Menu iconMenu iconIntroduction to Natural Language Processing with Transformers
Introduction to Natural Language Processing with Transformers

Chapter 4: The Transformer Architecture

4.2 Understanding the Encoder-Decoder Structure

A Transformer model follows the standard encoder-decoder structure. However, unlike traditional seq2seq models where the encoder and decoder are recurrent networks, the Transformer uses multiple layers of self-attention and point-wise, fully connected layers for both the encoder and decoder, shown in the following figure:

    Encoder:               Decoder:

    Input --> Embedding --> Self-Attention --> Add & Norm --> Feed Forward --> Add & Norm
                                              /         \\    |            /    /       \\
                                             /           \\   V           /    V         \\
                                          Multi-       Residual     Multi-  Residual   Output
                                         Head Attn.   Connection    Head Attn.  Connection

The model processes the input and output sequences in parallel, enhancing computational speed. It's essential to note that the decoder has an additional Multi-Head Attention layer to attend to the encoder's output alongside its input.

Let's break down these components:

4.2.1 The Encoder

The encoder in a Transformer is a stack of identical layers, each layer consisting of two sub-layers. The first sub-layer is a multi-head self-attention mechanism that enables the model to attend to different positions within the input sequence, allowing it to capture dependencies between different words of the input.

The second sub-layer is a position-wise fully connected feed-forward network that applies a nonlinear transformation to each position independently, allowing the model to learn complex, non-linear relationships between the input features. By stacking multiple layers on top of each other, the encoder is able to capture increasingly complex patterns in the input data, making it a powerful tool for a wide range of natural language processing tasks.

Example:

Let's write a code representation of a basic Transformer encoder:

class Encoder(nn.Module):
    "Core encoder is a stack of N layers"
    def __init__(self, layer, N):
        super(Encoder, self).__init__()
        self.layers = clones(layer, N)
        self.norm = LayerNorm(layer.size)

    def forward(self, x, mask):
        "Pass the input (and mask) through each layer in turn."
        for layer in self.layers:
            x = layer(x, mask)
        return self.norm(x)

In this code snippet, we define the Encoder class that accepts an input and a mask and passes them through each layer of the encoder. Layer normalization is applied at the end of each full pass.

4.2.2 The Decoder

In the Transformer, the decoder is composed of a stack of identical layers, just like the encoder. However, to allow for more complex computations and better performance, the decoder includes a third sub-layer that is not present in the encoder.

This additional sub-layer performs multi-head attention over the output of the encoder stack, and is crucial for the decoder to be able to generate high-quality output. By incorporating this extra sub-layer, the decoder can better "understand" the encoding of the input sequence, which in turn improves the quality of the output sequence.

This is because the decoder is better able to identify key features and patterns in the input, allowing it to generate more nuanced and accurate output.

Example:

Let's write a code representation of a basic Transformer decoder:

class Decoder(nn.Module):
    "Generic N layer decoder with masking."
    def __init__(self, layer, N):
        super(Decoder, self).__init__()
        self.layers = clones(layer, N)
        self.norm = LayerNorm(layer.size)

    def forward(self, x, memory, src_mask, tgt_mask):
        for layer in self.layers:
            x = layer(x, memory, src_mask, tgt_mask)
        return self.norm(x)

The Decoder class is a fundamental component of the neural network architecture used in this application. It plays a crucial role in the processing of information by accepting an input, a memory (which is the output of the encoder), and source and target masks. The decoder layer processes the input data, which is then passed through each decoder layer to perform a full pass.

During this process, the decoder applies layer normalization at the end of each pass, ensuring that the data is transformed in a way that is consistent with the overall architecture of the neural network. This critical step helps to optimize the performance of the network, ensuring that it can process data efficiently and accurately, even in the face of significant challenges and complexity.

The encoder-decoder structure is fundamental to the Transformer architecture, but it is the way these components are implemented in the model that sets it apart from other sequence-to-sequence models. The self-attention mechanism and the position-wise feed-forward networks are the two major innovations introduced in the Transformer that we will look at in greater detail in the coming sections.

The simplicity of this architecture, coupled with its parallelization ability and the abolishment of recurrence, is what makes the Transformer a truly transformative model. It is important to note that both the encoder and decoder consist of a stack of identical layers, enabling us to easily adjust the model's complexity by changing the number of layers.

Let's delve into the sub-layers of the Transformer and see how they contribute to the model's effectiveness. We start by understanding the self-attention mechanism, which is the cornerstone of the Transformer architecture.

4.2 Understanding the Encoder-Decoder Structure

A Transformer model follows the standard encoder-decoder structure. However, unlike traditional seq2seq models where the encoder and decoder are recurrent networks, the Transformer uses multiple layers of self-attention and point-wise, fully connected layers for both the encoder and decoder, shown in the following figure:

    Encoder:               Decoder:

    Input --> Embedding --> Self-Attention --> Add & Norm --> Feed Forward --> Add & Norm
                                              /         \\    |            /    /       \\
                                             /           \\   V           /    V         \\
                                          Multi-       Residual     Multi-  Residual   Output
                                         Head Attn.   Connection    Head Attn.  Connection

The model processes the input and output sequences in parallel, enhancing computational speed. It's essential to note that the decoder has an additional Multi-Head Attention layer to attend to the encoder's output alongside its input.

Let's break down these components:

4.2.1 The Encoder

The encoder in a Transformer is a stack of identical layers, each layer consisting of two sub-layers. The first sub-layer is a multi-head self-attention mechanism that enables the model to attend to different positions within the input sequence, allowing it to capture dependencies between different words of the input.

The second sub-layer is a position-wise fully connected feed-forward network that applies a nonlinear transformation to each position independently, allowing the model to learn complex, non-linear relationships between the input features. By stacking multiple layers on top of each other, the encoder is able to capture increasingly complex patterns in the input data, making it a powerful tool for a wide range of natural language processing tasks.

Example:

Let's write a code representation of a basic Transformer encoder:

class Encoder(nn.Module):
    "Core encoder is a stack of N layers"
    def __init__(self, layer, N):
        super(Encoder, self).__init__()
        self.layers = clones(layer, N)
        self.norm = LayerNorm(layer.size)

    def forward(self, x, mask):
        "Pass the input (and mask) through each layer in turn."
        for layer in self.layers:
            x = layer(x, mask)
        return self.norm(x)

In this code snippet, we define the Encoder class that accepts an input and a mask and passes them through each layer of the encoder. Layer normalization is applied at the end of each full pass.

4.2.2 The Decoder

In the Transformer, the decoder is composed of a stack of identical layers, just like the encoder. However, to allow for more complex computations and better performance, the decoder includes a third sub-layer that is not present in the encoder.

This additional sub-layer performs multi-head attention over the output of the encoder stack, and is crucial for the decoder to be able to generate high-quality output. By incorporating this extra sub-layer, the decoder can better "understand" the encoding of the input sequence, which in turn improves the quality of the output sequence.

This is because the decoder is better able to identify key features and patterns in the input, allowing it to generate more nuanced and accurate output.

Example:

Let's write a code representation of a basic Transformer decoder:

class Decoder(nn.Module):
    "Generic N layer decoder with masking."
    def __init__(self, layer, N):
        super(Decoder, self).__init__()
        self.layers = clones(layer, N)
        self.norm = LayerNorm(layer.size)

    def forward(self, x, memory, src_mask, tgt_mask):
        for layer in self.layers:
            x = layer(x, memory, src_mask, tgt_mask)
        return self.norm(x)

The Decoder class is a fundamental component of the neural network architecture used in this application. It plays a crucial role in the processing of information by accepting an input, a memory (which is the output of the encoder), and source and target masks. The decoder layer processes the input data, which is then passed through each decoder layer to perform a full pass.

During this process, the decoder applies layer normalization at the end of each pass, ensuring that the data is transformed in a way that is consistent with the overall architecture of the neural network. This critical step helps to optimize the performance of the network, ensuring that it can process data efficiently and accurately, even in the face of significant challenges and complexity.

The encoder-decoder structure is fundamental to the Transformer architecture, but it is the way these components are implemented in the model that sets it apart from other sequence-to-sequence models. The self-attention mechanism and the position-wise feed-forward networks are the two major innovations introduced in the Transformer that we will look at in greater detail in the coming sections.

The simplicity of this architecture, coupled with its parallelization ability and the abolishment of recurrence, is what makes the Transformer a truly transformative model. It is important to note that both the encoder and decoder consist of a stack of identical layers, enabling us to easily adjust the model's complexity by changing the number of layers.

Let's delve into the sub-layers of the Transformer and see how they contribute to the model's effectiveness. We start by understanding the self-attention mechanism, which is the cornerstone of the Transformer architecture.

4.2 Understanding the Encoder-Decoder Structure

A Transformer model follows the standard encoder-decoder structure. However, unlike traditional seq2seq models where the encoder and decoder are recurrent networks, the Transformer uses multiple layers of self-attention and point-wise, fully connected layers for both the encoder and decoder, shown in the following figure:

    Encoder:               Decoder:

    Input --> Embedding --> Self-Attention --> Add & Norm --> Feed Forward --> Add & Norm
                                              /         \\    |            /    /       \\
                                             /           \\   V           /    V         \\
                                          Multi-       Residual     Multi-  Residual   Output
                                         Head Attn.   Connection    Head Attn.  Connection

The model processes the input and output sequences in parallel, enhancing computational speed. It's essential to note that the decoder has an additional Multi-Head Attention layer to attend to the encoder's output alongside its input.

Let's break down these components:

4.2.1 The Encoder

The encoder in a Transformer is a stack of identical layers, each layer consisting of two sub-layers. The first sub-layer is a multi-head self-attention mechanism that enables the model to attend to different positions within the input sequence, allowing it to capture dependencies between different words of the input.

The second sub-layer is a position-wise fully connected feed-forward network that applies a nonlinear transformation to each position independently, allowing the model to learn complex, non-linear relationships between the input features. By stacking multiple layers on top of each other, the encoder is able to capture increasingly complex patterns in the input data, making it a powerful tool for a wide range of natural language processing tasks.

Example:

Let's write a code representation of a basic Transformer encoder:

class Encoder(nn.Module):
    "Core encoder is a stack of N layers"
    def __init__(self, layer, N):
        super(Encoder, self).__init__()
        self.layers = clones(layer, N)
        self.norm = LayerNorm(layer.size)

    def forward(self, x, mask):
        "Pass the input (and mask) through each layer in turn."
        for layer in self.layers:
            x = layer(x, mask)
        return self.norm(x)

In this code snippet, we define the Encoder class that accepts an input and a mask and passes them through each layer of the encoder. Layer normalization is applied at the end of each full pass.

4.2.2 The Decoder

In the Transformer, the decoder is composed of a stack of identical layers, just like the encoder. However, to allow for more complex computations and better performance, the decoder includes a third sub-layer that is not present in the encoder.

This additional sub-layer performs multi-head attention over the output of the encoder stack, and is crucial for the decoder to be able to generate high-quality output. By incorporating this extra sub-layer, the decoder can better "understand" the encoding of the input sequence, which in turn improves the quality of the output sequence.

This is because the decoder is better able to identify key features and patterns in the input, allowing it to generate more nuanced and accurate output.

Example:

Let's write a code representation of a basic Transformer decoder:

class Decoder(nn.Module):
    "Generic N layer decoder with masking."
    def __init__(self, layer, N):
        super(Decoder, self).__init__()
        self.layers = clones(layer, N)
        self.norm = LayerNorm(layer.size)

    def forward(self, x, memory, src_mask, tgt_mask):
        for layer in self.layers:
            x = layer(x, memory, src_mask, tgt_mask)
        return self.norm(x)

The Decoder class is a fundamental component of the neural network architecture used in this application. It plays a crucial role in the processing of information by accepting an input, a memory (which is the output of the encoder), and source and target masks. The decoder layer processes the input data, which is then passed through each decoder layer to perform a full pass.

During this process, the decoder applies layer normalization at the end of each pass, ensuring that the data is transformed in a way that is consistent with the overall architecture of the neural network. This critical step helps to optimize the performance of the network, ensuring that it can process data efficiently and accurately, even in the face of significant challenges and complexity.

The encoder-decoder structure is fundamental to the Transformer architecture, but it is the way these components are implemented in the model that sets it apart from other sequence-to-sequence models. The self-attention mechanism and the position-wise feed-forward networks are the two major innovations introduced in the Transformer that we will look at in greater detail in the coming sections.

The simplicity of this architecture, coupled with its parallelization ability and the abolishment of recurrence, is what makes the Transformer a truly transformative model. It is important to note that both the encoder and decoder consist of a stack of identical layers, enabling us to easily adjust the model's complexity by changing the number of layers.

Let's delve into the sub-layers of the Transformer and see how they contribute to the model's effectiveness. We start by understanding the self-attention mechanism, which is the cornerstone of the Transformer architecture.

4.2 Understanding the Encoder-Decoder Structure

A Transformer model follows the standard encoder-decoder structure. However, unlike traditional seq2seq models where the encoder and decoder are recurrent networks, the Transformer uses multiple layers of self-attention and point-wise, fully connected layers for both the encoder and decoder, shown in the following figure:

    Encoder:               Decoder:

    Input --> Embedding --> Self-Attention --> Add & Norm --> Feed Forward --> Add & Norm
                                              /         \\    |            /    /       \\
                                             /           \\   V           /    V         \\
                                          Multi-       Residual     Multi-  Residual   Output
                                         Head Attn.   Connection    Head Attn.  Connection

The model processes the input and output sequences in parallel, enhancing computational speed. It's essential to note that the decoder has an additional Multi-Head Attention layer to attend to the encoder's output alongside its input.

Let's break down these components:

4.2.1 The Encoder

The encoder in a Transformer is a stack of identical layers, each layer consisting of two sub-layers. The first sub-layer is a multi-head self-attention mechanism that enables the model to attend to different positions within the input sequence, allowing it to capture dependencies between different words of the input.

The second sub-layer is a position-wise fully connected feed-forward network that applies a nonlinear transformation to each position independently, allowing the model to learn complex, non-linear relationships between the input features. By stacking multiple layers on top of each other, the encoder is able to capture increasingly complex patterns in the input data, making it a powerful tool for a wide range of natural language processing tasks.

Example:

Let's write a code representation of a basic Transformer encoder:

class Encoder(nn.Module):
    "Core encoder is a stack of N layers"
    def __init__(self, layer, N):
        super(Encoder, self).__init__()
        self.layers = clones(layer, N)
        self.norm = LayerNorm(layer.size)

    def forward(self, x, mask):
        "Pass the input (and mask) through each layer in turn."
        for layer in self.layers:
            x = layer(x, mask)
        return self.norm(x)

In this code snippet, we define the Encoder class that accepts an input and a mask and passes them through each layer of the encoder. Layer normalization is applied at the end of each full pass.

4.2.2 The Decoder

In the Transformer, the decoder is composed of a stack of identical layers, just like the encoder. However, to allow for more complex computations and better performance, the decoder includes a third sub-layer that is not present in the encoder.

This additional sub-layer performs multi-head attention over the output of the encoder stack, and is crucial for the decoder to be able to generate high-quality output. By incorporating this extra sub-layer, the decoder can better "understand" the encoding of the input sequence, which in turn improves the quality of the output sequence.

This is because the decoder is better able to identify key features and patterns in the input, allowing it to generate more nuanced and accurate output.

Example:

Let's write a code representation of a basic Transformer decoder:

class Decoder(nn.Module):
    "Generic N layer decoder with masking."
    def __init__(self, layer, N):
        super(Decoder, self).__init__()
        self.layers = clones(layer, N)
        self.norm = LayerNorm(layer.size)

    def forward(self, x, memory, src_mask, tgt_mask):
        for layer in self.layers:
            x = layer(x, memory, src_mask, tgt_mask)
        return self.norm(x)

The Decoder class is a fundamental component of the neural network architecture used in this application. It plays a crucial role in the processing of information by accepting an input, a memory (which is the output of the encoder), and source and target masks. The decoder layer processes the input data, which is then passed through each decoder layer to perform a full pass.

During this process, the decoder applies layer normalization at the end of each pass, ensuring that the data is transformed in a way that is consistent with the overall architecture of the neural network. This critical step helps to optimize the performance of the network, ensuring that it can process data efficiently and accurately, even in the face of significant challenges and complexity.

The encoder-decoder structure is fundamental to the Transformer architecture, but it is the way these components are implemented in the model that sets it apart from other sequence-to-sequence models. The self-attention mechanism and the position-wise feed-forward networks are the two major innovations introduced in the Transformer that we will look at in greater detail in the coming sections.

The simplicity of this architecture, coupled with its parallelization ability and the abolishment of recurrence, is what makes the Transformer a truly transformative model. It is important to note that both the encoder and decoder consist of a stack of identical layers, enabling us to easily adjust the model's complexity by changing the number of layers.

Let's delve into the sub-layers of the Transformer and see how they contribute to the model's effectiveness. We start by understanding the self-attention mechanism, which is the cornerstone of the Transformer architecture.