我们引入了稀疏门控专家混合层Sparsely-Gated Mixture-of-Experts layer(MoE),由多达数千个前馈子网络组成。可训练的门控网络确定用于每个示例的这些专家的稀疏组合。我们将 MoE 应用于语言建模和机器翻译的任务,其中模型容量对于吸收训练语料库中的大量可用知识至关重要。我们提出了模型架构,其中具有多达 1370 亿个参数...
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 ...
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=1nG(x)iEi(x), 若G(x)i=0G(x)i=0, 则我们不需要计算 Ei(x)Ei(x). Ei(x)Ei(x) 可以...
我们引入了稀疏门控专家混合层(Sparsely-Gated Mixture-of-Experts Layer),包括数以千计的前馈子网络。对于每一个样本,有一个可训练的门控网络(gating network)会计算这些专家(指前馈子网络)的稀疏组合。 我们把专家混合(MoE)应用于语言建模和机器翻译任务中,对于这些任务,从训练语料库中吸收的巨量知识,是十分关键...
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 ...
1.2 Our Approach: The Sparsely-Gated Mixture-of-Experts Layer Our approach to conditional computation is to introduce a new type of general purpose neural network component: a Sparsely-Gated Mixture-of-Experts Layer (MoE). The MoE consists of a number of experts, each a simple feed-forward ne...
We in-troduce a Sparsely-Gated Mixture-of-Experts layer (MoE), consisting of up tothousands of feed-forward sub-networks. A trainable gating network determinesa sparse combination of these experts to use for each example. We apply the MoEto the tasks of language modeling and machine ...
importtorchfrommixture_of_expertsimportHeirarchicalMoEmoe=HeirarchicalMoE(dim=512,num_experts=(4,4),# 4 gates on the first layer, then 4 experts on the second, equaling 16 experts)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 ...