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(gpt-oss, oai, chat): Remove Harmony Integration and Implement Native GPT-OSS Tool Call Support (#9043)

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This commit is contained in:
Chang Su
2025-08-11 18:59:18 -07:00
committed by GitHub
parent 0eec4cb6cc
commit a218490136
9 changed files with 712 additions and 404 deletions
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322
python/sglang/srt/entrypoints/openai/serving_chat.py
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@@ -7,18 +7,8 @@ from typing import Any, AsyncGenerator, Dict, List, Optional, Union
from fastapi import Request
from fastapi.responses import ORJSONResponse, StreamingResponse
from openai_harmony import Message as OpenAIMessage
from sglang.srt.conversation import generate_chat_conv
from sglang.srt.entrypoints.harmony_utils import (
get_developer_message,
get_stop_tokens_for_assistant_actions,
get_streamable_parser_for_assistant,
get_system_message,
parse_chat_input,
parse_output_into_messages,
render_for_completion,
)
from sglang.srt.entrypoints.openai.protocol import (
ChatCompletionRequest,
ChatCompletionResponse,
@@ -57,30 +47,12 @@ class OpenAIServingChat(OpenAIServingBase):
"""Handler for /v1/chat/completions requests"""
def __init__(
self, tokenizer_manager: TokenizerManager, template_manager: TemplateManager
self,
tokenizer_manager: TokenizerManager,
template_manager: TemplateManager,
):
super().__init__(tokenizer_manager)
self.template_manager = template_manager
self.use_harmony = (
self.tokenizer_manager.model_config.hf_config.model_type == "gpt_oss"
)
if self.use_harmony:
from sglang.srt.function_call.harmony_tool_parser import (
HarmonyToolCallParser,
)
self.harmony_tool_parser = HarmonyToolCallParser()
# NOTE While OpenAI's chat completion API supports browsing
# for some models, currently vLLM doesn't support it. Please use the
# Responses API instead.
self.supports_browsing = False
self.browser_tool = None
# NOTE: Chat completion API does not support code interpreter.
# Please use the Responses API instead.
self.supports_code_interpreter = False
self.python_tool = None
def _request_id_prefix(self) -> str:
return "chatcmpl-"
@@ -97,6 +69,18 @@ class OpenAIServingChat(OpenAIServingBase):
):
return "Tools cannot be empty if tool choice is set to required."
max_output_tokens = request.max_completion_tokens or request.max_tokens
server_context_length = self.tokenizer_manager.server_args.context_length
if (
max_output_tokens
and server_context_length
and max_output_tokens > server_context_length
):
return (
f"max_completion_tokens is too large: {max_output_tokens}."
f"This model supports at most {server_context_length} completion tokens."
)
return None
def _convert_to_internal_request(
@@ -107,66 +91,43 @@ class OpenAIServingChat(OpenAIServingBase):
is_multimodal = self.tokenizer_manager.model_config.is_multimodal
# Process messages and apply chat template
if not self.use_harmony:
processed_messages = self._process_messages(request, is_multimodal)
processed_messages = self._process_messages(request, is_multimodal)
# Build sampling parameters
sampling_params = self._build_sampling_params(
request,
processed_messages.stop,
processed_messages.tool_call_constraint,
)
# Build sampling parameters
sampling_params = self._build_sampling_params(
request,
processed_messages.stop,
processed_messages.tool_call_constraint,
)
# Handle single vs multiple requests
if is_multimodal:
prompt_kwargs = {"text": processed_messages.prompt}
else:
if isinstance(processed_messages.prompt_ids, str):
prompt_kwargs = {"text": processed_messages.prompt_ids}
else:
prompt_kwargs = {"input_ids": processed_messages.prompt_ids}
adapted_request = GenerateReqInput(
**prompt_kwargs,
image_data=processed_messages.image_data,
video_data=processed_messages.video_data,
audio_data=processed_messages.audio_data,
sampling_params=sampling_params,
return_logprob=request.logprobs,
logprob_start_len=-1,
top_logprobs_num=request.top_logprobs or 0,
stream=request.stream,
return_text_in_logprobs=True,
modalities=processed_messages.modalities,
lora_path=request.lora_path,
bootstrap_host=request.bootstrap_host,
bootstrap_port=request.bootstrap_port,
bootstrap_room=request.bootstrap_room,
return_hidden_states=request.return_hidden_states,
rid=request.rid,
)
# Handle single vs multiple requests
if is_multimodal:
prompt_kwargs = {"text": processed_messages.prompt}
else:
processed_messages, prompt_ids = self._make_request_with_harmony(request)
if isinstance(processed_messages.prompt_ids, str):
prompt_kwargs = {"text": processed_messages.prompt_ids}
else:
prompt_kwargs = {"input_ids": processed_messages.prompt_ids}
adapted_request = GenerateReqInput(
input_ids=prompt_ids,
sampling_params=self._build_sampling_params(
request,
request.stop,
tool_call_constraint=None,
),
stream=request.stream,
return_logprob=request.logprobs,
logprob_start_len=-1,
top_logprobs_num=request.top_logprobs or 0,
return_text_in_logprobs=True,
lora_path=request.lora_path,
bootstrap_host=request.bootstrap_host,
bootstrap_port=request.bootstrap_port,
bootstrap_room=request.bootstrap_room,
return_hidden_states=request.return_hidden_states,
rid=request.rid,
)
adapted_request = GenerateReqInput(
**prompt_kwargs,
image_data=processed_messages.image_data,
video_data=processed_messages.video_data,
audio_data=processed_messages.audio_data,
sampling_params=sampling_params,
return_logprob=request.logprobs,
logprob_start_len=-1,
top_logprobs_num=request.top_logprobs or 0,
stream=request.stream,
return_text_in_logprobs=True,
modalities=processed_messages.modalities,
lora_path=request.lora_path,
bootstrap_host=request.bootstrap_host,
bootstrap_port=request.bootstrap_port,
bootstrap_room=request.bootstrap_room,
return_hidden_states=request.return_hidden_states,
rid=request.rid,
)
return adapted_request, request
@@ -251,14 +212,16 @@ class OpenAIServingChat(OpenAIServingBase):
tokenize=True,
add_generation_prompt=True,
tools=tools,
reasoning_effort=request.reasoning_effort,
builtin_tools=[],
**(
request.chat_template_kwargs if request.chat_template_kwargs else {}
),
)
except Exception:
# This except branch will be triggered when the chosen model
# has a different tools input format that is not compatible
# with openAI's apply_chat_template tool_call format, like Mistral.
# This except branch will be triggered when the chosen model
# has a different tools input format that is not compatible
# with openAI's apply_chat_template tool_call format, like Mistral.
tools = (
[t if "function" in t else {"function": t} for t in tools]
if tools
@@ -269,6 +232,8 @@ class OpenAIServingChat(OpenAIServingBase):
tokenize=True,
add_generation_prompt=True,
tools=tools,
reasoning_effort=request.reasoning_effort,
builtin_tools=[],
**(
request.chat_template_kwargs if request.chat_template_kwargs else {}
),
@@ -459,12 +424,6 @@ class OpenAIServingChat(OpenAIServingBase):
cached_tokens = {}
hidden_states = {}
# Harmony tracking
if self.use_harmony:
harmony_parsers = [
get_streamable_parser_for_assistant() for _ in range(request.n)
]
try:
async for content in self.tokenizer_manager.generate_request(
adapted_request, raw_request
@@ -511,58 +470,14 @@ class OpenAIServingChat(OpenAIServingBase):
)
yield f"data: {chunk.model_dump_json()}\n\n"
# Process content delta
if self.use_harmony:
harmony_parser = harmony_parsers[index]
new_token_ids = content["output_ids"]
for token_id in new_token_ids:
harmony_parser.process(token_id)
is_final = harmony_parser.current_channel == "final"
is_analysis = harmony_parser.current_channel == "analysis"
delta = harmony_parser.last_content_delta or ""
if is_analysis:
choice_data = ChatCompletionResponseStreamChoice(
index=index,
delta=DeltaMessage(reasoning_content=delta),
finish_reason=None,
)
chunk = ChatCompletionStreamResponse(
id=content["meta_info"]["id"],
created=int(time.time()),
choices=[choice_data],
model=request.model,
)
yield f"data: {chunk.model_dump_json()}\n\n"
continue
choice_data = ChatCompletionResponseStreamChoice(
index=index,
delta=DeltaMessage(content=delta if delta else None),
finish_reason=None,
matched_stop=None,
logprobs=choice_logprobs,
)
chunk = ChatCompletionStreamResponse(
id=content["meta_info"]["id"],
created=int(time.time()),
choices=[choice_data],
model=request.model,
)
yield f"data: {chunk.model_dump_json()}\n\n"
continue
else:
stream_buffer = stream_buffers.get(index, "")
delta = content["text"][len(stream_buffer) :]
stream_buffers[index] = stream_buffer + delta
stream_buffer = stream_buffers.get(index, "")
delta = content["text"][len(stream_buffer) :]
stream_buffers[index] = stream_buffer + delta
# Handle reasoning content
if (
self.tokenizer_manager.server_args.reasoning_parser
and request.separate_reasoning
and not self.use_harmony
):
reasoning_text, delta = self._process_reasoning_stream(
index, delta, reasoning_parser_dict, content, request
@@ -581,27 +496,8 @@ class OpenAIServingChat(OpenAIServingBase):
)
yield f"data: {chunk.model_dump_json()}\n\n"
if self.use_harmony and not is_final:
choice_data = ChatCompletionResponseStreamChoice(
index=index,
delta=DeltaMessage(reasoning_content=delta),
finish_reason=None,
)
chunk = ChatCompletionStreamResponse(
id=content["meta_info"]["id"],
created=int(time.time()),
choices=[choice_data],
model=request.model,
)
yield f"data: {chunk.model_dump_json()}\n\n"
# Handle tool calls
# TODO: support tool call parsing for harmony
if (
request.tool_choice != "none"
and request.tools
and not self.use_harmony
):
if request.tool_choice != "none" and request.tools:
async for chunk in self._process_tool_call_stream(
index,
delta,
@@ -765,76 +661,6 @@ class OpenAIServingChat(OpenAIServingBase):
finish_reason = ret_item["meta_info"]["finish_reason"]
text = ret_item["text"]
output_ids = ret_item["output_ids"]
if self.use_harmony:
parser = parse_output_into_messages(output_ids)
output_msgs = parser.messages
if len(output_msgs) == 0:
# The generation has stopped during reasoning.
is_tool_call = False
reasoning_content = parser.current_content
final_content = None
elif len(output_msgs) == 1:
# The generation has stopped during final message.
is_tool_call = False
reasoning_content = output_msgs[0].content[0].text
final_content = parser.current_content
else:
if len(output_msgs) != 2:
raise ValueError(
"Expected 2 output messages (reasoning and final), "
f"but got {len(output_msgs)}."
)
reasoning_msg, final_msg = output_msgs
reasoning_content = reasoning_msg.content[0].text
final_content = final_msg.content[0].text
is_tool_call = final_msg.recipient is not None
if is_tool_call:
# Extract tool call information from final message
tool_call = (
self.harmony_tool_parser.extract_tool_calls_from_message(
final_msg
)
)
tool_calls = [tool_call] if tool_call else []
message = ChatMessage(
role="assistant",
reasoning_content=reasoning_content,
content=None, # Tool calls don't have regular content
tool_calls=tool_calls,
)
else:
# Normal message
message = ChatMessage(
role="assistant",
reasoning_content=reasoning_content,
content=final_content,
)
if is_tool_call:
finish_reason_type = "tool_calls"
elif finish_reason:
finish_reason_type = (
finish_reason["type"] if finish_reason else "stop"
)
else:
finish_reason_type = "stop"
choice_data = ChatCompletionResponseChoice(
index=idx,
message=message,
logprobs=choice_logprobs,
finish_reason=finish_reason_type,
matched_stop=(
finish_reason["matched"]
if finish_reason and "matched" in finish_reason
else None
),
)
choices.append(choice_data)
continue
# Handle reasoning content
reasoning_text = None
@@ -1184,33 +1010,3 @@ class OpenAIServingChat(OpenAIServingBase):
return f"data: {chunk.model_dump_json()}\n\n"
return None
def _make_request_with_harmony(
self,
request: ChatCompletionRequest,
):
messages: list[OpenAIMessage] = []
# Add system message.
# In Chat Completion API, browsing is enabled by default if the model
# supports it.
assert not self.supports_browsing
assert not self.supports_code_interpreter
sys_msg = get_system_message(
reasoning_effort=request.reasoning_effort,
browser_description=None,
python_description=None,
)
messages.append(sys_msg)
# Add developer message.
dev_msg = get_developer_message()
messages.append(dev_msg)
# Add user message.
for chat_msg in request.messages:
messages.append(parse_chat_input(chat_msg))
# Render prompt token ids.
prompt_token_ids = render_for_completion(messages)
return messages, prompt_token_ids