Developed Triton Packed Sequence Operations for the FlagOpen/FlagGems repository, focusing on optimizing variable-length sequence handling in neural networks. Introduced a new operation to efficiently pack variable-length sequences into padded tensors, reducing memory usage and improving throughput. Subsequently, restored the unpacking functionality to enable seamless conversion back to original sequence forms, enhancing tensor manipulation and end-to-end model performance. The work involved Python, CUDA, and deep learning frameworks such as PyTorch, with careful attention to benchmarking and API design. These changes streamlined sequence processing flows, consolidated tensor transformations, and improved memory efficiency without introducing critical issues during the monitoring period.