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arXiv · 2411.19870

DeMo: Decoupled Momentum Optimization

Bowen Peng·Lizhang Chen·Baiyu Su·Jeffrey Quesnelle·Diederik P. Kingma·Qiang Liu
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Abstract

Scaling neural network training increasingly depends on synchronous data-parallelism, yet full-precision gradient all-reduce imposes a severe communication bottleneck. We propose Decoupled Momentum Optimization (DeMo), a drop-in replacement for any momentum-based optimizers that significantly reduces the communication bandwidth while maintaining convergence. DeMo (i) decouples local momentum updates, (ii) applies a fast orthonormal transform (e.g., DCT) followed by top-k sparsification, and (iii) reuses the momentum buffer as error feedback via momentum subtraction. This design reduces per-step communication by up to two orders of magnitude with minimal computational overhead. Experiments on 300M and 1B-parameter DeMo language models show DeMo transmits up to 85x less data per GPU than AdamW-DDP while achieving comparable loss and accuracy. DeMo is topology-agnostic and enables training across multi-datacenter or Ethernet-based setups. Code is available at https://github.com/bloc97/DeMo

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Bowen Peng, Lizhang Chen, Baiyu Su, Jeffrey Quesnelle, Diederik P. Kingma, Qiang Liu. 2026-02-06. DeMo: Decoupled Momentum Optimization. https://arxiv.org/abs/2411.19870

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