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...
主要提出了a Sparsely-Gated Mixture-of-Experts layer (MoE), 设计,提高模型容量,同时降低计算量,且获得了更好的效果(91年前就有MoE的研究了,不要误以为只有大模型后才有MoE,这对理解设计动机比较重要)。初学者,例如我,可能有几个误区: 1) 以为MoE是独立的网络结构,本文是设计在LSTM单元结合,它不用于改变时...
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...
论文出自:Shazeer N, Mirhoseini A, Maziarz K, et al. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer[J]. arXiv preprint arXiv:1701.06538, 2017. 摘要 神经网络的吸收信息的容量(capacity)受限于参数数目。 条件计算(conditional computation)针对于每个样本,激活网络的部分子...
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,) ...
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...
In this work, we address these challenges and finally realize the promise of conditional computation, achieving greater than 1000x improvements in model capacity with only minor losses in computational efficiency on modern GPU clusters. We introduce a Sparsely-Gated Mixture-of-Experts layer (MoE), ...
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 ...
In this work, we address these challenges and finally realize the promise of conditional computation, achieving greater than 1000x improvements in model capacity with only minor losses in computational efficiency on modern GPU clusters. We introduce a Sparsely-Gated Mixture-of-Experts layer (MoE), ...