feat - add a new endpoint get_tokenizer_info to provide tokenizer/chat-template information

tests/entrypoints/openai/test_tokenization.py

@@ -32,6 +32,7 @@ def server(zephyr_lora_added_tokens_files: str):  # noqa: F811
         f"zephyr-lora2={zephyr_lora_added_tokens_files}",
         "--max-lora-rank",
         "64",
+        "--enable-tokenizer-info-endpoint",
     ]
 
     with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
@@ -283,3 +284,106 @@ async def test_detokenize(
     response.raise_for_status()
 
     assert response.json() == {"prompt": prompt}
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "model_name,tokenizer_name",
+    [(MODEL_NAME, MODEL_NAME), ("zephyr-lora2", "zephyr-lora2")],
+    indirect=["tokenizer_name"],
+)
+async def test_tokenizer_info_basic(
+    server: RemoteOpenAIServer,
+    model_name: str,
+    tokenizer_name: str,
+):
+    """Test basic tokenizer info endpoint functionality."""
+    response = requests.get(server.url_for("tokenizer_info"))
+    response.raise_for_status()
+    result = response.json()
+    assert "tokenizer_class" in result
+    assert isinstance(result["tokenizer_class"], str)
+    assert result["tokenizer_class"]
+
+
+@pytest.mark.asyncio
+async def test_tokenizer_info_schema(server: RemoteOpenAIServer):
+    """Test that the response matches expected schema types."""
+    response = requests.get(server.url_for("tokenizer_info"))
+    response.raise_for_status()
+    result = response.json()
+    field_types = {
+        "add_bos_token": bool,
+        "add_prefix_space": bool,
+        "clean_up_tokenization_spaces": bool,
+        "split_special_tokens": bool,
+        "bos_token": str,
+        "eos_token": str,
+        "pad_token": str,
+        "unk_token": str,
+        "chat_template": str,
+        "errors": str,
+        "model_max_length": int,
+        "additional_special_tokens": list,
+        "added_tokens_decoder": dict,
+    }
+    for field, expected_type in field_types.items():
+        if field in result and result[field] is not None:
+            assert isinstance(
+                result[field],
+                expected_type), (f"{field} should be {expected_type.__name__}")
+
+
+@pytest.mark.asyncio
+async def test_tokenizer_info_added_tokens_structure(
+    server: RemoteOpenAIServer, ):
+    """Test added_tokens_decoder structure if present."""
+    response = requests.get(server.url_for("tokenizer_info"))
+    response.raise_for_status()
+    result = response.json()
+    added_tokens = result.get("added_tokens_decoder")
+    if added_tokens:
+        for token_id, token_info in added_tokens.items():
+            assert isinstance(token_id, str), "Token IDs should be strings"
+            assert isinstance(token_info, dict), "Token info should be a dict"
+            assert "content" in token_info, "Token info should have content"
+            assert "special" in token_info, (
+                "Token info should have special flag")
+            assert isinstance(token_info["special"],
+                              bool), ("Special flag should be boolean")
+
+
+@pytest.mark.asyncio
+async def test_tokenizer_info_consistency_with_tokenize(
+    server: RemoteOpenAIServer, ):
+    """Test that tokenizer info is consistent with tokenization endpoint."""
+    info_response = requests.get(server.url_for("tokenizer_info"))
+    info_response.raise_for_status()
+    info = info_response.json()
+    tokenize_response = requests.post(
+        server.url_for("tokenize"),
+        json={
+            "model": MODEL_NAME,
+            "prompt": "Hello world!"
+        },
+    )
+    tokenize_response.raise_for_status()
+    tokenize_result = tokenize_response.json()
+    info_max_len = info.get("model_max_length")
+    tokenize_max_len = tokenize_result.get("max_model_len")
+    if info_max_len and tokenize_max_len:
+        assert info_max_len >= tokenize_max_len, (
+            "Info max length should be >= tokenize max length")
+
+
+@pytest.mark.asyncio
+async def test_tokenizer_info_chat_template(server: RemoteOpenAIServer):
+    """Test chat template is properly included."""
+    response = requests.get(server.url_for("tokenizer_info"))
+    response.raise_for_status()
+    result = response.json()
+    chat_template = result.get("chat_template")
+    if chat_template:
+        assert isinstance(chat_template,
+                          str), ("Chat template should be a string")
+        assert chat_template.strip(), "Chat template should not be empty"

vllm/entrypoints/openai/api_server.py

@@ -522,6 +522,19 @@ async def detokenize(request: DetokenizeRequest, raw_request: Request):
     assert_never(generator)
 
 
+def maybe_register_tokenizer_info_endpoint(args):
+    """Conditionally register the tokenizer info endpoint if enabled."""
+    if getattr(args, 'enable_tokenizer_info_endpoint', False):
+
+        @router.get("/tokenizer_info")
+        async def get_tokenizer_info(raw_request: Request):
+            """Get comprehensive tokenizer information."""
+            result = await tokenization(raw_request).get_tokenizer_info()
+            return JSONResponse(content=result.model_dump(),
+                                status_code=result.code if isinstance(
+                                    result, ErrorResponse) else 200)
+
+
 @router.get("/v1/models")
 async def show_available_models(raw_request: Request):
     handler = models(raw_request)
@@ -1692,6 +1705,7 @@ async def run_server_worker(listen_address,
         uvicorn_kwargs['log_config'] = log_config
 
     async with build_async_engine_client(args, client_config) as engine_client:
+        maybe_register_tokenizer_info_endpoint(args)
         app = build_app(args)
 
         vllm_config = await engine_client.get_vllm_config()

vllm/entrypoints/openai/cli_args.py

@@ -182,6 +182,9 @@ class FrontendArgs:
     """If set to True, enable tracking server_load_metrics in the app state."""
     enable_force_include_usage: bool = False
     """If set to True, including usage on every request."""
+    enable_tokenizer_info_endpoint: bool = False
+    """Enable the /get_tokenizer_info endpoint. May expose chat
+    templates and other tokenizer configuration."""
 
     @staticmethod
     def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:

vllm/entrypoints/openai/protocol.py

@@ -1953,6 +1953,16 @@ class DetokenizeResponse(OpenAIBaseModel):
     prompt: str
 
 
+class TokenizerInfoResponse(OpenAIBaseModel):
+    """
+    Response containing tokenizer configuration 
+    equivalent to tokenizer_config.json
+    """
+
+    model_config = ConfigDict(extra="allow")
+    tokenizer_class: str
+
+
 class LoadLoRAAdapterRequest(BaseModel):
     lora_name: str
     lora_path: str

vllm/entrypoints/openai/serving_tokenization.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from typing import Final, Optional, Union
+from dataclasses import dataclass
+from typing import Any, Final, Optional, Union
 
 import jinja2
 from fastapi import Request
@@ -17,11 +17,13 @@
                                               ErrorResponse,
                                               TokenizeChatRequest,
                                               TokenizeRequest,
-                                              TokenizeResponse)
+                                              TokenizeResponse,
+                                              TokenizerInfoResponse)
 # yapf: enable
 from vllm.entrypoints.openai.serving_engine import OpenAIServing
 from vllm.entrypoints.openai.serving_models import OpenAIServingModels
 from vllm.logger import init_logger
+from vllm.transformers_utils.tokenizer import AnyTokenizer
 
 logger = init_logger(__name__)
 
@@ -155,3 +157,49 @@ async def create_detokenize(
         input_text = prompt_input["prompt"]
 
         return DetokenizeResponse(prompt=input_text)
+
+    async def get_tokenizer_info(
+        self, ) -> Union[TokenizerInfoResponse, ErrorResponse]:
+        """Get comprehensive tokenizer information."""
+        try:
+            tokenizer = await self.engine_client.get_tokenizer()
+            info = TokenizerInfo(tokenizer, self.chat_template).to_dict()
+            return TokenizerInfoResponse(**info)
+        except Exception as e:
+            return self.create_error_response(
+                f"Failed to get tokenizer info: {str(e)}")
+
+
+@dataclass
+class TokenizerInfo:
+    tokenizer: AnyTokenizer
+    chat_template: Optional[str]
+
+    def to_dict(self) -> dict[str, Any]:
+        """Return the tokenizer configuration."""
+        return self._get_tokenizer_config()
+
+    def _get_tokenizer_config(self) -> dict[str, Any]:
+        """Get tokenizer configuration directly from the tokenizer object."""
+        config = dict(getattr(self.tokenizer, "init_kwargs", None) or {})
+
+        # Remove file path fields
+        config.pop("vocab_file", None)
+        config.pop("merges_file", None)
+
+        config = self._make_json_serializable(config)
+        config["tokenizer_class"] = type(self.tokenizer).__name__
+        if self.chat_template:
+            config["chat_template"] = self.chat_template
+        return config
+
+    def _make_json_serializable(self, obj):
+        """Convert any non-JSON-serializable objects to serializable format."""
+        if hasattr(obj, "content"):
+            return obj.content
+        elif isinstance(obj, dict):
+            return {k: self._make_json_serializable(v) for k, v in obj.items()}
+        elif isinstance(obj, list):
+            return [self._make_json_serializable(item) for item in obj]
+        else:
+            return obj