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Training large language models (LLMs) encounters challenges in GPU memoryconsumption due to the high memory requirements of model states. The widelyused Zero Redundancy Optimizer (ZeRO) addresses this issue through strategicsharding but introduces communication challenges at scale. To tackle thisproblem, we propose AMSP, a system designed to optimize ZeRO for scalable LLMtraining. AMSP incorporates three flexible sharding strategies: Full-Replica,Full-Sharding, and Partial-Sharding, and allows each component within the modelstates (Parameters, Gradients, Optimizer States) to independently choose asharding strategy as well as the device mesh. We conduct a thorough analysis ofcommunication costs, formulating an optimization problem to discover theoptimal sharding strategy. Additionally, AMSP optimizes distributed LLMtraining by efficiently overlapping communication with computation. Evaluationsdemonstrate up to 52\% Model FLOPs Utilization (MFU) when training theLLaMA-based model on 1024 GPUs, resulting in a 1.56 times improvement intraining throughput compared to newly proposed systems like MiCS and ZeRO++.

AMSP: Reducing Communication Overhead of ZeRO for Efficient LLM Training