一种面向多模态模型的分区混合并行优化方法

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关键词：多模态模型；混合并行；并行优化；模块分区；调度优化中图分类号：TP311文献标志码：ADOI：10.7652/xjtuxb202602022 文章编号：0253-987X（2026）02-0229-12

Abstract： To address the challenges of complex architecture， large parameter size，and high computational demands in multimodal models，which lead to difficult training and low efficiency， as well as the limitations of existing data and model parallelism strategies in handling internal heterogeneity，a partitioned hybrid parallel optimization （MMHP） method for multimodal models is proposed. First，based on the heterogeneity of different submodules in the multimodal model， a module dependency graph is constructed to identify key partitioning points，achieving module partitioning and ensuring load balancing among submodules. Second， according to the parameter scale and computational requirements of different module partitions，a hybrid parallel optimization method is designed by integrating data parallelism and model paralelism to accommodate the diverse needs of heterogeneous computing tasks. Finally， a computational task scheduling optimization algorithm is developed based on dynamic programming to dynamically allocate computing resources，achieving a reasonable match between computational tasks and resources， further optimizing the utilization of computing resources， and improving model training efficiency. Experimental results show that，without compromising model training accuracy， the proposed

MMHP method can fully utilize computing resources and improve the training efficiency of multimodal models. Compared with existing mainstream paralll strategies，the training speed can be increased by up to 2 times.

Keywords： multimodal models； hybrid parallel； parallel optimization；module partitioning;scheduling optimization

多模态模型因其跨模态协同能力已成为人工智能研究新热点[1-3]。（剩余19455字）