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Fix standard GPU AD #103

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Merged
merged 1 commit into from
May 1, 2020
Merged

Fix standard GPU AD #103

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4 changes: 4 additions & 0 deletions src/SparseDiffTools.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -46,6 +46,10 @@ include("coloring/matrix2graph.jl")
include("differentiation/compute_jacobian_ad.jl")
include("differentiation/jaches_products.jl")

Base.@pure __parameterless_type(T) = Base.typename(T).wrapper
parameterless_type(x) = parameterless_type(typeof(x))
parameterless_type(x::Type) = __parameterless_type(x)

function __init__()
@require Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f" begin
export numback_hesvec, numback_hesvec!, autoback_hesvec, autoback_hesvec!
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9 changes: 5 additions & 4 deletions src/differentiation/compute_jacobian_ad.jl
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Expand Up @@ -18,6 +18,7 @@ end

getsize(::Val{N}) where N = N
getsize(N::Integer) = N
void_setindex!(args...) = (setindex!(args...); return)

function ForwardColorJacCache(f,x,_chunksize = nothing;
dx = nothing,
Expand All @@ -30,15 +31,15 @@ function ForwardColorJacCache(f,x,_chunksize = nothing;
chunksize = _chunksize
end

p = adapt.(typeof(x),generate_chunked_partials(x,colorvec,chunksize))
p = adapt.(parameterless_type(x),generate_chunked_partials(x,colorvec,chunksize))
_t = Dual{ForwardDiff.Tag(f,eltype(vec(x)))}.(vec(x),first(p))
t = ArrayInterface.restructure(x,_t)
if dx isa Nothing
fx = similar(t)
_dx = similar(x)
else
tup = first(first(p)) .* false
_pi = adapt.(typeof(dx),[tup for i in 1:length(dx)])
tup = ArrayInterface.allowed_getindex(ArrayInterface.allowed_getindex(p,1),1) .* false
_pi = adapt(parameterless_type(dx),[tup for i in 1:length(dx)])
fx = reshape(Dual{ForwardDiff.Tag(f,eltype(vec(x)))}.(vec(dx),_pi),size(dx)...)
_dx = dx
end
Expand Down Expand Up @@ -121,7 +122,7 @@ function forwarddiff_color_jacobian(f,x::AbstractArray{<:Number},jac_cache::Forw
for j in 1:chunksize
col_index = (i-1)*chunksize + j
(col_index > ncols) && return J
Ji = mapreduce(i -> i==col_index ? partials.(vec(fx), j) : zeros(nrows), hcat, 1:ncols)
Ji = mapreduce(i -> i==col_index ? partials.(vec(fx), j) : adapt(parameterless_type(J),zeros(eltype(J),nrows)), hcat, 1:ncols)
J = J + (size(Ji)!=size(J) ? reshape(Ji,size(J)) : Ji) #branch when size(dx) == (1,) => size(Ji) == (1,) while size(J) == (1,1)
end
end
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