Training Variable Long Sequences with Data-Centric Parallel
A new framework optimizes training efficiency for variable long sequences using dynamic data-driven runtime adjustments.
Training deep learning models on variable long sequences poses significant computational challenges. Existing methods force a difficult trade-off between efficiency and ease-of-use. Simple approaches use static configurations that cause workload imbalance low efficiency, while co...
ArXiv CS.AI
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Data-Centric Parallel (DCP) bridges the long-standing gap between training efficiency and implementation complexity for variable-length models. By enabling dynamic resource allocation based on input batch characteristics, this method significantly reduces compute waste without requiring massive architectural overhauls.
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