PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

FSDP addresses memory capacity challenges by sharding parameters across devices, employs communication optimizations to enhance efficiency, includes a rate limiter feature to control memory impact, offers user-friendly APIs for easy integration, achieved promising results on large models, enables broader applications in various domains, faces challenges in mathematical equivalence and handling shared parameters, and has potential research directions in adaptive sharding strategies, new communication primitives, and combining with other parallelism paradigms.

Read full paper: https://arxiv.org/abs/2304.11277

Tags: Systems and Performance, Deep Learning, Machine Learning