gistlib - implement a custom self attention layer in matlab in matlab

To implement a custom self-attention layer in MATLAB, follow these steps:

Define the class for the self-attention layer by inheriting from the nnet.layer.Layer class.


main.m
classdef SelfAttentionLayer < nnet.layer.Layer
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Define and initialize the layer properties. In this case, it includes the number of attention heads and the dimension of the hidden representation.


main.m
properties
    NumHeads
    HiddenDim
    Wq
    Wk
    Wv
end

methods
    function layer = SelfAttentionLayer(numHeads, hiddenDim)
        layer.NumHeads = numHeads;
        layer.HiddenDim = hiddenDim;
        layer.Name = 'self_attention_layer';
        layer.Description = ['Self-Attention Layer with ', ...
                             num2str(numHeads), ' heads'];
        layer.Wq = [];
        layer.Wk = [];
        layer.Wv = [];
    end
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Define the forward function for the layer. In this case, it applies the self-attention mechanism to the input.


main.m
function Z = predict(layer, X)
    % X has shape [sequenceLength, inputSize, miniBatchSize]
    [sequenceLength, inputSize, miniBatchSize] = size(X);
    
    % Compute queries, keys, and values
    Q = layer.Wq*X;
    K = layer.Wk*X;
    V = layer.Wv*X;
    
    % Reshape the matrices for multi-head attention
    Q = reshape(Q, [sequenceLength, layer.NumHeads, ...
                    layer.HiddenDim/layer.NumHeads, miniBatchSize]);
    K = reshape(K, [sequenceLength, layer.NumHeads, ...
                    layer.HiddenDim/layer.NumHeads, miniBatchSize]);
    V = reshape(V, [sequenceLength, layer.NumHeads, ...
                    layer.HiddenDim/layer.NumHeads, miniBatchSize]);

    % Compute the attention scores and softmax
    S = dot(Q, permute(K, [1, 2, 4, 3])) / sqrt(layer.HiddenDim/layer.NumHeads);
    A = softmax(S, 1);
    
    % Compute the output of the self-attention layer
    Z = dot(A, V);
    Z = reshape(Z, [sequenceLength, layer.HiddenDim, miniBatchSize]);
end
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Define the function to initialize the learnable parameters of the layer. In this case, it initializes the weight matrices for queries, keys, and values.


main.m
function layer = initialize(layer, inputSize)
    % Initialize learnable parameters
    layer.Wq = randn(layer.HiddenDim, inputSize);
    layer.Wk = randn(layer.HiddenDim, inputSize);
    layer.Wv = randn(layer.HiddenDim, inputSize);
end
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Define the function to update the learnable parameters of the layer during training.


main.m
function layer = update(layer, ~, ~)
    % No training is required for self-attention layer
end
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With these steps, you have implemented a custom self-attention layer in MATLAB that can be added to any deep learning network.

implement a custom self attention layer in matlab in matlab

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