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This paper is squarely about AI training, specifically memory and speed optimization for Mixture-of-Experts models, so it passes the topic gate. Its core contribution is updating expert tensors during the backward pass so gradients never accumulate across all experts, which reportedly cuts peak memory by 19% to 22% on OLMoE and Granite fine-tuning while keeping loss effectively unchanged. For practitioners, the key caveats are that results are limited to fine-tuning two models in bf16 and that the gains depend on batched tensor updates, so independent validation at larger scale would be needed before relying on the claimed speedups.
Sonar-MoE: In-Backward Expert Updates for Memory-Efficient Mixture-of-Experts Training
arXiv:2611.03577v1 Announce Type: cross Abstract: Most parameters of a Mixture-of-Experts (MoE) model belong to its experts, and standard training keeps every expert gradient in GPU memory until the optimizer step. This memory grows with the number and size of the experts, so it rises as MoE models scale. We introduce Sonar-MoE, which updates each expert tensor during the backward pass and frees its gradient right away. It brings optimizer-in-backward from dense models to batched MoE expert tensors, so the GPU never holds more than one layer of expert gradients. When we fine-tune OLMoE-1B-7B and Granite-3.1-3B-A800M in bf16 with two expert-kernel backends, Sonar-MoE lowers peak memory by 19% to 22%. Its updates are bitwise identical to standard training without expert gradient clipping, and held-out loss stays within 0.003 of all baselines. Each step also takes 17% to 23% less time than standard training with global-norm clipping. Updating whole batched tensors rather than individual experts is what keeps Sonar-MoE fast, as it avoids a 20% to 72% slowdown while using the same memory. Sonar-MoE matches optimizer-in-backward over all parameters in memory and speed while still clipping the dense parameters.
Abstract:Most parameters of a Mixture-of-Experts (MoE) model belong to its experts, and standard training keeps every expert gradient in GPU memory until the optimizer step. This memory grows with the number and size of the experts, so it rises as MoE models scale. We introduce Sonar-MoE, which updates each expert tensor during the backward pass and frees its gradient right away. It brings optimizer-in-backward from dense models to batched MoE expert tensors, so the GPU never holds more than one layer of expert gradients. When we fine-tune OLMoE-1B-7B and Granite-3.1-3B-A800M in bf16 with two expert-kernel backends, Sonar-MoE lowers peak memory by 19% to 22%. Its updates are bitwise identical to standard training without expert gradient clipping, and held-out loss stays within 0.003 of all baselines. Each step also takes 17% to 23% less time than standard training with global-norm clipping. Updating whole batched tensors rather than individual experts is what keeps Sonar-MoE fast, as it avoids a 20% to 72% slowdown while using the same memory. Sonar-MoE matches optimizer-in-backward over all parameters in memory and speed while still clipping the dense parameters.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Performance (cs.PF)
arXiv:2611.03577 [cs.LG]
(or arXiv:2611.03577v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2611.03577 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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