[Bugfix] Allow Gemma4 reasoning parser to have selective tokens to skip

[ashishdatta]

Purpose

When reasoning is enabled the adjust_request is called in the reasoning parser to allow channels tokens to not be skipped for reasoning, but this impacts when tool_choice="none" as it will add the tool call tokens <|tool_call> and <tool_call|> into the content.

So proposal here is to introduce a way for the reasoning parser to select which tokens should be skipped.

Test Plan

Unit Tests

pytest tests/reasoning/test_gemma4_reasoning_parser.py

Serving Test

MODEL = "google/gemma-4-26B-A4B-it"

TOOLS = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get the current weather",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {"type": "string"}
            },
            "required": ["location"],
        },
    },
}]

TOOL_CALL_TOKENS = {"<|tool_call>", "<tool_call|>", '<|"|>'}

resp = client.chat.completions.create(
    model=MODEL,
    messages=[{"role": "user", "content": "What's the weather in Paris?"}],
    tools=TOOLS,
    tool_choice="none",
    extra_body={"chat_template_kwargs": {"enable_thinking": True}},
    max_tokens=300,
    stream=False,
)

Test Result

Unit tests

pytest tests/reasoning/test_gemma4_reasoning_parser.py
32 passed, 16 warnings in 10.41s

Serving Test Result

finish_reason: stop
content: 'call:get_weather{location:Paris}'
reasoning: None
PASS: no tool-call tokens in content

---

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[gemini-code-assist]

Code Review

This pull request introduces a skip_token_ids mechanism to allow selective suppression of specific tokens during detokenization, even when skip_special_tokens is set to False. This is primarily utilized in the Gemma4ReasoningParser to hide tool-call delimiters while preserving reasoning channel markers. The changes span the OpenAI protocol definitions, sampling parameters, and the V1 engine's detokenizer logic. Feedback suggests removing the check for the presence of tools when tool_choice is set to "none" to ensure that structural tool-call delimiters are suppressed regardless of whether tools were explicitly provided, preventing them from leaking into the output when special token skipping is disabled.

[gemini-code-assist]

The condition and getattr(request, "tools", None) unnecessarily restricts the suppression of tool-call tokens. When tool_choice is "none", the model is explicitly instructed not to use tools, yet it may still emit structural tool-call delimiters due to training bias or prompt artifacts. Since skip_special_tokens is set to False to preserve reasoning markers, these tool-call tokens will leak into the visible content unless explicitly suppressed. Removing the tools check ensures they are hidden whenever the tool parser is not active, providing a cleaner output.

        if getattr(request, "tool_choice", None) == "none":