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...
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从后来的研究来看,比如谷歌文章Mixture-of-Experts with Expert Choice Routing, 2022 NIPS还是聚焦在设计Gating策略,优化算法精度,本质上是让token更加精准的路由到理想的expert是关键目标,不能让有些expert 饿死(undertrain)也不能让有的负载太高,否则计算系统设计上不容易设计解决均衡问题、分布式计算带来的动态通信均...
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...
A Pytorch implementation of Sparsely Gated Mixture 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 implementation here, with a few enhancements. Update: Yo...
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)...
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...
I -FaadeNet: An Illumination-invariant Faade Recognition Network Leveraging Sparsely Gated Mixture of Multi-color Space Experts for Aerial Oblique Imagerydoi:10.14358/PERS.23-00033R2SHENZHEN (Guangdong Sheng, China : East)ZURICH (Switzerland)COLOR space...
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...