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selfAttentionLayer

Self-attention layer

Since R2023a

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    Description

    A self-attention layer computes single-head or multihead self-attention of its input.

    The layer:

    1. Computes the queries, keys, and values from the input

    2. Computes the scaled dot-product attention across heads using the queries, keys, and values

    3. Merges the results from the heads

    4. Performs a linear transformation on the merged result

    Creation

    Syntax

    layer = selfAttentionLayer(numHeads,numKeyChannels)
    layer = selfAttentionLayer(numHeads,numKeyChannels,Name=Value)

    Description

    example

    layer = selfAttentionLayer(numHeads,numKeyChannels) creates a self-attention layer and sets the NumHeads and NumKeyChannels properties.

    layer = selfAttentionLayer(numHeads,numKeyChannels,Name=Value) sets the optional NumValueChannels, OutputSize, HasPaddingMaskInput, AttentionMask, DropoutProbability, HasScoresOutput, Parameters and Initialization, Learning Rate and Regularization, and Name properties.

    Properties

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    Self-Attention

    This property is read-only.

    Number of attention heads, specified as a positive integer that evenly divides NumKeyChannels.

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

    This property is read-only.

    Number of channels for the keys and queries, specified as a positive integer that is divisible by NumHeads.

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | char | string

    Number of channels for the values, specified as one of these values:

    • "auto" — Use NumKeyChannels.

    • Positive integer — Use the specified number of channels. This value must be divisible by NumHeads.

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | char | string

    Number of channels of the layer output, specified as one of these values:

    • "auto" — Use the number of channels in the layer input.

    • Positive integer — Use the specified number of channels.

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | char | string

    Flag indicating whether the layer has an input that represents the padding mask, specified as 0 (false) or 1 (true).

    If the HasPaddingMaskInput property is 0 (false), then the layer has one input with the name 'in', which corresponds to the input data. In this case, the layer treats all elements as data.

    If the HasPaddingMaskInput property is 1 (true), then the layer has two inputs with the names 'in' and 'mask', which correspond to the input data and the mask, respectively. In this case, the padding mask is an array of ones and zeros. The layer uses and ignores elements of the input when the corresponding element in the mask is one or zero, respectively.

    Mask preventing attention to elements in key-value pairs, specified as one of these values:

    • "none" — Do not prevent attention to elements based on their positions. If HasPaddingMaskInput is 1 (true), then the layer prevents attention to padding elements only.

    • "causal" — Prevent elements in position M from attending to elements in position N, where N is greater than M. Use this option for auto-regressive models.

    Dropout probability for attention scores, specified as a scalar in the range [0, 1).

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

    Flag indicating whether the layer has an output that represents the attention scores, specified as one of these values:

    • 0 (false) — The layer does not have an output that represents the attention scores.

    • 1 (true) — The layer has an output with the name "scores" that represents the attention scores.

    This property is read-only.

    Number of input channels, specified as one of these values:

    • "auto" — Automatically determine the number of input channels when you initialize the network

    • Positive integer — Configure the layer for the specified number of input channels. InputSize and the number of channels in the layer input data must match.

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | char | string

    Parameters and Initialization

    Function to initialize the query, key, value, and output weights, specified as one of these values:

    • "glorot" – Initialize the weights with the Glorot initializer (also known as Xavier initializer) [2]. The Glorot initializer independently samples from a uniform distribution with zero mean and a variance of 2/(numIn + numOut). The values of numIn and numOut depend on the weight matrix:

      WeightnumInnumOut
      QueryInputSizeNumKeyChannels
      KeyInputSizeNumKeyChannels
      ValueInputSizeNumValueChannels
      OutputNumValueChannelsOutputSize

    • "he" – Initialize the weights with the He initializer [3]. The He initializer samples from a normal distribution with zero mean and a variance of 2/numIn. The values of numIn and numOut depend on the weight matrix:

      WeightnumInnumOut
      QueryInputSizeNumKeyChannels
      KeyInputSizeNumKeyChannels
      ValueInputSizeNumValueChannels
      OutputNumValueChannelsOutputSize

    • "narrow-normal" — Initialize the weights by independently sampling from a normal distribution with zero mean and a standard deviation of 0.01.

    • "zeros" — Initialize the weights with zeros.

    • "ones" — Initialize the weights with ones.

    • Function handle — Initialize the weights with a custom function. If you specify a function handle, then the function must have the form weights = func(sz), where sz is the size of the weights. For an example, see Specify Custom Weight Initialization Function.

    The layer only initializes the weights when the corresponding weights property is empty.

    Data Types: char | string | function_handle

    Function to initialize the query, key, value, and output biases, specified as one of these values:

    • "zeros" — Initialize the biases with zeros.

    • "ones" — Initialize the biases with ones.

    • "narrow-normal" — Initialize the biases by independently sampling from a normal distribution with zero mean and a standard deviation of 0.01.

    • Function handle — Initialize the biases with a custom function. If you specify a function handle, then the function must have the form bias = func(sz), where sz is the size of the biases.

    The layer only initializes the biases when the corresponding bias property is empty.

    Data Types: char | string | function_handle

    Query weights, specified as a NumKeyChannels-by-numInputChannels matrix or [], where numInputChannels is the number of channels in the layer input.

    Data Types: single | double

    Key weights, specified as a NumKeyChannels-by-numInputChannels matrix or [], where numInputChannels is the number of channels in the layer input.

    Data Types: single | double

    Value weights, specified as a NumValueChannels-by-numInputChannels matrix or [], where numInputChannels is the number of channels in the layer input.

    Data Types: single | double

    Output weights, specified as an OutputSize-by-NumValueChannels matrix or [].

    Data Types: single | double

    Query biases, specified as a NumKeyChannels-by-1 vector or [].

    Data Types: single | double

    Key biases, specified as a NumKeyChannels-by-1 vector or [].

    Data Types: single | double

    Value biases, specified as a NumValueChannels-by-1 vector or [].

    Data Types: single | double

    Output biases, specified as an OutputSize-by-1 vector or [].

    Data Types: single | double

    Learning Rate and Regularization

    Learning rate factor for the query, key, value, and output weights, specified as a nonnegative scalar.

    The software multiplies this factor by the global learning rate to determine the learning rate for the weights in this layer. For example, if WeightLearnRateFactor is 2, then the learning rate for the weights in this layer is twice the current global learning rate. The software determines the global learning rate based on the settings you specify using the trainingOptions function.

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

    Learning rate factor for the query, key, value, and output biases, specified as a nonnegative scalar.

    The software multiplies this factor by the global learning rate to determine the learning rate for the biases in this layer. For example, if BiasLearnRateFactor is 2, then the learning rate for the biases in the layer is twice the current global learning rate. The software determines the global learning rate based on the settings you specify using the trainingOptions function.

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

    L2 regularization factor for the query, key, value, and output weights, specified as a nonnegative scalar.

    The software multiplies this factor by the global L2 regularization factor to determine the L2 regularization for the weights in this layer. For example, if WeightL2Factor is 2, then the L2 regularization for the weights in this layer is twice the global L2 regularization factor. You can specify the global L2 regularization factor using the trainingOptions function.

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

    L2 regularization factor for the query, key, value, and output biases, specified as a nonnegative scalar.

    The software multiplies this factor by the global L2 regularization factor to determine the L2 regularization for the biases in this layer. For example, if BiasL2Factor is 2, then the L2 regularization for the biases in this layer is twice the global L2 regularization factor. The software determines the global L2 regularization factor based on the settings you specify using the trainingOptions function.

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

    Layer

    Layer name, specified as a character vector or a string scalar. For Layer array input, the trainnet, trainNetwork, assembleNetwork, layerGraph, and dlnetwork functions automatically assign names to layers with the name "".

    The SelfAttentionLayer object stores this property as a character vector.

    Data Types: char | string

    This property is read-only.

    Number of inputs to the layer, returned as 1 or 2.

    If the HasPaddingMaskInput property is 0 (false), then the layer has one input with the name 'in', which corresponds to the input data. In this case, the layer treats all elements as data.

    If the HasPaddingMaskInput property is 1 (true), then the layer has two inputs with the names 'in' and 'mask', which correspond to the input data and the mask, respectively. In this case, the padding mask is an array of ones and zeros. The layer uses and ignores elements of the input when the corresponding element in the mask is one or zero, respectively.

    Data Types: double

    This property is read-only.

    Input names of the layer, returned as a cell array of character vectors.

    If the HasPaddingMaskInput property is 0 (false), then the layer has one input with the name 'in', which corresponds to the input data. In this case, the layer treats all elements as data.

    If the HasPaddingMaskInput property is 1 (true), then the layer has two inputs with the names 'in' and 'mask', which correspond to the input data and the mask, respectively. In this case, the padding mask is an array of ones and zeros. The layer uses and ignores elements of the input when the corresponding element in the mask is one or zero, respectively.

    This property is read-only.

    Number of outputs of the layer.

    If the HasScoresOutput property is 0 (false), then the layer has one output with the name 'out', which corresponds to the output data.

    If the HasScoresOutput property is 1 (true), then the layer has two inputs with the names 'out' and 'scores', which correspond to the output data and the attention scores, respectively.

    Data Types: double

    This property is read-only.

    Output names of the layer.

    If the HasScoresOutput property is 0 (false), then the layer has one output with the name 'out', which corresponds to the output data.

    If the HasScoresOutput property is 1 (true), then the layer has two inputs with the names 'out' and 'scores', which correspond to the output data and the attention scores, respectively.

    Examples

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    Open Live Script

    Create a self-attention layer with eight heads and 256 key and query channels.

    layer = selfAttentionLayer(8,256)
    layer = 
  SelfAttentionLayer with properties:

                   Name: ''
          AttentionMask: 'none'
    HasPaddingMaskInput: 0
        HasScoresOutput: 0

   Hyperparameters
              InputSize: 'auto'
               NumHeads: 8
         NumKeyChannels: 256
       NumValueChannels: 'auto'
             OutputSize: 'auto'
     DropoutProbability: 0

   Learnable Parameters
           QueryWeights: []
             KeyWeights: []
           ValueWeights: []
          OutputWeights: []
              QueryBias: []
                KeyBias: []
              ValueBias: []
             OutputBias: []

Use properties method to see a list of all properties.

    Include a self-attention layer in a layer array.

    layers = [
    sequenceInputLayer(12)
    selfAttentionLayer(4,12)
    layerNormalizationLayer
    fullyConnectedLayer(9)
    softmaxLayer];

    References

    [1] Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. "Attention is all you need." In Advances in neural information processing systems, Vol. 30. Curran Associates, Inc., 2017. https://papers.nips.cc/paper/7181-attention-is-all-you-need.

    [2] Glorot, Xavier, and Yoshua Bengio. "Understanding the Difficulty of Training Deep Feedforward Neural Networks." In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 249–356. Sardinia, Italy: AISTATS, 2010. https://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf

    [3] He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification." In 2015 IEEE International Conference on Computer Vision (ICCV), 1026–34. Santiago, Chile: IEEE, 2015. https://doi.org/10.1109/ICCV.2015.123

    Version History

    Introduced in R2023a

    See Also

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