深度学习-Transformer 黄海广 副教授 2 03 Transformer的训练 本章目录 01 Transformer介绍 02 Transformer的工作流程 04 BERT 3 1.Transformer介绍 01 Transformer介绍 03 Transformer的训练 02 Transformer的工作流程 4 1.Transformer介绍 为什么需要用transformer 其实在之前我们使用的是RNN（或者是其的单向或者双向变种LSTM/GRU等） 来 作为编解码器。RNN模块每次只能够吃进一个输入token和前一次的隐藏状态，然 后得到输出。它的时序结构使得这个模型能够得到长距离的依赖关系，但是这也 使得它不能够并行计算，模型效率十分低。 在没有transformer的时候，我们 5 1.Transformer介绍 Seq2Seq任务 Seq2Seq 任务指的是输入和输出都是 序列的任务，输出的长度不确定时采 用的模型，这种情况一般是在机器翻 译的任务中出现，将一句中文翻译成 英文，那么这句英文的长度有可能会 比中文短，也有可能会比中文长，所 以输出的长度就不确定了。 上图，输入的中文长度为4，输出的英文长度为2 6 1.Transformer介绍 Encoder-Decoder模型

1 2023年06月 深度学习-Vision Transformer (ViT) 黄海广 副教授 2 03 模型训练策略 本章目录 01 背景知识 02 模型介绍 04 模型的缺点与改进 05 模型的代码实现 3 1.背景知识 03 模型训练策略 01 背景知识 02 模型介绍 04 模型的缺点与改进 05 all you need的文章，开创性地提出了 在序列转录领域，完全抛弃 CNN和RNN，只依赖Attention-注 意力结构的简单的网络架构， 名为Transformer；论文实现的 任务是机器翻译。 Transformer结构 Multi-Head Attention Add & Norm Input Embedding Output Embedding Feed Inputs Outputs (shifted right) Positional Encoding Positional Encoding 1.背景知识 6 为什么需要用transformer Transformer原本是用来做 NLP的工作的，所以ViT的 首要任务是将图转换成词 的结构，这里采取的方法 是如上图左下角所示，将 图片分割成小块，每个小 块就相当于句子里的一个 词。这里把每个小块称作

Framework containers are no longer tested on Pascal GPU architectures. ‣ Transformer Engine is a library for accelerating Transformer models on NVIDIA GPUs. It includes support for 8-bit floating point (FP8) inference performance with lower memory utilization. Transformer Engine also includes a collection of highly optimized modules for popular Transformer architectures and an automatic mixed precision-like TransformerXL model: This transformer-based language model has a segment-level recurrence and a novel relative positional encoding. The enhancements that were introduced in Transformer-XL help capture better

Normalization: bn, gn, ln?  激活函数: relu, leaky_relu, swish ?  Backbone: resnet, hrnet, mobilenet, transformer?  多任务模型: share-bottom, mmoe, ple?  特征选择/生成: Age, sex, comment, click… 解决方案: 超参搜索 效果提升 模型理解 Blade  推荐模型优化: 千亿特征 3. 工程优化 RingAllReduce + 层级级联 EasyVision 多机多卡性能对比 工程优化: 数据并行  M6模型  Transformer模型: RapidFormer  人脸分类模型: 超大softmax  3D卷积模型 M6模型 RapidFormer性能 工程优化: 模型并行(Whale)  FP16 / Op融合(Fusion Stitch)  MILR: Blade Disc 工程优化: Blade模型推理 Dynamic Shape Compiler for Machine Learning Workloads EmbeddingVariable [No Hash Conflict] 特征准入/淘汰 Adaptive Embedding 训练: 推理: Ring All-reduc同步训练

a similar effect in the world of Natural Language Processing (NLP) (see Figure 1-2), where the Transformer architecture significantly beat previous benchmarks such as the General Language Understanding models like BERT4 and GPT5 models have demonstrated additional improvements on NLP-related tasks. BERT spawned several related model architectures optimizing its various aspects. GPT-3 has captured the attention over the Transformer Encoder architecture that is the leading architecture being used for complex NLP tasks such as translation. The NAS generated architecture, which is named Evolved Transformer8, achieves

meaningfully represent these inputs using a small number of numerical features, will help us solve tasks related to these inputs. Ideally this representation is such that similar inputs have similar representations quite a journey, let's pause and ponder over what we learnt. Some more thoughts and optimizations related to Embeddings So far we learnt about embeddings, how pre-trained embeddings are useful, and gave mechanism, which forms the backbone of the state of the art NLP model architectures such as the Transformer, which is now showing great promise in computer vision applications as well! Learn Long-Term Dependencies

Juniper Networks hardware and software products are Year 2000 compliant. Junos OS has no known time-related limitations through the year 2038. However, the NTP application is known to have some difficulty automates the creation and management of virtualized networks to connect, isolate, and secure cloud workloads and services seamlessly across private and public clouds. Cloud-Native Contrail Networking (CN2) end-to-end virtual networking and security for cloud-native containerized and virtual machine (VM) workloads, across multi-cluster compute and storage environments, all from a central point of control. It

Therefore, they can upgrade components on the server side with zero maintenance downtime, optimize workloads with a clear view of what end users are doing, ensure authentication, authorization, and auditing pure SQL for both data processing, e.g. ETL, and online analytics processing(OLAP), e.g. BI. All workloads can be done on one platform, using one copy of data, with one SQL interface. ADMIN GUIDE 1 Kyuubi enables simplified, secure access to any cluster resource through an entry point to deploy different workloads for end(remote) users. Behind this single entry, admin- istrators have a single point for configuration

Therefore, they can upgrade components on the server side with zero maintenance downtime, optimize workloads with a clear view of what end users are doing, ensure authentication, authorization, and auditing pure SQL for both data processing, e.g. ETL, and online analytics processing(OLAP), e.g. BI. All workloads can be done on one platform, using one copy of data, with one SQL interface. ADMIN GUIDE 1 Kyuubi enables simplified, secure access to any cluster resource through an entry point to deploy different workloads for end(remote) users. Behind this single entry, admin- istrators have a single point for configuration

Therefore, they can upgrade components on the server side with zero maintenance downtime, optimize workloads with a clear view of what end users are doing, ensure authentication, authorization, and auditing pure SQL for both data processing, e.g. ETL, and online analytics processing(OLAP), e.g. BI. All workloads can be done on one platform, using one copy of data, with one SQL interface. ADMIN GUIDE 1 Kyuubi enables simplified, secure access to any cluster resource through an entry point to deploy different workloads for end(remote) users. Behind this single entry, admin- istrators have a single point for configuration