This AI Paper Proposes Soft MoE: A Fully-Differentiable Sparse Transformer that Addresses these Challenges while Maintaining the Benefits of MoEs

Greater computational cost is required for larger Transformers to function well. Recent research suggests that model size and training data must be scaled simultaneously to use any training compute resource the most. Sparse mixes of experts are a possible substitute that enables model scalability without incurring their full computational cost. Language, vision, and multimodal models have recently developed methods for sparsely activating token pathways throughout the network. Choosing which modules to apply to each input token is the discrete optimization challenge at the heart of sparse MoE Transformers. 

These modules are often MLPs and are referred to as experts. Linear programs, reinforcement learning, deterministic fixed rules, optimum transport, greedy top-k experts per token, and greedy top-k tokens per expert are just a few methods used to identify appropriate token-to-expert pairings. Heuristic auxiliary losses are frequently needed to balance expert utilization and reduce unassigned tokens. Small inference batch sizes, unique inputs, or transfer learning can worsen these problems in out-of-distribution settings. Researchers from Google DeepMind provide a novel strategy called Soft MoE that addresses several of these issues. 

Soft MoEs carry out a soft assignment by combining tokens rather than using a sparse and discrete router that seeks a good hard assignment between tokens and experts. They specifically construct several weighted averages of all tokens, the weights of which rely on both the tokens and the experts, and then process each weighted average via the relevant expert. Most of the issues above, brought on by the discrete process at the center of sparse MoEs, are absent in soft MoE models. Auxiliary losses that impose some desirable behavior and depend on the routing scores are a common source of gradients for popular sparse MoE methods, which learn router parameters by post-multiplicating expert outputs with the chosen routing scores. 

These algorithms frequently perform similarly to random fixed routing, according to observations. Soft MoE avoids this problem by immediately updating each routing parameter depending on each input token. They observed that huge percentages of input tokens could concurrently alter discrete paths through the network, creating training problems during training. Soft routing can give stability when training a router. Hard routing can also be difficult with numerous specialists since most works only prepare with a small number. They demonstrate that Soft MoE is scalable to thousands of experts and is constructed to be balanced. 

Last but not least, there are no batch effects during inference, where a single input might influence the routing and prediction for multiple inputs. While taking roughly half as long to train, Soft MoE L/16 outperforms ViT H/14 in upstream, few-shot, and finetuning and is quicker at inference. Additionally, after a comparable amount of training, Soft MoE B/16 beats ViT H/14 on upstream measures and matches ViT H/14 on few-shot and finetuning. Even though Soft MoE B/16 has 5.5 times as many parameters as ViT H/14, it performs inference 5.7 times faster. 

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