[Rust Frontend] Fix macro-based content format detection

[reidliu41]

Purpose

The Rust chat template detector only recognized loops directly over
message.content. It missed templates that pass the content into a macro and
iterate with {% for item in content %}.

Qwen3.5 uses the macro form, causing auto mode to incorrectly select the
string content format. Multimodal messages consequently lost the
<|vision_start|> and <|vision_end|> wrappers around <|image_pad|>, which
could produce incorrect prompt tokens and multimodal positions.

Recognize loops over a content parameter while preserving the existing
direct message.content detection. Add regression coverage using both a
minimal macro template and the repository's Qwen3.5 template.

The corrected auto-detection matches the explicit OpenAI content format and
the corresponding Python detector behavior.

Test Plan

  cargo nextest run \
    -p vllm-chat \
    -E 'test(detects_openai_template_with_content_parameter_loop) | test(qwen35_template_auto_detects_openai_multimodal_content)'

Test Result

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[claude]

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[BugenZhao]

@codex review

[chatgpt-codex-connector]

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[chatgpt-codex-connector]

Restrict bare content loops to message-fed macro parameters

This condition treats every loop over a variable named content anywhere in the template as evidence that message content uses OpenAI parts, without checking that the variable is a macro parameter receiving message.content (or is related to messages at all). For example, an unused helper macro or {% set content = documents %}{% for item in content %} now switches auto detection to OpenAi; to_template_content then passes every message body as an array rather than the string the template expects, producing incorrect prompts or render failures. Track the macro call/argument relationship instead of matching the variable name globally.

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[reidliu41]

I took another look at the Python implementation and confirmed that it does not perform additional detection or trace macro call/argument relationships.
While such tracking could make the detection more precise, it would require broader AST analysis
and additional coverage for argument mapping, scoping, and related edge cases to avoid introducing new false negatives.
Given the added complexity and the current Python behavior, I think keeping the Rust implementation aligned with Python may be the better trade-off for now.

[BugenZhao]

Thanks for the fix.

I think in a longer term, we probably want to switch a more robust and less heuristic approach for detecting the format. For example, instead of doing static analysis on the template content, we can probe the desired behavior by seeding different inputs, like https://github.com/ai-dynamo/dynamo/blob/fa4916d7e8a5b192f7d6db71b934bec0330812be/lib/llm/src/preprocessor/prompt/template/formatters.rs#L15-L38