December 2025 (2025-12) Monthly Summary for apple/axlearn Overview: Focused on expanding the flexibility and configurability of the Splash Attention mechanism. Delivered a feature that enables variable head dimensions, supporting non-128 attention heads and enabling more adaptable configurations for different input sizes. This work lays the groundwork for broader experimentation and potential performance benefits across diverse workloads. Key feature delivered: - Splash Attention: Variable head dimensions support. Allows non-128 attention v heads in Splash Attention, enabling flexible configurations for attention models and potential performance gains across varying input sizes. Commit: dc4d4aea5a383cef282e48a568d9bcfcb84071cf (GitOrigin-RevId: 17e5478a639554f9adcdea1444e8388a392d1040). Bugs fixed: - No major bugs fixed this month. The focus was on delivering the new feature and validating integration with the existing codebase. Overall impact and accomplishments: - Enabled broader experimentation with attention configurations, increasing the model's adaptability to different data shapes and workloads. - Strengthened the project's architectural flexibility by introducing variable head support in Splash Attention, which can shorten future integration timelines for related features. Technologies/skills demonstrated: - Attention mechanism design and extension (Splash Attention). - Feature integration in a large ML framework with attention to backward compatibility and configurability. - Code-level changes and commit-level traceability for feature delivery.