该研究是一篇关于MoE在语言模型中的早期应用,它聚焦着循环神经网络上的MoE实现,含有一些固有的限制,比如专家过多,训练不完全,有些专家不干活的问题,后来的研究则一步一步解决了这些问题并且实现了MoE模型在Transformer这类先进模型上的应用,并取得了很好的效果。 Abstract 神经网络吸收信息的能力受到其参数数量的限制。...
在Transformer层里,初看也是针对Transformer的FFN这层比较耗费算力的sub layer,而不是在Transformer前面或者后面加的MoE层;2)Gating网络也有可以学习的参数,另外它的设计是MoE一个研究topic,文章的正文和附录都有大量篇幅讨论它的设计,对算法性能、训练效率都有较大影响;3)给MoE的input同时作为Gating和expert的输入; 4...
具体说来,如图一所示,我们把 MoE以卷积的方式(convolutionally)放在多层 LSTM 层之间。 在文本的每个位置上,就会调用 MoE 一次,进而可能选择不同的专家组合。 不同的专家会倾向于变得高度专业化(基于语法和语义)。 1.3 专家混合的相关工作 2 混合专家层的结构 MoE 层包括 : nn个“专家网络”:E1,⋯,EnE1,...
MoE 训练Shazeer N., Mirhoseini A., Maziarz K., Davis A., Le Q., Hinton G. and Dean J. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. ICLR, 2017.概Mixture-of-Experts (MoE).MoE通过一 gating network 选择不同的 expert: y=n∑i=1G(x)iEi(x),y=∑i...
The sparsely-gated Mixture of Experts (MoE) can magnify a network capacity with a little computational complexity. In this work, we investigate how multi-lingual Automatic Speech Recognition (ASR) networks can be scaled up with a simple routing algorithm in order to achieve better accuracy. More...
We present model architectures in which a MoE with up to 137 billion parameters is applied convolutionally between stacked LSTM layers. On large language modeling and machine translation benchmarks, these models achieve significantly better results than state-of-the-art at lower computational cost. ...
extra capacity in case gating is not perfectly balanced.capacity_factor_eval=2.,# capacity_factor_* should be set to a value >=1loss_coef=1e-2# multiplier on the auxiliary expert balancing auxiliary loss)inputs=torch.randn(4,1024,512)out,aux_loss=moe(inputs)# (4, 1024, 512), (1,...
We introduce a Sparsely-Gated Mixture-of-Experts layer (MoE), consisting of up to thousands of feed-forward sub-networks. A trainable gating network determines a sparse combination of these experts to use for each example. We apply the MoE to the tasks of language modeling and machine ...
We apply the MoEto the tasks of language modeling and machine translation, where model capacityis critical for absorbing the vast quantities of knowledge available in the trainingcorpora. We present model architectures in which a MoE with up to 137 billionparameters is applied convolutionally between...
We introduce a Sparsely-Gated Mixture-of-Experts layer (MoE), consisting of up to thousands of feed-forward sub-networks. A trainable gating network determines a sparse combination of these experts to use for each example. We apply the MoE to the tasks of language modeling and machine ...