feat: sglang guided decoding support

components/src/dynamo/sglang/args.py

@@ -353,7 +353,6 @@ async def parse_args(args: list[str]) -> Config:
         server_args.served_model_name = parsed_args.served_model_name
         server_args.enable_metrics = getattr(parsed_args, "enable_metrics", False)
         server_args.log_level = getattr(parsed_args, "log_level", "info")
-        server_args.skip_tokenizer_init = True
         server_args.kv_events_config = getattr(parsed_args, "kv_events_config", None)
         server_args.tp_size = getattr(parsed_args, "tp_size", 1)
         server_args.dp_size = getattr(parsed_args, "dp_size", 1)
@@ -389,15 +388,9 @@ async def parse_args(args: list[str]) -> Config:
             FutureWarning,
             stacklevel=2,
         )
-        logging.info(
-            "Using SGLang's built in tokenizer. Setting skip_tokenizer_init to False"
-        )
-        server_args.skip_tokenizer_init = False
+        logging.info("Using SGLang's built in tokenizer")
     else:
-        logging.info(
-            "Using dynamo's built in tokenizer. Setting skip_tokenizer_init to True"
-        )
-        server_args.skip_tokenizer_init = True
+        logging.info("Using dynamo's built in tokenizer")
 
     # Derive use_kv_events from server_args.kv_events_config
     # Check that kv_events_config exists AND publisher is not "null" ("zmq" or any future publishers)

components/src/dynamo/sglang/register.py

@@ -38,9 +38,9 @@ async def _register_model_with_runtime_config(
     """
     runtime_config = await _get_runtime_config(engine, server_args, dynamo_args)
 
-    if not server_args.skip_tokenizer_init:
+    if dynamo_args.use_sglang_tokenizer:
         logging.warning(
-            "The skip-tokenizer-init flag was not set. Using the sglang tokenizer/detokenizer instead. The dynamo tokenizer/detokenizer will not be used and only v1/chat/completions will be available"
+            "Using the sglang tokenizer/detokenizer instead. The dynamo tokenizer/detokenizer will not be used and only v1/chat/completions will be available"
         )
         input_type = ModelInput.Text
         # Only override output_type for chat models, not for embeddings

components/src/dynamo/sglang/request_handlers/handler_base.py

@@ -3,6 +3,7 @@
 
 import asyncio
 import inspect
+import json
 import logging
 import random
 from abc import ABC, abstractmethod
@@ -173,13 +174,13 @@ def __init__(
             self.metrics_publisher = publisher.metrics_publisher
             self.kv_publisher = publisher.kv_publisher
         self.serving_mode = config.serving_mode
-        self.skip_tokenizer_init = config.server_args.skip_tokenizer_init
+        self.use_sglang_tokenizer = config.dynamo_args.use_sglang_tokenizer
         self.enable_trace = config.server_args.enable_trace
 
         if engine is not None:
             self.input_param_manager = InputParamManager(
                 self.engine.tokenizer_manager.tokenizer
-                if not self.skip_tokenizer_init
+                if self.use_sglang_tokenizer
                 else None
             )
             self._engine_supports_priority = (
@@ -430,13 +431,24 @@ def cleanup(self) -> None:
 
     def _get_input_param(self, request: Dict[str, Any]) -> Dict[str, Any]:
         request_input = self.input_param_manager.get_input_param(
-            request, use_tokenizer=not self.skip_tokenizer_init
+            request, use_tokenizer=self.use_sglang_tokenizer
         )
 
         return {
             "prompt" if isinstance(request_input, str) else "input_ids": request_input
         }
 
+    @staticmethod
+    def _get_guided_decoding_params(
+        guided_decoding: Optional[Dict[str, Any]],
+    ) -> Dict[str, Any]:
+        """Extract guided decoding params (e.g. json_schema) for SGLang sampling_params."""
+        if isinstance(guided_decoding, dict):
+            json_schema = guided_decoding.get("json")
+            if json_schema is not None:
+                return {"json_schema": json.dumps(json_schema)}
+        return {}
+
     @staticmethod
     def _generate_bootstrap_room() -> int:
         """Generate a unique bootstrap room ID for disaggregated serving.

components/src/dynamo/sglang/request_handlers/llm/decode_handler.py

@@ -88,7 +88,7 @@ def _build_sampling_params(self, request: Dict[str, Any]) -> Dict[str, Any]:
         Returns:
             Dict of sampling parameters for SGLang engine.
         """
-        if self.skip_tokenizer_init:
+        if not self.use_sglang_tokenizer:
             # Token-based request format
             sampling_opts = request.get("sampling_options", {})
             stop_conditions = request.get("stop_conditions", {})
@@ -99,6 +99,9 @@ def _build_sampling_params(self, request: Dict[str, Any]) -> Dict[str, Any]:
                 "top_k": sampling_opts.get("top_k"),
                 "max_new_tokens": stop_conditions.get("max_tokens"),
                 "ignore_eos": stop_conditions.get("ignore_eos"),
+                **self._get_guided_decoding_params(
+                    sampling_opts.get("guided_decoding")
+                ),
             }
         else:
             # OpenAI request format
@@ -107,6 +110,7 @@ def _build_sampling_params(self, request: Dict[str, Any]) -> Dict[str, Any]:
                 "top_p": request.get("top_p"),
                 "top_k": request.get("top_k"),
                 "max_new_tokens": request.get("max_tokens"),
+                **self._get_guided_decoding_params(request.get("guided_decoding")),
             }
 
         return {k: v for k, v in param_mapping.items() if v is not None}
@@ -171,7 +175,7 @@ async def generate(
                 **self._priority_kwargs(priority),
             )
 
-            if self.skip_tokenizer_init:
+            if not self.use_sglang_tokenizer:
                 async for out in self._process_token_stream(decode, context):
                     yield out
             else:
@@ -202,7 +206,7 @@ async def generate(
                 data_parallel_rank=dp_rank,
                 **self._priority_kwargs(priority),
             )
-            if self.skip_tokenizer_init:
+            if not self.use_sglang_tokenizer:
                 async for out in self._process_token_stream(agg, context):
                     yield out
             else:

components/src/dynamo/sglang/request_handlers/llm/diffusion_handler.py

@@ -89,7 +89,7 @@ async def generate(
         )
 
         # Process stream output (token-based or text-based)
-        if self.skip_tokenizer_init:
+        if not self.use_sglang_tokenizer:
             async for out in self._process_token_stream(async_gen, context):
                 yield out
         else:

components/src/dynamo/sglang/request_handlers/llm/prefill_handler.py

@@ -86,6 +86,9 @@ async def generate(
                 "top_p": sampling_opts.get("top_p"),
                 "top_k": sampling_opts.get("top_k"),
                 "max_new_tokens": stop_conditions.get("max_tokens"),
+                **self._get_guided_decoding_params(
+                    sampling_opts.get("guided_decoding")
+                ),
             }
             sampling_params = {
                 k: v for k, v in sampling_params.items() if v is not None