_base + 2 + 1 + dp_rank