The Transformer Model

Last Updated on January 6, 2023

We have already familiarized ourselves with the thought of self-attention as carried out by the Transformer consideration mechanism for neural machine translation. We will now be shifting our focus to the small print of the Transformer construction itself to seek out how self-attention could also be carried out with out relying on utilizing recurrence and convolutions.

In this tutorial, you will uncover the group construction of the Transformer model.

After ending this tutorial, you will know:

How the Transformer construction implements an encoder-decoder development with out recurrence and convolutions
How the Transformer encoder and decoder work
How the Transformer self-attention compares to utilizing recurrent and convolutional layers

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The Transformer Model
Photo by Samule Sun, some rights reserved.

Tutorial Overview

This tutorial is break up into three elements; they’re:

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 simply’re already conscious of:

The concept of consideration
The consideration mechanism
The Transformer consideration mechanism

The Transformer Architecture

The Transformer construction follows an encoder-decoder development nevertheless does not rely on recurrence and convolutions as a approach to generate an output.

The encoder-decoder development of the Transformer construction
Taken from “Attention Is All You Need“

In a nutshell, the responsibility of the encoder, on the left half of the Transformer construction, is to map an enter sequence to a sequence of regular representations, which is then fed proper right into a decoder.

The decoder, on the very best half of the construction, receives the output of the encoder together with the decoder output on the sooner time step to generate an output sequence.

At each step the model is auto-regressive, consuming the beforehand generated symbols as additional enter when producing the next.
– Attention Is All You Need, 2023.

The Encoder

The encoder block of the Transformer construction
Taken from “Attention Is All You Need“

The encoder consists of a stack of $N$ = 6 an an identical layers, the place each layer consists of two sublayers:

The first sublayer implements a multi-head self-attention mechanism. You have seen that the multi-head mechanism implements $h$ heads that acquire a (utterly completely different) linearly projected mannequin of the queries, keys, and values, each to offer $h$ outputs in parallel which may be then used to generate a remaining consequence.

The second sublayer is a very associated feed-forward group consisting of two linear transformations with Rectified Linear Unit (ReLU) activation in between:

$$textual content material{FFN}(x) = textual content material{ReLU}(mathbf{W}_1 x + b_1) mathbf{W}_2 + b_2$$

The six layers of the Transformer encoder apply the an identical linear transformations to the entire phrases throughout the enter sequence, nevertheless each layer employs utterly completely different weight ($mathbf{W}_1, mathbf{W}_2$) and bias ($b_1, b_2$) parameters to take motion.

Furthermore, each of these two sublayers has a residual connection spherical it.

Each sublayer can be succeeded by a normalization layer, $textual content material{layernorm}(.)$, which normalizes the sum computed between the sublayer enter, $x$, and the output generated by the sublayer itself, $textual content material{sublayer}(x)$:

$$textual content material{layernorm}(x + textual content material{sublayer}(x))$$

An obligatory consideration to recollect is that the Transformer construction cannot inherently seize any particulars concerning the relative positions of the phrases throughout the sequence as a result of it does not make use of recurrence. This information must be injected by introducing positional encodings to the enter embeddings.

The positional encoding vectors are of the an identical dimension as a result of the enter embeddings and are generated using sine and cosine capabilities of assorted frequencies. Then, they’re merely summed to the enter embeddings as a approach to inject the positional information.

The Decoder

The decoder block of the Transformer construction
Taken from “Attention Is All You Need“

The decoder shares quite a few similarities with the encoder.

The decoder moreover consists of a stack of $N$ = 6 an an identical layers which may be each composed of three sublayers:

The first sublayer receives the sooner output of the decoder stack, augments it with positional information, and implements multi-head self-attention over it. While the encoder is designed to deal with all phrases throughout the enter sequence regardless of their place throughout the sequence, the decoder is modified to attend solely to the earlier phrases. Hence, the prediction for a phrase at place $i$ can solely depend on the acknowledged outputs for the phrases that come sooner than it throughout the sequence. In the multi-head consideration mechanism (which implements quite a few, single consideration capabilities in parallel), that’s achieved by introducing a masks over the values produced by the scaled multiplication of matrices $mathbf{Q}$ and $mathbf{Okay}$. This masking is carried out by suppressing the matrix values that will in every other case correspond to illegal connections:

$$
textual content material{masks}(mathbf{QK}^T) =
textual content material{masks} left( begin{bmatrix}
e_{11} & e_{12} & dots & e_{1n}
e_{21} & e_{22} & dots & e_{2n}
vdots & vdots & ddots & vdots
e_{m1} & e_{m2} & dots & e_{mn}
end{bmatrix} correct) =
begin{bmatrix}
e_{11} & -infty & dots & -infty
e_{21} & e_{22} & dots & -infty
vdots & vdots & ddots & vdots
e_{m1} & e_{m2} & dots & e_{mn}
end{bmatrix}
$$

The multi-head consideration throughout the decoder implements quite a few masked, single-attention capabilities
Taken from “Attention Is All You Need“

The masking makes the decoder unidirectional (not just like the bidirectional encoder).
– Advanced Deep Learning with Python, 2023.

The second layer implements a multi-head self-attention mechanism very like the one carried out throughout the first sublayer of the encoder. On the decoder facet, this multi-head mechanism receives the queries from the sooner decoder sublayer and the keys and values from the output of the encoder. This permits the decoder to deal with the entire phrases throughout the enter sequence.

The third layer implements a very associated feed-forward group, very like the one carried out throughout the second sublayer of the encoder.

Furthermore, the three sublayers on the decoder facet even have residual connections spherical them and are succeeded by a normalization layer.

Positional encodings are moreover added to the enter embeddings of the decoder within the an identical technique as beforehand outlined for the encoder.

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Sum Up: The Transformer Model

The Transformer model runs as follows:

Each phrase forming an enter sequence is reworked proper right into a $d_{textual content material{model}}$-dimensional embedding vector.

Each embedding vector representing an enter phrase is augmented by summing it (element-wise) to a positional encoding vector of the an identical $d_{textual content material{model}}$ dimension, due to this fact introducing positional information into the enter.

The augmented embedding vectors are fed into the encoder block consisting of the two sublayers outlined above. Since the encoder attends to all phrases throughout the enter sequence, irrespective within the occasion that they precede or succeed the phrase into consideration, then the Transformer encoder is bidirectional.

The decoder receives as enter its private predicted output phrase at time-step, $t – 1$.

The enter to the decoder can be augmented by positional encoding within the an identical technique carried out on the encoder facet.

The augmented decoder enter is fed into the three sublayers comprising the decoder block outlined above. Masking is utilized throughout the first sublayer as a approach to stop the decoder from attending to the succeeding phrases. At the second sublayer, the decoder moreover receives the output of the encoder, which now permits the decoder to deal with the entire phrases throughout the enter sequence.

The output of the decoder lastly passes through a very associated layer, adopted by a softmax layer, to generate a prediction for the next phrase of the output sequence.

Comparison to Recurrent and Convolutional Layers

Vaswani et al. (2023) make clear that their motivation for abandoning utilizing recurrence and convolutions was based mostly totally on quite a few components:

Self-attention layers have been found to be earlier than recurrent layers for shorter sequence lengths and could also be restricted to consider solely a neighborhood throughout the enter sequence for very prolonged sequence lengths.

The number of sequential operations required by a recurrent layer depends on the sequence dimension, whereas this amount stays fastened for a self-attention layer.

In convolutional neural networks, the kernel width immediately impacts the long-term dependencies which may be established between pairs of enter and output positions. Tracking long-term dependencies would require using large kernels or stacks of convolutional layers that may improve the computational worth.

Summary

In this tutorial, you discovered the group construction of the Transformer model.

Specifically, you realized:

How the Transformer construction implements an encoder-decoder development with out recurrence and convolutions
How the Transformer encoder and decoder work
How the Transformer self-attention compares to recurrent and convolutional layers

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