April 2026: Delivered performance optimization for ASCEND NPU Group Normalization in linkedin/Liger-Kernel. Implemented new block size selection functions and refined kernel configurations to maximize hardware utilization and throughput. Validated changes with unit tests, style checks, and convergence tests; no major bugs fixed this month, all changes pass CI. Impact: faster normalization path enabling improved training/inference performance and lower latency. Skills demonstrated: performance optimization, kernel tuning, hardware-aware development, and rigorous code quality practices.

July 2025 performance month focused on stability, consistency, and training correctness for volcengine/verl. Delivered memory-stability improvements for large Qwen models on ASCEND NPUs, standardized device configuration across modules, and fixed training pipeline issues affecting PPO/DP. These changes reduce runtime failures, improve reproducibility, and enable scalable experimentation with larger model sizes across hardware.

June 2025 – Volcengine Verl focused on stabilizing ASCEND NPU training and expanding model support to broaden hardware compatibility and accelerate value delivery. Key deliverables include a bug fix for distributed training loss calculation on ASCEND NPUs and the introduction of Qwen2.5 VL model support on ASCEND NPU, accompanied by CI workflow updates, documentation, and new training/testing scripts. A transformers library patch was applied to optimize performance on NPU hardware, further improving training throughput and reliability. These efforts resulted in more accurate training outcomes, reduced operational risk, and greater flexibility in deploying VL models on ASCEND-based pipelines.