Worked on the pytorch/executorch repository to deliver a performance-oriented optimization for transformer models, focusing on attention mechanism efficiency. Developed and integrated a feature that tracks the last valid token position during token generation, enabling more effective management of attention computation and reducing latency for long-sequence generation. This enhancement, implemented in the llama_transformer module, aligns with ongoing goals to improve throughput for large-scale sequence models. The work was executed using Python and PyTorch, leveraging deep learning and neural network expertise. Version control and pull request tracking were used to ensure disciplined delivery and maintainability of the new transformer attention feature.