[gpt-oss] Support chat completion api (#22342)

vllm/entrypoints/harmony_utils.py

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import datetime
+from collections.abc import Iterable
 from typing import Literal, Optional
 
 from openai.types.responses.tool import Tool
@@ -109,3 +110,36 @@ def get_stop_tokens_for_assistant_actions() -> list[int]:
 
 def get_streamable_parser_for_assistant() -> StreamableParser:
     return StreamableParser(get_encoding(), role=Role.ASSISTANT)
+
+
+def parse_output_into_messages(token_ids: Iterable[int]) -> StreamableParser:
+    parser = get_streamable_parser_for_assistant()
+    for token_id in token_ids:
+        parser.process(token_id)
+    return parser
+
+
+def parse_chat_output(
+        token_ids: list[int]) -> tuple[Optional[str], Optional[str], bool]:
+    parser = parse_output_into_messages(token_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
+    return reasoning_content, final_content, is_tool_call

vllm/entrypoints/openai/protocol.py

@@ -323,6 +323,7 @@ class ResponsesRequest(OpenAIBaseModel):
         if (top_p := self.top_p) is None:
             top_p = default_sampling_params.get(
                 "top_p", self._DEFAULT_SAMPLING_PARAMS["top_p"])
+        stop_token_ids = default_sampling_params.get("stop_token_ids")
 
         # Structured output
         guided_decoding = None
@@ -340,6 +341,7 @@ class ResponsesRequest(OpenAIBaseModel):
             top_p=top_p,
             max_tokens=max_tokens,
             logprobs=self.top_logprobs,
+            stop_token_ids=stop_token_ids,
             output_kind=(RequestOutputKind.DELTA
                          if self.stream else RequestOutputKind.FINAL_ONLY),
             guided_decoding=guided_decoding,
@@ -404,6 +406,8 @@ class ChatCompletionRequest(OpenAIBaseModel):
         Literal["required"],
         ChatCompletionNamedToolChoiceParam,
     ]] = "none"
+    reasoning_effort: Optional[Literal["low", "medium", "high"]] = None
+    include_reasoning: bool = True
 
     # NOTE this will be ignored by vLLM -- the model determines the behavior
     parallel_tool_calls: Optional[bool] = False

vllm/entrypoints/openai/serving_chat.py

@@ -12,6 +12,7 @@ import jinja2
 import partial_json_parser
 import regex as re
 from fastapi import Request
+from openai_harmony import Message as OpenAIMessage
 from pydantic import TypeAdapter
 
 from vllm.config import ModelConfig
@@ -19,6 +20,10 @@ from vllm.engine.protocol import EngineClient
 from vllm.entrypoints.chat_utils import (ChatTemplateContentFormatOption,
                                          ConversationMessage,
                                          random_tool_call_id)
+from vllm.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_chat_output, render_for_completion)
 from vllm.entrypoints.logger import RequestLogger
 from vllm.entrypoints.openai.protocol import (
     ChatCompletionLogProb, ChatCompletionLogProbs,
@@ -35,6 +40,7 @@ from vllm.entrypoints.openai.tool_parsers import ToolParser, ToolParserManager
 from vllm.entrypoints.openai.tool_parsers.mistral_tool_parser import (
     MistralToolCall)
 from vllm.entrypoints.utils import get_max_tokens
+from vllm.inputs.data import TokensPrompt as EngineTokensPrompt
 from vllm.logger import init_logger
 from vllm.outputs import CompletionOutput, RequestOutput
 from vllm.reasoning import ReasoningParser, ReasoningParserManager
@@ -125,6 +131,23 @@ class OpenAIServingChat(OpenAIServing):
             logger.info("Using default chat sampling params from %s: %s",
                         source, self.default_sampling_params)
 
+        self.use_harmony = model_config.hf_config.model_type == "gpt_oss"
+        if self.use_harmony:
+            if "stop_token_ids" not in self.default_sampling_params:
+                self.default_sampling_params["stop_token_ids"] = []
+            self.default_sampling_params["stop_token_ids"].extend(
+                get_stop_tokens_for_assistant_actions())
+
+        # NOTE(woosuk): 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(woosuk): Chat completion API does not support code interpreter.
+        # Please use the Responses API instead.
+        self.supports_code_interpreter = False
+        self.python_tool = None
+
     async def create_chat_completion(
         self,
         request: ChatCompletionRequest,
@@ -169,7 +192,8 @@ class OpenAIServingChat(OpenAIServing):
 
             if (request.tool_choice == "auto" and
                     not (self.enable_auto_tools and tool_parser is not None)
-                    and not isinstance(tokenizer, MistralTokenizer)):
+                    and not isinstance(tokenizer, MistralTokenizer)
+                    and not self.use_harmony):
                 # for hf tokenizers, "auto" tools requires
                 # --enable-auto-tool-choice and --tool-call-parser
                 return self.create_error_response(
@@ -184,25 +208,35 @@ class OpenAIServingChat(OpenAIServing):
             else:
                 tool_dicts = [tool.model_dump() for tool in request.tools]
 
-            (
+            if not self.use_harmony:
-                conversation,
+                # Common case.
-                request_prompts,
+                (
-                engine_prompts,
+                    conversation,
-            ) = await self._preprocess_chat(
+                    request_prompts,
-                request,
+                    engine_prompts,
-                tokenizer,
+                ) = await self._preprocess_chat(
-                request.messages,
+                    request,
-                chat_template=request.chat_template or self.chat_template,
+                    tokenizer,
-                chat_template_content_format=self.chat_template_content_format,
+                    request.messages,
-                add_generation_prompt=request.add_generation_prompt,
+                    chat_template=request.chat_template or self.chat_template,
-                continue_final_message=request.continue_final_message,
+                    chat_template_content_format=self.
-                tool_dicts=tool_dicts,
+                    chat_template_content_format,
-                documents=request.documents,
+                    add_generation_prompt=request.add_generation_prompt,
-                chat_template_kwargs=request.chat_template_kwargs,
+                    continue_final_message=request.continue_final_message,
-                tool_parser=tool_parser,
+                    tool_dicts=tool_dicts,
-                truncate_prompt_tokens=request.truncate_prompt_tokens,
+                    documents=request.documents,
-                add_special_tokens=request.add_special_tokens,
+                    chat_template_kwargs=request.chat_template_kwargs,
-            )
+                    tool_parser=tool_parser,
+                    truncate_prompt_tokens=request.truncate_prompt_tokens,
+                    add_special_tokens=request.add_special_tokens,
+                )
+            else:
+                # For GPT-OSS.
+                (
+                    conversation,
+                    request_prompts,
+                    engine_prompts,
+                ) = self._make_request_with_harmony(request)
         except (ValueError, TypeError, RuntimeError,
                 jinja2.TemplateError) as e:
             logger.exception("Error in preprocessing prompt inputs")
@@ -436,6 +470,11 @@ class OpenAIServingChat(OpenAIServing):
         finish_reason_sent = [False] * num_choices
         num_prompt_tokens = 0
         num_cached_tokens = None
+        if self.use_harmony:
+            harmony_parsers = [
+                get_streamable_parser_for_assistant()
+                for _ in range(num_choices)
+            ]
 
         if isinstance(request.tool_choice, ChatCompletionNamedToolChoiceParam):
             tool_choice_function_name = request.tool_choice.function.name
@@ -597,7 +636,18 @@ class OpenAIServingChat(OpenAIServing):
                     else:
                         logprobs = None
 
-                    delta_text = output.text
+                    if self.use_harmony:
+                        harmony_parser = harmony_parsers[i]
+                        for token_id in output.token_ids:
+                            harmony_parser.process(token_id)
+                        # FIXME(woosuk): Support function calling
+                        is_final = harmony_parser.current_channel == "final"
+                        if not (request.include_reasoning or is_final):
+                            # Skip the reasoning content.
+                            continue
+                        delta_text = harmony_parser.last_content_delta or ""
+                    else:
+                        delta_text = output.text
 
                     if not delta_text and not output.token_ids and \
                         not previous_num_tokens[i]:
@@ -607,7 +657,8 @@ class OpenAIServingChat(OpenAIServing):
                     delta_message: Optional[DeltaMessage]
 
                     # just update previous_texts and previous_token_ids
-                    if tool_choice_auto or self.reasoning_parser:
+                    if ((tool_choice_auto or self.reasoning_parser)
+                            and not self.use_harmony):
                         assert previous_texts is not None
                         assert all_previous_token_ids is not None
                         previous_text = previous_texts[i]
@@ -621,8 +672,14 @@ class OpenAIServingChat(OpenAIServing):
                         else:
                             current_token_ids = list(output.token_ids)
 
+                    if self.use_harmony:
+                        if is_final:
+                            delta_message = DeltaMessage(content=delta_text)
+                        else:
+                            delta_message = DeltaMessage(
+                                reasoning_content=delta_text)
                     # handle streaming deltas for tools with named tool_choice
-                    if tool_choice_function_name:
+                    elif tool_choice_function_name:
                         if (self.reasoning_parser and not reasoning_end_arr[i]
                                 and not reasoning_parser.is_reasoning_end(
                                     previous_token_ids)):
@@ -990,7 +1047,38 @@ class OpenAIServingChat(OpenAIServing):
                 )
             else:
                 logprobs = None
-            auto_tools_called = False
+
+            if self.use_harmony:
+                reasoning_content, final_content, is_tool_call = (
+                    parse_chat_output(token_ids))
+                if not request.include_reasoning:
+                    reasoning_content = None
+
+                if is_tool_call:
+                    # TODO(woosuk): Implement tool call for gpt-oss.
+                    # For now, only Responses API supports tool call for
+                    # gpt-oss.
+                    raise NotImplementedError(
+                        "Tool call in Chat Completion API is not supported "
+                        "for gpt-oss yet. Please use Responses API instead.")
+                else:
+                    # Normal message
+                    message = ChatMessage(
+                        role=role,
+                        reasoning_content=reasoning_content,
+                        content=final_content,
+                    )
+
+                choice_data = ChatCompletionResponseChoice(
+                    index=output.index,
+                    message=message,
+                    logprobs=logprobs,
+                    finish_reason="tool_calls" if is_tool_call else
+                    output.finish_reason if output.finish_reason else "stop",
+                    stop_reason=output.stop_reason,
+                )
+                choices.append(choice_data)
+                continue
 
             if self.reasoning_parser:
                 try:
@@ -1003,10 +1091,13 @@ class OpenAIServingChat(OpenAIServing):
                 reasoning_content, content = (
                     reasoning_parser.extract_reasoning_content(
                         output.text, request=request))
+                if not request.include_reasoning:
+                    reasoning_content = None
             else:
                 reasoning_content = None
                 content = output.text
 
+            auto_tools_called = False
             # if auto tools are not enabled, and a named tool choice using
             #   outlines is not being used
             if (not self.enable_auto_tools or not self.tool_parser) and \
@@ -1261,3 +1352,33 @@ class OpenAIServingChat(OpenAIServing):
             and delta_message.tool_calls[0].function
             and delta_message.tool_calls[0].function.arguments is not None
         )
+
+    def _make_request_with_harmony(
+        self,
+        request: ChatCompletionRequest,
+    ):
+        messages: list[OpenAIMessage] = []
+
+        # 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
+        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)
+        engine_prompt = EngineTokensPrompt(prompt_token_ids=prompt_token_ids)
+        return messages, [prompt_token_ids], [engine_prompt]