Qyz contributed to the pytorch/torchrec repository by enhancing the Triton TBE embedding backend, focusing on multi-feature table support and improved performance parity with CUDA TBE. They developed the TritonBatchedFusedEmbeddingBag module and integrated feature_table_map logic, refining batch-size calculations and embedding lookups. Their work included implementing robust input validation, bounds checking, and addressing FP16-to-FP32 precision issues to ensure numerical stability and correctness. Qyz also fixed backward kernel handling for accurate gradient aggregation and expanded unit testing coverage. Using Python, CUDA, and PyTorch, they delivered targeted improvements that addressed both reliability and compatibility for evolving distributed deep learning workloads.