[Frontend] add FunctionGemma tool parser support (#31218)

Signed-off-by: gateremark <gateremg@gmail.com>

docs/features/tool_calling.md

@@ -372,6 +372,30 @@ Supported models:
 
 Flags: `--tool-call-parser glm47`
 
+### FunctionGemma Models (`functiongemma`)
+
+Google's FunctionGemma is a lightweight (270M parameter) model specifically designed for function calling.
+It's built on Gemma 3 and optimized for edge deployment on devices like laptops and phones.
+
+Supported models:
+
+* `google/functiongemma-270m-it`
+
+FunctionGemma uses a unique output format with `<start_function_call>` and `<end_function_call>` tags:
+
+```text
+<start_function_call>call:get_weather{location:<escape>London<escape>}<end_function_call>
+```
+
+The model is designed to be fine-tuned for specific function-calling tasks for best results.
+
+Flags: `--tool-call-parser functiongemma --chat-template examples/tool_chat_template_functiongemma.jinja`
+
+!!! note
+    FunctionGemma is intended to be fine-tuned for your specific function-calling task.
+    The base model provides general function calling capabilities, but best results
+    are achieved with task-specific fine-tuning. See Google's [FunctionGemma documentation](https://ai.google.dev/gemma/docs/functiongemma) for fine-tuning guides.
+
 ### Qwen3-Coder Models (`qwen3_xml`)
 
 Supported models:

examples/tool_chat_template_functiongemma.jinja

+{%- set ns = namespace(developer_content='', has_tools=false) -%}
+
+{%- if tools is defined and tools | length > 0 -%}
+    {%- set ns.has_tools = true -%}
+{%- endif -%}
+
+{%- for message in messages -%}
+    {%- if message.role == 'developer' or message.role == 'system' -%}
+<start_of_turn>user
+{{ message.content }}
+{%- if ns.has_tools %}
+
+Available functions:
+{%- for tool in tools %}
+{%- if tool.type == 'function' %}
+
+Function: {{ tool.function.name }}
+Description: {{ tool.function.description | default('No description provided') }}
+Parameters: {{ tool.function.parameters | tojson }}
+{%- endif %}
+{%- endfor %}
+{%- endif %}
+<end_of_turn>
+    {%- elif message.role == 'user' -%}
+<start_of_turn>user
+{{ message.content }}<end_of_turn>
+    {%- elif message.role == 'assistant' -%}
+        {%- if message.tool_calls is defined and message.tool_calls | length > 0 -%}
+<start_of_turn>model
+{%- for tool_call in message.tool_calls %}
+<start_function_call>call:{{ tool_call.function.name }}{
+{%- set args = tool_call.function.arguments -%}
+{%- if args is string -%}
+{%- set args = args | fromjson -%}
+{%- endif -%}
+{%- for key, value in args.items() -%}
+{{ key }}:<escape>{{ value }}<escape>{% if not loop.last %},{% endif %}
+{%- endfor -%}
+}<end_function_call>
+{%- endfor %}
+<end_of_turn>
+        {%- else -%}
+<start_of_turn>model
+{{ message.content }}<end_of_turn>
+        {%- endif -%}
+    {%- elif message.role == 'tool' -%}
+<start_of_turn>user
+Function result for {{ message.name | default('function') }}: {{ message.content }}<end_of_turn>
+    {%- endif -%}
+{%- endfor -%}
+
+{%- if add_generation_prompt -%}
+<start_of_turn>model
+{%- endif -%}

tests/tool_parsers/test_functiongemma_tool_parser.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from unittest.mock import MagicMock
+
+import pytest
+
+from vllm.entrypoints.openai.protocol import ChatCompletionRequest
+from vllm.tool_parsers.functiongemma_tool_parser import FunctionGemmaToolParser
+
+
+@pytest.fixture
+def mock_tokenizer():
+    tokenizer = MagicMock()
+    tokenizer.encode.return_value = [1, 2, 3]
+    tokenizer.get_vocab.return_value = {}
+    return tokenizer
+
+
+@pytest.fixture
+def parser(mock_tokenizer):
+    return FunctionGemmaToolParser(mock_tokenizer)
+
+
+@pytest.fixture
+def mock_request():
+    request = MagicMock(spec=ChatCompletionRequest)
+    request.tools = []
+    request.tool_choice = "auto"
+    return request
+
+
+class TestExtractToolCalls:
+    def test_no_tool_calls(self, parser, mock_request):
+        model_output = "Hello, how can I help you today?"
+        result = parser.extract_tool_calls(model_output, mock_request)
+
+        assert result.tools_called is False
+        assert result.tool_calls == []
+        assert result.content == model_output
+
+    def test_single_tool_call(self, parser, mock_request):
+        model_output = (
+            "<start_function_call>call:get_weather{location:<escape>London<escape>}"
+            "<end_function_call>"
+        )
+        result = parser.extract_tool_calls(model_output, mock_request)
+
+        assert result.tools_called is True
+        assert len(result.tool_calls) == 1
+        assert result.tool_calls[0].function.name == "get_weather"
+        assert '"location": "London"' in result.tool_calls[0].function.arguments
+
+    def test_multiple_arguments(self, parser, mock_request):
+        model_output = (
+            "<start_function_call>call:get_weather{"
+            "location:<escape>San Francisco<escape>,"
+            "unit:<escape>celsius<escape>}"
+            "<end_function_call>"
+        )
+        result = parser.extract_tool_calls(model_output, mock_request)
+
+        assert result.tools_called is True
+        assert len(result.tool_calls) == 1
+        assert result.tool_calls[0].function.name == "get_weather"
+        args = result.tool_calls[0].function.arguments
+        assert "San Francisco" in args
+        assert "celsius" in args
+
+    def test_text_before_tool_call(self, parser, mock_request):
+        model_output = (
+            "Let me check the weather for you. "
+            "<start_function_call>call:get_weather{location:<escape>Paris<escape>}"
+            "<end_function_call>"
+        )
+        result = parser.extract_tool_calls(model_output, mock_request)
+
+        assert result.tools_called is True
+        assert result.content == "Let me check the weather for you."
+
+    def test_multiple_tool_calls(self, parser, mock_request):
+        model_output = (
+            "<start_function_call>call:get_weather{location:<escape>London<escape>}"
+            "<end_function_call>"
+            "<start_function_call>call:get_time{timezone:<escape>UTC<escape>}"
+            "<end_function_call>"
+        )
+        result = parser.extract_tool_calls(model_output, mock_request)
+
+        assert result.tools_called is True
+        assert len(result.tool_calls) == 2
+        assert result.tool_calls[0].function.name == "get_weather"
+        assert result.tool_calls[1].function.name == "get_time"
+
+
+class TestParseArguments:
+    def test_empty_arguments(self, parser):
+        result = parser._parse_arguments("")
+        assert result == {}
+
+    def test_single_string_argument(self, parser):
+        result = parser._parse_arguments("city:<escape>Tokyo<escape>")
+        assert result == {"city": "Tokyo"}
+
+    def test_multiple_arguments(self, parser):
+        args_str = "city:<escape>Tokyo<escape>,country:<escape>Japan<escape>"
+        result = parser._parse_arguments(args_str)
+        assert result == {"city": "Tokyo", "country": "Japan"}
+
+    def test_numeric_argument(self, parser):
+        result = parser._parse_arguments("count:<escape>42<escape>")
+        assert result == {"count": 42}
+
+    def test_boolean_argument(self, parser):
+        result = parser._parse_arguments("enabled:<escape>true<escape>")
+        assert result == {"enabled": True}
+
+    def test_argument_with_spaces(self, parser):
+        result = parser._parse_arguments("message:<escape>Hello World<escape>")
+        assert result == {"message": "Hello World"}
+
+
+class TestAdjustRequest:
+    def test_skip_special_tokens_disabled(self, parser, mock_request):
+        mock_request.tools = [{"type": "function", "function": {"name": "test"}}]
+        mock_request.tool_choice = "auto"
+        mock_request.skip_special_tokens = True
+
+        result = parser.adjust_request(mock_request)
+        assert result.skip_special_tokens is False
+
+    def test_skip_special_tokens_when_tool_choice_none(self, parser, mock_request):
+        mock_request.tools = [{"type": "function", "function": {"name": "test"}}]
+        mock_request.tool_choice = "none"
+        mock_request.skip_special_tokens = True
+
+        result = parser.adjust_request(mock_request)
+        assert result.skip_special_tokens is True
+
+
+class TestBufferDeltaText:
+    def test_regular_text_not_buffered(self, parser):
+        result = parser._buffer_delta_text("hello")
+        assert result == "hello"
+        assert parser.buffered_delta_text == ""
+
+    def test_complete_tag_flushed(self, parser):
+        parser.buffered_delta_text = "<start_function_"
+        result = parser._buffer_delta_text("call>")
+        assert "<start_function_call>" in result
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])

vllm/tool_parsers/__init__.py

@@ -142,6 +142,10 @@ _TOOL_PARSERS_TO_REGISTER = {
         "gigachat3_tool_parser",
         "GigaChat3ToolParser",
     ),
+    "functiongemma": (
+        "functiongemma_tool_parser",
+        "FunctionGemmaToolParser",
+    ),
 }
 
 

vllm/tool_parsers/functiongemma_tool_parser.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import json
+from collections.abc import Sequence
+
+import regex as re
+
+from vllm.entrypoints.chat_utils import make_tool_call_id
+from vllm.entrypoints.openai.protocol import (
+    ChatCompletionRequest,
+    DeltaFunctionCall,
+    DeltaMessage,
+    DeltaToolCall,
+    ExtractedToolCallInformation,
+    FunctionCall,
+    ToolCall,
+)
+from vllm.logger import init_logger
+from vllm.tokenizers import TokenizerLike
+from vllm.tool_parsers.abstract_tool_parser import ToolParser
+
+logger = init_logger(__name__)
+
+
+class FunctionGemmaToolParser(ToolParser):
+    """
+    Tool parser for Google's FunctionGemma model (google/functiongemma-270m-it).
+
+    Handles the FunctionGemma function call format:
+    <start_function_call>call:func_name{param:<escape>value<escape>}<end_function_call>
+    """
+
+    def __init__(self, tokenizer: TokenizerLike):
+        super().__init__(tokenizer)
+
+        # Streaming state
+        self.current_tool_name_sent: bool = False
+        self.prev_tool_call_arr: list[dict] = []
+        self.current_tool_id: int = -1
+        self.streamed_args_for_tool: list[str] = []
+
+        # FunctionGemma tokens
+        self.tool_call_start_token: str = "<start_function_call>"
+        self.tool_call_end_token: str = "<end_function_call>"
+
+        # Regex patterns
+        self.tool_call_regex = re.compile(
+            r"<start_function_call>call:(\w+)\{(.*?)\}<end_function_call>"
+            r"|<start_function_call>call:(\w+)\{(.*)",
+            re.DOTALL,
+        )
+        self.arg_regex = re.compile(
+            r"(\w+):<escape>(.*?)<escape>",
+            re.DOTALL,
+        )
+
+        if self.model_tokenizer:
+            self.tool_call_start_token_ids = self.model_tokenizer.encode(
+                self.tool_call_start_token, add_special_tokens=False
+            )
+            self.tool_call_end_token_ids = self.model_tokenizer.encode(
+                self.tool_call_end_token, add_special_tokens=False
+            )
+        else:
+            self.tool_call_start_token_ids = []
+            self.tool_call_end_token_ids = []
+
+        self.buffered_delta_text = ""
+
+    def _parse_arguments(self, args_str: str) -> dict:
+        """Parse FunctionGemma argument string into a dictionary."""
+        arguments = {}
+        if not args_str:
+            return arguments
+
+        matches = self.arg_regex.findall(args_str)
+        for key, value in matches:
+            try:
+                parsed_value = json.loads(value)
+                arguments[key] = parsed_value
+            except json.JSONDecodeError:
+                arguments[key] = value
+
+        return arguments
+
+    def adjust_request(self, request: ChatCompletionRequest) -> ChatCompletionRequest:
+        request = super().adjust_request(request)
+        if request.tools and request.tool_choice != "none":
+            request.skip_special_tokens = False
+        return request
+
+    def extract_tool_calls(
+        self,
+        model_output: str,
+        request: ChatCompletionRequest,
+    ) -> ExtractedToolCallInformation:
+        if self.tool_call_start_token not in model_output:
+            return ExtractedToolCallInformation(
+                tools_called=False, tool_calls=[], content=model_output
+            )
+
+        try:
+            matches = self.tool_call_regex.findall(model_output)
+
+            if not matches:
+                return ExtractedToolCallInformation(
+                    tools_called=False, tool_calls=[], content=model_output
+                )
+
+            tool_calls: list[ToolCall] = []
+
+            for match in matches:
+                func_name = match[0] if match[0] else match[2]
+                args_str = match[1] if match[1] else match[3]
+
+                if not func_name:
+                    continue
+
+                arguments = self._parse_arguments(args_str)
+
+                tool_calls.append(
+                    ToolCall(
+                        type="function",
+                        function=FunctionCall(
+                            name=func_name,
+                            arguments=json.dumps(arguments, ensure_ascii=False),
+                        ),
+                    )
+                )
+
+            if tool_calls:
+                content_end = model_output.find(self.tool_call_start_token)
+                content = (
+                    model_output[:content_end].strip() if content_end > 0 else None
+                )
+
+                return ExtractedToolCallInformation(
+                    tools_called=True,
+                    tool_calls=tool_calls,
+                    content=content if content else None,
+                )
+
+            return ExtractedToolCallInformation(
+                tools_called=False, tool_calls=[], content=model_output
+            )
+
+        except Exception:
+            logger.exception("Error extracting tool calls from FunctionGemma response")
+            return ExtractedToolCallInformation(
+                tools_called=False, tool_calls=[], content=model_output
+            )
+
+    def _buffer_delta_text(self, delta_text: str) -> str:
+        """Buffer incoming delta text to handle multi-token special sequences."""
+        potential_start = "<start_function_call>"
+        potential_end = "<end_function_call>"
+
+        combined = self.buffered_delta_text + delta_text
+
+        if combined.endswith(potential_start) or combined.endswith(potential_end):
+            self.buffered_delta_text = ""
+            return combined
+
+        for tag in [potential_start, potential_end]:
+            for i in range(1, len(tag)):
+                if combined.endswith(tag[:i]):
+                    self.buffered_delta_text = combined[-(i):]
+                    return combined[:-i]
+
+        self.buffered_delta_text = ""
+        return combined
+
+    def extract_tool_calls_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+        request: ChatCompletionRequest,
+    ) -> DeltaMessage | None:
+        delta_text = self._buffer_delta_text(delta_text)
+        current_text = previous_text + delta_text
+
+        if self.tool_call_start_token not in current_text:
+            if delta_text:
+                return DeltaMessage(content=delta_text)
+            return None
+
+        try:
+            start_count = current_text.count(self.tool_call_start_token)
+            end_count = current_text.count(self.tool_call_end_token)
+            prev_start_count = previous_text.count(self.tool_call_start_token)
+            prev_end_count = previous_text.count(self.tool_call_end_token)
+
+            if self.tool_call_start_token not in current_text:
+                return DeltaMessage(content=delta_text)
+
+            # Starting a new function call
+            if start_count > prev_start_count and start_count > end_count:
+                self.current_tool_id += 1
+                self.current_tool_name_sent = False
+                self.streamed_args_for_tool.append("")
+                self.prev_tool_call_arr.append({})
+                logger.debug("Starting new tool call %d", self.current_tool_id)
+                return None
+
+            # In the middle of a function call
+            if start_count > end_count:
+                last_start = current_text.rfind(self.tool_call_start_token)
+                partial_call = current_text[
+                    last_start + len(self.tool_call_start_token) :
+                ]
+
+                if partial_call.startswith("call:"):
+                    func_part = partial_call[5:]
+
+                    if "{" in func_part:
+                        func_name = func_part.split("{")[0]
+                        args_part = (
+                            func_part.split("{", 1)[1] if "{" in func_part else ""
+                        )
+
+                        if not self.current_tool_name_sent and func_name:
+                            self.current_tool_name_sent = True
+                            self.prev_tool_call_arr[self.current_tool_id] = {
+                                "name": func_name,
+                                "arguments": {},
+                            }
+                            return DeltaMessage(
+                                tool_calls=[
+                                    DeltaToolCall(
+                                        index=self.current_tool_id,
+                                        type="function",
+                                        id=make_tool_call_id(),
+                                        function=DeltaFunctionCall(
+                                            name=func_name
+                                        ).model_dump(exclude_none=True),
+                                    )
+                                ]
+                            )
+
+                        if self.current_tool_name_sent and args_part:
+                            current_args = self._parse_arguments(args_part)
+                            if current_args:
+                                current_args_json = json.dumps(
+                                    current_args, ensure_ascii=False
+                                )
+                                prev_streamed = self.streamed_args_for_tool[
+                                    self.current_tool_id
+                                ]
+
+                                if len(current_args_json) > len(prev_streamed):
+                                    diff = current_args_json[len(prev_streamed) :]
+                                    self.streamed_args_for_tool[
+                                        self.current_tool_id
+                                    ] = current_args_json
+                                    self.prev_tool_call_arr[self.current_tool_id][
+                                        "arguments"
+                                    ] = current_args
+
+                                    return DeltaMessage(
+                                        tool_calls=[
+                                            DeltaToolCall(
+                                                index=self.current_tool_id,
+                                                function=DeltaFunctionCall(
+                                                    arguments=diff
+                                                ).model_dump(exclude_none=True),
+                                            )
+                                        ]
+                                    )
+
+                return None
+
+            # Function call just ended
+            if end_count > prev_end_count:
+                if self.current_tool_id >= 0 and self.current_tool_id < len(
+                    self.prev_tool_call_arr
+                ):
+                    all_calls = self.tool_call_regex.findall(current_text)
+                    args = {}
+                    if self.current_tool_id < len(all_calls):
+                        match = all_calls[self.current_tool_id]
+                        if match[0]:
+                            args_str = match[1]
+                            args = self._parse_arguments(args_str)
+                            self.prev_tool_call_arr[self.current_tool_id][
+                                "arguments"
+                            ] = args
+
+                    if args:
+                        args_json = json.dumps(args, ensure_ascii=False)
+                        prev_streamed = self.streamed_args_for_tool[
+                            self.current_tool_id
+                        ]
+                        if len(args_json) > len(prev_streamed):
+                            diff = args_json[len(prev_streamed) :]
+                            self.streamed_args_for_tool[self.current_tool_id] = (
+                                args_json
+                            )
+                            return DeltaMessage(
+                                tool_calls=[
+                                    DeltaToolCall(
+                                        index=self.current_tool_id,
+                                        function=DeltaFunctionCall(
+                                            arguments=diff
+                                        ).model_dump(exclude_none=True),
+                                    )
+                                ]
+                            )
+                return None
+
+            if delta_text:
+                return DeltaMessage(content=delta_text)
+            return None
+
+        except Exception:
+            logger.exception("Error in streaming tool call extraction")
+            return None