All is Not Lost: LLM Recovery without Checkpoints

TL;DR

This paper introduces CheckFree and CheckFree+ methods for recovering large language models during decentralized training without checkpoints, reducing overhead and improving efficiency.

Contribution

Proposes checkpoint-free recovery techniques for LLM training that outperform traditional checkpointing and redundancy in efficiency and scalability.

Findings

CheckFree achieves up to 12% faster convergence than redundant computation.

CheckFree+ extends recovery to first and last stages using out-of-order pipelining.

Both methods require no additional computation or storage, only small overhead for embedding layers.

Abstract

Training LLMs on decentralized nodes or on-spot instances, lowers the training cost and enables model democratization. The inevitable challenge here is the transient churns of nodes due to failures and the operator's scheduling policies, leading to losing parts of the model (some layers). The conventional approaches to recover from failures is to either use checkpointing, where periodically a copy of the entire model is sent to an additional storage, or redundant computation. These approaches yield significant communication and/or computation overhead even in non-failure cases and scale poorly in settings with large models. In this paper we propose CheckFree, an efficient recovery method where a failing stage is substituted by weighted averaging of the closest neighboring stages. In contrast to the state of the art, CheckFree requires no additional computation or storage. However, because of the nature of averaging neighbouring stages, it can only recover failures of intermediate stages. We further extend our method to CheckFree+ with out-of-order pipeline execution to tolerate crashes of the first and last stages. Thanks to out-of-order pipelining, behaviour of the first and last stages are mimicked by their neighboring ones, which allows CheckFree+ to recover them by copying the neighboring stages. To recover the (de-)embedding layers, CheckFree+ copies those layers in the neighboring stages, which requires relatively small storage overhead. We extensively evaluate our method on LLaMa models of model sizes from 124M to 1.5B with varying failure frequencies. In the case of low and medium failure rates (5-10%), CheckFree and CheckFree+ outperform both checkpointing and redundant computation in terms of convergence wall-clock time, achieving up to 12% improvement over redundant computation. Both of our proposals can be ran via our code available at: https://github.com/gensyn-ai/CheckFree