Neural Network primitives from NNlib.jl

dot_product_attention(query, key, value, [bias]; [fdrop, mask, nheads])

Multihead dot product attention used in transformer architectures.

The input arrays must have the first two dimensions given by the number of features and the sequence length, then an arbitrary number of batch dimensions or none.

Returns the attention output array of size (v_dim, q_len, batch_size...) and the attention scores of size (kv_len, q_len, nheads, batch_size...).

See also dot_product_attention_scores if you only need the attention scores.

Arguments

• query: Query array of size (qk_dim, q_len, batch_size...).
• key: Key array of size (qk_dim, kv_len, batch_size...).
• value: Value array of size (v_dim, kv_len, batch_size...).
• bias: Either nothing or an array broadcastable to size (kv_len, q_len, nheads, batch_size). It will be added to the attention scores before applying the softmax. Default nothing.
• fdrop: A dropout function or layer to be applied on the attention scores right after the softmax. Default identity (no dropout).
• mask: Either nothing or a boolean array broadcastable to size (kv_len, q_len, nheads, batch_size). The mask is applied to the attention scores just before the softmax. See make_causal_mask fore creating causal masks. Default nothing.
• nheads: Number of heads to split the input arrays into. Default 1.

Examples

q, k, v = rand(10, 20, 2), rand(10, 30, 2), rand(20, 30, 2)
y, α = dot_product_attention(q, k, v)

dot_product_attention_scores(query, key, [bias]; [fdrop, mask])

Return the attention scores for the dot_product_attention. Input arrays must have dimensions (num_features ÷ nheads, nheads, sequence_length, batch_size).

See dot_product_attention for more details.

make_causal_mask(x, dims=2)

Return a boolean square matrix m of the same type as x and of side size(x, dims). Its elements are set such that m[i, j] == i ≤ j.

Can be used to mask the attention scores in dot_product_attention.

softmax(x; dims = 1)

Softmax turns input array x into probability distributions that sum to 1 along the dimensions specified by dims. It is semantically equivalent to the following:

softmax(x; dims = 1) = exp.(x) ./ sum(exp.(x), dims = dims)

with additional manipulations enhancing numerical stability.

For a matrix input x it will by default (dims = 1) treat it as a batch of vectors, with each column independent. Keyword dims = 2 will instead treat rows independently, and so on.

See also logsoftmax.

Examples

julia> softmax([1, 2, 3])
3-element Vector{Float64}:
 0.09003057317038046
 0.24472847105479764
 0.6652409557748218

julia> softmax([1 2 3; 2 2 2])  # dims=1
2×3 Matrix{Float64}:
 0.268941  0.5  0.731059
 0.731059  0.5  0.268941

julia> softmax([1 2 3; 2 2 2]; dims=2)
2×3 Matrix{Float64}:
 0.0900306  0.244728  0.665241
 0.333333   0.333333  0.333333

Note that, when used with Flux.jl, softmax must not be passed to layers like Dense which accept an activation function. The activation is broadcasted over the result, thus applies to individual numbers. But softmax always needs to see the whole column.

julia> using Flux

julia> x = randn(Float32, 4, 4, 3, 13);

julia> model = Chain(Conv((4, 4), 3 => 8, tanh), Flux.flatten, Dense(8 => 7), softmax);

julia> model(x) |> size
(7, 13)

julia> Dense(4 => 7, softmax)(x)
ERROR: `softmax(x)` called with a number, but it expects an array. 

logsoftmax(x; dims = 1)

Computes the log of softmax in a more numerically stable way than directly taking log.(softmax(xs)). Commonly used in computing cross entropy loss.

It is semantically equivalent to the following:

logsoftmax(x; dims = 1) = x .- log.(sum(exp.(x), dims = dims))

See also softmax.

PoolDims(x_size::NTuple{M}, k::Union{NTuple{L, Int}, Int};
         stride=k, padding=0, dilation=1)  where {M, L}

Dimensions for a "pooling" operation that can have an arbitrary input size, kernel size, stride, dilation, and channel count. Used to dispatch onto efficient implementations at compile-time.

lpnormpool(x, p::Real, k::NTuple{N, Integer}; pad=0, stride=k)

Perform Lp pool operation with value of the Lp norm p and window size k on input tensor x, also known as LPPool in pytorch. This pooling operator from Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks.

Arguments:

• x and k: Expects ndim(x) ∈ 3:5, and always length(k) == ndim(x) - 2
• p is restricted to 0 < p < Inf.
• pad: See pad_zeros for details.
• stride: Either a tuple with the same length as k, or one integer for all directions. Default is k.

For all elements x in a size k window, lpnormpool computes (∑ᵢ xᵢ^p)^(1 / p) as an element of the output.

Thus lpnormpool(x, 1, k) ./ prod(k) ≈ meanpool(x, k) and lpnormpool(x, 2, k).^2 ./ prod(k) ≈ meanpool(x.^2, k).

maxpool(x, k::NTuple{N, Integer}; pad=0, stride=k)

Perform max pool operation with window size k on input tensor x.

Arguments:

• x and k: Expects ndim(x) ∈ 3:5, and always length(k) == ndim(x) - 2
• pad: See pad_zeros for details.
• stride: Either a tuple with the same length as k, or one integer for all directions. Default is k.

meanpool(x, k::NTuple{N, Integer}; pad=0, stride=k)

Perform mean pool operation with window size k on input tensor x.

Arguments:

• x and k: Expects ndim(x) ∈ 3:5, and always length(k) == ndim(x) - 2
• pad: See pad_zeros for details.
• stride: Either a tuple with the same length as k, or one integer for all directions. Default is k.

pad_circular(x, pad::Tuple; [dims])
pad_circular(x, pad::Int; [dims])

Pad the array x "circularly" across the border by wrapping around values from the opposite side of x.

pad can a tuple of integers (l1, r1, ..., ln, rn) of some length 2n that specifies the left and right padding size for each of the dimensions in dims. If dims is not given, it defaults to the first n dimensions.

If pad is an integer, it is applied on both sides on every dimension in dims. In this case, dims defaults to the first ndims(x)-2 dimensions (i.e. excludes the channel and batch dimension).

The pad length on either side in any dimension must not exceed the size of x in that dimension, i.e. pad_circular is not able to create abitrary sized tilings of x.

See also pad_repeat, pad_reflect, pad_symmetric, and pad_constant.

julia> r = reshape(1:9, 3, 3)
3×3 reshape(::UnitRange{Int64}, 3, 3) with eltype Int64:
 1  4  7
 2  5  8
 3  6  9

julia> pad_circular(r, (1,2,1,2))
6×6 Matrix{Int64}:
 9  3  6  9  3  6
 7  1  4  7  1  4
 8  2  5  8  2  5
 9  3  6  9  3  6
 7  1  4  7  1  4
 8  2  5  8  2  5

pad_constant(x, pad::Tuple, val = 0; [dims = :])
pad_constant(x, pad::Int, val = 0; [dims = :])

Pad the array x with the constant value val.

pad can be a tuple of integers. If it is of some length 2 * length(dims) that specifies the left and right padding size for each of the dimensions in dims as (l1, r1, ..., ln, rn). If supplied with a tuple of length length(dims) instead, it applies symmetric padding. If dims is not given, it defaults to all dimensions.

For integer pad input, it is applied on both sides on every dimension in dims.

See also pad_zeros, pad_repeat, pad_reflect, pad_symmetric, and pad_circular.

julia> r = reshape(1:4, 2, 2)
2×2 reshape(::UnitRange{Int64}, 2, 2) with eltype Int64:
 1  3
 2  4

julia> pad_constant(r, (1, 2, 3, 4), 8)
5×9 Matrix{Int64}:
 8  8  8  8  8  8  8  8  8
 8  8  8  1  3  8  8  8  8
 8  8  8  2  4  8  8  8  8
 8  8  8  8  8  8  8  8  8
 8  8  8  8  8  8  8  8  8

julia> pad_constant(r, 1, 8)
4×4 Matrix{Int64}:
 8  8  8  8
 8  1  3  8
 8  2  4  8
 8  8  8  8

julia> r = reshape(1:27, 3, 3, 3)
3×3×3 reshape(::UnitRange{Int64}, 3, 3, 3) with eltype Int64:
[:, :, 1] =
 1  4  7
 2  5  8
 3  6  9

[:, :, 2] =
 10  13  16
 11  14  17
 12  15  18

[:, :, 3] =
 19  22  25
 20  23  26
 21  24  27

julia> pad_constant(r, (2,1), dims = 1) # assymetric padding
6×3×3 Array{Int64, 3}:
[:, :, 1] =
 0  0  0
 0  0  0
 1  4  7
 2  5  8
 3  6  9
 0  0  0

[:, :, 2] =
  0   0   0
  0   0   0
 10  13  16
 11  14  17
 12  15  18
  0   0   0

[:, :, 3] =
  0   0   0
  0   0   0
 19  22  25
 20  23  26
 21  24  27
  0   0   0

julia> pad_constant(r, (2,1, 3), dims = (1,2)) # padding must always be either the same length as dims, or double it
ERROR: ArgumentError: Could not parse padding (2, 1, 3) and dims (1, 2)
Stacktrace:
[...]

pad_reflect(x, pad::Tuple; [dims])
pad_reflect(x, pad::Int; [dims])

Pad the array x reflecting its values across the border.

pad can a tuple of integers (l1, r1, ..., ln, rn) of some length 2n that specifies the left and right padding size for each of the dimensions in dims. If dims is not given, it defaults to the first n dimensions.

If pad is an integer, it is applied on both sides on every dimension in dims. In this case, dims defaults to the first ndims(x)-2 dimensions (i.e. excludes the channel and batch dimension).

See also pad_repeat, pad_symmetric, pad_circular, and pad_constant.

julia> r = reshape(1:9, 3, 3)
3×3 reshape(::UnitRange{Int64}, 3, 3) with eltype Int64:
 1  4  7
 2  5  8
 3  6  9

julia> pad_reflect(r, (1,2,1,2))
6×6 Matrix{Int64}:
 5  2  5  8  5  2
 4  1  4  7  4  1
 5  2  5  8  5  2
 6  3  6  9  6  3
 5  2  5  8  5  2
 4  1  4  7  4  1

pad_repeat(x, pad::Tuple; [dims])
pad_repeat(x, pad::Int; [dims])

Pad the array x repeating the values on the border.

pad can a tuple of integers (l1, r1, ..., ln, rn) of some length 2n that specifies the left and right padding size for each of the dimensions in dims. If dims is not given, it defaults to the first n dimensions.

If pad is an integer, it is applied on both sides on every dimension in dims. In this case, dims defaults to the first ndims(x)-2 dimensions (i.e. excludes the channel and batch dimension).

See also pad_reflect, pad_symmetric, pad_circular, and pad_constant.

julia> r = reshape(1:9, 3, 3)
3×3 reshape(::UnitRange{Int64}, 3, 3) with eltype Int64:
 1  4  7
 2  5  8
 3  6  9

julia> pad_repeat(r, (1,2,3,4))
6×10 Matrix{Int64}:
 1  1  1  1  4  7  7  7  7  7
 1  1  1  1  4  7  7  7  7  7
 2  2  2  2  5  8  8  8  8  8
 3  3  3  3  6  9  9  9  9  9
 3  3  3  3  6  9  9  9  9  9
 3  3  3  3  6  9  9  9  9  9

pad_symmetric(x, pad::Tuple; [dims])
pad_symmetric(x, pad::Int; [dims])

Pad the array x reflecting its values symmetrically across the border, i.e. the border values of x are present in the padding values, in contrast to pad_reflect.

pad can a tuple of integers (l1, r1, ..., ln, rn) of some length 2n that specifies the left and right padding size for each of the dimensions in dims. If dims is not given, it defaults to the first n dimensions.

If pad is an integer, it is applied on both sides on every dimension in dims. In this case, dims defaults to the first ndims(x)-2 dimensions (i.e. excludes the channel and batch dimension).

See also pad_repeat, pad_reflect, pad_circular, and pad_constant.

julia> r = reshape(1:9, 3, 3)
3×3 reshape(::UnitRange{Int64}, 3, 3) with eltype Int64:
 1  4  7
 2  5  8
 3  6  9

julia> pad_symmetric(r, (1,2,1,2))
6×6 Matrix{Int64}:
 1  1  4  7  7  4
 1  1  4  7  7  4
 2  2  5  8  8  5
 3  3  6  9  9  6
 3  3  6  9  9  6
 2  2  5  8  8  5

pad_zeros(x, pad::Tuple; [dims])
pad_zeros(x, pad::Int; [dims])

Pad the array x with zeros. Equivalent to pad_constant with the constant equal to 0.

conv(x, w; stride = 1, pad = 0, dilation = 1, flipped = false, groups = 1)

Apply convolution filter w to input x. x and w are 3d/4d/5d tensors in 1d/2d/3d convolutions respectively. x and w may have real or complex element types.

ConvDims

Type system-level information about convolution dimensions. Critical for things like im2col!() to generate efficient code, and helpful to reduce the number of kwargs getting passed around.

depthwiseconv(x, w; stride=1, pad=0, dilation=1, flipped=false)

Depthwise convolution operation with filter w on input x. x and w are 3d/4d/5d tensors in 1d/2d/3d convolutions respectively.

DepthwiseConvDims

Concrete subclass of ConvDims for a depthwise convolution. Differs primarily due to characterization by C_in, C_mult, rather than C_in, C_out. Useful to be separate from DenseConvDims primarily for channel calculation differences.

DenseConvDims

Concrete subclass of ConvDims for a normal, dense, conv2d/conv3d.

dropout([rng], A, p; [dims])

Returns an array in which each element of A is either replaced with zero, with probability p, or else multiplied by 1/(1-p).

By default every element is treated independently. With keyword dims=1, a choice is made for every value of the 1st index i.e. each row of a matrix is either zero or not.

Optional first argument is the random number generator used.

Examples

julia> dropout(ones(2, 10), 0.2)
2×10 Matrix{Float64}:
 1.25  1.25  0.0   1.25  1.25  1.25  1.25  1.25  1.25  1.25
 1.25  1.25  1.25  0.0   1.25  1.25  0.0   1.25  1.25  1.25

julia> mean(dropout(ones(10^4, 5), 0.2), dims=1)
1×5 Matrix{Float64}:
 0.998  1.00075  0.99125  0.99575  1.00075

julia> dropout(ones(5, 5), 0.7, dims=1)  # whole row the same
5×5 Matrix{Float64}:
 3.33333  3.33333  3.33333  3.33333  3.33333
 0.0      0.0      0.0      0.0      0.0
 0.0      0.0      0.0      0.0      0.0
 3.33333  3.33333  3.33333  3.33333  3.33333
 0.0      0.0      0.0      0.0      0.0

julia> mean(dropout(ones(10^4, 5), 0.3, dims=1), dims=1)
1×5 Matrix{Float64}:
 1.00571  1.00571  1.00571  1.00571  1.00571

dropout!(B, A, p; [dims])

This does exactly B .= dropout(A, p; dims), or rather, it's the implementation of out-of-place dropout.

upsample_nearest(x, scale::NTuple{S,Int})
upsample_nearest(x; size::NTuple{S,Int})

Upsamples the array x by integer multiples along the first S dimensions. Subsequent dimensions of x are not altered.

Either the scale factors or the final output size can be specified.

See also upsample_bilinear, for two dimensions of an N=4 array.

Example

julia> upsample_nearest([1 2 3; 4 5 6], (2, 3))
4×9 Matrix{Int64}:
 1  1  1  2  2  2  3  3  3
 1  1  1  2  2  2  3  3  3
 4  4  4  5  5  5  6  6  6
 4  4  4  5  5  5  6  6  6

julia> ans == upsample_nearest([1 2 3; 4 5 6]; size=(4, 9))  # equivalent
true

julia> upsample_nearest([1 2 3; 4 5 6], (2,))
4×3 Matrix{Int64}:
 1  2  3
 1  2  3
 4  5  6
 4  5  6

julia> ans == upsample_nearest([1 2 3; 4 5 6], size=(4,))
true

upsample_linear(x::AbstractArray{T,3}, scale::Real; align_corners::Bool = true)
upsample_linear(x::AbstractArray{T,3}; size::Integer, align_corners::Bool = true)

Upsamples the first dimension of the array x by the upsample provided scale, using linear interpolation. As an alternative to using scale, the resulting array size can be directly specified with a keyword argument.

The size of the output is equal to (scale*S1, S2, S3), where S1, S2, S3 = size(x).

∇upsample_linear(Δ::AbstractArray{T,3}; size::Integer, align_corners::Bool = true) where T

Arguments

• Δ: Incoming gradient array, backpropagated from downstream layers
• size: Size of the image upsampled in the first place

Outputs

• dx: Downsampled version of Δ

upsample_bilinear(x::AbstractArray{T,4}, scale::NTuple{2,Real}; align_corners::Bool = true)
upsample_bilinear(x::AbstractArray{T,4}; size::NTuple{2,Integer}, align_corners::Bool = true)

Upsamples the first 2 dimensions of the array x by the upsample factors stored in scale, using bilinear interpolation. As an alternative to using scale, the resulting image size can be directly specified with a keyword argument.

The size of the output is equal to (scale[1]*S1, scale[2]*S2, S3, S4), where S1, S2, S3, S4 = size(x).

Examples

julia> x = reshape(Float32[1 2 3; 4 5 6], (2,3,1,1))
2×3×1×1 Array{Float32, 4}:
[:, :, 1, 1] =
 1.0  2.0  3.0
 4.0  5.0  6.0

julia> upsample_bilinear(x, (2, 3))
4×9×1×1 Array{Float32, 4}:
[:, :, 1, 1] =
 1.0  1.25  1.5  1.75  2.0  2.25  2.5  2.75  3.0
 2.0  2.25  2.5  2.75  3.0  3.25  3.5  3.75  4.0
 3.0  3.25  3.5  3.75  4.0  4.25  4.5  4.75  5.0
 4.0  4.25  4.5  4.75  5.0  5.25  5.5  5.75  6.0

julia> ans == upsample_bilinear(x; size=(4, 9))  # specify ouput size instead
true

julia> upsample_bilinear(x, (2.5, 3.5))  # non-integer scaling factors are allowed
5×10×1×1 Array{Float32, 4}:
[:, :, 1, 1] =
 1.0   1.22222  1.44444  1.66667  1.88889  …  2.33333  2.55556  2.77778  3.0
 1.75  1.97222  2.19444  2.41667  2.63889     3.08333  3.30556  3.52778  3.75
 2.5   2.72222  2.94444  3.16667  3.38889     3.83333  4.05556  4.27778  4.5
 3.25  3.47222  3.69444  3.91667  4.13889     4.58333  4.80556  5.02778  5.25
 4.0   4.22222  4.44444  4.66667  4.88889     5.33333  5.55556  5.77778  6.0

∇upsample_bilinear(Δ::AbstractArray{T,4}; size::NTuple{2,Integer}, align_corners::Bool = true) where T

Arguments

• Δ: Incoming gradient array, backpropagated from downstream layers
• size: Lateral (W,H) size of the image upsampled in the first place

Outputs

• dx: Downsampled version of Δ

upsample_trilinear(x::AbstractArray{T,5}, scale::NTuple{3,Real}; align_corners::Bool = true)
upsample_trilinear(x::AbstractArray{T,5}; size::NTuple{3,Integer}, align_corners::Bool = true)

Upsamples the first 3 dimensions of the array x by the upsample factors stored in scale, using trilinear interpolation. As an alternative to using scale, the resulting image size can be directly specified with a keyword argument.

The size of the output is equal to (scale[1]*S1, scale[2]*S2, scale[3]*S3, S4, S5), where S1, S2, S3, S4, S5 = size(x).

Examples

upsample_trilinear(x, (2, 3, 4))
upsample_trilinear(x; size=(4, 9, 11))  # specify ouput size instead
upsample_trilinear(x, (2.5, 3.5, pi))  # non-integer scaling factors are allowed

∇upsample_trilinear(Δ::AbstractArray{T,5}; size::NTuple{3,Integer}, align_corners::Bool = true) where T

Arguments

• Δ: Incoming gradient array, backpropagated from downstream layers
• size: Lateral size & depth (W,H,D) of the image upsampled in the first place

Outputs

• dx: Downsampled version of Δ

pixel_shuffle(x, r::Integer)

Pixel shuffling operation, upscaling by a factor r.

For 4-arrays representing N images, the operation converts input size(x) == (W, H, r^2*C, N) to output of size (r*W, r*H, C, N). For D-dimensional data, it expects ndims(x) == D+2 with channel and batch dimensions, and divides the number of channels by r^D.

Used in super-resolution networks to upsample towards high resolution features. Reference: Shi et. al., "Real-Time Single Image and Video Super-Resolution ...", CVPR 2016, https://arxiv.org/abs/1609.05158

Examples

julia> x = [10i + j + channel/10 for i in 1:2, j in 1:3, channel in 1:4, batch in 1:1]
2×3×4×1 Array{Float64, 4}:
[:, :, 1, 1] =
 11.1  12.1  13.1
 21.1  22.1  23.1

[:, :, 2, 1] =
 11.2  12.2  13.2
 21.2  22.2  23.2

[:, :, 3, 1] =
 11.3  12.3  13.3
 21.3  22.3  23.3

[:, :, 4, 1] =
 11.4  12.4  13.4
 21.4  22.4  23.4

julia> pixel_shuffle(x, 2)  # 4 channels used up as 2x upscaling of image dimensions
4×6×1×1 Array{Float64, 4}:
[:, :, 1, 1] =
 11.1  11.3  12.1  12.3  13.1  13.3
 11.2  11.4  12.2  12.4  13.2  13.4
 21.1  21.3  22.1  22.3  23.1  23.3
 21.2  21.4  22.2  22.4  23.2  23.4

julia> y = [i + channel/10 for i in 1:3, channel in 1:6, batch in 1:1]
3×6×1 Array{Float64, 3}:
[:, :, 1] =
 1.1  1.2  1.3  1.4  1.5  1.6
 2.1  2.2  2.3  2.4  2.5  2.6
 3.1  3.2  3.3  3.4  3.5  3.6

julia> pixel_shuffle(y, 2)  # 1D image, with 6 channels reduced to 3
6×3×1 Array{Float64, 3}:
[:, :, 1] =
 1.1  1.3  1.5
 1.2  1.4  1.6
 2.1  2.3  2.5
 2.2  2.4  2.6
 3.1  3.3  3.5
 3.2  3.4  3.6

batched_mul(A, B) -> C
A ⊠ B  # \boxtimes

Batched matrix multiplication. Result has C[:,:,k...] == A[:,:,k...] * B[:,:,k...] where k... represent any indices in the last dimensions.

If ndims(A) == ndims(B) == 3 and size(B,3) == 1 then instead C[:,:,k] == A[:,:,k] * B[:,:,1], and similarly for A.

To transpose each matrix, apply batched_transpose to the array, or batched_adjoint for conjugate-transpose:

julia> A, B = randn(2,5,17), randn(5,9,17);

julia> A ⊠ B |> size
(2, 9, 17)

julia> batched_adjoint(A) |> size
(5, 2, 17)

julia> batched_mul(A, batched_adjoint(randn(9,5,17))) |> size
(2, 9, 17)

julia> A ⊠ randn(5,9,1) |> size
(2, 9, 17)

julia> batched_transpose(A) == PermutedDimsArray(A, (2,1,3))
true

The equivalent PermutedDimsArray may be used in place of batched_transpose. Other permutations are also handled by BLAS, provided that the batch index k is not the first dimension of the underlying array. Thus PermutedDimsArray(::Array, (1,3,2)) and PermutedDimsArray(::Array, (3,1,2)) are fine.

However, A = PermutedDimsArray(::Array, (3,2,1)) is not acceptable to BLAS, since the batch dimension is the contiguous one: stride(A,3) == 1. This will be copied, as doing so is faster than batched_mul_generic!.

Both this copy and batched_mul_generic! produce @debug messages, and setting for instance ENV["JULIA_DEBUG"] = NNlib will display them.

batched_mul(A::Array{T,3}, B::Matrix)
batched_mul(A::Matrix, B::Array{T,3})
A ⊠ B

This is always matrix-matrix multiplication, but either A or B may lack a batch index.

• When B is a matrix, result has C[:,:,k] == A[:,:,k] * B[:,:] for all k.

• When A is a matrix, then C[:,:,k] == A[:,:] * B[:,:,k]. This can also be done by reshaping and calling *, for instance A ⊡ B using TensorCore.jl, but is implemented here using batched_gemm instead of gemm.

julia> randn(16,8,32) ⊠ randn(8,4) |> size
(16, 4, 32)

julia> randn(16,8,32) ⊠ randn(8,4,1) |> size  # equivalent
(16, 4, 32)

julia> randn(16,8) ⊠ randn(8,4,32) |> size
(16, 4, 32)

See also batched_vec to regard B as a batch of vectors, A[:,:,k] * B[:,k].

batched_mul!(C, A, B) -> C
batched_mul!(C, A, B, α=1, β=0)

In-place batched matrix multiplication, equivalent to mul!(C[:,:,k], A[:,:,k], B[:,:,k], α, β) for all k. If size(B,3) == 1 then every batch uses B[:,:,1] instead.

This will call batched_gemm! whenever possible. For real arrays this means that, for X ∈ [A,B,C], either stride(X,1)==1 or stride(X,2)==1, the latter may be caused by batched_transpose or by for instance PermutedDimsArray(::Array, (3,1,2)). Unlike batched_mul this will never make a copy.

For complex arrays, the wrapper made by batched_adjoint must be outermost to be seen. In this case the strided accepted by BLAS are more restricted, if stride(C,1)==1 then only stride(AorB::BatchedAdjoint,2) == 1 is accepted.

batched_transpose(A::AbstractArray{T,3})
batched_adjoint(A)

Equivalent to applying transpose or adjoint to each matrix A[:,:,k].

These exist to control how batched_mul behaves, as it operates on such matrix slices of an array with ndims(A)==3.

PermutedDimsArray(A, (2,1,3)) is equivalent to batched_transpose(A), and is also understood by batched_mul (and more widely supported elsewhere).

BatchedTranspose{T, S} <: AbstractBatchedMatrix{T, 3}
BatchedAdjoint{T, S}

Lazy wrappers analogous to Transpose and Adjoint, returned by batched_transpose etc.

batched_transpose(A::AbstractArray{T,3})
batched_adjoint(A)

Equivalent to applying transpose or adjoint to each matrix A[:,:,k].

These exist to control how batched_mul behaves, as it operates on such matrix slices of an array with ndims(A)==3.

PermutedDimsArray(A, (2,1,3)) is equivalent to batched_transpose(A), and is also understood by batched_mul (and more widely supported elsewhere).

BatchedTranspose{T, S} <: AbstractBatchedMatrix{T, 3}
BatchedAdjoint{T, S}

Lazy wrappers analogous to Transpose and Adjoint, returned by batched_transpose etc.

batched_vec(A::Array{T,3}, B::Matrix)
batched_vec(A::Array{T,3}, b::Vector)

Batched matrix-vector multiplication: the result has C[:,:,k] == A[:,:,k] * B[:,k] for all k, or else C[:,:,k] == A[:,:,k] * b for b::Vector.

With the same argument types, batched_mul(A, B) would regard B as a fixed matrix, not a batch of vectors. Both reshape and then call batched_mul(::Array{T,3}, ::Array{T,3}).

julia> A, B, b = randn(16,8,32), randn(8,32), randn(8);

julia> batched_vec(A,B) |> size
(16, 32)

julia> batched_vec(A,b) |> size
(16, 32)

NNlib.gather(src, idx) -> dst

Reverse operation of scatter. Gathers data from source src and writes it in a destination dst according to the index array idx. For each k in CartesianIndices(idx), assign values to dst according to

dst[:, ... , k] .= src[:, ... , idx[k]...]

Notice that if idx is a vector containing integers and src is a matrix, previous expression simplifies to

dst[:, k] .= src[:, idx[k]]

and k will run over 1:length(idx).

The elements of idx can be integers or integer tuples and may be repeated. A single src column can end up being copied into zero, one, or multiple dst columns.

See gather! for an in-place version.

Examples

julia> NNlib.gather([1,20,300,4000], [2,4,2])
3-element Vector{Int64}:
   20
 4000
   20

julia> NNlib.gather([1 2 3; 4 5 6], [1,3,1,3,1])
2×5 Matrix{Int64}:
 1  3  1  3  1
 4  6  4  6  4

gather(src, IJK...)

Convert the tuple of integer vectors IJK to a tuple of CartesianIndex and call gather on it: gather(src, CartesianIndex.(IJK...)).

Examples

julia> src = reshape([1:15;], 3, 5)
3×5 Matrix{Int64}:
 1  4  7  10  13
 2  5  8  11  14
 3  6  9  12  15

julia> NNlib.gather(src, [1, 2], [2, 4])
2-element Vector{Int64}:
  4
 11

NNlib.gather!(dst, src, idx)

Reverse operation of scatter!. Gathers data from source src and writes it in destination dst according to the index array idx. For each k in CartesianIndices(idx), assign values to dst according to

dst[:, ... , k] .= src[:, ... , idx[k]...]

Notice that if idx is a vector containing integers, and both dst and src are matrices, previous expression simplifies to

dst[:, k] .= src[:, idx[k]]

and k will run over 1:length(idx).

The elements of idx can be integers or integer tuples and may be repeated. A single src column can end up being copied into zero, one, or multiple dst columns.

See gather for an allocating version.

NNlib.scatter(op, src, idx; [init, dstsize])

Scatter operation allocating a destination array dst and calling scatter!(op, dst, src, idx) on it.

• If keyword init is provided, it is used to initialize the content of dst. Otherwise, the init values is inferred from the reduction operator op for some common operators (e.g. init = 0 for op = +).

• If dstsize is provided, it will be used to define the size of destination array, otherwise it will be inferred by src and idx.

See scatter! for full details on how idx works.

Examples

julia> NNlib.scatter(+, [10,100,1000], [3,1,2])
3-element Vector{Int64}:
  100
 1000
   10

julia> NNlib.scatter(+, [1 2 3 4; 5 6 7 8], [2,1,1,5])
2×5 Matrix{Int64}:
  5  1  0  0  4
 13  5  0  0  8

julia> NNlib.scatter(*, [10,200,3000], [1,4,2]; init = 10, dstsize = 6)
6-element Vector{Int64}:
   100
 30000
    10
  2000
    10
    10

NNlib.scatter!(op, dst, src, idx)

Scatter operation, which writes data in src into dst at locations idx. A binary reduction operator op is applied during the scatter. For each index k in idx, accumulates values in dst according to

dst[:, ..., idx[k]...] = (op).(dst[:, ..., idx[k]...], src[:, ..., k...])

See also scatter, gather.

Arguments

• op: Operations to be applied on dst and src, e.g. +, -, *, /, max, min and mean.
• dst: The destination for src to aggregate to. This argument will be mutated.
• src: The source data for aggregating.
• idx: The mapping for aggregation from source (index) to destination (value). The idx array can contain either integers or tuples.

Examples

julia> NNlib.scatter!(+, ones(3), [10,100], [1,3])
3-element Vector{Float64}:
  11.0
   1.0
 101.0

julia> NNlib.scatter!(*, fill(0.5, 2, 4), [1 10; 100 1000], [3,2])
2×4 Matrix{Float64}:
 0.5    5.0   0.5  0.5
 0.5  500.0  50.0  0.5

grid_sample(input::AbstractArray{T, 4}, grid::AbstractArray{T, 4}; padding_mode = :zeros)
grid_sample(input::AbstractArray{T, 5}, grid::AbstractArray{T, 4}; padding_mode = :zeros)

Given `input`, compute output by sampling `input` values at pixel
locations from `grid`. Uses bilinear interpolation to calculate output values.

This implementation assumes the extrema (`-1` and `1`) are considered
as referring to the center points of the input’s corner pixels
(i.e. align corners is `true`).

# Arguments

- `input`: Input array in `(W_in, H_in, [D_in,] C, N)` shape.
- `grid`: Input grid in `(2, W_out, H_out, [D_out,] N)` shape.
    Where for each `(W_out, H_out, [D_out,] N)` grid contains `(x, y [,z])`
    coordinates that specify sampling locations normalized by the `input` shape.

    Therefore, `x`, `y` and [`z`] should have values in `[-1, 1]` range.
    For example, `(x = -1, y = -1, [z = -1])` is the left-top[-front] pixel of `input`,
    and `(x = 1, y = 1, [z = 1])` is the right-bottom-back pixel of `input`.

    Out-of-bound values are handled according to the `padding_mode`.
- `padding_mode`: Out-of-bound padding.
    `:zeros` to use `0` for out-of-bound grid locations.
    `:border` to use border values for out-of-bound grid locations.
    Default is `:zeros`.

# Returns

`(W_out, H_out, [D_out,] C, N)` sampled grid from `input`.

# Examples

In the example below, grid contains two out-of-bound sampling locations,
which are handled differently, depending on the `padding_mode`.

```jldoctest
julia> x = reshape(collect(1.0:4.0), (2, 2, 1, 1))
2×2×1×1 Array{Float64, 4}:
[:, :, 1, 1] =
1.0  3.0
2.0  4.0

julia> grid = Array{Float64}(undef, 2, 3, 2, 1);

julia> grid[:, 1, 1, 1] .= (-3, -1);

julia> grid[:, 2, 1, 1] .= (0, -1);

julia> grid[:, 3, 1, 1] .= (1, -1);

julia> grid[:, 1, 2, 1] .= (-1, 1);

julia> grid[:, 2, 2, 1] .= (0, 1);

julia> grid[:, 3, 2, 1] .= (3, 1);

julia> grid_sample(x, grid; padding_mode=:zeros)
3×2×1×1 Array{Float64, 4}:
[:, :, 1, 1] =
0.0  3.0
1.5  3.5
2.0  0.0

julia> grid_sample(x, grid; padding_mode=:border)
3×2×1×1 Array{Float64, 4}:
[:, :, 1, 1] =
1.0  3.0
1.5  3.5
2.0  4.0
```

∇grid_sample(Δ::AbstractArray{T, 4}, input::AbstractArray{T, 4}, grid::AbstractArray{T, 4}; padding_mode = :zeros) where T

Arguments

• Δ: Input gradient in (W_out, H_out, C, N) shape (same as output of the primal computation).
• input: Input from primal computation in (W_in, H_in, C, N) shape.
• grid: Grid from primal computation in (2, W_out, H_out, N) shape.
• padding_mode: Out-of-bound padding. :zeros to use 0 for out-of-bound grid locations. :border to use border values for out-of-bound grid locations. Should be the same as in primal computation. Default is :zeros.

Returns

dinput (same shape as input) and dgrid (same shape as grid) gradients.

ctc_loss(ŷ, y)

Computes the connectionist temporal classification loss between ŷ and y. ŷ must be a classes-by-time matrices, i.e., each row represents a class and each column represents a time step. Additionally, the logsoftmax function will be applied to ŷ, so ŷ must be the raw activation values from the neural network and not, for example, the activations after being passed through a softmax activation function. y must be a 1D array of the labels associated with ŷ. The blank label is assumed to be the last label category in ŷ, so it is equivalent to size(ŷ, 1). Used for sequence-to-sequence classification problems such as speech recognition and handwriting recognition where the exact time-alignment of the output (e.g., letters) is not needed to solve the problem. See Graves et al. (2006) or Graves (2012) for mathematical details.

logsumexp(x; dims = :)

Computes log.(sum(exp.(x); dims)) in a numerically stable way. Without dims keyword this returns a scalar.

See also logsoftmax.

glu(x, dim = 1)

The gated linear unit from the "Language Modeling with Gated Convolutional Networks" paper.

Calculates a .* sigmoid(b), where x is split in half along given dimension dim to form a and b.