selfAttentionLayer
Description
A self-attention layer computes single-head or multihead self-attention of its input.
The layer:
Computes the queries, keys, and values from the input
Computes the scaled dot-product attention across heads using the queries, keys, and values
Merges the results from the heads
Performs a linear transformation on the merged result
Creation
Syntax
Description
creates a self-attention layer and sets the layer
= selfAttentionLayer(numHeads,numKeyChannels)NumHeads
and NumKeyChannels
properties.
sets the optional layer
= selfAttentionLayer(numHeads,numKeyChannels,Name=Value
)NumValueChannels
, OutputSize
, HasPaddingMaskInput
, AttentionMask
, DropoutProbability
, HasScoresOutput
, Parameters and Initialization, Learning Rate and Regularization, and Name
properties.
Properties
Self-Attention
NumHeads
— Number of attention heads
positive integer
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
NumKeyChannels
— Number of channels for keys and queries
positive integer
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
NumValueChannels
— Number of channels for values
"auto"
(default) | positive integer
Number of channels for the values, specified as one of these values:
"auto"
— UseNumKeyChannels
.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
OutputSize
— Number of channels of layer output
"auto"
(default) | positive integer
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
HasPaddingMaskInput
— Flag indicating whether layer has mask input
0
(false
) (default) | 1
(true
)
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.
The format of the padding mask must match that of the input. The size of the "S"
(spatial), "T"
(time), and "B"
(batch) dimensions of the padding mask must match the size of the corresponding dimensions in the input.
The padding mask can have any number of channels. The software uses the values in the first channel only to indicate padding values.
AttentionMask
— Mask preventing attention to elements in key-value pairs
"none"
(default) | "causal"
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. IfHasPaddingMaskInput
is1
(true
), then the layer prevents attention to padding elements only."causal"
— Prevent elements in positionM
from attending to elements in positionN
, whereN
is greater thanM
. Use this option for auto-regressive models.
DropoutProbability
— Dropout probability for attention scores
0
(default) | scalar in the range [0, 1)
Probability of dropping out attention scores, specified as a scalar in the range [0, 1).
During training, the software randomly sets values in the attention scores to zero using the specified probability. These dropouts can encourage the model to learn more robust and generalizable representations by preventing it from relying too heavily on specific dependencies.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
HasScoresOutput
— Flag indicating whether layer has scores output
0
(false
) (default) | 1
(true
)
Flag indicating whether the layer has an output that represents the scores (also known as the
attention weights), specified as 0
(false
) or
1
(true
).
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.
InputSize
— Number of input channels
"auto"
(default) | positive integer
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 networkPositive 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
WeightsInitializer
— Function to initialize weights
"glorot"
(default) | "he"
| "narrow-normal"
| "zeros"
| "ones"
| function handle
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 of2/(numIn + numOut)
. The values ofnumIn
andnumOut
depend on the weight matrix:Weight numIn
numOut
Query InputSize
NumKeyChannels
Key InputSize
NumKeyChannels
Value InputSize
NumValueChannels
Output NumValueChannels
OutputSize
"he"
– Initialize the weights with the He initializer [3]. The He initializer samples from a normal distribution with zero mean and a variance of2/numIn
. The values ofnumIn
andnumOut
depend on the weight matrix:Weight numIn
numOut
Query InputSize
NumKeyChannels
Key InputSize
NumKeyChannels
Value InputSize
NumValueChannels
Output NumValueChannels
OutputSize
"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)
, wheresz
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
BiasInitializer
— Function to initialize biases
"zeros"
(default) | "narrow-normal"
| "ones"
| 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)
, wheresz
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
QueryWeights
— Query weights
[]
(default) | matrix
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
KeyWeights
— Key weights
[]
(default) | matrix
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
ValueWeights
— Value weights
[]
(default) | matrix
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
OutputWeights
— Output weights
[]
(default) | matrix
Output weights, specified as an OutputSize
-by-NumValueChannels
matrix or []
.
Data Types: single
| double
QueryBias
— Query biases
[]
(default) | vector
Query biases, specified as a NumKeyChannels
-by-1
vector or
[]
.
Data Types: single
| double
KeyBias
— Key biases
[]
(default) | vector
Key biases, specified as a NumKeyChannels
-by-1
vector or
[]
.
Data Types: single
| double
ValueBias
— Value biases
[]
(default) | vector
Value biases, specified as a NumValueChannels
-by-1
vector or
[]
.
Data Types: single
| double
OutputBias
— Output biases
[]
(default) | vector
Output biases, specified as an OutputSize
-by-1
vector or
[]
.
Data Types: single
| double
Learning Rate and Regularization
WeightLearnRateFactor
— Learning rate factor for weights
1
(default) | nonnegative scalar
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
BiasLearnRateFactor
— Learning rate factor for biases
1
(default) | nonnegative scalar
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
WeightL2Factor
— L2 regularization factor for weights
1
(default) | nonnegative scalar
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
BiasL2Factor
— L2 regularization factor for biases
0
(default) | nonnegative scalar
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
Name
— Layer name
""
(default) | character vector | string scalar
NumInputs
— Number of inputs
1
| 2
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.
The format of the padding mask must match that of the input. The size of the "S"
(spatial), "T"
(time), and "B"
(batch) dimensions of the padding mask must match the size of the corresponding dimensions in the input.
The padding mask can have any number of channels. The software uses the values in the first channel only to indicate padding values.
Data Types: double
InputNames
— Input names
"in"
| ["in" "mask"]
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.
The format of the padding mask must match that of the input. The size of the "S"
(spatial), "T"
(time), and "B"
(batch) dimensions of the padding mask must match the size of the corresponding dimensions in the input.
The padding mask can have any number of channels. The software uses the values in the first channel only to indicate padding values.
The SelfAttentionLayer
object stores this property as a cell array of character
vectors.
NumOutputs
— Number of outputs
1
(default) | 2
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
OutputNames
— Output names
"out"
(default) | ["out" "scores"]
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.
The SelfAttentionLayer
object stores this property as a cell array of character
vectors.
Examples
Create Self-Attention Layer
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];
Algorithms
Dot-Product Attention
The attention operation focuses on parts of the input using weighted multiplication operations.
The single-head dot-product attention operation is given by
where:
Q denotes the queries.
K denotes the keys.
V denotes the values.
denotes the scaling factor.
M is a mask array of ones and zeros.
p is the dropout probability.
The mask operation includes or excludes the values of the matrix multiplication setting values of the input to for zero-valued mask elements. The mask is the union of the padding and attention masks. The softmax function normalizes the value of the input data across the channel dimension such that it sums to one. The dropout operation sets elements to zero with probability p.
Multihead Self-Attention
The multihead self-attention operation for the input X is given by
where:
h is the number of heads.
WQ is a learnable projection matrix for the queries.
WK is a learnable projection matrix for the keys.
WV is a learnable projection matrix for the values.
WO is a learnable projection matrix for the output.
Each weight matrix is composed of concatenated weight matrices Wi for each head. Each denotes the output of the head operation given by
Layer Input and Output Formats
Layers in a layer array or layer graph pass data to subsequent layers as formatted dlarray
objects.
The format of a dlarray
object is a string of characters in which each
character describes the corresponding dimension of the data. The formats consist of one or
more of these characters:
"S"
— Spatial"C"
— Channel"B"
— Batch"T"
— Time"U"
— Unspecified
For example, you can describe 2-D image data that is represented as a 4-D array, where the
first two dimensions correspond to the spatial dimensions of the images, the third
dimension corresponds to the channels of the images, and the fourth dimension
corresponds to the batch dimension, as having the format "SSCB"
(spatial, spatial, channel, batch).
You can interact with these dlarray
objects in automatic differentiation
workflows, such as those for developing a custom layer, using a functionLayer
object, or using the forward
and predict
functions with
dlnetwork
objects.
This table shows the supported input formats of SelfAttentionLayer
objects and the
corresponding output format. If the software passes the output of the layer to a custom
layer that does not inherit from the nnet.layer.Formattable
class, or a
FunctionLayer
object with the Formattable
property
set to 0
(false
), then the layer receives an
unformatted dlarray
object with dimensions ordered according to the formats
in this table. The formats listed here are only a subset. The layer may support additional
formats such as formats with additional "S"
(spatial) or
"U"
(unspecified) dimensions.
Input Format | Output Format |
---|---|
"CB" (channel, batch) | "CB" (channel, batch) |
"SCB" (spatial, channel, batch) | "SCB" (spatial, channel, batch) |
"CBT" (channel, batch, time) | "CBT" (channel, batch, time) |
"SC" (spatial, channel) | "SC" (spatial, channel) |
"CT" (channel, time) | "CT" (channel, time) |
"SB" (spatial, batch) | "SCB" (spatial, channel, batch) |
"BT" (batch, time) | "CBT" (channel, batch, time) |
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
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
Usage notes and limitations:
Code generation is not supported when
HasScoresOutput
is set totrue
.Code generation does not support passing
dlarray
objects with unspecified (U) dimensions to this layer.For code generation, you must pass a
dlarray
object with a channel (C) dimension as the input to this layer. For example, code generation supports data format such as "SSC" or "SSCBT".
GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.
Refer to the usage notes and limitations in the C/C++ Code Generation section. The same limitations apply to GPU code generation.
Version History
Introduced in R2023a
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