# Chapter 3: Transition to Transformers: Attention Mechanisms

## 3.4 Detailed Breakdown of the Transformer Architecture

Transformers consist of an encoder and a decoder, each composed of multiple layers of sub-modules. The encoder is responsible for generating a representation of the input sequence, while the decoder is responsible for generating the output sequence.

In the encoder, the input sequence is first passed through a self-attention mechanism, which allows each position in the sequence to attend to all the other positions, learning a representation of the input sequence that takes into account the context of each position.

The output of the self-attention mechanism is passed through a feed-forward neural network that applies a non-linear transformation to each position independently. This process is repeated for multiple layers, allowing the encoder to capture increasingly complex representations of the input sequence.

The decoder, on the other hand, takes the representation generated by the encoder and uses it to generate the output sequence. At each step of the decoding process, the decoder attends to the encoder's output and the previously generated output to generate the next output token. The decoder also uses self-attention to allow each position in the output sequence to attend to all the other positions, ensuring that the generated output takes into account the context of each position. Like the encoder, the decoder also consists of multiple layers of sub-modules that apply non-linear transformations to the input at each position.

In this section, we will examine each of these sub-modules in detail. Note that these explanations will include a combination of pseudo-code and simplified Python code to illustrate the concepts.

### 3.4.1 Self-Attention Mechanism

As previously stated, the self-attention mechanism is a fundamental aspect of the Transformer model. This mechanism is responsible for determining the degree to which each word in the input sequence should be attended to when encoding a particular word.

The self-attention mechanism is a complex process that involves a series of calculations to determine the importance of each word in the sequence. This process can be broken down into several steps, including calculating the attention scores for each word, normalizing these scores, and finally computing the weighted average of the input sequence based on these scores.

By doing so, the self-attention mechanism can effectively capture the most relevant information from the input sequence and use it to generate a more accurate representation of the original text.

**Example:**

Here's a simplified Python code that could represent a single-head self-attention mechanism:

`import numpy as np`

import torch.nn.functional as F

def scaled_dot_product_attention(query, key, value):

# Calculate the dot product of the query and key

attention_logits = np.dot(query, key.T)

# Scale the logits by the square root of the dimension of the key

attention_logits = attention_logits / np.sqrt(key.shape[-1])

# Apply a softmax to the logits to get the attention weights

attention_weights = F.softmax(attention_logits, dim=-1)

# Multiply the values by the attention weights to get the output

output = np.dot(attention_weights, value)

return output, attention_weights

This simplified function represents the core of the self-attention mechanism: it takes as input the query, key, and value matrices, computes the attention weights, and returns a weighted sum of the values.

### 3.4.2 Multi-Head Attention

The Transformer model, a neural network architecture introduced in 2017, marked a significant breakthrough in natural language processing. One of the key features of the Transformer model is its use of multi-head attention. In traditional self-attention mechanisms, the input sequence is mapped to a sequence of queries, keys, and values with a single learned projection.

However, multi-head attention performs multiple such projections in parallel, each with a different learned linear projection of the input. By doing so, the model can capture different types of attention from different parts of the input.

The outputs of these parallel attention "heads" are then concatenated and linearly transformed to produce the final output. This allows the model to capture different aspects of the input in a more nuanced way.

For instance, one attention head may learn to focus on syntactic relationships, another on semantic relationships, and another on certain types of named entities. By combining the outputs of these attention heads, the Transformer model can learn to capture a wide range of linguistic phenomena, making it a powerful tool for natural language processing tasks.

**Example:**

Here's an example of how one might implement multi-head attention:

`class MultiHeadAttention(nn.Module):`

def __init__(self, d_model, num_heads):

super(MultiHeadAttention, self).__init__()

self.num_heads = num_heads

self.d_model = d_model

assert d_model % self.num_heads == 0

self.depth = d_model // self.num_heads

self.wq = nn.Linear(d_model, d_model)

self.wk = nn.Linear(d_model, d_model)

self.wv = nn.Linear(d_model, d_model)

self.dense = nn.Linear(d_model, d_model)

def split_heads(self, x, batch_size):

"""Split the last dimension into (num_heads, depth).

Transpose the result such that the shape is (batch_size, num_heads, seq_len, depth)

"""

x = x.view(batch_size, -1, self.num_heads, self.depth)

return x.permute(0, 2, 1, 3)

def forward(self, v, k, q, mask):

batch_size = q.shape[0]

q = self.wq(q) # (batch_size, seq_len, d_model)

k

= self.wk(k) # (batch_size, seq_len, d_model)

v = self.wv(v) # (batch_size, seq_len, d_model)

q = self.split_heads(q, batch_size) # (batch_size, num_heads, seq_len_q, depth)

k = self.split_heads(k, batch_size) # (batch_size, num_heads, seq_len_k, depth)

v = self.split_heads(v, batch_size) # (batch_size, num_heads, seq_len_v, depth)

# scaled_attention.shape == (batch_size, num_heads, seq_len_q, depth)

# attention_weights.shape == (batch_size, num_heads, seq_len_q, seq_len_k)

scaled_attention, attention_weights = scaled_dot_product_attention(

q, k, v, mask)

scaled_attention = scaled_attention.permute(0, 2, 1, 3) # (batch_size, seq_len_q, num_heads, depth)

concat_attention = scaled_attention.reshape(batch_size, -1, self.d_model) # (batch_size, seq_len_q, d_model)

output = self.dense(concat_attention) # (batch_size, seq_len_q, d_model)

return output, attention_weights

This code provides a practical example of how multi-head attention can be implemented using PyTorch. It's important to note that the exact implementation details can vary, particularly in larger models and different variations of Transformers.

### 3.4.3 Position-wise Feed-Forward Networks

In addition to the attention mechanism, the Transformer's encoder and decoder have a fully connected feed-forward network in each layer. This network is applied identically and independently to each position. It consists of two linear transformations, each with a ReLU activation in between.

These feed-forward networks are important because they allow the model to capture more complex relationships between the input and output sequences. They also serve as a form of regularization, helping to prevent overfitting. Moreover, the feed-forward networks are used to introduce nonlinearity into the model, allowing it to learn more complex functions.

**Example:**

Here's a simple implementation of a position-wise feed-forward network:

`import torch.nn as nn`

class PositionwiseFeedForward(nn.Module):

def __init__(self, d_model, d_ff, dropout=0.1):

super(PositionwiseFeedForward, self).__init__()

self.w_1 = nn.Linear(d_model, d_ff)

self.w_2 = nn.Linear(d_ff, d_model)

self.dropout = nn.Dropout(dropout)

def forward(self, x):

return self.w_2(self.dropout(F.relu(self.w_1(x))))

Here, `d_model`

is the dimensionality of the input and output, and `d_ff`

is the dimensionality of the intermediate layer. The ReLU activation function provides the non-linearity in the network.

### 3.4.4 Residual Connections & Layer Normalization

Residual connections, also known as skip connections, play a significant role in deep networks. They enable models to have a greater depth without encountering the vanishing gradient problem, which tends to occur when gradients become too small, making it hard for the model to learn. By allowing the gradient to be directly backpropagated to earlier layers, residual connections help to mitigate this problem.

In the context of Transformers, each sublayer (consisting of self-attention and feed-forward layers) in each encoder and decoder layer is accompanied by a residual connection, which helps to preserve the important information learned in earlier layers.

The residual connection in each sublayer is followed by layer normalization, which is a normalization technique similar to batch normalization. However, while batch normalization normalizes across the batch dimension, layer normalization normalizes across the feature dimension.

This normalization technique helps to ensure that the model can learn more efficiently by reducing the internal covariate shift that can occur during training.

**Example:**

Here is how you might implement a Transformer layer with residual connections and layer normalization:

`class TransformerLayer(nn.Module):`

def __init__(self, d_model, num_heads, d_ff, dropout=0.1):

super(TransformerLayer, self).__init__()

self.self_attn = MultiHeadAttention(d_model, num_heads)

self.feed_forward = PositionwiseFeedForward(d_model, d_ff, dropout)

self.layer_norm1 = nn.LayerNorm(d_model)

self.layer_norm2 = nn.LayerNorm(d_model)

self.dropout = nn.Dropout(dropout)

def forward(self, x, mask):

# Sublayer 1: multi-head self-attention and layer normalization

attn_output, _ = self.self_attn(x, x, x, mask)

out1 = self.layer_norm1(x + self.dropout(attn_output))

# Sublayer 2: position-wise feed-forward and layer normalization

ff_output = self.feed_forward(out1)

out2 = self.layer_norm2(out1 + self.dropout(ff_output))

return out2

This `TransformerLayer`

first applies multi-head self-attention, followed by dropout, adds the residual connection, and applies layer normalization. It then applies the position-wise feed-forward network, dropout, adds the second residual connection, and applies layer normalization again.

### 3.4.5 Positional Encoding

The Transformer's self-attention mechanism is a key aspect of its success in natural language processing. However, this mechanism does not take into account the inherent order of the input sequence. To address this issue, the Transformer introduces positional encodings to the input embeddings.

These encodings provide information about the position of each word in the sequence, allowing the self-attention mechanism to take this information into account when processing the input. The positional encodings have the same dimension as the embeddings, and are added to the embeddings in order to create the final input representation.

This combination of embeddings and positional encodings allows the Transformer to effectively process sequences of variable length, and has been shown to be highly effective in a wide range of natural language processing tasks.

**Example:**

Here's how you might generate positional encodings:

`class PositionalEncoding(nn.Module):`

def __init__(self, d_model, dropout=0.1, max_len=5000):

super(PositionalEncoding, self).__init__()

self.dropout = nn.Dropout(p=dropout)

# Compute

the positional encodings once in log space.

pe = torch.zeros(max_len, d_model)

position = torch.arange(0, max_len).unsqueeze(1)

div_term = torch.exp(torch.arange(0, d_model, 2) *

-(np.log(10000.0) / d_model))

pe[:, 0::2] = torch.sin(position * div_term)

pe[:, 1::2] = torch.cos(position * div_term)

pe = pe.unsqueeze(0)

self.register_buffer('pe', pe)

def forward(self, x):

x = x + self.pe[:, :x.size(1)]

return self.dropout(x)

The positional encoding module uses sine and cosine functions of different frequencies to create a unique positional encoding for each time step.

At this stage, we have broken down the major components of the Transformer architecture and provided illustrative code snippets for each. However, before proceeding to the next section on building the Transformer encoder, it may be useful to take a moment to discuss one more important aspect: the configuration and tuning of the Transformer model.

## 3.4 Detailed Breakdown of the Transformer Architecture

Transformers consist of an encoder and a decoder, each composed of multiple layers of sub-modules. The encoder is responsible for generating a representation of the input sequence, while the decoder is responsible for generating the output sequence.

In the encoder, the input sequence is first passed through a self-attention mechanism, which allows each position in the sequence to attend to all the other positions, learning a representation of the input sequence that takes into account the context of each position.

The output of the self-attention mechanism is passed through a feed-forward neural network that applies a non-linear transformation to each position independently. This process is repeated for multiple layers, allowing the encoder to capture increasingly complex representations of the input sequence.

The decoder, on the other hand, takes the representation generated by the encoder and uses it to generate the output sequence. At each step of the decoding process, the decoder attends to the encoder's output and the previously generated output to generate the next output token. The decoder also uses self-attention to allow each position in the output sequence to attend to all the other positions, ensuring that the generated output takes into account the context of each position. Like the encoder, the decoder also consists of multiple layers of sub-modules that apply non-linear transformations to the input at each position.

In this section, we will examine each of these sub-modules in detail. Note that these explanations will include a combination of pseudo-code and simplified Python code to illustrate the concepts.

### 3.4.1 Self-Attention Mechanism

As previously stated, the self-attention mechanism is a fundamental aspect of the Transformer model. This mechanism is responsible for determining the degree to which each word in the input sequence should be attended to when encoding a particular word.

The self-attention mechanism is a complex process that involves a series of calculations to determine the importance of each word in the sequence. This process can be broken down into several steps, including calculating the attention scores for each word, normalizing these scores, and finally computing the weighted average of the input sequence based on these scores.

By doing so, the self-attention mechanism can effectively capture the most relevant information from the input sequence and use it to generate a more accurate representation of the original text.

**Example:**

Here's a simplified Python code that could represent a single-head self-attention mechanism:

`import numpy as np`

import torch.nn.functional as F

def scaled_dot_product_attention(query, key, value):

# Calculate the dot product of the query and key

attention_logits = np.dot(query, key.T)

# Scale the logits by the square root of the dimension of the key

attention_logits = attention_logits / np.sqrt(key.shape[-1])

# Apply a softmax to the logits to get the attention weights

attention_weights = F.softmax(attention_logits, dim=-1)

# Multiply the values by the attention weights to get the output

output = np.dot(attention_weights, value)

return output, attention_weights

This simplified function represents the core of the self-attention mechanism: it takes as input the query, key, and value matrices, computes the attention weights, and returns a weighted sum of the values.

### 3.4.2 Multi-Head Attention

The Transformer model, a neural network architecture introduced in 2017, marked a significant breakthrough in natural language processing. One of the key features of the Transformer model is its use of multi-head attention. In traditional self-attention mechanisms, the input sequence is mapped to a sequence of queries, keys, and values with a single learned projection.

However, multi-head attention performs multiple such projections in parallel, each with a different learned linear projection of the input. By doing so, the model can capture different types of attention from different parts of the input.

The outputs of these parallel attention "heads" are then concatenated and linearly transformed to produce the final output. This allows the model to capture different aspects of the input in a more nuanced way.

For instance, one attention head may learn to focus on syntactic relationships, another on semantic relationships, and another on certain types of named entities. By combining the outputs of these attention heads, the Transformer model can learn to capture a wide range of linguistic phenomena, making it a powerful tool for natural language processing tasks.

**Example:**

Here's an example of how one might implement multi-head attention:

`class MultiHeadAttention(nn.Module):`

def __init__(self, d_model, num_heads):

super(MultiHeadAttention, self).__init__()

self.num_heads = num_heads

self.d_model = d_model

assert d_model % self.num_heads == 0

self.depth = d_model // self.num_heads

self.wq = nn.Linear(d_model, d_model)

self.wk = nn.Linear(d_model, d_model)

self.wv = nn.Linear(d_model, d_model)

self.dense = nn.Linear(d_model, d_model)

def split_heads(self, x, batch_size):

"""Split the last dimension into (num_heads, depth).

Transpose the result such that the shape is (batch_size, num_heads, seq_len, depth)

"""

x = x.view(batch_size, -1, self.num_heads, self.depth)

return x.permute(0, 2, 1, 3)

def forward(self, v, k, q, mask):

batch_size = q.shape[0]

q = self.wq(q) # (batch_size, seq_len, d_model)

k

= self.wk(k) # (batch_size, seq_len, d_model)

v = self.wv(v) # (batch_size, seq_len, d_model)

q = self.split_heads(q, batch_size) # (batch_size, num_heads, seq_len_q, depth)

k = self.split_heads(k, batch_size) # (batch_size, num_heads, seq_len_k, depth)

v = self.split_heads(v, batch_size) # (batch_size, num_heads, seq_len_v, depth)

# scaled_attention.shape == (batch_size, num_heads, seq_len_q, depth)

# attention_weights.shape == (batch_size, num_heads, seq_len_q, seq_len_k)

scaled_attention, attention_weights = scaled_dot_product_attention(

q, k, v, mask)

scaled_attention = scaled_attention.permute(0, 2, 1, 3) # (batch_size, seq_len_q, num_heads, depth)

concat_attention = scaled_attention.reshape(batch_size, -1, self.d_model) # (batch_size, seq_len_q, d_model)

output = self.dense(concat_attention) # (batch_size, seq_len_q, d_model)

return output, attention_weights

This code provides a practical example of how multi-head attention can be implemented using PyTorch. It's important to note that the exact implementation details can vary, particularly in larger models and different variations of Transformers.

### 3.4.3 Position-wise Feed-Forward Networks

In addition to the attention mechanism, the Transformer's encoder and decoder have a fully connected feed-forward network in each layer. This network is applied identically and independently to each position. It consists of two linear transformations, each with a ReLU activation in between.

These feed-forward networks are important because they allow the model to capture more complex relationships between the input and output sequences. They also serve as a form of regularization, helping to prevent overfitting. Moreover, the feed-forward networks are used to introduce nonlinearity into the model, allowing it to learn more complex functions.

**Example:**

Here's a simple implementation of a position-wise feed-forward network:

`import torch.nn as nn`

class PositionwiseFeedForward(nn.Module):

def __init__(self, d_model, d_ff, dropout=0.1):

super(PositionwiseFeedForward, self).__init__()

self.w_1 = nn.Linear(d_model, d_ff)

self.w_2 = nn.Linear(d_ff, d_model)

self.dropout = nn.Dropout(dropout)

def forward(self, x):

return self.w_2(self.dropout(F.relu(self.w_1(x))))

Here, `d_model`

is the dimensionality of the input and output, and `d_ff`

is the dimensionality of the intermediate layer. The ReLU activation function provides the non-linearity in the network.

### 3.4.4 Residual Connections & Layer Normalization

Residual connections, also known as skip connections, play a significant role in deep networks. They enable models to have a greater depth without encountering the vanishing gradient problem, which tends to occur when gradients become too small, making it hard for the model to learn. By allowing the gradient to be directly backpropagated to earlier layers, residual connections help to mitigate this problem.

In the context of Transformers, each sublayer (consisting of self-attention and feed-forward layers) in each encoder and decoder layer is accompanied by a residual connection, which helps to preserve the important information learned in earlier layers.

The residual connection in each sublayer is followed by layer normalization, which is a normalization technique similar to batch normalization. However, while batch normalization normalizes across the batch dimension, layer normalization normalizes across the feature dimension.

This normalization technique helps to ensure that the model can learn more efficiently by reducing the internal covariate shift that can occur during training.

**Example:**

Here is how you might implement a Transformer layer with residual connections and layer normalization:

`class TransformerLayer(nn.Module):`

def __init__(self, d_model, num_heads, d_ff, dropout=0.1):

super(TransformerLayer, self).__init__()

self.self_attn = MultiHeadAttention(d_model, num_heads)

self.feed_forward = PositionwiseFeedForward(d_model, d_ff, dropout)

self.layer_norm1 = nn.LayerNorm(d_model)

self.layer_norm2 = nn.LayerNorm(d_model)

self.dropout = nn.Dropout(dropout)

def forward(self, x, mask):

# Sublayer 1: multi-head self-attention and layer normalization

attn_output, _ = self.self_attn(x, x, x, mask)

out1 = self.layer_norm1(x + self.dropout(attn_output))

# Sublayer 2: position-wise feed-forward and layer normalization

ff_output = self.feed_forward(out1)

out2 = self.layer_norm2(out1 + self.dropout(ff_output))

return out2

This `TransformerLayer`

first applies multi-head self-attention, followed by dropout, adds the residual connection, and applies layer normalization. It then applies the position-wise feed-forward network, dropout, adds the second residual connection, and applies layer normalization again.

### 3.4.5 Positional Encoding

The Transformer's self-attention mechanism is a key aspect of its success in natural language processing. However, this mechanism does not take into account the inherent order of the input sequence. To address this issue, the Transformer introduces positional encodings to the input embeddings.

These encodings provide information about the position of each word in the sequence, allowing the self-attention mechanism to take this information into account when processing the input. The positional encodings have the same dimension as the embeddings, and are added to the embeddings in order to create the final input representation.

This combination of embeddings and positional encodings allows the Transformer to effectively process sequences of variable length, and has been shown to be highly effective in a wide range of natural language processing tasks.

**Example:**

Here's how you might generate positional encodings:

`class PositionalEncoding(nn.Module):`

def __init__(self, d_model, dropout=0.1, max_len=5000):

super(PositionalEncoding, self).__init__()

self.dropout = nn.Dropout(p=dropout)

# Compute

the positional encodings once in log space.

pe = torch.zeros(max_len, d_model)

position = torch.arange(0, max_len).unsqueeze(1)

div_term = torch.exp(torch.arange(0, d_model, 2) *

-(np.log(10000.0) / d_model))

pe[:, 0::2] = torch.sin(position * div_term)

pe[:, 1::2] = torch.cos(position * div_term)

pe = pe.unsqueeze(0)

self.register_buffer('pe', pe)

def forward(self, x):

x = x + self.pe[:, :x.size(1)]

return self.dropout(x)

The positional encoding module uses sine and cosine functions of different frequencies to create a unique positional encoding for each time step.

At this stage, we have broken down the major components of the Transformer architecture and provided illustrative code snippets for each. However, before proceeding to the next section on building the Transformer encoder, it may be useful to take a moment to discuss one more important aspect: the configuration and tuning of the Transformer model.

## 3.4 Detailed Breakdown of the Transformer Architecture

Transformers consist of an encoder and a decoder, each composed of multiple layers of sub-modules. The encoder is responsible for generating a representation of the input sequence, while the decoder is responsible for generating the output sequence.

In the encoder, the input sequence is first passed through a self-attention mechanism, which allows each position in the sequence to attend to all the other positions, learning a representation of the input sequence that takes into account the context of each position.

The output of the self-attention mechanism is passed through a feed-forward neural network that applies a non-linear transformation to each position independently. This process is repeated for multiple layers, allowing the encoder to capture increasingly complex representations of the input sequence.

The decoder, on the other hand, takes the representation generated by the encoder and uses it to generate the output sequence. At each step of the decoding process, the decoder attends to the encoder's output and the previously generated output to generate the next output token. The decoder also uses self-attention to allow each position in the output sequence to attend to all the other positions, ensuring that the generated output takes into account the context of each position. Like the encoder, the decoder also consists of multiple layers of sub-modules that apply non-linear transformations to the input at each position.

In this section, we will examine each of these sub-modules in detail. Note that these explanations will include a combination of pseudo-code and simplified Python code to illustrate the concepts.

### 3.4.1 Self-Attention Mechanism

As previously stated, the self-attention mechanism is a fundamental aspect of the Transformer model. This mechanism is responsible for determining the degree to which each word in the input sequence should be attended to when encoding a particular word.

The self-attention mechanism is a complex process that involves a series of calculations to determine the importance of each word in the sequence. This process can be broken down into several steps, including calculating the attention scores for each word, normalizing these scores, and finally computing the weighted average of the input sequence based on these scores.

By doing so, the self-attention mechanism can effectively capture the most relevant information from the input sequence and use it to generate a more accurate representation of the original text.

**Example:**

Here's a simplified Python code that could represent a single-head self-attention mechanism:

`import numpy as np`

import torch.nn.functional as F

def scaled_dot_product_attention(query, key, value):

# Calculate the dot product of the query and key

attention_logits = np.dot(query, key.T)

# Scale the logits by the square root of the dimension of the key

attention_logits = attention_logits / np.sqrt(key.shape[-1])

# Apply a softmax to the logits to get the attention weights

attention_weights = F.softmax(attention_logits, dim=-1)

# Multiply the values by the attention weights to get the output

output = np.dot(attention_weights, value)

return output, attention_weights

This simplified function represents the core of the self-attention mechanism: it takes as input the query, key, and value matrices, computes the attention weights, and returns a weighted sum of the values.

### 3.4.2 Multi-Head Attention

The Transformer model, a neural network architecture introduced in 2017, marked a significant breakthrough in natural language processing. One of the key features of the Transformer model is its use of multi-head attention. In traditional self-attention mechanisms, the input sequence is mapped to a sequence of queries, keys, and values with a single learned projection.

However, multi-head attention performs multiple such projections in parallel, each with a different learned linear projection of the input. By doing so, the model can capture different types of attention from different parts of the input.

The outputs of these parallel attention "heads" are then concatenated and linearly transformed to produce the final output. This allows the model to capture different aspects of the input in a more nuanced way.

For instance, one attention head may learn to focus on syntactic relationships, another on semantic relationships, and another on certain types of named entities. By combining the outputs of these attention heads, the Transformer model can learn to capture a wide range of linguistic phenomena, making it a powerful tool for natural language processing tasks.

**Example:**

Here's an example of how one might implement multi-head attention:

`class MultiHeadAttention(nn.Module):`

def __init__(self, d_model, num_heads):

super(MultiHeadAttention, self).__init__()

self.num_heads = num_heads

self.d_model = d_model

assert d_model % self.num_heads == 0

self.depth = d_model // self.num_heads

self.wq = nn.Linear(d_model, d_model)

self.wk = nn.Linear(d_model, d_model)

self.wv = nn.Linear(d_model, d_model)

self.dense = nn.Linear(d_model, d_model)

def split_heads(self, x, batch_size):

"""Split the last dimension into (num_heads, depth).

Transpose the result such that the shape is (batch_size, num_heads, seq_len, depth)

"""

x = x.view(batch_size, -1, self.num_heads, self.depth)

return x.permute(0, 2, 1, 3)

def forward(self, v, k, q, mask):

batch_size = q.shape[0]

q = self.wq(q) # (batch_size, seq_len, d_model)

k

= self.wk(k) # (batch_size, seq_len, d_model)

v = self.wv(v) # (batch_size, seq_len, d_model)

q = self.split_heads(q, batch_size) # (batch_size, num_heads, seq_len_q, depth)

k = self.split_heads(k, batch_size) # (batch_size, num_heads, seq_len_k, depth)

v = self.split_heads(v, batch_size) # (batch_size, num_heads, seq_len_v, depth)

# scaled_attention.shape == (batch_size, num_heads, seq_len_q, depth)

# attention_weights.shape == (batch_size, num_heads, seq_len_q, seq_len_k)

scaled_attention, attention_weights = scaled_dot_product_attention(

q, k, v, mask)

scaled_attention = scaled_attention.permute(0, 2, 1, 3) # (batch_size, seq_len_q, num_heads, depth)

concat_attention = scaled_attention.reshape(batch_size, -1, self.d_model) # (batch_size, seq_len_q, d_model)

output = self.dense(concat_attention) # (batch_size, seq_len_q, d_model)

return output, attention_weights

This code provides a practical example of how multi-head attention can be implemented using PyTorch. It's important to note that the exact implementation details can vary, particularly in larger models and different variations of Transformers.

### 3.4.3 Position-wise Feed-Forward Networks

In addition to the attention mechanism, the Transformer's encoder and decoder have a fully connected feed-forward network in each layer. This network is applied identically and independently to each position. It consists of two linear transformations, each with a ReLU activation in between.

These feed-forward networks are important because they allow the model to capture more complex relationships between the input and output sequences. They also serve as a form of regularization, helping to prevent overfitting. Moreover, the feed-forward networks are used to introduce nonlinearity into the model, allowing it to learn more complex functions.

**Example:**

Here's a simple implementation of a position-wise feed-forward network:

`import torch.nn as nn`

class PositionwiseFeedForward(nn.Module):

def __init__(self, d_model, d_ff, dropout=0.1):

super(PositionwiseFeedForward, self).__init__()

self.w_1 = nn.Linear(d_model, d_ff)

self.w_2 = nn.Linear(d_ff, d_model)

self.dropout = nn.Dropout(dropout)

def forward(self, x):

return self.w_2(self.dropout(F.relu(self.w_1(x))))

Here, `d_model`

is the dimensionality of the input and output, and `d_ff`

is the dimensionality of the intermediate layer. The ReLU activation function provides the non-linearity in the network.

### 3.4.4 Residual Connections & Layer Normalization

Residual connections, also known as skip connections, play a significant role in deep networks. They enable models to have a greater depth without encountering the vanishing gradient problem, which tends to occur when gradients become too small, making it hard for the model to learn. By allowing the gradient to be directly backpropagated to earlier layers, residual connections help to mitigate this problem.

In the context of Transformers, each sublayer (consisting of self-attention and feed-forward layers) in each encoder and decoder layer is accompanied by a residual connection, which helps to preserve the important information learned in earlier layers.

The residual connection in each sublayer is followed by layer normalization, which is a normalization technique similar to batch normalization. However, while batch normalization normalizes across the batch dimension, layer normalization normalizes across the feature dimension.

This normalization technique helps to ensure that the model can learn more efficiently by reducing the internal covariate shift that can occur during training.

**Example:**

Here is how you might implement a Transformer layer with residual connections and layer normalization:

`class TransformerLayer(nn.Module):`

def __init__(self, d_model, num_heads, d_ff, dropout=0.1):

super(TransformerLayer, self).__init__()

self.self_attn = MultiHeadAttention(d_model, num_heads)

self.feed_forward = PositionwiseFeedForward(d_model, d_ff, dropout)

self.layer_norm1 = nn.LayerNorm(d_model)

self.layer_norm2 = nn.LayerNorm(d_model)

self.dropout = nn.Dropout(dropout)

def forward(self, x, mask):

# Sublayer 1: multi-head self-attention and layer normalization

attn_output, _ = self.self_attn(x, x, x, mask)

out1 = self.layer_norm1(x + self.dropout(attn_output))

# Sublayer 2: position-wise feed-forward and layer normalization

ff_output = self.feed_forward(out1)

out2 = self.layer_norm2(out1 + self.dropout(ff_output))

return out2

This `TransformerLayer`

first applies multi-head self-attention, followed by dropout, adds the residual connection, and applies layer normalization. It then applies the position-wise feed-forward network, dropout, adds the second residual connection, and applies layer normalization again.

### 3.4.5 Positional Encoding

The Transformer's self-attention mechanism is a key aspect of its success in natural language processing. However, this mechanism does not take into account the inherent order of the input sequence. To address this issue, the Transformer introduces positional encodings to the input embeddings.

These encodings provide information about the position of each word in the sequence, allowing the self-attention mechanism to take this information into account when processing the input. The positional encodings have the same dimension as the embeddings, and are added to the embeddings in order to create the final input representation.

This combination of embeddings and positional encodings allows the Transformer to effectively process sequences of variable length, and has been shown to be highly effective in a wide range of natural language processing tasks.

**Example:**

Here's how you might generate positional encodings:

`class PositionalEncoding(nn.Module):`

def __init__(self, d_model, dropout=0.1, max_len=5000):

super(PositionalEncoding, self).__init__()

self.dropout = nn.Dropout(p=dropout)

# Compute

the positional encodings once in log space.

pe = torch.zeros(max_len, d_model)

position = torch.arange(0, max_len).unsqueeze(1)

div_term = torch.exp(torch.arange(0, d_model, 2) *

-(np.log(10000.0) / d_model))

pe[:, 0::2] = torch.sin(position * div_term)

pe[:, 1::2] = torch.cos(position * div_term)

pe = pe.unsqueeze(0)

self.register_buffer('pe', pe)

def forward(self, x):

x = x + self.pe[:, :x.size(1)]

return self.dropout(x)

The positional encoding module uses sine and cosine functions of different frequencies to create a unique positional encoding for each time step.

At this stage, we have broken down the major components of the Transformer architecture and provided illustrative code snippets for each. However, before proceeding to the next section on building the Transformer encoder, it may be useful to take a moment to discuss one more important aspect: the configuration and tuning of the Transformer model.

## 3.4 Detailed Breakdown of the Transformer Architecture

### 3.4.1 Self-Attention Mechanism

**Example:**

Here's a simplified Python code that could represent a single-head self-attention mechanism:

`import numpy as np`

import torch.nn.functional as F

def scaled_dot_product_attention(query, key, value):

# Calculate the dot product of the query and key

attention_logits = np.dot(query, key.T)

# Scale the logits by the square root of the dimension of the key

attention_logits = attention_logits / np.sqrt(key.shape[-1])

# Apply a softmax to the logits to get the attention weights

attention_weights = F.softmax(attention_logits, dim=-1)

# Multiply the values by the attention weights to get the output

output = np.dot(attention_weights, value)

return output, attention_weights

### 3.4.2 Multi-Head Attention

**Example:**

Here's an example of how one might implement multi-head attention:

`class MultiHeadAttention(nn.Module):`

def __init__(self, d_model, num_heads):

super(MultiHeadAttention, self).__init__()

self.num_heads = num_heads

self.d_model = d_model

assert d_model % self.num_heads == 0

self.depth = d_model // self.num_heads

self.wq = nn.Linear(d_model, d_model)

self.wk = nn.Linear(d_model, d_model)

self.wv = nn.Linear(d_model, d_model)

self.dense = nn.Linear(d_model, d_model)

def split_heads(self, x, batch_size):

"""Split the last dimension into (num_heads, depth).

Transpose the result such that the shape is (batch_size, num_heads, seq_len, depth)

"""

x = x.view(batch_size, -1, self.num_heads, self.depth)

return x.permute(0, 2, 1, 3)

def forward(self, v, k, q, mask):

batch_size = q.shape[0]

q = self.wq(q) # (batch_size, seq_len, d_model)

k

= self.wk(k) # (batch_size, seq_len, d_model)

v = self.wv(v) # (batch_size, seq_len, d_model)

q = self.split_heads(q, batch_size) # (batch_size, num_heads, seq_len_q, depth)

k = self.split_heads(k, batch_size) # (batch_size, num_heads, seq_len_k, depth)

v = self.split_heads(v, batch_size) # (batch_size, num_heads, seq_len_v, depth)

# scaled_attention.shape == (batch_size, num_heads, seq_len_q, depth)

# attention_weights.shape == (batch_size, num_heads, seq_len_q, seq_len_k)

scaled_attention, attention_weights = scaled_dot_product_attention(

q, k, v, mask)

scaled_attention = scaled_attention.permute(0, 2, 1, 3) # (batch_size, seq_len_q, num_heads, depth)

concat_attention = scaled_attention.reshape(batch_size, -1, self.d_model) # (batch_size, seq_len_q, d_model)

output = self.dense(concat_attention) # (batch_size, seq_len_q, d_model)

return output, attention_weights

### 3.4.3 Position-wise Feed-Forward Networks

**Example:**

Here's a simple implementation of a position-wise feed-forward network:

`import torch.nn as nn`

class PositionwiseFeedForward(nn.Module):

def __init__(self, d_model, d_ff, dropout=0.1):

super(PositionwiseFeedForward, self).__init__()

self.w_1 = nn.Linear(d_model, d_ff)

self.w_2 = nn.Linear(d_ff, d_model)

self.dropout = nn.Dropout(dropout)

def forward(self, x):

return self.w_2(self.dropout(F.relu(self.w_1(x))))

`d_model`

is the dimensionality of the input and output, and `d_ff`

is the dimensionality of the intermediate layer. The ReLU activation function provides the non-linearity in the network.

### 3.4.4 Residual Connections & Layer Normalization

**Example:**

`class TransformerLayer(nn.Module):`

def __init__(self, d_model, num_heads, d_ff, dropout=0.1):

super(TransformerLayer, self).__init__()

self.self_attn = MultiHeadAttention(d_model, num_heads)

self.feed_forward = PositionwiseFeedForward(d_model, d_ff, dropout)

self.layer_norm1 = nn.LayerNorm(d_model)

self.layer_norm2 = nn.LayerNorm(d_model)

self.dropout = nn.Dropout(dropout)

def forward(self, x, mask):

# Sublayer 1: multi-head self-attention and layer normalization

attn_output, _ = self.self_attn(x, x, x, mask)

out1 = self.layer_norm1(x + self.dropout(attn_output))

# Sublayer 2: position-wise feed-forward and layer normalization

ff_output = self.feed_forward(out1)

out2 = self.layer_norm2(out1 + self.dropout(ff_output))

return out2

`TransformerLayer`

first applies multi-head self-attention, followed by dropout, adds the residual connection, and applies layer normalization. It then applies the position-wise feed-forward network, dropout, adds the second residual connection, and applies layer normalization again.

### 3.4.5 Positional Encoding

**Example:**

Here's how you might generate positional encodings:

`class PositionalEncoding(nn.Module):`

def __init__(self, d_model, dropout=0.1, max_len=5000):

super(PositionalEncoding, self).__init__()

self.dropout = nn.Dropout(p=dropout)

# Compute

the positional encodings once in log space.

pe = torch.zeros(max_len, d_model)

position = torch.arange(0, max_len).unsqueeze(1)

div_term = torch.exp(torch.arange(0, d_model, 2) *

-(np.log(10000.0) / d_model))

pe[:, 0::2] = torch.sin(position * div_term)

pe[:, 1::2] = torch.cos(position * div_term)

pe = pe.unsqueeze(0)

self.register_buffer('pe', pe)

def forward(self, x):

x = x + self.pe[:, :x.size(1)]

return self.dropout(x)