class OnlineRenderer:
def __init__(
self,
model_config: ModelConfig,
renderer: BaseRenderer,
*,
request_logger: RequestLogger | None,
chat_template: str | None,
chat_template_content_format: ChatTemplateContentFormatOption,
trust_request_chat_template: bool = False,
trust_request_mm_kwargs: bool = False,
enable_auto_tools: bool = False,
exclude_tools_when_tool_choice_none: bool = False,
tool_parser: str | None = None,
reasoning_parser: str | None = None,
tool_strict_level: str = "auto",
default_chat_template_kwargs: dict[str, Any] | None = None,
log_error_stack: bool = False,
) -> None:
self.model_config = model_config
self.renderer = renderer
self.request_logger = request_logger
self.enable_auto_tools = enable_auto_tools
self.exclude_tools_when_tool_choice_none = exclude_tools_when_tool_choice_none
self.use_harmony = model_config.hf_config.model_type == "gpt_oss"
self.parser: type[Parser] | None = ParserManager.get_parser(
tool_parser_name=tool_parser,
reasoning_parser_name=reasoning_parser,
enable_auto_tools=enable_auto_tools,
tool_strict_level=tool_strict_level,
model_name=model_config.model,
is_harmony=self.use_harmony,
tokenizer=renderer.tokenizer,
)
self.chat_template = chat_template
self.chat_template_content_format: ChatTemplateContentFormatOption = (
chat_template_content_format
)
self.default_chat_template_kwargs: dict[str, Any] = (
default_chat_template_kwargs or {}
)
self.trust_request_chat_template = trust_request_chat_template
self.trust_request_mm_kwargs = trust_request_mm_kwargs
self.log_error_stack = log_error_stack
self.supports_browsing = False
self.supports_code_interpreter = False
def warmup(self) -> None:
self.renderer.warmup(
ChatParams(
chat_template=self.chat_template,
chat_template_content_format=self.chat_template_content_format,
chat_template_kwargs=self.default_chat_template_kwargs,
)
)
async def render_chat(
self,
request: ChatCompletionRequest,
*,
skip_mm_cache: bool = False,
) -> tuple[list[ConversationMessage], list[EngineInput]] | ErrorResponse:
"""Core preprocessing logic for chat requests (no model/engine check).
Called directly by render_chat_request and delegated to by
OpenAIServingChat.render_chat_request after its engine-aware checks.
Decode-side token reuse (ids forwarded in ``kv_transfer_params``) is
handled deeper, in ``preprocess_chat`` / ``_make_request_with_harmony``,
so it skips only templating and tokenization while tool-choice
validation and ``adjust_request`` still run and the output is
detokenized (text-out).
"""
tokenizer = self.renderer.tokenizer
tool_parser = self.parser.tool_parser_cls if self.parser is not None else None
if is_mistral_tokenizer(tokenizer):
# because of issues with pydantic we need to potentially
# re-serialize the tool_calls field of the request
_mt.maybe_serialize_tool_calls(request) # type: ignore[arg-type]
_mt.truncate_tool_call_ids(request) # type: ignore[arg-type]
_mt.validate_request_params(request)
# Check if tool parsing is unavailable (common condition)
tool_parsing_unavailable = (
tool_parser is None
and not is_mistral_tokenizer(tokenizer)
and not self.use_harmony
)
# Validate tool_choice when tool parsing is required but unavailable
if tool_parsing_unavailable and request.tool_choice not in (
None,
"none",
):
if request.tool_choice == "auto" and not self.enable_auto_tools:
# for hf tokenizers, "auto" tools requires
# --enable-auto-tool-choice and --tool-call-parser
return self.create_error_response(
'"auto" tool choice requires '
"--enable-auto-tool-choice and --tool-call-parser to be set"
)
elif request.tool_choice != "auto":
# "required" or named tool requires tool parser
if isinstance(request.tool_choice, ChatCompletionNamedToolChoiceParam):
tool_choice_desc = f'function "{request.tool_choice.function.name}"'
else:
tool_choice_desc = f'"{request.tool_choice}"'
return self.create_error_response(
f"tool_choice={tool_choice_desc} requires "
"--tool-call-parser to be set"
)
if request.tools is None or (
request.tool_choice == "none" and self.exclude_tools_when_tool_choice_none
):
tool_dicts = None
else:
tool_dicts = [tool.model_dump() for tool in request.tools]
if not self.use_harmony:
# Common case.
error_check_ret = self.validate_chat_template(
request_chat_template=request.chat_template,
chat_template_kwargs=request.chat_template_kwargs,
trust_request_chat_template=self.trust_request_chat_template,
)
if error_check_ret is not None:
return error_check_ret
conversation, engine_inputs = await self.preprocess_chat(
request,
request.messages,
default_template=self.chat_template,
default_template_content_format=self.chat_template_content_format,
default_template_kwargs=self.default_chat_template_kwargs,
tool_dicts=tool_dicts,
parser=self.parser,
skip_mm_cache=skip_mm_cache,
)
else:
# For GPT-OSS.
self.adjust_harmony_request(request)
should_include_tools = tool_dicts is not None
conversation, engine_inputs = self._make_request_with_harmony(
request, should_include_tools
)
return conversation, engine_inputs
async def render_responses(
self,
request: ResponsesRequest,
*,
previous_messages: ResponsesPreviousMessages | None = None,
previous_response_outputs: list[ResponseOutputItem] | None = None,
tool_server: "ToolServer | None" = None,
skip_mm_cache: bool = False,
) -> ResponsesRenderResult | ErrorResponse:
"""Render a Responses request using only explicitly supplied history."""
template_error = self.validate_chat_template(
request_chat_template=None,
chat_template_kwargs=request.chat_template_kwargs,
trust_request_chat_template=self.trust_request_chat_template,
)
if template_error is not None:
return template_error
if self.use_harmony:
return self._render_responses_with_harmony(
request,
previous_messages=previous_messages,
previous_response_outputs=previous_response_outputs,
tool_server=tool_server,
)
if previous_messages is not None and any(
isinstance(message, OpenAIMessage) for message in previous_messages
):
return self.create_error_response(
"Non-Harmony Responses history must use chat messages.",
err_type="invalid_request_error",
param="previous_response_id",
)
tool_dicts = construct_tool_dicts(
request.tools,
request.tool_choice,
exclude_tools_when_tool_choice_none=(
self.exclude_tools_when_tool_choice_none
),
)
messages = construct_input_messages(
request_instructions=request.instructions,
request_input=request.input,
prev_msg=list(previous_messages) if previous_messages is not None else None,
prev_response_output=(
list(previous_response_outputs)
if previous_response_outputs is not None
else None
),
)
chat_template_kwargs = (
request.build_chat_params(
self.chat_template,
self.chat_template_content_format,
)
.with_defaults(self.default_chat_template_kwargs)
.chat_template_kwargs
)
_, engine_inputs = await self.preprocess_chat(
request,
messages,
default_template=self.chat_template,
default_template_content_format=self.chat_template_content_format,
default_template_kwargs=chat_template_kwargs,
tool_dicts=tool_dicts,
parser=self.parser,
skip_mm_cache=skip_mm_cache,
)
return self._responses_render_result(messages, engine_inputs)
def _render_responses_with_harmony(
self,
request: ResponsesRequest,
*,
previous_messages: ResponsesPreviousMessages | None,
previous_response_outputs: list[ResponseOutputItem] | None,
tool_server: "ToolServer | None",
) -> ResponsesRenderResult | ErrorResponse:
self.adjust_harmony_request(request)
if previous_messages is not None and any(
not isinstance(message, OpenAIMessage) for message in previous_messages
):
return self.create_error_response(
"Harmony Responses history must use Harmony messages.",
err_type="invalid_request_error",
param="previous_response_id",
)
messages: list[OpenAIMessage] = []
request_input = request.input
if previous_messages is None:
tool_types = extract_tool_types(request.tools)
with_custom_tools = has_custom_tools(tool_types)
instructions = request.instructions
if instructions is None and isinstance(request_input, list):
instructions, request_input = extract_instructions_from_messages(
request_input
)
descriptions = self._get_harmony_builtin_tool_descriptions(
request.tools,
tool_types,
tool_server,
)
messages.extend(
build_harmony_preamble(
instructions=instructions,
tools=request.tools if with_custom_tools else None,
reasoning_effort=(
request.reasoning.effort if request.reasoning else None
),
with_custom_tools=with_custom_tools,
**descriptions,
)
)
messages.extend(construct_harmony_previous_input_messages(request))
else:
messages.extend(previous_messages)
previous_outputs = list(previous_response_outputs or ())
try:
if isinstance(request_input, str):
if request_input or not request.previous_input_messages:
messages.append(get_user_message(request_input))
else:
for response_message in request_input:
new_message = response_input_to_harmony(
response_message,
previous_outputs,
)
if new_message is not None:
messages.append(new_message)
if isinstance(response_message, ResponseFunctionToolCall):
previous_outputs.append(response_message)
except (ValueError, VLLMValidationError) as exc:
return self.create_error_response(
str(exc),
err_type="invalid_request_error",
param="input",
)
return ResponsesRenderResult(
messages=messages,
engine_input=self.render_responses_harmony_messages(
messages,
cache_salt=request.cache_salt,
tok_params=request.build_tok_params(self.model_config),
),
)
def _get_harmony_builtin_tool_descriptions(
self,
tools: list[Tool],
tool_types: set[str],
tool_server: "ToolServer | None",
) -> dict[str, ToolNamespaceConfig | None]:
allowed_tools = _extract_allowed_tools_from_mcp_requests(tools)
descriptions: dict[str, ToolNamespaceConfig | None] = {}
for server_name, request_name in BUILTIN_TOOL_TO_MCP_SERVER_LABEL.items():
description = (
tool_server.get_tool_description(
server_name,
allowed_tools.get(request_name),
)
if request_name in tool_types
and tool_server is not None
and tool_server.has_tool(server_name)
else None
)
descriptions[f"{server_name}_description"] = description
return descriptions
def render_responses_harmony_messages(
self,
messages: list[OpenAIMessage],
*,
cache_salt: str | None,
tok_params: TokenizeParams | None = None,
) -> EngineInput:
arrival_time = time.time()
prompt = TokensPrompt(prompt_token_ids=render_for_completion(messages))
if tok_params is not None:
tok_params.apply_post_tokenization(
self.renderer.tokenizer,
prompt,
)
engine_input = tokens_input(prompt["prompt_token_ids"], cache_salt=cache_salt)
engine_input["arrival_time"] = arrival_time
return engine_input
def _responses_render_result(
self,
messages: list[ChatCompletionMessageParam],
engine_inputs: list[EngineInput],
) -> ResponsesRenderResult | ErrorResponse:
if len(engine_inputs) != 1:
return self.create_error_response(
f"Expected exactly 1 engine prompt, got {len(engine_inputs)}"
)
return ResponsesRenderResult(
messages=messages,
engine_input=engine_inputs[0],
)
def adjust_harmony_request(
self, request: ChatCompletionRequest | ResponsesRequest
) -> None:
"""Apply the Harmony parser's ``adjust_request`` to ``request`` in place.
Every Harmony render path must call this before sampling params are
built from ``request``.
"""
if self.parser is None:
return
# HarmonyParser doesn't need chat_template_kwargs
# TODO: Unify adjust_request() call with non-harmony branch
self.parser(
self.renderer.get_tokenizer(),
request.tools,
model_config=self.model_config,
).adjust_request(request=request)
def _make_request_with_harmony(
self,
request: ChatCompletionRequest,
should_include_tools: bool = True,
):
"""Build Harmony (GPT-OSS) messages and engine prompt from a chat request."""
reuse_ids = _reused_prompt_token_ids(request, self.renderer)
if reuse_ids:
# Decode-side token reuse: feed the forwarded ids straight to the
# engine. Harmony has no adjust_request hook to preserve.
engine_input = tokens_input(reuse_ids, cache_salt=request.cache_salt)
return [], [engine_input]
messages: list[OpenAIMessage] = []
# because of issues with pydantic we need to potentially
# re-serialize the tool_calls field of the request
# for more info: see comment in `maybe_serialize_tool_calls`
_mt.maybe_serialize_tool_calls(request) # type: ignore[arg-type]
chat_messages = list(request.messages)
instructions, chat_messages = extract_instructions_from_messages(chat_messages)
# Add system message.
# NOTE: In Chat Completion API, browsing is enabled by default
# if the model supports it. TODO: Support browsing.
assert not self.supports_browsing
assert not self.supports_code_interpreter
if (reasoning_effort := request.reasoning_effort) == "none":
raise VLLMValidationError(
f"Harmony does not support {reasoning_effort=}",
parameter="reasoning_effort",
)
tools = request.tools if should_include_tools else None
messages.extend(
build_harmony_preamble(
instructions=instructions,
tools=tools, # type: ignore[arg-type]
reasoning_effort=reasoning_effort,
with_custom_tools=should_include_tools,
)
)
# Add remaining conversation messages.
messages.extend(parse_chat_inputs_to_harmony_messages(chat_messages))
# Render prompt token ids.
prompt_token_ids = render_for_completion(messages)
engine_input = tokens_input(prompt_token_ids, cache_salt=request.cache_salt)
return messages, [engine_input]
async def render_completion(
self,
request: CompletionRequest,
*,
skip_mm_cache: bool = False,
) -> list[EngineInput] | ErrorResponse:
"""Core preprocessing logic for completion requests (no model/engine check).
Called directly by render_completion_request and delegated to by
OpenAIServingCompletion.render_completion_request after its engine-aware checks.
"""
prompt_input = request.prompt
if request.suffix is not None:
if request.echo:
return self.create_error_response(
"Echo is unsupported with suffix.",
param="suffix",
)
if request.prompt_embeds is not None:
return self.create_error_response(
"suffix is not supported with prompt_embeds",
param="suffix",
)
if request.truncate_prompt_tokens is not None:
return self.create_error_response(
"suffix is not supported with truncate_prompt_tokens",
param="suffix",
)
if isinstance(request.prompt, str):
rendered_prompt = self.renderer.render_completion_suffix(
request.prompt, request.suffix
)
if rendered_prompt is None:
return self.create_error_response(
"suffix is only supported for models with FIM completion "
"rendering",
param="suffix",
)
prompt_input = rendered_prompt
elif isinstance(request.prompt, list) and all(
isinstance(prompt, str) for prompt in request.prompt
):
rendered_prompts = []
for prompt in cast(list[str], request.prompt):
rendered_prompt = self.renderer.render_completion_suffix(
prompt, request.suffix
)
if rendered_prompt is None:
return self.create_error_response(
"suffix is only supported for models with FIM completion "
"rendering",
param="suffix",
)
rendered_prompts.append(rendered_prompt)
prompt_input = rendered_prompts
else:
return self.create_error_response(
"suffix requires text prompt input for FIM completion rendering",
param="suffix",
)
if request.echo and request.prompt_embeds is not None:
return self.create_error_response("Echo is unsupported with prompt embeds.")
if request.prompt_logprobs is not None and request.prompt_embeds is not None:
return self.create_error_response(
"prompt_logprobs is not compatible with prompt embeds."
)
engine_inputs = await self.preprocess_completion(
request,
prompt_input=prompt_input,
prompt_embeds=request.prompt_embeds,
skip_mm_cache=skip_mm_cache,
)
return engine_inputs
def create_error_response(
self,
message: str | Exception,
err_type: str = "BadRequestError",
status_code: HTTPStatus = HTTPStatus.BAD_REQUEST,
param: str | None = None,
) -> ErrorResponse:
return create_error_response(message, err_type, status_code, param)
def validate_chat_template(
self,
request_chat_template: str | None,
chat_template_kwargs: dict[str, Any] | None,
trust_request_chat_template: bool,
) -> ErrorResponse | None:
"""Copied from GenerateBaseServing._validate_chat_template."""
if not trust_request_chat_template and (
request_chat_template is not None
or (
chat_template_kwargs
and chat_template_kwargs.get("chat_template") is not None
)
):
return self.create_error_response(
"Chat template is passed with request, but "
"--trust-request-chat-template is not set. "
"Refused request with untrusted chat template."
)
return None
async def preprocess_completion(
self,
request: Any,
prompt_input: str | list[str] | list[int] | list[list[int]] | None,
prompt_embeds: bytes | list[bytes] | None,
*,
skip_mm_cache: bool = False,
) -> list[EngineInput]:
"""Copied from GenerateBaseServing._preprocess_completion."""
prompts = list[SingletonPrompt | bytes]()
if prompt_embeds is not None: # embeds take higher priority
prompts.extend(prompt_to_seq(prompt_embeds))
if prompt_input is not None:
prompts.extend(prompt_to_seq(prompt_input))
return await self.preprocess_cmpl(request, prompts, skip_mm_cache=skip_mm_cache)
async def preprocess_cmpl(
self,
request: Any,
prompts: Sequence[PromptType | bytes],
*,
skip_mm_cache: bool = False,
) -> list[EngineInput]:
"""Copied from GenerateBaseServing._preprocess_cmpl."""
renderer = self.renderer
model_config = self.model_config
validate_request_mm_kwargs(
mm_processor_kwargs=getattr(request, "mm_processor_kwargs", None),
media_io_kwargs=getattr(request, "media_io_kwargs", None),
trust_request_mm_kwargs=self.trust_request_mm_kwargs,
)
parsed_prompts = [
(
prompt
if isinstance(prompt, bytes)
else parse_model_prompt(model_config, prompt)
)
for prompt in prompts
]
tok_params = request.build_tok_params(model_config)
return await renderer.render_cmpl_async(
parsed_prompts,
tok_params,
prompt_extras={
k: v
for k in ("mm_processor_kwargs", "cache_salt")
if (v := getattr(request, k, None)) is not None
},
skip_mm_cache=skip_mm_cache,
)
async def preprocess_chat(
self,
request: Any,
messages: list[Any],
default_template: str | None,
default_template_content_format: ChatTemplateContentFormatOption,
default_template_kwargs: dict[str, Any] | None,
tool_dicts: list[dict[str, Any]] | None = None,
parser: type[Parser] | None = None,
*,
skip_mm_cache: bool = False,
) -> tuple[list[ConversationMessage], list[EngineInput]]:
"""Copied from GenerateBaseServing._preprocess_chat."""
renderer = self.renderer
validate_request_mm_kwargs(
mm_processor_kwargs=getattr(request, "mm_processor_kwargs", None),
media_io_kwargs=getattr(request, "media_io_kwargs", None),
trust_request_mm_kwargs=self.trust_request_mm_kwargs,
)
mm_config = self.model_config.multimodal_config
default_template_kwargs = merge_kwargs(
default_template_kwargs,
dict(
tools=tool_dicts,
tokenize=(
is_mistral_tokenizer(renderer.tokenizer)
or self.model_config.enable_prompt_embeds
),
),
)
tok_params = request.build_tok_params(self.model_config)
chat_params = request.build_chat_params(
default_template, default_template_content_format
).with_defaults(
default_template_kwargs,
default_media_io_kwargs=(mm_config.media_io_kwargs if mm_config else None),
default_mm_processor_kwargs=getattr(request, "mm_processor_kwargs", None),
)
reuse_ids = _reused_prompt_token_ids(request, renderer, messages)
if reuse_ids:
# Decode-side token reuse: feed the forwarded ids straight to the
# engine, skipping templating and tokenization. ``messages`` are not
# tokenized, so conversation is empty. The adjust_request tail below
# still runs.
conversation: list[ConversationMessage] = []
engine_input = tokens_input(
reuse_ids, cache_salt=getattr(request, "cache_salt", None)
)
else:
(conversation,), (engine_input,) = await renderer.render_chat_async(
[messages],
chat_params,
tok_params,
prompt_extras={
k: v
for k in ("mm_processor_kwargs", "cache_salt")
if (v := getattr(request, k, None)) is not None
},
skip_mm_cache=skip_mm_cache,
)
# tool parsing is done only if a tool_parser has been set and if
# tool_choice is not "none" (if tool_choice is "none" but a tool_parser
# is set, we want to prevent parsing a tool_call hallucinated by the LLM
#
# Exception: Mistral grammar-capable tokenizers always call
# adjust_request — even for tool_choice="none" — so that the grammar
# factory can prevent special-token leakage.
if parser is not None:
tokenizer = renderer.get_tokenizer()
tool_parser = parser.tool_parser_cls
tool_choice = getattr(request, "tool_choice", "none")
is_mistral_grammar_eligible = (
tool_parser is not None
and is_mistral_tool_parser(tool_parser)
and is_mistral_tokenizer(tokenizer)
and tokenizer.supports_grammar
)
should_adjust_request = (
parser.always_adjust_request
or parser.reasoning_parser_cls is not None
or tool_choice != "none"
or is_mistral_grammar_eligible
)
if should_adjust_request:
if not isinstance(request, ChatCompletionRequest | ResponsesRequest):
msg = (
"Tool usage is only supported "
"for Chat Completions API or Responses API requests, "
f"but got {type(request).__name__}"
)
raise NotImplementedError(msg)
request = parser(
tokenizer,
request.tools,
model_config=self.model_config,
chat_template_kwargs=chat_params.chat_template_kwargs,
).adjust_request(
request=request,
)
return conversation, [engine_input]