Illustrated Guide to Transformers Neural Network: A step by step explanation

Summary

The video explains how transformer models utilize an attention mechanism to achieve state-of-the-art natural language processing, overcoming limitations with recurrent neural networks. It provides an intuitive understanding of the attention mechanism using a text generation example. The transformer architecture contains encoder and decoder modules with multi-headed self-attention and point-wise feedforward layers. Attention allows the model to focus on relevant words in the context. The video walks through how the modules encode input into an abstract representation and decode output step-by-step using attention weights and masking to prevent seeing future words.

Takeaways

• 😲 Transformers are attention-based encoder-decoder neural networks that outperform RNNs for sequence tasks.
• 👀 The attention mechanism allows the model to focus on relevant words when encoding and decoding.
• 📝 Transformer encoder maps input to an abstract representation with positional encodings.
• 🔁 Encoder layers have multi-headed self-attention and feedforward layers.
• ⏭ Decoder generates output sequences auto-regressively.
• 🤝 Decoder attends to encoder output to focus on relevant input.
• 😎 Masking prevents decoder from conditioning on future tokens.
• 🧠 Multiple stacked encoder and decoder layers boost representational power.
• 🤖 Transformers achieve state-of-the-art results for tasks like translation and text generation.
• ⬆️ The attention mechanism is key to the transformer architecture.

Q & A

How do transformers work compared to RNNs like GRUs and LSTMs?

Transformers use an attention mechanism that allows them to learn dependencies regardless of distance between words in a sequence. This gives them a potentially infinite context window compared to RNNs like GRUs and LSTMs which have a fixed size hidden state and struggle with longer term dependencies.

What is the encoder-decoder architecture in transformers?

The encoder maps an input sequence to a continuous representation with all learned information. The decoder then takes this representation to generate an output sequence step-by-step while attending to the input sequence via the encoder output.

What are multi-headed self attention and masking in transformers?

Multi-headed self attention allows the model to associate different words in the input. Masking prevents the decoder from attending to future words not yet generated.

How does the decoder generate the output sequence?

The decoder is auto-regressive, using its own previous outputs as additional inputs. It attends to the relevant encoder output and its own previous outputs to predict the next word probabilistically.

What is the purpose of having multiple stacked encoder and decoder layers?

Stacking layers allows the model to learn different combinations of attention, increasing its representational power and predictive abilities.

Why use residual connections and layer normalization?

Residual connections help gradients flow during training. Layer normalization helps stabilize training over many epochs.

What are the key components of the transformer architecture?

The key components are the encoder-decoder structure, multi-headed self attention, positional encodings, residual connections, layer normalization and masking.

How are transformers used in NLP applications?

Transformers have led to state-of-the-art results in tasks like machine translation, question answering, summarization and many others.

Why were transformers introduced?

To overcome the limitations of RNN models like GRUs/LSTMs in learning long-range dependencies, via the self-attention mechanism.

What was the breakthrough application of transformers?

The paper 'Attention is All You Need' introduced transformers for neural machine translation, massively outperforming prior sequence-to-sequence models.

Outlines

00:00

😃 Introducing Transformers

The first paragraph introduces transformers, describing them as powerful natural language processing models that are breaking records and pushing state-of-the-art in various applications like machine translation, chatbots, and search engines. It notes transformers are popular in deep learning today.

05:03

😊 Explaining Attention Mechanism

The second paragraph explains the attention mechanism, which is key to understanding transformers. It provides an intuitive example of a text generation model writing a sci-fi story, and how it can reference relevant words when generating new text using attention. The power of attention is it has a theoretically infinite window to reference.

10:04

🤓 Walkthrough of Transformer Model

The third paragraph provides a walkthrough explaining the transformer model mechanics on a conversational chatbot example. It steps through the encoder and decoder layers, explaining embedding layers, multi-headed self-attention, masking, residual connections, and more.

Keywords

💡Transformers

Transformers are a novel neural network architecture introduced in the paper 'Attention Is All You Need'. They rely entirely on attention mechanisms and have no recurrence, unlike previous models like RNNs and LSTMs. Transformers have revolutionized natural language processing by achieving state-of-the-art results on tasks like machine translation. The video explains how transformers work by walking through an example of using them to build a conversational chatbot.

💡Attention mechanism

The attention mechanism is the key innovation that enables transformers to achieve superior performance. It allows the model to learn what words or parts of the input sequence to 'attend' to when generating each output. For example, when outputting the word 'fine' in the response 'I am fine', the model can learn to heavily attend to the input word 'you' to determine the appropriate response. Attention allows transformers to incorporate context from the entire input sequence.

💡Encoder

The encoder is the component of a transformer that processes the input sequence. It uses stacked encoder layers, each consisting of a multi-headed self-attention module followed by a feedforward network. The encoder maps the input into a continuous vector representation containing information about all words and their relationships, as determined by the attention mechanism.

💡Decoder

The decoder is the component that uses the encoder output to generate the target sequence. Like the encoder, it stacks decoder layers containing multi-headed attention. The decoder attends to the encoder output as well as its own previous outputs. It is auto-regressive, generating the output sequence one word at a time while preventing attention to future words.

💡Multi-headed attention

Multi-headed attention is used in both the encoder and decoder. It allows the model to focus on different aspects of the sequence by splitting the input into multiple 'heads' and applying scaled dot-product attention to each. For example, one head may focus on positional relationships while another attends to semantic similarity. The multiple outputs are concatenated, allowing the model to integrate different types of relevant information.

💡Residual connections

Residual connections are used throughout the transformer, where the output of each sub-layer is added to its input and then normalized. These connections allow information and gradients to flow directly across stacks of layers, avoiding the vanishing gradient problem and enabling effective training of very deep models.

💡Positional encoding

Since transformers have no recurrence, positional encoding is used to inject information about the order of sequence elements. For example, sine and cosine functions are used to encode the position of each word. This allows the model to incorporate sequential relationships learned during training.

💡Masking

Masking prevents the decoder from attending to future sequence positions that it has not yet generated. For example, when generating the word 'am', the decoder multi-headed attention is masked to prevent attention to the subsequent word 'fine'. This forces a uni-directional generation order.

💡Backpropagation

Backpropagation is the algorithm used to train neural networks, including transformers. The loss from the output is propagated backwards through the network layers to update the model weights and minimize the loss. Multiple passes of backpropagation on large datasets enable transformers to learn complex language relationships captured through its attention mechanisms.

💡Recurrent neural networks

Recurrent neural networks like LSTMs were previously state-of-the-art for sequence tasks like language modeling. The video explains how transformers have surpassed RNNs in performance. RNNs suffer from short-term memory, while transformers use attention to incorporate long-range context. This gives transformers superior ability to process long sequences.

Highlights

Transformers are taking the NLP world by storm with their incredible performance

Transformers use an attention mechanism to achieve state-of-the-art results by associating words with other relevant words

Attention allows models to reference the entire context while RNNs have a limited short-term memory

The Transformer introduced a novel attention-based encoder-decoder architecture

The encoder maps inputs to an abstract representation, the decoder generates outputs using that representation

Multi-headed self attention allows the model to associate each word with every other word

Queries, keys and values are computed to determine which words to focus attention on

Residual connections allow gradients to flow directly through the network layers

The decoder generates text autoregressively, attending only to past generated words

Masking prevents the decoder from looking ahead at future words

The decoder matches encoder outputs to its own previous outputs

Multiple encoder and decoder layers allow learning different attention representations

The Transformer outperforms RNNs for longer sequences due to lack of short-term memory

Transformers now enable unprecedented NLP achievements across many applications

The attention mechanism is key to the Transformer's state-of-the-art performance

Transcripts

transformers are taking the natural

language processing world by storm these

incredible models are breaking multiple

NLP records and pushing the

state-of-the-art they are used in many

applications like machine language

translation conversational chat BOTS and

even a power better search engines

transformers are the rage and deep

learning nowadays but how do they work

why are they outperformed a previous

king of sequence problems like recurrent

neural networks gr use and LS tiens

you've probably heard of different

famous transformer models like Burt CBT

and GB t2 in this video we'll focus on

the one paper that started it all

attention is all you need to understand

transformers we first must understand

the attention mechanism to get an

intuitive understanding of the attention

mechanism let's start with a fun text

generation model that's capable of

writing its own sci-fi novel we'll need

to prime in a model with an arbitrary

input and a model will generate the rest

okay

let's make the story interesting as

aliens entered our planet and began to

colonize earth a certain group of

extraterrestrials begin to manipulate

our society through their influence of a

certain number of the elite of the

country to keep an iron grip over the

populace by the way I then just make

this up this was actually generated by

open AI is GPT to transformer model

shout out to hugging face for an awesome

interface to play with I'll provide a

link in description okay so the model is

a little dark but what's interesting is

how it works as a model generate tax

word by word it has the ability to

reference or tend to words that is

relevant to the generated word how the

model knows which were to attend to is

all learned while training with

backpropagation our intends are also

capable of looking at previous inputs

too but the power of the attention

mechanism is that it doesn't suffer from

short-term memory rnns have a shorter

window to reference from so when a story

gets longer rnns can't access word

generated earlier in the sequence

this is still true for gr use and L

STM's although they do have a bigger

capacity to achieve longer term memory

therefore having a longer window to

reference from the attention mechanism

in theory and given enough compute

resources have an infinite window to

reference from therefore being capable

of using the entire context of the story

while generating the text this power was

demonstrated in the paper attention is

all you need when the author's introduce

a new novel neural network called the

Transformers which is an attention based

encoder decoder type architecture on a

high level the encoder Maps an input

sequence into an abstract continuous

representation that holds all the

learned information of that input to

decoder then takes our continuous

representation and step by step

generates a single output while also

being fed to previous output let's walk

through an example

the attention is all you need paper

applied to transformer model on a neuro

machine translation problem our

demonstration of the transformer model

would be a conversational chat bot the

example with taking an input tax hi how

are you and generate the response I am

fine

let's break down the mechanics of the

network step by step the first step is

feeding our input into a word embedded

layer a word embedding layer can be

thought of as a lookup table to grab a

learn factor of representation of each

word neural networks learned through

numbers so each word maps to a vector

with continuous values to represent that

word

next step is to inject positional

information into the embeddings because

a transformer encoder has no recurrence

like recurrent known networks we must

add information about the positions into

the input embeddings

this is done using positional encoding

the authors came up with a clever trick

using sine and cosine functions we won't

go into the mathematical details of the

positional codings in this video but

here are the basics for every odd time

step create a vector using the cosine

function for every even time step create

a vector using the sine function then

add those vectors to their corresponding

embedding vector this successfully gives

the network information on two positions

of each vector the sine and cosine

functions were chosen in tandem because

they have linear properties the model

can easily learn to attend to now we

have the encoder layer the encoder

layers job is to map all input sequence

into an abstract continuous

representation that holds the learned

information for that entire sequence it

contains two sub modules multi-headed

attention followed by a fully connected

network there are also residual

connections around each of the two sub

modules followed by a layer

normalization to break this down let's

look at the multi headed attention

module multi-headed attention Indian

code applies a specific attention

mechanism called self attention self

attention allows a model to associate

each individual word in the input to

other words in the input so in our

example it's possible that our model can

learn to associate the word you with how

M are it's also possible that the model

learns that word structured in this

pattern are typically a question

so respond appropriately to achieve self

attention we feed the input into three

distinct fully connected layers to

create the query key and value vectors

what are these vectors exactly I found a

good explanation on stock-exchange

stating the query key and value concept

comes from the retrieval system for

example when you type a query to search

for some video on YouTube

the search engine will map your query

against a set of keys for example video

title description etc associated with

candidate videos in the database then

present you with the best match video

let's see how this relates to self

attention the queries and keys undergoes

a dot product matrix multiplication to

produce a score matrix the score matrix

determines how much focus should a word

be put on other words so each word will

have a score to correspond to other

words in the time step the higher score

the more the focus this is how queries

are mapped to keys then the scores get

scaled down by getting divided by the

square root of the dimension of the

queries and the keys this is to allow

for more stable gradients as multiplying

values can have exploding effects next

you take the softmax the scaled score to

get the attention weights which gives

you probability values between 0 & 1

by doing the softmax the higher scores

get heightened and the lower scores are

depressed this allows the model to be

more confident on which words to attend

to then you take the attention weights

and multiply it by your value vector to

get an output vector the higher softmax

scores will keep the value of the words

the model learn is more important the

lower scores will drown out their

irrelevant words you feed the output

vector into a linear layer to process to

make this a multi-headed attention

computation you need to split the query

key in value into adding vectors before

applying self attention to split vectors

that goes through the same self

attention process individually each self

attention process is called a head each

head produces an output vector that gets

concatenated into a single vector before

go through in a final linear layer in

theory each head would learn something

different therefore giving the encounter

model more representation power okay so

that's multi-headed attention to sum it

up multi-headed attention is a module in

a transformer network that

you to the attention waits for the input

and produces an output vector with

encoded information on how each word

should attend to all other words in a

sequence

next step the multi-headed attention

output vector is added to the original

input this is called a residual

connection the output of the residual

connection goes through a layer

normalization the normalized residual

output gets fed into a point-wise

feed-forward network for further

processing the point-wise feed-forward

network are a couple of linear layers

with a relict evasion in between the

output of that is again added to the

input of the point-wise feed-forward

network and further normalized the

residual connections helps the network

train by allowing gradients to flow

through the networks directly the layer

normalizations are used to stabilize the

network which results in sustained

producing the training time necessary

and a point-wise feed-forward layer are

used to further process the attention

output potentially giving it a richer

representation

and that wraps up the encoded layer all

these operations is for the purpose of

encoding the input to a continuous

representation with attention

information this will help the decoder

focus on the appropriate words in the

input during the decoding process you

can stack the encoder and times to

further encode the information where

each layer has the opportunity to learn

different attention representations

therefore potentially boosting the

predictive power of the transformer

network now we move on to the decoder

the decoders job is to generate text

sequences the decoder has similar sub

layers as the encoder it has two

multi-headed attention layers a

point-wise feed-forward layer with

residual connections and layer

normalization after each sub layer these

sub layers behave similarly to layers in

the encoder but each multi-headed

attention layer has a different job it's

capped off with a linear layer that acts

like a classifier and a soft Max to get

the word probabilities the decoder is

auto regressive it takes in the list of

previous outputs as inputs as well as

the encoder outputs that contains the

attention information from the input the

decoder stops decoding when it generates

an end token as an output let's walk

through the decoding steps the input

goes through an embedding layer in a

position on coding layer to get

positional embeddings the positional

embeddings gets fed into the first

multi-headed attention layer which

computes the attention score for the

decoders input this multi-headed

attention layer operates slightly

different since the decoders

autoregressive and generates the

sequence word-by-word you need to

prevent it from condition into future

tokens for example when computing

attention scores on the word am you

should not have access to the word fine

because our word is a future word that

was generated after the word am should

only have access to itself and the words

before this is true for all other words

where they can only attend to previous

words we need a method to prevent

computing attention scores for future

words this method is called masking

to prevent the decoder from looking at

future tokens you apply a look-ahead

mask the mask is added before

calculating the softmax and after

scaling the scores let's take a look at

how this works the mask is a matrix

that's the same size as the attention

scores filled with values of materials

and negative infinity x' when you add

the mask to the scale attention scores

you get a matrix of scores with the top

right triangle filled with negative

infinity x' the reason for this is once

you take the softmax of the mask scores

the negative infinity is get zeroed out

leaving a zero attention score for

future tokens as you can see the

attention scores for M have values for

itself and all other words before it but

zero for the word fine this essentially

tells the model to put no focus on those

words this masking is the only

difference on how the attention scores

are calculated in the first multi-headed

attention layer this layers still have

multiple heads that the masks are being

applied to before getting concatenated

and fed through a linear layer for

further processing the output of the

first multi-headed attention is a mask

output vector with information on how

the model should attend on the decoders

inputs

now on to the second multi-headed

attention layer for this layer the

encoders output are the queries in the

keys in the first multi-headed attention

layer outputs are the values this

process matches the encoders input to

the decoders input allowing the decoder

to decide which encoder input is

relevant to put focus on the output of

the second multi-headed attention goes

through a point wise feed-forward layer

for further processing the output of the

final point wise feed-forward layer goes

through a final linear layer that access

a classifier the classifier is as

biggest number of classes you have for

example if you have 10,000 classes for

10,000 words the output of that

classifier will be of size 10,000 the

output of the classifier again gets fed

into a soft max layer the soft max layer

produced probability scores between 0

and 1 for each class we take the index

of the highest probability score and

that equals our predicted word the

decoder didn't taste the output and adds

it to the list of decoder inputs and

continue decoding again until end token

is predicted for our case the highest

probability prediction is the final

class which is assigned to the end token

this is how the decoder generates the

output the decoder can be stacked n

layers high each layer taking in inputs

from the encoder and the layers before

it by stacking layers the model can

learn to extract and focus on different

combinations of attention from its

attention heads potentially boosting its

predictive power and that's it that's

the mechanics of the transformers

transformers leverage the power of the

attention mechanism to make better

predictions recur known networks trying

to achieve similar things but because

they suffer from short term memory

transformers are usually better

especially if you want to encode or

generate longer sequences because of the

transformer architecture the natural

language processing industry can now

achieve unprecedented results if you

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