Worked on performance optimization for the ROCm/pytorch repository, focusing on the LLM quantization pipeline. Developed a feature that introduced caching for the assert_and_get_unique_device function, which accelerated the prepare and convert steps during quantization preparation. This change reduced latency and improved throughput for large language model workflows on ROCm. The implementation leveraged Python and applied principles of performance optimization and quantization to streamline the deployment process. The work was delivered as a single, targeted commit, reflecting a focused approach to solving a specific bottleneck in the quantization workflow and demonstrating depth in optimizing Python-based machine learning infrastructure.