feat: support multiple model names in --served_model_name

Allow specifying multiple served model names so that requests using any
alias are accepted and the /v1/models endpoint returns all names.

Changes:
- serve.py: --served_model_name is now multiple=True (specify flag
  multiple times); launch_server/launch_grpc_server accept Sequence[str];
  passes list to OpenAIServer
- openai_server.py: __init__ accepts Union[str, Sequence[str]]; stores
  self.model (primary) and self.served_model_names (all aliases);
  get_model() returns a ModelCard for each name; added
  _resolve_model_name() to echo back the client-requested name in
  responses if it matches a known alias

Usage:
  trtllm-serve model --served_model_name my-model --served_model_name alias1

Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>

Author: nvyutwu

tensorrt_llm/commands/serve.py

@@ -7,7 +7,7 @@
 import subprocess  # nosec B404
 import sys
 from pathlib import Path
-from typing import Any, Dict, Literal, Mapping, Optional, Sequence
+from typing import Any, Dict, List, Literal, Mapping, Optional, Sequence, Tuple
 
 import click
 import torch
@@ -267,10 +267,13 @@ def launch_server(
         server_role: Optional[ServerRole] = None,
         disagg_cluster_config: Optional[DisaggClusterConfig] = None,
         multimodal_server_config: Optional[MultimodalServerConfig] = None,
-        served_model_name: Optional[str] = None):
+        served_model_name: Optional[Sequence[str]] = None):
 
     backend = llm_args["backend"]
-    model = served_model_name or llm_args["model"]
+    # served_model_name may be a sequence (tuple from click's multiple=True, or list).
+    # Normalize to a list; empty sequence is treated as None.
+    served_model_names: List[str] = list(served_model_name) if served_model_name else []
+    model = served_model_names[0] if served_model_names else llm_args["model"]
     addr_info = socket.getaddrinfo(host, port, socket.AF_UNSPEC,
                                    socket.SOCK_STREAM)
     address_family = socket.AF_INET6 if all(
@@ -303,7 +306,7 @@ def launch_server(
                 param_hint="backend")
 
         server = OpenAIServer(generator=llm,
-                              model=model,
+                              model=served_model_names if served_model_names else model,
                               tool_parser=tool_parser,
                               server_role=server_role,
                               metadata_server_cfg=metadata_server_cfg,
@@ -321,7 +324,7 @@ def launch_server(
 def launch_grpc_server(host: str,
                        port: int,
                        llm_args: dict,
-                       served_model_name: Optional[str] = None):
+                       served_model_name: Optional[Sequence[str]] = None):
     """
     Launch a gRPC server for TensorRT-LLM.
 
@@ -350,7 +353,8 @@ async def serve_grpc_async():
         logger.info("Initializing TensorRT-LLM gRPC server...")
 
         backend = llm_args.get("backend")
-        model_path = served_model_name or llm_args.get("model", "")
+        _names = list(served_model_name) if served_model_name else []
+        model_path = _names[0] if _names else llm_args.get("model", "")
 
         if backend == "pytorch":
             llm_args.pop("build_config", None)
@@ -766,11 +770,12 @@ def convert(self, value: Any, param: Optional["click.Parameter"],
 @click.option(
     "--served_model_name",
     type=str,
+    multiple=True,
     default=None,
     help=help_info_with_stability_tag(
-        "The model name used in the API. If not specified, the model path is "
-        "used as the model name. This is useful when the model path is long or "
-        "when you want to expose a custom name to clients.", "prototype"))
+        "The model name(s) used in the API. Can be specified multiple times for aliases. "
+        "The first name is primary; additional names are aliases that the server also accepts. "
+        "If not specified, the model path is used as the model name.", "prototype"))
 @click.option("--extra_visual_gen_options",
               type=str,
               default=None,
@@ -794,7 +799,7 @@ def serve(
         enable_attention_dp: bool, disagg_cluster_uri: Optional[str],
         media_io_kwargs: Optional[str], video_pruning_rate: Optional[float],
         custom_module_dirs: list[Path], chat_template: Optional[str],
-        grpc: bool, served_model_name: Optional[str],
+        grpc: bool, served_model_name: Tuple[str, ...],
         extra_visual_gen_options: Optional[str]):
     """Running an OpenAI API compatible server
 

tensorrt_llm/serve/openai_server.py

@@ -15,7 +15,7 @@
 from http import HTTPStatus
 from pathlib import Path
 from typing import (Annotated, Any, AsyncGenerator, AsyncIterator, List,
-                    Optional, Union)
+                    Optional, Sequence, Union)
 
 import uvicorn
 from fastapi import Body, FastAPI, Request
@@ -175,7 +175,7 @@ class OpenAIServer:
     def __init__(
             self,
             generator: Union[LLM, MultimodalEncoder, VisualGen],
-            model: str,
+            model: Union[str, Sequence[str]],
             tool_parser: Optional[str],
             server_role: Optional[ServerRole],
             metadata_server_cfg: MetadataServerConfig,
@@ -194,11 +194,20 @@ def __init__(
         self.host = None
         self.port = None
 
-        model_dir = Path(model)
-        if model_dir.exists() and model_dir.is_dir():
-            self.model = model_dir.name
+        # Normalize model names: accept either a single string or a list/tuple of strings.
+        # self.model is the primary name; self.served_model_names includes all aliases.
+        if isinstance(model, (list, tuple)):
+            names = list(model)
         else:
-            self.model = model
+            names = [model]
+        primary = names[0] if names else ""
+        model_dir = Path(primary)
+        if model_dir.exists() and model_dir.is_dir():
+            primary = model_dir.name
+        self.model = primary
+        self.served_model_names: List[str] = [primary] + [
+            n for n in names[1:] if n != primary
+        ]
         self.metrics_collector = None
         self.perf_metrics = None
         self.perf_metrics_lock = None
@@ -607,7 +616,7 @@ async def health_generate(self, raw_request: Request) -> Response:
                     "role": "user",
                     "content": "hi"
                 }],  # Minimal prompt (often > 1 token after tokenization)
-                model=self.model,
+                model=self.model,  # Use primary model name for health checks
                 max_completion_tokens=1,  # Request only 1 token out
                 stream=False,
                 temperature=0.0,  # Deterministic output
@@ -644,8 +653,14 @@ async def version(self) -> JSONResponse:
         ver = {"version": VERSION}
         return JSONResponse(content=ver)
 
+    def _resolve_model_name(self, requested: Optional[str]) -> str:
+        """Return the requested model name if it is a known alias, else the primary name."""
+        if requested and requested in self.served_model_names:
+            return requested
+        return self.model
+
     async def get_model(self) -> JSONResponse:
-        model_list = ModelList(data=[ModelCard(id=self.model)])
+        model_list = ModelList(data=[ModelCard(id=name) for name in self.served_model_names])
         return JSONResponse(content=model_list.model_dump())
 
     async def get_iteration_stats(self) -> JSONResponse:
@@ -1062,7 +1077,7 @@ async def create_mm_embedding_response(promise: RequestOutput):
                 int(h["tensor_size"][0]) for h in mm_embedding_handles)
             return ChatCompletionResponse(
                 id=str(promise.request_id),
-                model=self.model,
+                model=self._resolve_model_name(request.model),
                 choices=[
                     ChatCompletionResponseChoice(
                         index=0,
@@ -1173,7 +1188,7 @@ def merge_completion_responses(
                     cached_tokens=num_cached_tokens, ),
             )
             merged_rsp = CompletionResponse(
-                model=self.model,
+                model=self._resolve_model_name(request.model),
                 choices=all_choices,
                 usage=usage_info,
                 prompt_token_ids=all_prompt_token_ids,
@@ -1447,7 +1462,7 @@ async def create_response(
                     generator=promise,
                     request=request,
                     sampling_params=args.sampling_params,
-                    model_name=self.model,
+                    model_name=self._resolve_model_name(request.model),
                     conversation_store=self.conversation_store,
                     generation_result=None,
                     enable_store=self.enable_store and request.store,
@@ -1516,7 +1531,7 @@ async def create_streaming_generator(promise: RequestOutput,
                 streaming_processor = ResponsesStreamingProcessor(
                     request=request,
                     sampling_params=sampling_params,
-                    model_name=self.model,
+                    model_name=self._resolve_model_name(request.model),
                     conversation_store=self.conversation_store,
                     enable_store=self.enable_store and request.store,
                     use_harmony=self.use_harmony,
@@ -1525,7 +1540,7 @@ async def create_streaming_generator(promise: RequestOutput,
                 )
 
             postproc_args = ResponsesAPIPostprocArgs(
-                model=self.model,
+                model=self._resolve_model_name(request.model),
                 request=request,
                 sampling_params=sampling_params,
                 use_harmony=self.use_harmony,