MOE

Links: https://arxiv.org/abs/2101.03961 “SWITCH TRANSFORMERS: SCALING TO TRILLION PARAMETER MODELS WITH SIMPLE AND EFFICIENT SPARSITY”，提出了一种可以扩展到万亿参数的网络，有两个比较大的创新，基于Transformer MoE网络结构，简化了MoE的routing机制，降低了计算量；进一步通过数据并行+模型并行+expert并行的方式降低了训练通信量，提升训练性能。 模型 Simplifying Sparse Routing Mixture of Expert Routing which takes as an input a token representation x and then routes this to the best deter- mined top-k experts Switch Routing: route to only a single expert, this simplification preserves model quality, reduces routing computation and performs better. Sparse routing通过参数Wr计算出一个在N个experts上的softmax分布，对每个token输入筛选概率最高的 top k 个 experts，对应的是MOE中的门控机制。这样对算力的需求并没有随着参数量的增加而大幅增长，使得这个模型更加容易训练。 EFFICIENT SPARSE ROUTING 并行Switch实现 tensor shapes are statically determined at compilation time computation is dynamic due to the routing decisions at training and inference....

Mixture of Experts (MOE) MOE属于Ensemble Method中的一个方法, 采用分治思想： 将复杂的建模任务分解为多个相对简单的子任务，为每个子任务训练专门的模型：涉及子任务分解，或者Clustering 需要一个门控模型，基于数据输入选择如何组合多个专家模型的结果 Mixture of experts aims at increasing the accuracy of a function approximation by replacing a single global model by a weighted sum of local models (experts). It is based on a partition of the problem domain into several subdomains via clustering algorithms followed by a local expert training on each subdomain. Local Models & Global Models Hinton的课件介绍了模型拟合分布的两个极端方式: Very local models: 使用很多非常局部化的模型, e....