In this chapter, we’ll explore the concept of task-agnostic transformer models that can be applied to various NLP tasks without any significant modifications. We’ll start by understanding how multi-head attention works in detail and then move on to building our own transformer model from scratch using PyTorch.

So Let’s get right into it with it!

First, we need to understand what self-attention is and why it’s important for NLP tasks. Self-attention allows the model to focus on specific parts of an input sequence based on their relevance to a particular task or output. This can be especially useful in language processing where words often have complex relationships with each other, and understanding these relationships is crucial for accurate prediction.

Now multi-head attention specifically. Multi-head attention involves applying multiple self-attention mechanisms simultaneously on the input sequence to capture different aspects of the data. This can be useful in situations where there are multiple important features or patterns that need to be extracted from the input, and a single head may not be sufficient to capture them all.

Here’s an example: let’s say we have a sentence “The quick brown fox jumps over the lazy dog.” We want our model to predict whether this sentence is positive or negative based on its sentiment. To do this, we can use multi-head attention to focus on different parts of the input sequence that are most relevant for sentiment analysis. For example, one head might attend primarily to words like “quick” and “jumps,” which tend to have a more positive connotation in English language, while another head might attend primarily to words like “lazy” and “dog,” which tend to have a more negative connotation. By combining the output of these different heads, we can get a better overall sentiment prediction for the entire sentence.

In terms of implementation, multi-head attention involves applying multiple self-attention mechanisms in parallel on the input sequence using separate sets of parameters (i.e., weights). This allows us to capture different aspects of the data simultaneously and combine them into a single output. Here’s an example code snippet from the PyTorch documentation that shows how multi-head attention can be implemented:

# MultiHeadAttention class with annotations
class MultiHeadAttention(nn.Module):
    # Initialize the class with the specified number of heads
    def __init__(self, num_heads):
        # Call the parent class constructor
        super().__init__()
        # Store the number of heads as an attribute
        self.num_heads = num_heads
        # Create a MultiheadAttention layer with the specified number of heads
        # and an unspecified embedding dimension
        self.attn = nn.MultiheadAttention(embed_dim, num_heads)
        
    # Define the forward method for the class
    def forward(self, query, key, value, mask=None):
        # ...
        # Perform the multi-head attention operation using the stored layer
        # and the given query, key, and value tensors
        attn_output, attn_weights = self.attn(query, key, value, mask=mask)
        # Return the output and weights from the multi-head attention operation
        return attn_output, attn_weights

In this code snippet, we’re using the `nn.MultiheadAttention()` function from PyTorch to implement multi-head attention with a given number of heads (i.e., self-attention mechanisms). The input arguments are as follows:

– `query`, `key`, and `value`: These are the inputs for each head, which can be thought of as queries, keys, and values in traditional database terminology. In this case, they represent different parts of the input sequence that we want to focus on based on their relevance to a particular task or output.