March 2026 monthly summary for pytorch/torchrec: Delivered Muon optimizer integration into MVAI trainer, enabling specialized handling of 2D weight matrices with a safe fallback for non-2D parameters; expanded optimizer factory to support MUON for both CUDA and CPU paths; introduced MuonConfig dataclass and OptimType.MUON; ensured FSDP2 compatibility while avoiding FSDP1 where necessary; added comprehensive unit tests and updated configuration defaults. This work enhances MVAI optimization capabilities, broadens PyTorch's 2D-weight optimization support, and reduces manual tuning for 2D-heavy models.

February 2026 monthly summary for pytorch/FBGEMM: focus on performance optimization of sparse data kernels with vectorization in permute_2D_data_kernel; major improvement in latency for 2D sparse feature permutations; collaboration via PR #5370; no major bug fixes this month.

Month: 2025-11 | Focused on stabilizing evaluation integrity in pytorch/FBGEMM while delivering significant performance optimizations for permutation operations used in recommender systems. Key actions included reverting a problematic bucket_permute kernel to fix evaluation mismatch and implementing a vectorized permute_1D_data_kernel with an accompanying benchmark for assessing performance gains. The work reduced latency in embedding reordering and improved benchmarking capabilities, contributing to more reliable evaluation and higher throughput for sparse data workloads.

Month 2025-10: Delivered CUDA-backed rebatching optimizations in pytorch/FBGEMM, unifying CUDA and AMD capabilities and improving preprocessing throughput for training pipelines. Implemented two new CUDA kernels and resolved CUDA misalignment issues affecting rebatching and bucketing paths, enabling smoother production workloads.