The Sparse Mixture of Experts (SMoE) has been widely employed to enhance the
efficiency of training and inference for Transformer-based foundational models,
yielding promising results. However, the performance of SMoE heavily depends on
the choice of hyper-parameters, such as the number of experts and the number of
experts to be activated (referred to as top-k), resulting in significant
computational overhead due to the extensive model training by searching over
various hyper-parameter configurations. As a remedy, we introduce the Dynamic
Mixture of Experts (DynMoE) technique. DynMoE incorporates (1) a novel gating
method that enables each token to automatically determine the number of experts
to activate. (2) An adaptive process automatically adjusts the number of
experts during training. Extensive numerical results across Vision, Language,
and Vision-Language tasks demonstrate the effectiveness of our approach to
achieve competitive performance compared to GMoE for vision and language tasks,
and MoE-LLaVA for vision-language tasks, while maintaining efficiency by
activating fewer parameters. Our code is available at
this https URL

通过引入动态的专家混合技术（DynMoE），我们的方法在视觉、语言和视觉 - 语言任务中取得了竞争性能，同时通过激活更少的参数来保持效率。

动态专家混合：高效 Transformer 模型的自动调优方法

Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient  Transformer Models

Sparsely activated Mixture-of-Experts (SMoE) has shown promise to scale up
the learning capacity of neural networks, however, they have issues like (a)
High Memory Usage, due to duplication of the network layers into multiple
copies as experts; and (b) Redundancy in Experts, as common learning-based
routing policies suffer from representational collapse. Therefore, vanilla SMoE
models are memory inefficient and non-scalable, especially for
resource-constrained downstream scenarios. In this paper, we ask: Can we craft
a compact SMoE model by consolidating expert information? What is the best
recipe to merge multiple experts into fewer but more knowledgeable experts? Our
pilot investigation reveals that conventional model merging methods fail to be
effective in such expert merging for SMoE. The potential reasons are: (1)
redundant information overshadows critical experts; (2) appropriate neuron
permutation for each expert is missing to bring all of them in alignment. To
address this, we propose M-SMoE, which leverages routing statistics to guide
expert merging. Specifically, it starts with neuron permutation alignment for
experts; then, dominant experts and their "group members" are formed; lastly,
every expert group is merged into a single expert by utilizing each expert's
activation frequency as their weight for merging, thus diminishing the impact
of insignificant experts. Moreover, we observed that our proposed merging
promotes a low dimensionality in the merged expert's weight space, naturally
paving the way for additional compression. Hence, our final method, MC-SMoE
(i.e., Merge, then Compress SMoE), further decomposes the merged experts into
low-rank and structural sparse alternatives. Extensive experiments across 8
benchmarks validate the effectiveness of MC-SMoE. For instance, our MC-SMoE
achieves up to 80% memory and a 20% FLOPs reduction, with virtually no loss in
performance.

通过专家融合和神经元排列对信息进行压缩，MC-SMoE 模型实现了高效的神经网络学习，降低了内存和计算资源的需求。