在这项工作中,我们解决了这些挑战,最终实现了条件计算的承诺,模型容量提高了 1000 倍以上,而现代 GPU 集群的计算效率仅略有损失。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...
概述 提出了一个专家混合网络。通过门控系统来确定不同专家系统的加权组合,以在不同的场景中激活不同的专家模块。 动机 神经网络吸收信息的能力受到参数量的限制,在理论上,有人提出了条件计算的方法,即网络的某些部分在每个实例都基础上处于活跃的状态。因此可以通过门
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...
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...
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), ...
In thiswork, we address these challenges and f inally realize the promise of conditionalcomputation, achieving greater than 1000x improvements in model capacity withonly minor losses in computational eff iciency on modern GPU clusters. We in-troduce a Sparsely-Gated Mixture-of-Experts layer (MoE)...
A Pytorch implementation of Sparsely GatedMixture of Experts, for massively increasing the capacity (parameter count) of a language model while keeping the computation constant. It will mostly be a line-by-line transcription of the tensorflow implementationhere, with a few enhancements. ...
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...
Sparse MoE backbone:稀疏MoE层可以看一下《The Sparsely-Gated Mixture-of-Experts Layer》这篇文章。专家(以输入激活的模型部分)是MLP。LIMoE包含多个MoE层。在这些层,每个token\textbf{x}\in \mathbb{R}^D由E个专家中的K个进行稀疏处理。为了选择K,轻量路由预测每个token的门权重:g(\textbf{x})=\mathtt...