research-article

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Authors:

Chien-Chin Huang,

Hamid Shojanazeri,

Alban Desmaison,

Pritam Damania,

Bernard Nguyen,

Shen LiAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 16, Issue 12

Pages 3848 - 3860

https://doi.org/10.14778/3611540.3611569

Published: 01 August 2023 Publication History

Abstract

It is widely acknowledged that large models have the potential to deliver superior performance across a broad range of domains. Despite the remarkable progress made in the field of machine learning systems research, which has enabled the development and exploration of large models, such abilities remain confined to a small group of advanced users and industry leaders, resulting in an implicit technical barrier for the wider community to access and leverage these technologies. In this paper, we introduce PyTorch Fully Sharded Data Parallel (FSDP) as an industry-grade solution for large model training. FSDP has been closely co-designed with several key PyTorch core components including Tensor implementation, dispatcher system, and CUDA memory caching allocator, to provide non-intrusive user experiences and high training efficiency. Additionally, FSDP natively incorporates a range of techniques and settings to optimize resource utilization across a variety of hardware configurations. The experimental results demonstrate that FSDP is capable of achieving comparable performance to Distributed Data Parallel while providing support for significantly larger models with near-linear scalability in terms of TFLOPS.

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Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 16, Issue 12

August 2023

685 pages

ISSN:2150-8097

Editors:
Georgia Koutrika
Athena Research Center
,
Jun Yang
Duke University

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VLDB Endowment

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Published: 01 August 2023

Published in PVLDB Volume 16, Issue 12

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Cited By

Hayes TRao RAkin HSofroniew NOktay DLin ZVerkuil RTran VDeaton JWiggert MBadkundri RShafkat IGong JDerry AMolina RThomas NKhan YMishra CKim CBartie LNemeth MHsu PSercu TCandido SRives A(2025)Simulating 500 million years of evolution with a language modelScience10.1126/science.ads0018Online publication date: 16-Jan-2025
https://doi.org/10.1126/science.ads0018
Huang CRianto BSun JFu ZLee C(2025)Privacy Protection and Standardization of Electronic Medical Records Using Large Language ModelLarge Language Models for Automatic Deidentification of Electronic Health Record Notes10.1007/978-981-97-7966-6_5(60-71)Online publication date: 26-Jan-2025
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https://dl.acm.org/doi/10.5555/3692070.3693410
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