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。