Ahsan Farooqui's Blog

Introduction

A transformer layer is a fundamental building block of transformer-based neural networks. It consists of two main components: an attention mechanism and a feed-forward network.

The attention mechanism allows the transformer layer to attend to different parts of the input sequence, which allows it to learn long-range dependencies. The feed-forward network allows the transformer layer to learn non-linear relationships between the input and output sequences.

Implementation

Here is the code to implement a transformer layer in Python:

Python

import torch

class TransformerLayer(torch.nn.Module):
    def __init__(self, d_model, heads, dropout):
        super(TransformerLayer, self).__init__()

        self.attention = torch.nn.MultiheadAttention(d_model, heads)
        self.feed_forward = torch.nn.Sequential(
            torch.nn.Linear(d_model, d_model * 4),
            torch.nn.ReLU(),
            torch.nn.Linear(d_model * 4, d_model),
        )
        self.dropout = torch.nn.Dropout(dropout)

    def forward(self, x):
        # Attention
        attention_output = self.attention(x, x, x)

        # Feed-forward
        feed_forward_output = self.feed_forward(attention_output)

        # Dropout
        output = self.dropout(feed_forward_output)

        return output + x

Explanation

The __init__ method initializes the transformer layer. This method takes three arguments: the dimension of the input and output sequences (d_model), the number of attention heads (heads), and the dropout rate (dropout).

The attention method implements the attention mechanism. This method takes three arguments: the input sequence (x), the query sequence (q), and the key sequence (k). The attention mechanism computes a weighted sum of the input sequence, where the weights are determined by the similarity between the query sequence and the key sequence.

The feed_forward method implements the feed-forward network. This method takes one argument: the input sequence (x). The feed-forward network consists of two linear layers, with a ReLU activation function in between.

The forward method is the main method of the transformer layer. This method takes one argument: the input sequence (x). The forward method first computes the attention output. Then, it computes the feed-forward output. Finally, it adds the attention output and the feed-forward output, and it applies dropout. The output of the forward method is the output of the transformer layer.

Conclusion

In this blog, we have shown how to implement a transformer layer in Python. The transformer layer is a fundamental building block of transformer-based neural networks, and it is used in a variety of natural language processing tasks, such as machine translation, text summarization, and question answering.