[gpt-oss][2/N] Support input_messages in responsesRequest (#26962)

Signed-off-by: Andrew Xia <axia@fb.com>
Co-authored-by: Andrew Xia <axia@fb.com>

tests/entrypoints/openai/test_response_api_with_harmony.py

@@ -535,11 +535,17 @@ def get_place_to_travel():
     return "Paris"
 
 
+def get_horoscope(sign):
+    return f"{sign}: Next Tuesday you will befriend a baby otter."
+
+
 def call_function(name, args):
     if name == "get_weather":
         return get_weather(**args)
     elif name == "get_place_to_travel":
         return get_place_to_travel()
+    elif name == "get_horoscope":
+        return get_horoscope(**args)
     else:
         raise ValueError(f"Unknown function: {name}")
 
@@ -828,3 +834,126 @@ async def test_output_messages_enabled(client: OpenAI, model_name: str, server):
     assert response.status == "completed"
     assert len(response.input_messages) > 0
     assert len(response.output_messages) > 0
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+async def test_function_call_with_previous_input_messages(
+    client: OpenAI, model_name: str
+):
+    """Test function calling using previous_input_messages
+    for multi-turn conversation with a function call"""
+
+    # Define the get_horoscope tool
+    tools = [
+        {
+            "type": "function",
+            "name": "get_horoscope",
+            "description": "Get today's horoscope for an astrological sign.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "sign": {"type": "string"},
+                },
+                "required": ["sign"],
+                "additionalProperties": False,
+            },
+            "strict": True,
+        }
+    ]
+
+    # Step 1: First call with the function tool
+    stream_response = await client.responses.create(
+        model=model_name,
+        input="What is the horoscope for Aquarius today?",
+        tools=tools,
+        extra_body={"enable_response_messages": True},
+        stream=True,
+    )
+
+    response = None
+    async for event in stream_response:
+        if event.type == "response.completed":
+            response = event.response
+
+    assert response is not None
+    assert response.status == "completed"
+
+    # Step 2: Parse the first output to find the function_call type
+    function_call = None
+    for item in response.output:
+        if item.type == "function_call":
+            function_call = item
+            break
+
+    assert function_call is not None, "Expected a function_call in the output"
+    assert function_call.name == "get_horoscope"
+    assert function_call.call_id is not None
+
+    # Verify the format matches expectations
+    args = json.loads(function_call.arguments)
+    assert "sign" in args
+
+    # Step 3: Call the get_horoscope function
+    result = call_function(function_call.name, args)
+    assert "Aquarius" in result
+    assert "baby otter" in result
+
+    # Get the input_messages and output_messages from the first response
+    first_input_messages = response.input_messages
+    first_output_messages = response.output_messages
+
+    # Construct the full conversation history using previous_input_messages
+    previous_messages = (
+        first_input_messages
+        + first_output_messages
+        + [
+            {
+                "role": "tool",
+                "name": "functions.get_horoscope",
+                "content": [{"type": "text", "text": str(result)}],
+            }
+        ]
+    )
+
+    # Step 4: Make another responses.create() call with previous_input_messages
+    stream_response_2 = await client.responses.create(
+        model=model_name,
+        tools=tools,
+        input="",
+        extra_body={
+            "previous_input_messages": previous_messages,
+            "enable_response_messages": True,
+        },
+        stream=True,
+    )
+
+    async for event in stream_response_2:
+        if event.type == "response.completed":
+            response_2 = event.response
+
+    assert response_2 is not None
+    assert response_2.status == "completed"
+    assert response_2.output_text is not None
+
+    # verify only one system message / developer message
+    num_system_messages_input = 0
+    num_developer_messages_input = 0
+    num_function_call_input = 0
+    for message_dict in response_2.input_messages:
+        message = Message.from_dict(message_dict)
+        if message.author.role == "system":
+            num_system_messages_input += 1
+        elif message.author.role == "developer":
+            num_developer_messages_input += 1
+        elif message.author.role == "tool":
+            num_function_call_input += 1
+    assert num_system_messages_input == 1
+    assert num_developer_messages_input == 1
+    assert num_function_call_input == 1
+
+    # Verify the output makes sense - should contain information about the horoscope
+    output_text = response_2.output_text.lower()
+    assert (
+        "aquarius" in output_text or "otter" in output_text or "tuesday" in output_text
+    )

tests/entrypoints/openai/test_serving_responses.py

@@ -125,6 +125,28 @@ class TestInitializeToolSessions:
         # Verify that init_tool_sessions was called
         assert mock_context.init_tool_sessions_called
 
+    def test_validate_create_responses_input(
+        self, serving_responses_instance, mock_context, mock_exit_stack
+    ):
+        request = ResponsesRequest(
+            input="test input",
+            previous_input_messages=[
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "text",
+                            "text": "What is my horoscope? I am an Aquarius.",
+                        }
+                    ],
+                }
+            ],
+            previous_response_id="lol",
+        )
+        error = serving_responses_instance._validate_create_responses_input(request)
+        assert error is not None
+        assert error.error.type == "invalid_request_error"
+
 
 class TestValidateGeneratorInput:
     """Test class for _validate_generator_input method"""

tests/entrypoints/test_harmony_utils.py

@@ -0,0 +1,254 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from openai_harmony import Role
+
+from vllm.entrypoints.harmony_utils import parse_input_to_harmony_message
+
+
+class TestParseInputToHarmonyMessage:
+    """Tests for parse_input_to_harmony_message function."""
+
+    def test_assistant_message_with_tool_calls(self):
+        """Test parsing assistant message with tool calls."""
+        chat_msg = {
+            "role": "assistant",
+            "tool_calls": [
+                {
+                    "function": {
+                        "name": "get_weather",
+                        "arguments": '{"location": "San Francisco"}',
+                    }
+                },
+                {
+                    "function": {
+                        "name": "search_web",
+                        "arguments": '{"query": "latest news"}',
+                    }
+                },
+            ],
+        }
+
+        messages = parse_input_to_harmony_message(chat_msg)
+
+        assert len(messages) == 2
+
+        # First tool call
+        assert messages[0].author.role == Role.ASSISTANT
+        assert messages[0].content[0].text == '{"location": "San Francisco"}'
+        assert messages[0].channel == "commentary"
+        assert messages[0].recipient == "functions.get_weather"
+        assert messages[0].content_type == "json"
+
+        # Second tool call
+        assert messages[1].author.role == Role.ASSISTANT
+        assert messages[1].content[0].text == '{"query": "latest news"}'
+        assert messages[1].channel == "commentary"
+        assert messages[1].recipient == "functions.search_web"
+        assert messages[1].content_type == "json"
+
+    def test_assistant_message_with_empty_tool_call_arguments(self):
+        """Test parsing assistant message with tool call having None arguments."""
+        chat_msg = {
+            "role": "assistant",
+            "tool_calls": [
+                {
+                    "function": {
+                        "name": "get_current_time",
+                        "arguments": None,
+                    }
+                }
+            ],
+        }
+
+        messages = parse_input_to_harmony_message(chat_msg)
+
+        assert len(messages) == 1
+        assert messages[0].content[0].text == ""
+        assert messages[0].recipient == "functions.get_current_time"
+
+    def test_tool_message_with_string_content(self):
+        """Test parsing tool message with string content."""
+        chat_msg = {
+            "role": "tool",
+            "name": "get_weather",
+            "content": "The weather in San Francisco is sunny, 72°F",
+        }
+
+        messages = parse_input_to_harmony_message(chat_msg)
+
+        assert len(messages) == 1
+        assert messages[0].author.role == Role.TOOL
+        assert messages[0].author.name == "functions.get_weather"
+        assert (
+            messages[0].content[0].text == "The weather in San Francisco is sunny, 72°F"
+        )
+        assert messages[0].channel == "commentary"
+
+    def test_tool_message_with_array_content(self):
+        """Test parsing tool message with array content."""
+        chat_msg = {
+            "role": "tool",
+            "name": "search_results",
+            "content": [
+                {"type": "text", "text": "Result 1: "},
+                {"type": "text", "text": "Result 2: "},
+                {
+                    "type": "image",
+                    "url": "http://example.com/img.png",
+                },  # Should be ignored
+                {"type": "text", "text": "Result 3"},
+            ],
+        }
+
+        messages = parse_input_to_harmony_message(chat_msg)
+
+        assert len(messages) == 1
+        assert messages[0].author.role == Role.TOOL
+        assert messages[0].content[0].text == "Result 1: Result 2: Result 3"
+
+    def test_tool_message_with_empty_content(self):
+        """Test parsing tool message with None content."""
+        chat_msg = {
+            "role": "tool",
+            "name": "empty_tool",
+            "content": None,
+        }
+
+        messages = parse_input_to_harmony_message(chat_msg)
+
+        assert len(messages) == 1
+        assert messages[0].content[0].text == ""
+
+    def test_system_message(self):
+        """Test parsing system message."""
+        chat_msg = {
+            "role": "system",
+            "content": "You are a helpful assistant",
+        }
+
+        messages = parse_input_to_harmony_message(chat_msg)
+
+        assert len(messages) == 1
+        # System messages are converted using Message.from_dict
+        # which should preserve the role
+        assert messages[0].author.role == Role.SYSTEM
+
+    def test_developer_message(self):
+        """Test parsing developer message."""
+        chat_msg = {
+            "role": "developer",
+            "content": "Use concise language",
+        }
+
+        messages = parse_input_to_harmony_message(chat_msg)
+
+        assert len(messages) == 1
+        assert messages[0].author.role == Role.DEVELOPER
+
+    def test_user_message_with_string_content(self):
+        """Test parsing user message with string content."""
+        chat_msg = {
+            "role": "user",
+            "content": "What's the weather in San Francisco?",
+        }
+
+        messages = parse_input_to_harmony_message(chat_msg)
+
+        assert len(messages) == 1
+        assert messages[0].author.role == Role.USER
+        assert messages[0].content[0].text == "What's the weather in San Francisco?"
+
+    def test_user_message_with_array_content(self):
+        """Test parsing user message with array content."""
+        chat_msg = {
+            "role": "user",
+            "content": [
+                {"text": "What's in this image? "},
+                {"text": "Please describe it."},
+            ],
+        }
+
+        messages = parse_input_to_harmony_message(chat_msg)
+
+        assert len(messages) == 1
+        assert messages[0].author.role == Role.USER
+        assert len(messages[0].content) == 2
+        assert messages[0].content[0].text == "What's in this image? "
+        assert messages[0].content[1].text == "Please describe it."
+
+    def test_assistant_message_with_string_content(self):
+        """Test parsing assistant message with string content (no tool calls)."""
+        chat_msg = {
+            "role": "assistant",
+            "content": "Hello! How can I help you today?",
+        }
+
+        messages = parse_input_to_harmony_message(chat_msg)
+
+        assert len(messages) == 1
+        assert messages[0].author.role == Role.ASSISTANT
+        assert messages[0].content[0].text == "Hello! How can I help you today?"
+
+    def test_pydantic_model_input(self):
+        """Test parsing Pydantic model input (has model_dump method)."""
+
+        class MockPydanticModel:
+            def model_dump(self, exclude_none=True):
+                return {
+                    "role": "user",
+                    "content": "Test message",
+                }
+
+        chat_msg = MockPydanticModel()
+        messages = parse_input_to_harmony_message(chat_msg)
+
+        assert len(messages) == 1
+        assert messages[0].author.role == Role.USER
+        assert messages[0].content[0].text == "Test message"
+
+    def test_message_with_empty_content(self):
+        """Test parsing message with empty string content."""
+        chat_msg = {
+            "role": "user",
+            "content": "",
+        }
+
+        messages = parse_input_to_harmony_message(chat_msg)
+
+        assert len(messages) == 1
+        assert messages[0].content[0].text == ""
+
+    def test_tool_call_with_missing_function_fields(self):
+        """Test parsing tool call with missing name or arguments."""
+        chat_msg = {
+            "role": "assistant",
+            "tool_calls": [
+                {
+                    "function": {}  # Missing both name and arguments
+                }
+            ],
+        }
+
+        messages = parse_input_to_harmony_message(chat_msg)
+
+        assert len(messages) == 1
+        assert messages[0].recipient == "functions."
+        assert messages[0].content[0].text == ""
+
+    def test_array_content_with_missing_text(self):
+        """Test parsing array content where text field is missing."""
+        chat_msg = {
+            "role": "user",
+            "content": [
+                {},  # Missing text field
+                {"text": "actual text"},
+            ],
+        }
+
+        messages = parse_input_to_harmony_message(chat_msg)
+
+        assert len(messages) == 1
+        assert len(messages[0].content) == 2
+        assert messages[0].content[0].text == ""
+        assert messages[0].content[1].text == "actual text"

vllm/entrypoints/harmony_utils.py

@@ -38,11 +38,14 @@ from openai_harmony import (
     ToolDescription,
     load_harmony_encoding,
 )
+from openai_harmony import Message as OpenAIHarmonyMessage
+from openai_harmony import Role as OpenAIHarmonyRole
 
 from vllm import envs
 from vllm.entrypoints.openai.protocol import (
     ChatCompletionToolsParam,
     ResponseInputOutputItem,
+    ResponsesRequest,
 )
 from vllm.utils import random_uuid
 
@@ -228,7 +231,7 @@ def parse_response_input(
     return msg
 
 
-def parse_chat_input(chat_msg) -> list[Message]:
+def parse_input_to_harmony_message(chat_msg) -> list[Message]:
     if not isinstance(chat_msg, dict):
         # Handle Pydantic models
         chat_msg = chat_msg.model_dump(exclude_none=True)
@@ -279,6 +282,40 @@ def parse_chat_input(chat_msg) -> list[Message]:
     return [msg]
 
 
+def construct_harmony_previous_input_messages(
+    request: ResponsesRequest,
+) -> list[OpenAIHarmonyMessage]:
+    messages: list[OpenAIHarmonyMessage] = []
+    if request.previous_input_messages:
+        for message in request.previous_input_messages:
+            # Handle both OpenAIHarmonyMessage objects and dictionary inputs
+            if isinstance(message, OpenAIHarmonyMessage):
+                message_role = message.author.role
+                # To match OpenAI, instructions, reasoning and tools are
+                # always taken from the most recent Responses API request
+                # not carried over from previous requests
+                if (
+                    message_role == OpenAIHarmonyRole.SYSTEM
+                    or message_role == OpenAIHarmonyRole.DEVELOPER
+                ):
+                    continue
+                messages.append(message)
+            else:
+                harmony_messages = parse_input_to_harmony_message(message)
+                for harmony_msg in harmony_messages:
+                    message_role = harmony_msg.author.role
+                    # To match OpenAI, instructions, reasoning and tools are
+                    # always taken from the most recent Responses API request
+                    # not carried over from previous requests
+                    if (
+                        message_role == OpenAIHarmonyRole.SYSTEM
+                        or message_role == OpenAIHarmonyRole.DEVELOPER
+                    ):
+                        continue
+                    messages.append(harmony_msg)
+    return messages
+
+
 def render_for_completion(messages: list[Message]) -> list[int]:
     conversation = Conversation.from_messages(messages)
     token_ids = get_encoding().render_conversation_for_completion(

vllm/entrypoints/openai/protocol.py

@@ -47,6 +47,7 @@ from openai.types.responses import (
 from openai.types.responses.response_reasoning_item import (
     Content as ResponseReasoningTextContent,
 )
+from openai_harmony import Message as OpenAIHarmonyMessage
 
 from vllm.utils.serial_utils import (
     EmbedDType,
@@ -383,10 +384,15 @@ class ResponsesRequest(OpenAIBaseModel):
         default=False,
         description=(
             "Dictates whether or not to return messages as part of the "
-            "response object. Currently only supported for non-streaming "
+            "response object. Currently only supported for"
             "non-background and gpt-oss only. "
         ),
     )
+    # similar to input_messages / output_messages in ResponsesResponse
+    # we take in previous_input_messages (ie in harmony format)
+    # this cannot be used in conjunction with previous_response_id
+    # TODO: consider supporting non harmony messages as well
+    previous_input_messages: list[OpenAIHarmonyMessage | dict] | None = None
     # --8<-- [end:responses-extra-params]
 
     _DEFAULT_SAMPLING_PARAMS = {

vllm/entrypoints/openai/serving_chat.py

@@ -27,8 +27,8 @@ from vllm.entrypoints.harmony_utils import (
     get_stop_tokens_for_assistant_actions,
     get_streamable_parser_for_assistant,
     get_system_message,
-    parse_chat_input,
     parse_chat_output,
+    parse_input_to_harmony_message,
     render_for_completion,
 )
 from vllm.entrypoints.logger import RequestLogger
@@ -1351,11 +1351,13 @@ class OpenAIServingChat(OpenAIServing):
                     index=output.index,
                     message=message,
                     logprobs=logprobs,
-                    finish_reason="tool_calls"
-                    if (tool_call_info is not None and tool_call_info.tools_called)
-                    else output.finish_reason
-                    if output.finish_reason
-                    else "stop",
+                    finish_reason=(
+                        "tool_calls"
+                        if (tool_call_info is not None and tool_call_info.tools_called)
+                        else output.finish_reason
+                        if output.finish_reason
+                        else "stop"
+                    ),
                     stop_reason=output.stop_reason,
                 )
                 choices.append(choice_data)
@@ -1522,11 +1524,13 @@ class OpenAIServingChat(OpenAIServing):
                 index=output.index,
                 message=message,
                 logprobs=logprobs,
-                finish_reason="tool_calls"
-                if auto_tools_called
-                else output.finish_reason
-                if output.finish_reason
-                else "stop",
+                finish_reason=(
+                    "tool_calls"
+                    if auto_tools_called
+                    else output.finish_reason
+                    if output.finish_reason
+                    else "stop"
+                ),
                 stop_reason=output.stop_reason,
                 token_ids=(
                     as_list(output.token_ids) if request.return_token_ids else None
@@ -1685,9 +1689,11 @@ class OpenAIServingChat(OpenAIServing):
                             should_return_as_token_id,
                         ),
                         logprob=max(step_token.logprob, -9999.0),
-                        bytes=None
-                        if step_decoded is None
-                        else list(step_decoded.encode("utf-8", errors="replace")),
+                        bytes=(
+                            None
+                            if step_decoded is None
+                            else list(step_decoded.encode("utf-8", errors="replace"))
+                        ),
                         top_logprobs=self._get_top_logprobs(
                             step_top_logprobs,
                             num_output_top_logprobs,
@@ -1764,7 +1770,7 @@ class OpenAIServingChat(OpenAIServing):
 
         # Add user message.
         for chat_msg in request.messages:
-            messages.extend(parse_chat_input(chat_msg))
+            messages.extend(parse_input_to_harmony_message(chat_msg))
 
         # Render prompt token ids.
         prompt_token_ids = render_for_completion(messages)

vllm/entrypoints/openai/serving_responses.py

@@ -63,6 +63,7 @@ from vllm.entrypoints.context import (
     StreamingHarmonyContext,
 )
 from vllm.entrypoints.harmony_utils import (
+    construct_harmony_previous_input_messages,
     get_developer_message,
     get_stop_tokens_for_assistant_actions,
     get_system_message,
@@ -248,6 +249,13 @@ class OpenAIServingResponses(OpenAIServing):
                 ),
                 status_code=HTTPStatus.BAD_REQUEST,
             )
+        if request.previous_input_messages and request.previous_response_id:
+            return self.create_error_response(
+                err_type="invalid_request_error",
+                message="Only one of `previous_input_messages` and "
+                "`previous_response_id` can be set.",
+                status_code=HTTPStatus.BAD_REQUEST,
+            )
         return None
 
     async def create_responses(
@@ -941,6 +949,8 @@ class OpenAIServingResponses(OpenAIServing):
                     instructions=request.instructions, tools=request.tools
                 )
                 messages.append(dev_msg)
+            messages += construct_harmony_previous_input_messages(request)
+
         else:
             # Continue the previous conversation.
             # FIXME(woosuk): Currently, request params like reasoning and