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Fully Sharded Data Parallelism (FSDP) shards only weight matrices with a per-layer AllGather, producing ","title":"Scaling Neural Network Verification with Tensor Parallelism and Fully Sharded Data Parallelism","url":"https://arxiv.org/abs/2606.09377","vendor":"arxiv_cs_ai"},"summary":"arXiv:2606.09377v2 Announce Type: replace-cross \nAbstract: Formal neural network verification -- proving that a network satisfies safety properties for *all* inputs in a specified domain -- is bounded in practice by GPU memory: standard implementations of bound-propagation algorithms (IBP, CROWN, $\\alpha$-CROWN) require weight and relaxation-coefficient matrices to reside entirely on one accelerator. We adapt two parallelism techniques originally developed for large-scale model training to the auto_LiRPA / $\\alpha,\\beta$-CROWN verification framework. 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