[FEATURE] Comprehensive Contribution Guidelines and Community Infrastructure

[LoserCheems]

Is your feature request related to a problem? Please describe.

Currently, the Flash-DMA project lacks comprehensive contribution guidelines and community infrastructure that would help attract and onboard new contributors effectively. While we have basic documentation, we need:

• Detailed contribution guidelines for code, documentation, and community participation
• Issue and PR templates to standardize submissions
• Code of conduct to ensure a welcoming community environment
• Security policy for vulnerability reporting
• Citation guidelines for academic use

This makes it difficult for new contributors to understand how to participate and can lead to inconsistent issue reporting and code submissions.

Describe alternatives you've considered

Alternative 1: Minimal approach

• Keep only basic README contribution section
• Handle contributions on a case-by-case basis
• ❌ Lacks scalability and consistency

Alternative 2: Copy from other projects directly

• Use generic templates from other repositories
• ❌ Doesn't address Flash-DMA's specific CUDA/AI requirements

Alternative 3: Comprehensive contribution framework (Proposed)

• Create detailed, Flash-DMA-specific contribution guidelines
• Implement GitHub issue/PR templates
• Establish community standards and security policies
• ✅ Professional, scalable, and tailored to our needs

Implementation details

Documentation Requirements:

• CONTRIBUTING.md: Detailed contribution guidelines including:

  • Development environment setup for CUDA development
  • Code style and testing requirements
  • Performance benchmarking guidelines
  • GPU architecture compatibility requirements

• CODE_OF_CONDUCT.md: Community standards based on Contributor Covenant

• SECURITY.md: Security vulnerability reporting procedures

GitHub Templates:

• Issue templates:

  • Bug report template with CUDA environment details
  • Feature request template (like this one)
  • Performance issue template for optimization requests

• Pull request template with:

  • Code quality checklist
  • Performance impact assessment
  • GPU architecture testing requirements

Process Improvements:

• No CUDA kernel changes required
• No Python API changes needed
• Performance implications: Better organized contributions should lead to higher quality optimizations
• Compatibility: Guidelines will emphasize multi-GPU architecture testing

Use case

Target contributors:

• Academic researchers working with long sequences (128K+ tokens)
• Industry developers integrating into production systems
• CUDA developers wanting to contribute performance optimizations
• Documentation contributors improving user experience

Specific applications:

• Long document processing: Contributors working on legal, scientific, or technical documents
• Code generation: Developers building AI coding assistants
• Research: Academic teams needing standardized citation and contribution processes

Workflow improvements:

• Faster onboarding: Clear guidelines reduce time-to-first-contribution
• Higher quality submissions: Templates ensure comprehensive issue/PR information
• Better collaboration: Code of conduct fosters inclusive community
• Academic integration: Proper citation guidelines encourage research use

Additional context

Current project status:

• Active development with Support-backward branch
• Growing interest from academic and industry users
• Need to scale community management as project gains traction

Success metrics:

• Increased number of quality contributions
• Reduced time spent on clarifying incomplete issues/PRs
• More diverse contributor base
• Better documentation coverage

Related work

Inspiration from successful projects:

• Hugging Face Transformers: Excellent example of comprehensive contribution guidelines for AI/ML projects
• Flash-Attention: Good reference for CUDA-based contribution requirements
• PyTorch: Best practices for large-scale CUDA/AI project management

Why valuable for Flash-DMA:

• Academic credibility: Proper citation and contribution standards
• Industry adoption: Professional contribution process encourages enterprise use
• Community growth: Welcoming guidelines attract diverse contributors
• Quality assurance: Structured templates improve code and documentation quality
• Maintainability: Clear processes reduce maintainer burden

Implementation priority:

1. Core contribution guidelines (CONTRIBUTING.md)
2. Community standards (CODE_OF_CONDUCT.md)
3. GitHub templates (issues/PRs)
4. Security and citation policies

This infrastructure investment will pay dividends as Flash-DMA grows and attracts more contributors from both academic and industry backgrounds.