feat(frontend): allowing passing vllm chat processor specific flags in frontend (#7896)

components/src/dynamo/frontend/main.py

@@ -114,6 +114,17 @@ def parse_args() -> tuple[FrontendConfig, Optional[Namespace], Optional[Namespac
     vllm_flags = None
     sglang_flags = None
 
+    # --trust-remote-code is only meaningful with --dyn-chat-processor vllm.
+    # Warn and strip it when a different (or no) chat processor is active so
+    # it does not propagate as an unknown-argument error below.
+    if "--trust-remote-code" in unknown and config.chat_processor != "vllm":
+        logger.warning(
+            "--trust-remote-code has no effect without '--dyn-chat-processor vllm'. "
+            "It is only supported by the vLLM chat processor. "
+            "Pass '--dyn-chat-processor vllm' to enable trust_remote_code."
+        )
+        unknown = [arg for arg in unknown if arg != "--trust-remote-code"]
+
     # parse extra vllm flags using vllm native parser.
     if config.chat_processor == "vllm":
         try:

components/src/dynamo/frontend/prepost.py

@@ -80,6 +80,7 @@ def _prepare_request(
     tokenizer: TokenizerLike,
     tool_parser_class: type[ToolParser] | None,
     exclude_tools_when_tool_choice_none: bool = True,
+    enable_auto_tool_choice: bool = False,
 ) -> tuple[ChatCompletionRequest, ToolParser | None, dict[str, Any], Any, ChatParams]:
     """Validate request and build arguments for template rendering.
 
@@ -103,7 +104,11 @@ def _prepare_request(
         request_for_sampling = ChatCompletionRequest.model_validate(request)
 
     tool_parser: ToolParser | None = None
-    if tool_parser_class and request_for_sampling.tools:
+    # With enable_auto_tool_choice the model may emit tool calls even when the
+    # client did not supply an explicit `tools` list, so we activate the parser
+    # whenever the tool_parser_class is available.
+    has_tools = bool(request_for_sampling.tools)
+    if tool_parser_class and (has_tools or enable_auto_tool_choice):
         if request_for_sampling.tool_choice != "none":
             tool_parser = tool_parser_class(tokenizer)
             request_for_sampling = tool_parser.adjust_request(request_for_sampling)
@@ -163,6 +168,7 @@ async def preprocess_chat_request(
     renderer,
     tool_parser_class: type[ToolParser] | None,
     exclude_tools_when_tool_choice_none: bool = True,
+    enable_auto_tool_choice: bool = False,
 ) -> PreprocessResult:
     (
         request_for_sampling,
@@ -175,6 +181,7 @@ async def preprocess_chat_request(
         tokenizer=tokenizer,
         tool_parser_class=tool_parser_class,
         exclude_tools_when_tool_choice_none=exclude_tools_when_tool_choice_none,
+        enable_auto_tool_choice=enable_auto_tool_choice,
     )
 
     _, engine_prompt = await renderer.render_messages_async(messages, chat_params)

components/src/dynamo/frontend/vllm_processor.py

@@ -77,6 +77,7 @@ class VllmProcessor:
         output_processor: OutputProcessor,
         tool_parser_class: type[ToolParser] | None,
         reasoning_parser_class: type[ReasoningParser] | None,
+        enable_auto_tool_choice: bool = False,
     ):
         self.tokenizer = tokenizer
         self.input_processor = input_processor
@@ -86,6 +87,7 @@ class VllmProcessor:
         self.tool_parser_class = tool_parser_class
         self.reasoning_parser_class = reasoning_parser_class
         self.exclude_tools_when_tool_choice_none = True
+        self.enable_auto_tool_choice = enable_auto_tool_choice
 
     def _get_eos_token_ids(self) -> list[int]:
         """Return EOS token ids using tokenizer metadata.
@@ -144,6 +146,7 @@ class VllmProcessor:
             renderer=self.input_processor.renderer,
             tool_parser_class=self.tool_parser_class,
             exclude_tools_when_tool_choice_none=self.exclude_tools_when_tool_choice_none,
+            enable_auto_tool_choice=self.enable_auto_tool_choice,
         )
 
         request_for_sampling = pre.request_for_sampling
@@ -433,11 +436,14 @@ class EngineFactory:
         tokenizer_mode = getattr(self.flags, "tokenizer_mode", None) or "auto"
         config_format = getattr(self.flags, "config_format", None) or "auto"
         load_format = getattr(self.flags, "load_format", None) or "dummy"
+        trust_remote_code = getattr(self.flags, "trust_remote_code", False)
+        enable_auto_tool_choice = getattr(self.flags, "enable_auto_tool_choice", False)
 
         model_config = ModelConfig(
             model=source_path,
             tokenizer_mode=tokenizer_mode,
             config_format=config_format,
+            trust_remote_code=trust_remote_code,
         )
         vllm_config = VllmConfig(
             model_config=model_config,
@@ -496,6 +502,7 @@ class EngineFactory:
             output_processor,
             tool_parser_class,
             reasoning_parser_class,
+            enable_auto_tool_choice=enable_auto_tool_choice,
         )
         gen.exclude_tools_when_tool_choice_none = (
             self.config.exclude_tools_when_tool_choice_none