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...
首先需要明确的是 MoE 肯定不是非常新的架构,因为早在 2017 年,谷歌就已经引入了 MoE,当时是稀疏门控专家混合层,全称为 Sparsely-Gated Mixture-of-Experts Layer,这直接带来了比之前最先进 LSTM 模型少 10 倍计算量的优化。2021 年,谷歌的 Switch Transformers 将 MoE 结构融入 Transformer,与密集的 T5-Base ...
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...
import torch from torch import nn from mixture_of_experts import MoE # a 3 layered MLP as the experts class Experts(nn.Module): def __init__(self, dim, num_experts = 16): super().__init__() self.w1 = nn.Parameter(torch.randn(num_experts, dim, dim * 4)) self.w2 = nn.Param...
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 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. ...
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 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. ...
图1:嵌入循环语言模型中的专家混合 (MoE) 层。在这种情况下,稀疏门函数选择两个专家来执行计算。它们的输出由门控网络的输出调制。 1.2 我们的方法:稀疏门控专家混合层 我们的条件计算方法是引入一种新型通用神经网络组件:稀疏门控专家混合层(MoE)。 MoE 由许多专家组成,每个专家都是一个简单的前馈神经网络,以及...