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...
· 《熬夜整理》保姆级系列教程-玩转Wireshark抓包神器教程(8)-Wireshark的TCP包详 · 一个有趣的插件,让写代码变成打怪升级的游戏 · 任务系统之任务流程可视化 Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer 笔记 2024-10-14 14:0228004970:59 ~ 1:39 MENU 博客...
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...
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 ...
Update: You should now useST Mixture of Experts $ pip install mixture_of_experts Usage importtorchfromtorchimportnnfrommixture_of_expertsimportMoEmoe=MoE(dim=512,num_experts=16,# increase the experts (# parameters) of your model without increasing computationhidden_dim=512*4,# size of hidden ...
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), ...
The sparsely-gated Mixture of Experts (MoE) can magnify a network capacity with a little computational complexity. In this work, we investigate how multi-lingual Automatic Speech Recognition (ASR) networks can be scaled up with a simple routing algorithm in order to achieve better accuracy. More...
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)...