From Transformers to Performers: Approximating Attention

A few weeks ago researchers from Google, the University of Cambridge, DeepMind and the Alan Turin Institute released the paper Rethinking Attention with Performers, which seeks to find a solution to the softmax bottleneck problem in transformers [1]. Their approach exploits a clever mathematical trick, which I will explain in this article.

Prerequisites:

  • Some knowledge of transformers
  • Kernel functions

Topics covered:

  • Why transformers?
  • The problem with transformers
  • Sidestepping the softmax bottleneck

Why transformers?

In essence, the Transformer is a model designed to work efficiently with sequential data, and it is in fact employed heavily in Natural Language Processing (NLP) tasks, which require handling sequences of words/letters. Unlike other sequential models, the transformer exploits attention mechanisms to process sequential data in parallel (ie: not needing to go through one word/input at a time) [2].

#complexity #machine-learning #transformers #nlp #performer

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From Transformers to Performers: Approximating Attention

From Transformers to Performers: Approximating Attention

A few weeks ago researchers from Google, the University of Cambridge, DeepMind and the Alan Turin Institute released the paper Rethinking Attention with Performers, which seeks to find a solution to the softmax bottleneck problem in transformers [1]. Their approach exploits a clever mathematical trick, which I will explain in this article.

Prerequisites:

  • Some knowledge of transformers
  • Kernel functions

Topics covered:

  • Why transformers?
  • The problem with transformers
  • Sidestepping the softmax bottleneck

Why transformers?

In essence, the Transformer is a model designed to work efficiently with sequential data, and it is in fact employed heavily in Natural Language Processing (NLP) tasks, which require handling sequences of words/letters. Unlike other sequential models, the transformer exploits attention mechanisms to process sequential data in parallel (ie: not needing to go through one word/input at a time) [2].

#complexity #machine-learning #transformers #nlp #performer

Ajay Kapoor

1624252974

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Chelsie  Towne

Chelsie Towne

1596716340

A Deep Dive Into the Transformer Architecture – The Transformer Models

Transformers for Natural Language Processing

It may seem like a long time since the world of natural language processing (NLP) was transformed by the seminal “Attention is All You Need” paper by Vaswani et al., but in fact that was less than 3 years ago. The relative recency of the introduction of transformer architectures and the ubiquity with which they have upended language tasks speaks to the rapid rate of progress in machine learning and artificial intelligence. There’s no better time than now to gain a deep understanding of the inner workings of transformer architectures, especially with transformer models making big inroads into diverse new applications like predicting chemical reactions and reinforcement learning.

Whether you’re an old hand or you’re only paying attention to transformer style architecture for the first time, this article should offer something for you. First, we’ll dive deep into the fundamental concepts used to build the original 2017 Transformer. Then we’ll touch on some of the developments implemented in subsequent transformer models. Where appropriate we’ll point out some limitations and how modern models inheriting ideas from the original Transformer are trying to overcome various shortcomings or improve performance.

What Do Transformers Do?

Transformers are the current state-of-the-art type of model for dealing with sequences. Perhaps the most prominent application of these models is in text processing tasks, and the most prominent of these is machine translation. In fact, transformers and their conceptual progeny have infiltrated just about every benchmark leaderboard in natural language processing (NLP), from question answering to grammar correction. In many ways transformer architectures are undergoing a surge in development similar to what we saw with convolutional neural networks following the 2012 ImageNet competition, for better and for worse.

#natural language processing #ai artificial intelligence #transformers #transformer architecture #transformer models

Carmen  Grimes

Carmen Grimes

1595505720

How to Test Mobile App Performance: 3 Key Components - DZone Performance

You’ve probably interacted with an app on your phone or tablet that’s slow, takes a long time to load, freezes, or even crashes on you altogether.

Frustrating, right?

On the flip side, you can probably think of an app that you love to use because from day one, it’s never given you any trouble.

Or maybe you never paid any mind to an app that works quickly, because isn’t that how it’s supposed to be?

So, what causes one app to be crash-prone and another, fast, and reliable?

Whether an app has good or bad performance depends on three factors: the backend, the network, and the app itself running on the device.

A developer or mobile tester can measure the performance of an application in different scenarios.

For example, they can test for when there’s a concurrency of users on the app at the same time, on different devices (which vary in hardware resources and screen sizes), and multiple networks such as 3G, 4G, Wifi, and more.

The reality is that many variables affect the performance of a mobile application. Moreover, a user may have a very bad experience with your app and the cause might not even have anything to do with the code or its implementation.

But, by running performance tests for each of these three factors, you’ll be able to identify problems and optimize your app for the best user experience possible.

Keep reading as we’ll cover the different types of tests for each factor, what to measure, and what tools are available to help you along the way.

1st Mobile Performance Factor: The Backend

A mobile app’s backend architecture is generally based on an application server, a web server, and a database.

**When it comes to the backend, the things related to performance that are important to know when an app is under load are the server’s response times, database queries times, and the server’s resource usage. **

Using this information, it’s easier to detect issues such as:

  • High server response times
  • Bottlenecks or breakpoints in the database and application server resources
  • Poor implementation of escalation policies

So what kind of tests are normally run to check the app’s backend performance? Load tests.

This is when you simulate load on the backend in different ways, whether it be through stress testing, peak testing, endurance testing, load testing, etc.

In general, the objective of these tests is to understand how the backend systems of an app behave and handle a certain volume of concurrent users.

Several tools allow you to load test your mobile app. The most commonly used ones include:

Apache JMeter – the number one open-source load testing tool

Gatling– a developer-friendly, open-source load testing tool with scripts written in Scala

BlazeMeter – a cloud performance testing platform that scales your JMeter or Gatling tests for a greater amount of concurrent users

2nd Mobile Performance Factor: The Network

With regards to the network that the device is connected to, there are two key things to measure: latency and bandwidth.

  • Latency is the time that elapses when information is sent on the network (measured in milliseconds).
  • Bandwidth is the maximum capacity (the amount of data) that can be transmitted through the network (measured in bits per second).

For mobile performance, the lower the latency and the higher the bandwidth, the better.

An app’s performance can vary depending on, for example, whether it’s connected to a 3G network or a 4G network, and unfortunately, this is beyond an app developer or tester’s control.

But, it is possible to incorporate the network during the mobile app performance testing process, simulating the different types of networks and measuring their impact on the response times, both on the server-side and the client-side.

#tutorial #performance #mobile apps #load testing #mobile testing #mobile app performance #client side performance

Edna  Bernhard

Edna Bernhard

1596525540

A Deep Dive Into the Transformer Architecture

Transformers for Natural Language Processing

It may seem like a long time since the world of natural language processing (NLP) was transformed by the seminal “Attention is All You Need” paper by Vaswani et al., but in fact that was less than 3 years ago. The relative recency of the introduction of transformer architectures and the ubiquity with which they have upended language tasks speaks to the rapid rate of progress in machine learning and artificial intelligence. There’s no better time than now to gain a deep understanding of the inner workings of transformer architectures, especially with transformer models making big inroads into diverse new applications like predicting chemical reactions and reinforcement learning.

Whether you’re an old hand or you’re only paying attention to transformer style architecture for the first time, this article should offer something for you. First, we’ll dive deep into the fundamental concepts used to build the original 2017 Transformer. Then we’ll touch on some of the developments implemented in subsequent transformer models. Where appropriate we’ll point out some limitations and how modern models inheriting ideas from the original Transformer are trying to overcome various shortcomings or improve performance.

What Do Transformers Do?

Transformers are the current state-of-the-art type of model for dealing with sequences. Perhaps the most prominent application of these models is in text processing tasks, and the most prominent of these is machine translation. In fact, transformers and their conceptual progeny have infiltrated just about every benchmark leaderboard in natural language processing (NLP), from question answering to grammar correction. In many ways transformer architectures are undergoing a surge in development similar to what we saw with convolutional neural networks following the 2012 ImageNet competition, for better and for worse.

#natural language processing #ai artificial intelligence #transformers #transformer architecture #transformer models #ai