Transformer architecture

Introduction Large-language models (LLMs) have gained tons of popularity lately with the releases of ChatGPT, GPT-4, Bard, and more. All these LLMs are based on the transformer neural network architecture. The transformer architecture was first introduced in the paper " The most popular variety of transformers are currently these GPT models. The only purpose of these models is to receive a prompt (an input) and predict the next token/word that comes after this input. Nothing more, nothing less. Note: Not all large-language models use a transformer architecture. However, models such as GPT-3, ChatGPT, GPT-4 & LaMDa use the (decoder-only) transformer architecture. Overview of the (decoder-only) Transformer model It is key first to understand the input and output of a transformer: • The input is a prompt (often referred to as context) fed into the transformer as a whole. There is no recurrence. • The output depends on the goal of the model. For GPT models, the output is a probability distribution of the next token/word that comes after the prompt. It outputs one prediction for the complete input. Next, it is essential to understand the key components that make up the decoder-only transformer architecture: • The embedding: the input of the transformer model is a prompt. This prompt needs to be embedded into something that the model can use. • The block(s): This is the main source of complexity. Each block contains a masked multi-head attention submodule, a feedforward network,...

Background [ ] Before transformers, most state-of-the-art NLP systems relied on gated RNNs, such as The terms "query", "key", "value" are borrowed from Previous work [ ] In 1992, In a fast weight controller, a Sequential processing [ ] Gated RNNs process n . Theoretically, the information from one token can propagate arbitrarily far down the sequence, if at every point the state continues to encode contextual information about the token. In practice this mechanism is flawed: the Self-attention [ ] These problems were addressed by attention mechanisms. Attention mechanisms let a model draw from the state at any preceding point along the sequence. The attention layer can access all previous states and weigh them according to a learned measure of relevance, providing relevant information about far-away tokens. A clear example of the value of attention is in last English word. Theoretically, this vector can encode information about the whole English sentence, giving the model all the necessary knowledge. In practice, this information is often poorly preserved by the LSTM. An attention mechanism can be added to address this problem: the decoder is given access to the state vectors of every English input word, not just the last, and can learn attention weights that dictate how much to attend to each English input state vector. When added to RNNs, attention mechanisms increase performance. In 2016, a new type of highly parallelizable decomposable attention was successfully combi...

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 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 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 ...

Abstract This paper introduces a novel transformer-based network architecture, FlowFormer, along with the Masked Cost Volume AutoEncoding (MCVA) for pretraining it to tackle the problem of optical flow estimation. FlowFormer tokenizes the 4D cost-volume built from the source-target image pair and iteratively refines flow estimation with a cost-volume encoder-decoder architecture. The cost-volume encoder derives a cost memory with alternate-group transformer~(AGT) layers in a latent space and the decoder recurrently decodes flow from the cost memory with dynamic positional cost queries. On the Sintel benchmark, FlowFormer architecture achieves 1.16 and 2.09 average end-point-error~(AEPE) on the clean and final pass, a 16.5\% and 15.5\% error reduction from the GMA~(1.388 and 2.47). MCVA enhances FlowFormer by pretraining the cost-volume encoder with a masked autoencoding scheme, which further unleashes the capability of FlowFormer with unlabeled data. This is especially critical in optical flow estimation because ground truth flows are more expensive to acquire than labels in other vision tasks. MCVA improves FlowFormer all-sided and FlowFormer+MCVA ranks 1st among all published methods on both Sintel and KITTI-2015 benchmarks and achieves the best generalization performance. Specifically, FlowFormer+MCVA achieves 1.07 and 1.94 AEPE on the Sintel benchmark, leading to 7.76\% and 7.18\% error reductions from FlowFormer. Publication:

Tweet Tweet Share Share Last Updated on January 6, 2023 We have already familiarized ourselves with the concept of self-attention as implemented by the Transformer attention mechanism for neural machine translation. We will now be shifting our focus to the details of the Transformer architecture itself to discover how self-attention can be implemented without relying on the use of recurrence and convolutions. In this tutorial, you will discover the network architecture of the Transformer model. After completing this tutorial, you will know: • How the Transformer architecture implements an encoder-decoder structure without recurrence and convolutions • How the Transformer encoder and decoder work • How the Transformer self-attention compares to the use of recurrent and convolutional layers Kick-start your project with my book self-study tutorials with working code to guide you into building a fully-working transformer model that can translate sentences from one language to another... Let’s get started. The Transformer Model Photo by Tutorial Overview This tutorial is divided into three parts; they are: • The Transformer Architecture • The Encoder • The Decoder • Sum Up: The Transformer Model • Comparison to Recurrent and Convolutional Layers Prerequisites For this tutorial, we assume that you are already familiar with: • • • The Transformer Architecture The Transformer architecture follows an encoder-decoder structure but does not rely on recurrence and convolutions in orde...

Transformer language models are an emerging technology that is gaining increasingly broad real-world use, for example in systems like GPT-3 , LaMDA , Codex , Meena , Gopher , and similar models. However, as these models scale, their open-endedness and high capacity creates an increasing scope for unexpected and sometimes harmful behaviors. Even years after a large model is trained, both creators and users routinely discover model capabilities – including problematic behaviors – they were previously unaware of. One avenue for addressing these issues is mechanistic interpretability, attempting to reverse engineer the detailed computations performed by transformers, similar to how a programmer might try to reverse engineer complicated binaries into human-readable source code. If this were possible, it could potentially provide a more systematic approach to explaining current safety problems, identifying new ones, and perhaps even anticipating the safety problems of powerful future models that have not yet been built. A previous project, the Circuits thread , has attempted to reverse engineer vision models, but so far there hasn’t been a comparable project for transformers or language models. In this paper, we attempt to take initial, very preliminary steps towards reverse-engineering transformers. Given the incredible complexity and size of modern language models, we have found it most fruitful to start with the simplest possible models and work our way up from there. Our aim...

From NLP to CV: Vision Transformer For a long time, convolutional neural networks (CNNs) have been the de facto standard in computer vision. On the other hand, in natural language processing (NLP), Transformer is today's prevalent architecture. Its spectacular success in the language domain inspired scientists to look for ways to adapt them for computer vision. Vision Transformer ( ViT) is proposed in the paper: An image is worth 16x16 words: transformers for image recognition at scale. It is the convolution-free architecture where transformers are applied to the image classification task. The idea is to represent an image as a sequence of image patches (tokens). Next, we process it by a transformer encoder as used in NLP. Figure 1: Vision Transformer ( Vit). Source: [1] The standard NLP transformer receives as input a 1D sequence of token embeddings. Let the initial 2D image have the shape (H, W, C). We need to convert it to the appropriate format: • Split the initial image into image patches of shape (P, P, C), where P is a patch size, and flatten them. Let N be the total number of patches. • Map flatten image patches to D dimensions with a trainable linear projection. The D is a constant latent vector size of all Transformer's layers. The output of this projection is called patch embeddings. • In akin to BERT's [class] token, we append a learnable class embedding (CLS) to the sequence of embedded patches. We will use only this class embedding to predict the output. • We...

In a previous post, we discussed a key innovation in sequence to sequence neural network architectures: each time step in the decoder network. This provides significantly more information bandwidth to the decoder via a learnable mechanism which is capable of determining which time-steps in the input sequence are relevant at each time-step being generated in the output sequence. Let's stop and think about that at a high-level. We have a mechanism which allows us to take a variable-length sequence and merge this information together to output a fixed-size vector. Doesn't that sound a lot like to role of a recurrent neural network? We introduced recurrence as a way to learn from variable-length sequences; but in an effort to squeeze more performance out of those recurrent networks, we accidentally stumbled across another way to process variable-length sequences. This begs the question, what if we tried to get rid of the recurrent layers and simply used attention everywhere? This is the premise behind a seminal paper from 2017, Overview • • • • • • • • • • • • • Swapping out recurrent layers with attention Let's first walk through what exactly it means to "replace the recurrent layers with attention". Recall that a standard recurrent neural network architecture involves evolving a hidden state across a sequence of inputs. We process each time step by combining information from the current input with information from the previous hidden state. This allows us to pass information...