Yao focused on performance engineering within the pytorch/pytorch repository, developing a fused atomic add kernel to optimize the compute_grad_weight operation for cases with few but large segments. Leveraging CUDA and C++, Yao designed the kernel to dynamically set grid size based on segment characteristics, improving parallelism and reducing per-iteration latency from approximately 40ms to 6ms in production. The work included comprehensive unit testing, end-to-end performance validation, and cross-architecture checks, ensuring numerical parity and robust reliability. Yao’s patch was landed with documented test infrastructure and verified on AMD MI300, demonstrating depth in deep learning and performance optimization.