Implementing Multi-Head Attention with Model Subclassing in TensorFlow Keras

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Alright, multi-head attention one of the most popular techniques used in transformer models for natural language processing (NLP). But instead of boring you with technical jargon and equations, we’re going to take a more casual approach and explain it like your favorite barista explaining how they make their signature latte.

So, imagine that you have a bunch of coffee beans from different regions around the world each bean has its unique flavor profile. Now, let’s say you want to brew a cup of coffee using all these beans together but still maintain their distinct flavors. That’s where multi-head attention comes in!

In transformer models for NLP, we have multiple self-attention heads that work simultaneously and independently on different parts of the input sequence. Each head is like one of those baristas who specializes in a particular type of coffee bean they know how to extract its unique flavor profile without affecting other beans’ flavors.

Now Let’s begin exploring with some technical details (but still keeping it casual). In TensorFlow Keras, we can implement multi-head attention using model subclassing. Here’s an example:

# Import necessary libraries
import tensorflow as tf
from tensorflow.keras import layers
import numpy as np

# Define a class for Multi-Head Attention layer
class MultiHeadAttention(layers.Layer):
    # Initialize the layer with default values for number of heads and model dimension
    def __init__(self, num_heads=8, d_model=512, **kwargs):
        super().__init__(**kwargs)
        self.num_heads = num_heads
        self.d_model = d_model
        
    # Build the layer by defining the weights for each head
    def build(self, input_shape):
        # Define the weights for query, key and value matrices for each head
        self.W1 = layers.Dense(units=self.num_heads * self.d_model)
        self.W2 = layers.Dense(units=self.num_heads * self.d_model)
        
    # Define the call function to perform multi-head attention
    def call(self, inputs):
        # Calculate the query, key and value matrices for each head
        qkv = tf.matmul(inputs, self.W1)
        # Reshape the output to have the shape of (batch_size, num_heads, sqrt(d_model), input_shape[-1])
        qkv = tf.reshape(qkv, (-1, self.num_heads, int(np.sqrt(self.d_model)), inputs.shape[-1]))
        
    # Define the output shape based on the number of heads and input dimensions
    def compute_output_shape(self, input_shape):
        return (input_shape[0], self.num_heads, int(np.sqrt(input_shape[-1])))

In this example, we’re defining a custom layer called `MultiHeadAttention`. We pass in two arguments the number of heads and the dimension size for each head (d_model). The `build()` function defines the weights for each head. In the `call()` function, we calculate the query, key, and value matrices for each head using a dot product with the input matrix.

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