chore: import upstream snapshot with attribution

python/sglang/srt/entrypoints/harmony_utils.py

@@ -0,0 +1,389 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Adapted from vLLM: https://github.com/vllm-project/vllm/blob/1b9902806915040ac9b3029f2ab7522ec505afc3/vllm/entrypoints/harmony_utils.py
+# Slight differences in processing chat messages
+import datetime
+import logging
+from collections.abc import Iterable
+from typing import Literal, Optional, Union
+
+import orjson
+from openai.types.responses import (
+    ResponseOutputItem,
+    ResponseOutputMessage,
+    ResponseOutputText,
+    ResponseReasoningItem,
+)
+from openai.types.responses.response_function_tool_call import ResponseFunctionToolCall
+from openai.types.responses.response_function_web_search import (
+    ActionFind,
+    ActionOpenPage,
+    ActionSearch,
+    ResponseFunctionWebSearch,
+)
+from openai.types.responses.response_reasoning_item import (
+    Content as ResponseReasoningTextContent,
+)
+from openai.types.responses.tool import Tool
+from openai_harmony import (
+    Author,
+    Conversation,
+    DeveloperContent,
+    HarmonyEncodingName,
+    Message,
+    ReasoningEffort,
+    Role,
+    StreamableParser,
+    SystemContent,
+    TextContent,
+    ToolDescription,
+    load_harmony_encoding,
+)
+
+from sglang.srt.entrypoints.openai.protocol import ResponseInputOutputItem
+from sglang.srt.utils import random_uuid
+
+logger = logging.getLogger(__name__)
+
+REASONING_EFFORT = {
+    "high": ReasoningEffort.HIGH,
+    "medium": ReasoningEffort.MEDIUM,
+    "low": ReasoningEffort.LOW,
+}
+
+_harmony_encoding = None
+
+
+def get_encoding():
+    global _harmony_encoding
+    if _harmony_encoding is None:
+        _harmony_encoding = load_harmony_encoding(HarmonyEncodingName.HARMONY_GPT_OSS)
+    return _harmony_encoding
+
+
+def get_system_message(
+    model_identity: Optional[str] = None,
+    reasoning_effort: Optional[Literal["high", "medium", "low"]] = None,
+    start_date: Optional[str] = None,
+    browser_description: Optional[str] = None,
+    python_description: Optional[str] = None,
+) -> Message:
+    sys_msg_content = SystemContent.new()
+    if model_identity is not None:
+        sys_msg_content = sys_msg_content.with_model_identity(model_identity)
+    if reasoning_effort is not None:
+        sys_msg_content = sys_msg_content.with_reasoning_effort(
+            REASONING_EFFORT[reasoning_effort]
+        )
+    if start_date is None:
+        start_date = datetime.datetime.now().strftime("%Y-%m-%d")
+    sys_msg_content = sys_msg_content.with_conversation_start_date(start_date)
+    if browser_description is not None:
+        sys_msg_content = sys_msg_content.with_tools(browser_description)
+    if python_description is not None:
+        sys_msg_content = sys_msg_content.with_tools(python_description)
+    sys_msg = Message.from_role_and_content(Role.SYSTEM, sys_msg_content)
+    return sys_msg
+
+
+def get_developer_message(
+    instructions: Optional[str] = None, tools: Optional[list[Tool]] = None
+) -> Message:
+    dev_msg_content = DeveloperContent.new()
+    if instructions is not None:
+        dev_msg_content = dev_msg_content.with_instructions(instructions)
+    if tools is not None:
+        function_tools = []
+        for tool in tools:
+            if tool.type in (
+                "web_search",
+                "web_search_preview",
+                "code_interpreter",
+            ):
+                # These are built-in tools that are added to the system message.
+                pass
+            elif tool.type == "function":
+                function_tools.append(tool)
+            else:
+                # No harmony prompt template for the remaining built-ins;
+                # drop them so the request still runs.
+                logger.debug(
+                    "harmony: ignoring unsupported response tool type %r",
+                    tool.type,
+                )
+        if function_tools:
+            function_tool_descriptions = [
+                ToolDescription.new(
+                    name=tool.name,
+                    description=tool.description,
+                    parameters=tool.parameters,
+                )
+                for tool in function_tools
+            ]
+            dev_msg_content = dev_msg_content.with_function_tools(
+                function_tool_descriptions
+            )
+    dev_msg = Message.from_role_and_content(Role.DEVELOPER, dev_msg_content)
+    return dev_msg
+
+
+def get_user_message(content: str) -> Message:
+    return Message.from_role_and_content(Role.USER, content)
+
+
+def parse_response_input(
+    response_msg: ResponseInputOutputItem,
+    prev_responses: list[Union[ResponseOutputItem, ResponseReasoningItem]],
+) -> Message:
+    if not isinstance(response_msg, dict):
+        response_msg = response_msg.model_dump()
+    if "type" not in response_msg or response_msg["type"] == "message":
+        role = response_msg["role"]
+        content = response_msg["content"]
+        if role == "system":
+            # User is trying to set a system message. Change it to:
+            # <|start|>developer<|message|># Instructions
+            # {instructions}<|end|>
+            role = "developer"
+            text_prefix = "Instructions:\n"
+        else:
+            text_prefix = ""
+        if isinstance(content, str):
+            msg = Message.from_role_and_content(role, text_prefix + content)
+        else:
+            # Filter to text parts first, then enumerate, so the surviving first
+            # text chunk always carries the system→developer text_prefix even if
+            # earlier parts were non-text (image/audio) and got dropped.
+            text_chunks = [
+                c for c in content if c.get("type") in ("text", "input_text")
+            ]
+            contents = [
+                TextContent(text=(text_prefix if i == 0 else "") + c.get("text", ""))
+                for i, c in enumerate(text_chunks)
+            ]
+            msg = Message.from_role_and_contents(role, contents)
+    elif response_msg["type"] == "function_call_output":
+        call_id = response_msg["call_id"]
+        call_response: Optional[ResponseFunctionToolCall] = None
+        for prev_response in reversed(prev_responses):
+            if (
+                isinstance(prev_response, ResponseFunctionToolCall)
+                and prev_response.call_id == call_id
+            ):
+                call_response = prev_response
+                break
+        if call_response is None:
+            raise ValueError(f"No call message found for {call_id}")
+        msg = Message.from_author_and_content(
+            Author.new(Role.TOOL, f"functions.{call_response.name}"),
+            response_msg["output"],
+        )
+    elif response_msg["type"] == "reasoning":
+        content = response_msg["content"]
+        assert len(content) == 1
+        msg = Message.from_role_and_content(Role.ASSISTANT, content[0]["text"])
+    elif response_msg["type"] == "function_call":
+        msg = Message.from_role_and_content(Role.ASSISTANT, response_msg["arguments"])
+        msg = msg.with_channel("commentary")
+        msg = msg.with_recipient(f"functions.{response_msg['name']}")
+        msg = msg.with_content_type("json")
+    else:
+        raise ValueError(f"Unknown input type: {response_msg['type']}")
+    return msg
+
+
+def parse_response_output(output: ResponseOutputItem) -> Message:
+    if isinstance(output, ResponseOutputMessage):
+        role = output.role
+        contents = [TextContent(text=c.text) for c in output.content]
+        msg = Message.from_role_and_contents(role, contents)
+        return msg
+    elif isinstance(output, ResponseFunctionToolCall):
+        msg = Message.from_role_and_content(Role.ASSISTANT, output.arguments)
+        msg = msg.with_channel("commentary")
+        msg = msg.with_recipient(output.name)
+        msg = msg.with_content_type("json")
+        return msg
+    else:
+        raise ValueError(f"Unknown output type: {type(output)}")
+
+
+def parse_chat_input(chat_msg) -> Message:
+    role = chat_msg.role
+    content = chat_msg.content
+    if isinstance(content, str):
+        contents = [TextContent(text=content)]
+    else:
+        # TODO: Support refusal.
+        contents = [TextContent(text=c.text) for c in content]
+    msg = Message.from_role_and_contents(role, contents)
+    return msg
+
+
+def render_for_completion(messages: list[Message]) -> list[int]:
+    conversation = Conversation.from_messages(messages)
+    token_ids = get_encoding().render_conversation_for_completion(
+        conversation, Role.ASSISTANT
+    )
+    return token_ids
+
+
+def get_stop_tokens_for_assistant_actions() -> list[int]:
+    return get_encoding().stop_tokens_for_assistant_actions()
+
+
+def get_streamable_parser_for_assistant() -> StreamableParser:
+    return StreamableParser(get_encoding(), role=Role.ASSISTANT)
+
+
+def parse_output_message(message: Message):
+    if message.author.role != "assistant":
+        # This is a message from a tool to the assistant (e.g., search result).
+        # Don't include it in the final output for now. This aligns with
+        # OpenAI's behavior on models like o4-mini.
+        return []
+
+    output_items = []
+    recipient = message.recipient
+    if recipient is not None and recipient.startswith("browser."):
+        if len(message.content) != 1:
+            raise ValueError("Invalid number of contents in browser message")
+        content = message.content[0]
+        browser_call = orjson.loads(content.text)
+        # TODO: translate to url properly!
+        if recipient == "browser.search":
+            action = ActionSearch(
+                query=f"cursor:{browser_call.get('query', '')}", type="search"
+            )
+        elif recipient == "browser.open":
+            action = ActionOpenPage(
+                url=f"cursor:{browser_call.get('url', '')}", type="open_page"
+            )
+        elif recipient == "browser.find":
+            action = ActionFind(
+                pattern=browser_call["pattern"],
+                url=f"cursor:{browser_call.get('url', '')}",
+                type="find",
+            )
+        else:
+            raise ValueError(f"Unknown browser action: {recipient}")
+        web_search_item = ResponseFunctionWebSearch(
+            id=f"ws_{random_uuid()}",
+            action=action,
+            status="completed",
+            type="web_search_call",
+        )
+        output_items.append(web_search_item)
+    elif message.channel == "analysis":
+        for content in message.content:
+            reasoning_item = ResponseReasoningItem(
+                id=f"rs_{random_uuid()}",
+                type="reasoning",
+                summary=[],
+                content=[
+                    ResponseReasoningTextContent(
+                        text=content.text, type="reasoning_text"
+                    )
+                ],
+                status=None,
+            )
+            output_items.append(reasoning_item)
+    elif message.channel == "commentary":
+        if message.recipient.startswith("functions."):
+            function_name = message.recipient.split(".")[-1]
+            for content in message.content:
+                random_id = random_uuid()
+                response_item = ResponseFunctionToolCall(
+                    arguments=content.text,
+                    call_id=f"call_{random_id}",
+                    type="function_call",
+                    name=function_name,
+                    id=f"ft_{random_id}",
+                )
+                output_items.append(response_item)
+        elif message.recipient.startswith("python") or message.recipient.startswith(
+            "browser"
+        ):
+            for content in message.content:
+                reasoning_item = ResponseReasoningItem(
+                    id=f"rs_{random_uuid()}",
+                    type="reasoning",
+                    summary=[],
+                    content=[
+                        ResponseReasoningTextContent(
+                            text=content.text, type="reasoning_text"
+                        )
+                    ],
+                    status=None,
+                )
+                output_items.append(reasoning_item)
+        else:
+            raise ValueError(f"Unknown recipient: {message.recipient}")
+    elif message.channel == "final":
+        contents = []
+        for content in message.content:
+            output_text = ResponseOutputText(
+                text=content.text,
+                annotations=[],  # TODO
+                type="output_text",
+                logprobs=None,  # TODO
+            )
+            contents.append(output_text)
+        text_item = ResponseOutputMessage(
+            id=f"msg_{random_uuid()}",
+            content=contents,
+            role=message.author.role,
+            status="completed",
+            type="message",
+        )
+        output_items.append(text_item)
+    else:
+        raise ValueError(f"Unknown channel: {message.channel}")
+    return output_items
+
+
+def parse_remaining_state(parser: StreamableParser):
+    if not parser.current_content:
+        return []
+    if parser.current_role != Role.ASSISTANT:
+        return []
+    current_recipient = parser.current_recipient
+    if current_recipient is not None and current_recipient.startswith("browser."):
+        return []
+
+    if parser.current_channel == "analysis":
+        reasoning_item = ResponseReasoningItem(
+            id=f"rs_{random_uuid()}",
+            type="reasoning",
+            summary=[],
+            content=[
+                ResponseReasoningTextContent(
+                    text=parser.current_content, type="reasoning_text"
+                )
+            ],
+            status=None,
+        )
+        return [reasoning_item]
+    elif parser.current_channel == "final":
+        output_text = ResponseOutputText(
+            text=parser.current_content,
+            annotations=[],  # TODO
+            type="output_text",
+            logprobs=None,  # TODO
+        )
+        text_item = ResponseOutputMessage(
+            id=f"msg_{random_uuid()}",
+            content=[output_text],
+            role="assistant",
+            status="completed",
+            type="message",
+        )
+        return [text_item]
+    return []
+
+
+def parse_output_into_messages(token_ids: Iterable[int]):
+    parser = get_streamable_parser_for_assistant()
+    for token_id in token_ids:
+        parser.process(token_id)
+    return parser