[CI] expand issue labeler rules for feature/model triage

[drizzlezyk]

• Replace minimal label rules with a comprehensive keyword-based issue labeler taxonomy.
• Add grouped labels for core features and advanced capabilities to improve issue routing.
• Expand model-related matching for LLM, multimodal generation, multimodal understanding, audio, and omni scenarios.
• Add/normalize regex patterns for common model families (DeepSeek, Kimi, GLM, Qwen, 310p, etc.) to increase auto-label coverage and consistency.

What this PR does / why we need it?

• Expands .github/issue-labeler.yml from a minimal set of rules to a richer keyword-based labeling configuration.
• Adds grouped label dimensions for:
  • Core features (e.g., PD disaggregation, KV cache pool, ACLGraph, async scheduler, CPU binding, quantization)
  • Advanced features (e.g., long sequence, DPC/PCP, MTP/speculative decode)
  • Model categories (LLM, multimodal generation, multimodal understanding, audio, omni, etc.)
  • Specific model families (e.g., DeepSeek, Kimi, GLM, Qwen, 310p)
• Improves automatic issue triage accuracy and reduces manual label maintenance effort.
• Makes issue categorization more consistent for maintainers and contributors.

Why needed:

• Existing labeler rules were too limited and could not adequately cover current feature/model issue distribution.
• Broader and more structured matching helps faster routing, prioritization, and ownership assignment.

Fixes #N/A

Does this PR introduce any user-facing change?

• No runtime/API user-facing changes.
• This PR only updates GitHub issue automation rules.

How was this patch tested?

• Performed static validation and review of .github/issue-labeler.yml structure and regex entries.
• Verified that rule groups and label keys are correctly formatted for GitHub issue labeler consumption.
• Confirmed that legacy minimal rules were replaced by expanded taxonomy without syntax-breaking YAML changes.
• No unit/e2e tests were added because this is repository automation configuration (GitHub labeling rules) rather than application runtime logic.
• vLLM version: v0.17.0
• vLLM main: vllm-project/vllm@4034c3d

[gemini-code-assist]

Summary of Changes

Hello, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly enhances the repository's issue labeling automation by transitioning from a limited set of rules to a rich, keyword-based configuration. The primary goal is to improve the accuracy and consistency of automatic issue triage, thereby reducing manual maintenance effort and facilitating faster routing, prioritization, and ownership assignment for maintainers and contributors.

Highlights

• Issue Labeler Expansion: Replaced minimal issue labeler rules with a comprehensive keyword-based taxonomy.
• Grouped Labels: Added grouped labels for core features and advanced capabilities to improve issue routing.
• Model-Related Matching: Expanded model-related matching for LLM, multimodal generation, multimodal understanding, audio, and omni scenarios.
• Regex Pattern Normalization: Added and normalized regex patterns for common model families (e.g., DeepSeek, Kimi, GLM, Qwen, 310p) to increase auto-label coverage and consistency.

🧠 New Feature in Public Preview: You can now enable Memory to help Gemini Code Assist learn from your team's feedback. This makes future code reviews more consistent and personalized to your project's style. Click here to enable Memory in your admin console.

Changelog

• .github/issue-labeler.yml
  • Replaced the existing minimal set of issue labeling rules with an extensive keyword-based taxonomy.
  • Introduced new grouped labels for core features (e.g., PD disaggregation, KV cache pool, ACLGraph, async scheduler, CPU binding, quantization) and advanced features (e.g., long sequence, DPC/PCP, MTP/speculative decode).
  • Added comprehensive model categories, including LLM, multimodal generation, multimodal understanding, audio, and omni models.
  • Normalized and expanded regex patterns for specific model families like DeepSeek, Kimi, GLM, Qwen, and 310p to enhance auto-label coverage.

Activity

• No specific activity (comments, reviews, progress) has been recorded for this pull request yet.

Using Gemini Code Assist

The full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips.

Invoking Gemini

You can request assistance from Gemini at any point by creating a comment using either /gemini <command> or @gemini-code-assist <command>. Below is a summary of the supported commands on the current page.

Feature	Command	Description
Code Review	/gemini review	Performs a code review for the current pull request in its current state.
Pull Request Summary	/gemini summary	Provides a summary of the current pull request in its current state.
Comment	@gemini-code-assist	Responds in comments when explicitly tagged, both in pull request comments and review comments.
Help	/gemini help	Displays a list of available commands.

Customization

To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a .gemini/ folder in the base of the repository. Detailed instructions can be found here.

Limitations & Feedback

Gemini Code Assist may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments. If you're interested in giving your feedback about your experience with Gemini Code Assist for GitHub and other Google products, sign up here.

You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension.

Footnotes

1. Review the Privacy Notices, Generative AI Prohibited Use Policy, Terms of Service, and learn how to configure Gemini Code Assist in GitHub here. Gemini can make mistakes, so double check it and use code with caution. ↩

[github-actions]

👋 Hi! Thank you for contributing to the vLLM Ascend project. The following points will speed up your PR merge:‌‌

• A PR should do only one thing, smaller PRs enable faster reviews.
• Every PR should include unit tests and end-to-end tests ‌to ensure it works and is not broken by other future PRs.
• Write the commit message by fulfilling the PR description to help reviewer and future developers understand.

If CI fails, you can run linting and testing checks locally according Contributing and Testing.

[gemini-code-assist]

Code Review

This pull request significantly expands the issue labeling rules in .github/issue-labeler.yml, introducing a more comprehensive and structured taxonomy for automatic issue triage. The changes are well-organized and greatly improve upon the previous minimal ruleset. I've identified a few issues related to unescaped special characters in regular expressions and inconsistencies between meta-labels and specific labels, which could lead to incorrect or missed labels. Addressing these will ensure the new labeling system functions as intended.