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tracker.jl
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tracker.jl
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using .Tracker: Tracker,
TrackedReal,
TrackedVector,
TrackedMatrix,
TrackedArray,
TrackedVecOrMat,
@grad,
track,
data,
param
using Compat: eachcol
using LinearAlgebra
maporbroadcast(f, x::TrackedArray...) = f.(x...)
function maporbroadcast(
f,
x1::TrackedArray{T, N},
x::AbstractArray{<:TrackedReal}...,
) where {T, N}
return f.(convert(Array{TrackedReal{T}, N}, x1), x...)
end
_eps(::Type{<:TrackedReal{T}}) where {T} = _eps(T)
function Base.minimum(d::LocationScale{<:TrackedReal})
m = minimum(d.ρ)
if isfinite(m)
return d.μ + d.σ * m
else
return m
end
end
function Base.maximum(d::LocationScale{<:TrackedReal})
m = maximum(d.ρ)
if isfinite(m)
return d.μ + d.σ * m
else
return m
end
end
# AD implementations
function jacobian(
b::Union{<:ADBijector{<:TrackerAD}, Inverse{<:ADBijector{<:TrackerAD}}},
x::Real
)
return data(Tracker.gradient(b, x)[1])
end
function jacobian(
b::Union{<:ADBijector{<:TrackerAD}, Inverse{<:ADBijector{<:TrackerAD}}},
x::AbstractVector{<:Real}
)
# We extract `data` so that we don't return a `Tracked` type
return data(Tracker.jacobian(b, x))
end
# implementations for Shift bijector
function _logabsdetjac_shift(a::TrackedReal, x::Real, ::Val{0})
return tracker_shift_logabsdetjac(a, x, Val(0))
end
function _logabsdetjac_shift(a::TrackedReal, x::AbstractVector{<:Real}, ::Val{0})
return tracker_shift_logabsdetjac(a, x, Val(0))
end
function _logabsdetjac_shift(
a::Union{TrackedReal, TrackedVector{<:Real}},
x::AbstractVector{<:Real},
::Val{1}
)
return tracker_shift_logabsdetjac(a, x, Val(1))
end
function _logabsdetjac_shift(
a::Union{TrackedReal, TrackedVector{<:Real}},
x::AbstractMatrix{<:Real},
::Val{1}
)
return tracker_shift_logabsdetjac(a, x, Val(1))
end
function tracker_shift_logabsdetjac(a, x, ::Val{N}) where {N}
return param(_logabsdetjac_shift(data(a), data(x), Val(N)))
end
# Log bijector
@grad function logabsdetjac(b::Log{1}, x::AbstractVector)
return -sum(log, data(x)), Δ -> (nothing, -Δ ./ data(x))
end
@grad function logabsdetjac(b::Log{1}, x::AbstractMatrix)
return -vec(sum(log, data(x); dims = 1)), Δ -> (nothing, .- Δ' ./ data(x))
end
@grad function logabsdetjac(b::Log{2}, x::AbstractMatrix)
return -sum(log, data(x)), Δ -> (nothing, -Δ ./ data(x))
end
# implementations for Scale bijector
# Adjoints for 0-dim and 1-dim `Scale` using `Real`
function _logabsdetjac_scale(a::TrackedReal, x::Real, ::Val{0})
return track(_logabsdetjac_scale, a, data(x), Val(0))
end
@grad function _logabsdetjac_scale(a::Real, x::Real, ::Val{0})
return _logabsdetjac_scale(data(a), data(x), Val(0)), Δ -> (inv(data(a)) .* Δ, nothing, nothing)
end
# Need to treat `AbstractVector` and `AbstractMatrix` separately due to ambiguity errors
function _logabsdetjac_scale(a::TrackedReal, x::AbstractVector, ::Val{0})
return track(_logabsdetjac_scale, a, data(x), Val(0))
end
@grad function _logabsdetjac_scale(a::Real, x::AbstractVector, ::Val{0})
da = data(a)
J = fill(inv.(da), length(x))
return _logabsdetjac_scale(da, data(x), Val(0)), Δ -> (transpose(J) * Δ, nothing, nothing)
end
function _logabsdetjac_scale(a::TrackedReal, x::AbstractMatrix, ::Val{0})
return track(_logabsdetjac_scale, a, data(x), Val(0))
end
@grad function _logabsdetjac_scale(a::Real, x::AbstractMatrix, ::Val{0})
da = data(a)
J = fill(size(x, 1) / da, size(x, 2))
return _logabsdetjac_scale(da, data(x), Val(0)), Δ -> (transpose(J) * Δ, nothing, nothing)
end
# adjoints for 1-dim and 2-dim `Scale` using `AbstractVector`
function _logabsdetjac_scale(a::TrackedVector, x::AbstractVector, ::Val{1})
return track(_logabsdetjac_scale, a, data(x), Val(1))
end
@grad function _logabsdetjac_scale(a::TrackedVector, x::AbstractVector, ::Val{1})
# ∂ᵢ (∑ⱼ log|aⱼ|) = ∑ⱼ δᵢⱼ ∂ᵢ log|aⱼ|
# = ∂ᵢ log |aᵢ|
# = (1 / aᵢ) ∂ᵢ aᵢ
# = (1 / aᵢ)
da = data(a)
J = inv.(da)
return _logabsdetjac_scale(da, data(x), Val(1)), Δ -> (J .* Δ, nothing, nothing)
end
function _logabsdetjac_scale(a::TrackedVector, x::AbstractMatrix, ::Val{1})
return track(_logabsdetjac_scale, a, data(x), Val(1))
end
@grad function _logabsdetjac_scale(a::TrackedVector, x::AbstractMatrix, ::Val{1})
da = data(a)
Jᵀ = repeat(inv.(da), 1, size(x, 2))
return _logabsdetjac_scale(da, data(x), Val(1)), Δ -> (Jᵀ * Δ, nothing, nothing)
end
# TODO: implement analytical gradient for scaling a vector using a matrix
# function _logabsdetjac_scale(a::TrackedMatrix, x::AbstractVector, ::Val{1})
# track(_logabsdetjac_scale, a, data(x), Val{1})
# end
# @grad function _logabsdetjac_scale(a::TrackedMatrix, x::AbstractVector, ::Val{1})
# throw
# end
# implementations for Stacked bijector
function logabsdetjac(b::Stacked, x::TrackedMatrix{<:Real})
return map(eachcol(x)) do c
logabsdetjac(b, c)
end
end
# TODO: implement custom adjoint since we can exploit block-diagonal nature of `Stacked`
function (sb::Stacked)(x::TrackedMatrix{<:Real})
return eachcolmaphcat(sb, x)
end
# Simplex adjoints
function _simplex_bijector(X::TrackedVecOrMat, b::SimplexBijector{1})
return track(_simplex_bijector, X, b)
end
function _simplex_inv_bijector(Y::TrackedVecOrMat, b::SimplexBijector{1})
return track(_simplex_inv_bijector, Y, b)
end
@grad function _simplex_bijector(X::AbstractVector, b::SimplexBijector{1})
Xd = data(X)
return _simplex_bijector(Xd, b), Δ -> (simplex_link_jacobian(Xd)' * Δ, nothing)
end
@grad function _simplex_inv_bijector(Y::AbstractVector, b::SimplexBijector{1})
Yd = data(Y)
return _simplex_inv_bijector(Yd, b), Δ -> (simplex_invlink_jacobian(Yd)' * Δ, nothing)
end
@grad function _simplex_bijector(X::AbstractMatrix, b::SimplexBijector{1})
Xd = data(X)
return _simplex_bijector(Xd, b), Δ -> begin
maphcat(eachcol(Xd), eachcol(Δ)) do c1, c2
simplex_link_jacobian(c1)' * c2
end, nothing
end
end
@grad function _simplex_inv_bijector(Y::AbstractMatrix, b::SimplexBijector{1})
Yd = data(Y)
return _simplex_inv_bijector(Yd, b), Δ -> begin
maphcat(eachcol(Yd), eachcol(Δ)) do c1, c2
simplex_invlink_jacobian(c1)' * c2
end, nothing
end
end
replace_diag(::typeof(log), X::TrackedMatrix) = track(replace_diag, log, X)
@grad function replace_diag(::typeof(log), X)
Xd = data(X)
f(i, j) = i == j ? log(Xd[i, j]) : Xd[i, j]
out = f.(1:size(Xd, 1), (1:size(Xd, 2))')
out, ∇ -> begin
g(i, j) = i == j ? ∇[i, j]/Xd[i, j] : ∇[i, j]
return (nothing, g.(1:size(Xd, 1), (1:size(Xd, 2))'))
end
end
replace_diag(::typeof(exp), X::TrackedMatrix) = track(replace_diag, exp, X)
@grad function replace_diag(::typeof(exp), X)
Xd = data(X)
f(i, j) = ifelse(i == j, exp(Xd[i, j]), Xd[i, j])
out = f.(1:size(Xd, 1), (1:size(Xd, 2))')
out, ∇ -> begin
g(i, j) = ifelse(i == j, ∇[i, j]*exp(Xd[i, j]), ∇[i, j])
return (nothing, g.(1:size(Xd, 1), (1:size(Xd, 2))'))
end
end
logabsdetjac(b::SimplexBijector{1}, x::TrackedVecOrMat) = track(logabsdetjac, b, x)
@grad function logabsdetjac(b::SimplexBijector{1}, x::AbstractVector)
xd = data(x)
return logabsdetjac(b, xd), Δ -> begin
(nothing, simplex_logabsdetjac_gradient(xd) * Δ)
end
end
@grad function logabsdetjac(b::SimplexBijector{1}, x::AbstractMatrix)
xd = data(x)
return logabsdetjac(b, xd), Δ -> begin
(nothing, maphcat(eachcol(xd), Δ) do c, g
simplex_logabsdetjac_gradient(c) * g
end)
end
end
for header in [
(:(u::TrackedArray), :w),
(:u, :(w::TrackedArray)),
(:(u::TrackedArray), :(w::TrackedArray)),
]
@eval begin
function get_u_hat($(header...))
if u isa TrackedArray
T = typeof(u)
else
T = typeof(w)
end
x = w' * u
return (u .+ (planar_flow_m(x) - x) .* w ./ sum(abs2, w))::T
end
end
end
for header in [
(:(z::TrackedArray), :w, :b),
(:z, :(w::TrackedArray), :b),
(:z, :w, :(b::TrackedReal)),
(:(z::TrackedArray), :(w::TrackedArray), :b),
(:(z::TrackedArray), :w, :(b::TrackedReal)),
(:z, :(w::TrackedArray), :(b::TrackedReal)),
(:(z::TrackedArray), :(w::TrackedArray), :(b::TrackedReal)),
]
@eval begin
function ψ($(header...))
if z isa AbstractMatrix
if z isa TrackedMatrix
T = typeof(z)
elseif w isa TrackedVector
T = matrixof(typeof(w))
else
T = matrixof(typeof(b))
end
else
if z isa TrackedVector
T = typeof(z)
elseif w isa TrackedVector
T = typeof(w)
else
T = vectorof(typeof(b))
end
end
return ((1 .- tanh.(w' * z .+ b).^2) .* w)::T # for planar flow from eq(11)
end
end
end
for header in [
(:(u::TrackedArray), :w, :b, :(z::AbstractVecOrMat)),
(:u, :(w::TrackedArray), :b, :(z::AbstractVecOrMat)),
(:u, :w, :(b::TrackedReal), :(z::AbstractVecOrMat)),
(:u, :w, :b, :(z::TrackedVecOrMat)),
(:(u::TrackedArray), :(w::TrackedArray), :b, :(z::AbstractVecOrMat)),
(:(u::TrackedArray), :w, :(b::TrackedReal), :(z::AbstractVecOrMat)),
(:(u::TrackedArray), :w, :b, :(z::TrackedVecOrMat)),
(:u, :(w::TrackedArray), :(b::TrackedReal), :(z::AbstractVecOrMat)),
(:u, :(w::TrackedArray), :b, :(z::TrackedVecOrMat)),
(:u, :w, :(b::TrackedArray), :(z::TrackedVecOrMat)),
(:(u::TrackedArray), :(w::TrackedArray), :(b::TrackedReal), :(z::AbstractVecOrMat)),
(:(u::TrackedArray), :(w::TrackedArray), :b, :(z::TrackedVecOrMat)),
(:(u::TrackedArray), :w, :(b::TrackedReal), :(z::TrackedVecOrMat)),
(:u, :(w::TrackedArray), :(b::TrackedReal), :(z::TrackedVecOrMat)),
(:(u::TrackedArray), :(w::TrackedArray), :(b::TrackedReal), :(z::TrackedVecOrMat)),
]
@eval begin
function _planar_transform($(header...))
u_hat = get_u_hat(u, w)
if z isa AbstractVector
temp = w' * z + b + zero(eltype(u_hat))
if z isa TrackedVector
T = typeof(z)
elseif u_hat isa TrackedVector
T = typeof(u_hat)
else
T = vectorof(typeof(temp))
end
else
temp = w' * z .+ (b + zero(eltype(u_hat)))
if z isa TrackedMatrix
T = typeof(z)
elseif u_hat isa TrackedVector
T = matrixof(typeof(u_hat))
else
T = matrixof(typeof(temp'))
end
end
transformed::T = z .+ u_hat .* tanh.(temp) # from eq(10)
return (transformed = transformed, u_hat = u_hat)
end
end
end
for header in [
(:(α_::TrackedReal), :β, :z_0, :(z::AbstractVector)),
(:α_, :(β::TrackedReal), :z_0, :(z::AbstractVector)),
(:α_, :β, :(z_0::TrackedVector), :(z::AbstractVector)),
(:α_, :β, :z_0, :(z::TrackedVector)),
(:(α_::TrackedReal), :(β::TrackedReal), :z_0, :(z::AbstractVector)),
(:(α_::TrackedReal), :β, :(z_0::TrackedVector), :(z::AbstractVector)),
(:(α_::TrackedReal), :β, :z_0, :(z::TrackedVecOrMat)),
(:(α_::TrackedReal), :(β::TrackedReal), :(z_0::TrackedVector), :(z::AbstractVector)),
(:(α_::TrackedReal), :(β::TrackedReal), :z_0, :(z::TrackedVector)),
(:(α_::TrackedReal), :β, :(z_0::TrackedVector), :(z::TrackedVector)),
(:α_, :(β::TrackedReal), :(z_0::TrackedVector), :(z::TrackedVector)),
(:(α_::TrackedReal), :(β::TrackedReal), :(z_0::TrackedVector), :(z::TrackedVector)),
]
@eval begin
function _radial_transform($(header...))
α = softplus(α_) # from A.2
β_hat = -α + softplus(β) # from A.2
if β_hat isa TrackedReal
TV = vectorof(typeof(β_hat))
T = vectorof(typeof(β_hat))
elseif z_0 isa TrackedVector
TV = typeof(z_0)
T = typeof(z_0)
else
T = TV = typeof(z)
end
Tr = promote_type(eltype(z), eltype(z_0))
r::Tr = norm((z .- z_0)::TV)
transformed::T = z .+ β_hat ./ (α .+ r') .* (z .- z_0) # from eq(14)
return (transformed = transformed, α = α, β_hat = β_hat, r = r)
end
end
end
for header in [
(:(α_::TrackedReal), :β, :z_0, :(z::AbstractMatrix)),
(:α_, :(β::TrackedReal), :z_0, :(z::AbstractMatrix)),
(:α_, :β, :(z_0::TrackedVector), :(z::AbstractMatrix)),
(:α_, :β, :z_0, :(z::TrackedMatrix)),
(:(α_::TrackedReal), :(β::TrackedReal), :z_0, :(z::AbstractMatrix)),
(:(α_::TrackedReal), :β, :(z_0::TrackedVector), :(z::AbstractMatrix)),
(:(α_::TrackedReal), :β, :z_0, :(z::TrackedMatrix)),
(:(α_::TrackedReal), :(β::TrackedReal), :(z_0::TrackedVector), :(z::AbstractMatrix)),
(:(α_::TrackedReal), :(β::TrackedReal), :z_0, :(z::TrackedMatrix)),
(:(α_::TrackedReal), :β, :(z_0::TrackedVector), :(z::TrackedMatrix)),
(:α_, :(β::TrackedReal), :(z_0::TrackedVector), :(z::TrackedMatrix)),
(:(α_::TrackedReal), :(β::TrackedReal), :(z_0::TrackedVector), :(z::TrackedMatrix)),
]
@eval begin
function _radial_transform($(header...))
α = softplus(α_) # from A.2
β_hat = -α + softplus(β) # from A.2
if β_hat isa TrackedReal
TV = vectorof(typeof(β_hat))
T = matrixof(TV)
elseif z_0 isa TrackedVector
TV = typeof(z_0)
T = matrixof(TV)
else
T = typeof(z)
TV = vectorof(T)
end
r::TV = eachcolnorm(z .- z_0)
transformed::T = z .+ β_hat ./ (α .+ r') .* (z .- z_0) # from eq(14)
return (transformed = transformed, α = α, β_hat = β_hat, r = r)
end
end
end
eachcolnorm(X) = map(norm, eachcol(X))
eachcolnorm(X::TrackedMatrix) = track(eachcolnorm, X)
@grad function eachcolnorm(X)
Xd = data(X)
y = map(norm, eachcol(Xd))
y, Δ -> begin
(Xd .* (Δ ./ y)',)
end
end
function matrixof(::Type{<:TrackedArray{T,1,Vector{T}}}) where {T<:Real}
return TrackedArray{T,2,Matrix{T}}
end
function matrixof(::Type{TrackedReal{T}}) where {T<:Real}
return TrackedArray{T,2,Matrix{T}}
end
function vectorof(::Type{<:TrackedArray{T,2,Matrix{T}}}) where {T<:Real}
return TrackedArray{T,1,Vector{T}}
end
function vectorof(::Type{TrackedReal{T}}) where {T<:Real}
return TrackedArray{T,1,Vector{T}}
end
(b::Exp{0})(x::TrackedVector) = exp.(x)::vectorof(float(eltype(x)))
(b::Exp{1})(x::TrackedVector) = exp.(x)::vectorof(float(eltype(x)))
(b::Exp{1})(x::TrackedMatrix) = exp.(x)::matrixof(float(eltype(x)))
(b::Exp{2})(x::TrackedMatrix) = exp.(x)::matrixof(float(eltype(x)))
(b::Log{0})(x::TrackedVector) = log.(x)::vectorof(float(eltype(x)))
(b::Log{1})(x::TrackedVector) = log.(x)::vectorof(float(eltype(x)))
(b::Log{1})(x::TrackedMatrix) = log.(x)::matrixof(float(eltype(x)))
(b::Log{2})(x::TrackedMatrix) = log.(x)::matrixof(float(eltype(x)))
logabsdetjac(b::Log{0}, x::TrackedVector) = .-log.(x)::vectorof(float(eltype(x)))
logabsdetjac(b::Log{1}, x::TrackedMatrix) = - vec(sum(log.(x); dims = 1))
getpd(X::TrackedMatrix) = track(getpd, X)
@grad function getpd(X::AbstractMatrix)
Xd = data(X)
return LowerTriangular(Xd) * LowerTriangular(Xd)', Δ -> begin
Xl = LowerTriangular(Xd)
return (LowerTriangular(Δ' * Xl + Δ * Xl),)
end
end
lower(A::TrackedMatrix) = track(lower, A)
@grad function lower(A::AbstractMatrix)
Ad = data(A)
return lower(Ad), Δ -> (lower(Δ),)
end
_inv_link_chol_lkj(y::TrackedMatrix) = track(_inv_link_chol_lkj, y)
@grad function _inv_link_chol_lkj(y_tracked)
y = data(y_tracked)
K = LinearAlgebra.checksquare(y)
w = similar(y)
z_mat = similar(y) # cache for adjoint
tmp_mat = similar(y)
@inbounds for j in 1:K
w[1, j] = 1
for i in 2:j
z = tanh(y[i-1, j])
tmp = w[i-1, j]
z_mat[i, j] = z
tmp_mat[i, j] = tmp
w[i-1, j] = z * tmp
w[i, j] = tmp * sqrt(1 - z^2)
end
for i in (j+1):K
w[i, j] = 0
end
end
function pullback_inv_link_chol_lkj(Δw)
LinearAlgebra.checksquare(Δw)
Δy = zero(y)
@inbounds for j in 1:K
Δtmp = Δw[j,j]
for i in j:-1:2
Δz = Δw[i-1, j] * tmp_mat[i, j] - Δtmp * tmp_mat[i, j] / sqrt(1 - z_mat[i, j]^2) * z_mat[i, j]
Δy[i-1, j] = Δz / cosh(y[i-1, j])^2
Δtmp = Δw[i-1, j] * z_mat[i, j] + Δtmp * sqrt(1 - z_mat[i, j]^2)
end
end
return (Δy,)
end
return w, pullback_inv_link_chol_lkj
end
_link_chol_lkj(w::TrackedMatrix) = track(_link_chol_lkj, w)
@grad function _link_chol_lkj(w_tracked)
w = data(w_tracked)
K = LinearAlgebra.checksquare(w)
z = similar(w)
@inbounds z[1, 1] = 0
tmp_mat = similar(w) # cache for pullback.
@inbounds for j=2:K
z[1, j] = atanh(w[1, j])
tmp = sqrt(1 - w[1, j]^2)
tmp_mat[1, j] = tmp
for i in 2:(j - 1)
p = w[i, j] / tmp
tmp *= sqrt(1 - p^2)
tmp_mat[i, j] = tmp
z[i, j] = atanh(p)
end
z[j, j] = 0
end
function pullback_link_chol_lkj(Δz)
LinearAlgebra.checksquare(Δz)
Δw = similar(w)
@inbounds Δw[1,1] = zero(eltype(Δz))
@inbounds for j=2:K
Δw[j, j] = 0
Δtmp = zero(eltype(Δz)) # Δtmp_mat[j-1,j]
for i in (j-1):-1:2
p = w[i, j] / tmp_mat[i-1, j]
ftmp = sqrt(1 - p^2)
d_ftmp_p = -p / ftmp
d_p_tmp = -w[i,j] / tmp_mat[i-1, j]^2
Δp = Δz[i,j] / (1-p^2) + Δtmp * tmp_mat[i-1, j] * d_ftmp_p
Δw[i, j] = Δp / tmp_mat[i-1, j]
Δtmp = Δp * d_p_tmp + Δtmp * ftmp # update to "previous" Δtmp
end
Δw[1, j] = Δz[1, j] / (1-w[1,j]^2) - Δtmp / sqrt(1 - w[1,j]^2) * w[1,j]
end
return (Δw,)
end
return z, pullback_link_chol_lkj
end