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Computer Science > Machine Learning

arXiv:2311.08105 (cs)
[Submitted on 14 Nov 2023 (v1), last revised 2 Dec 2023 (this
version, v2)]

Title:DiLoCo: Distributed Low-Communication Training of Language
Models

Authors:Arthur Douillard, Qixuan Feng, Andrei A. Rusu, Rachita
Chhaparia, Yani Donchev, Adhiguna Kuncoro, Marc'Aurelio Ranzato,
Arthur Szlam, Jiajun Shen
Download a PDF of the paper titled DiLoCo: Distributed
Low-Communication Training of Language Models, by Arthur Douillard
and 8 other authors
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    Abstract:Large language models (LLM) have become a critical
    component in many applications of machine learning. However,
    standard approaches to training LLM require a large number of
    tightly interconnected accelerators, with devices exchanging
    gradients and other intermediate states at each optimization
    step. While it is difficult to build and maintain a single
    computing cluster hosting many accelerators, it might be easier
    to find several computing clusters each hosting a smaller number
    of devices. In this work, we propose a distributed optimization
    algorithm, Distributed Low-Communication (DiLoCo), that enables
    training of language models on islands of devices that are poorly
    connected. The approach is a variant of federated averaging,
    where the number of inner steps is large, the inner optimizer is
    AdamW, and the outer optimizer is Nesterov momentum. On the
    widely used C4 dataset, we show that DiLoCo on 8 workers performs
    as well as fully synchronous optimization while communicating 500
    times less. DiLoCo exhibits great robustness to the data
    distribution of each worker. It is also robust to resources
    becoming unavailable over time, and vice versa, it can seamlessly
    leverage resources that become available during training.

Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL)
Cite as:  arXiv:2311.08105 [cs.LG]
          (or arXiv:2311.08105v2 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2311.08105
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Arthur Douillard [view email]
[v1] Tue, 14 Nov 2023 12:05:45 UTC (1,609 KB)
[v2] Sat, 2 Dec 2023 14:10:14 UTC (1,610 KB)
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