Shuyao enhanced the pytorch/FBGEMM repository by addressing a critical bug in the CPU inference path for unified embeddings. Using C++ and leveraging expertise in CPU optimization and embedded systems, Shuyao implemented a robust bounds-check mechanism that accurately calculates num_rows even when weights_offsets are non-monotonic due to a non-identity feature_table_map. The solution involved preparing a sorted, deduplicated weights_offsets vector and applying per-table upper_bound logic, which eliminated false-positive out-of-bounds errors on ARM CPUs while preserving x86 behavior. This targeted fix improved production reliability and simplified future maintenance, reflecting a deep understanding of cross-architecture inference challenges.