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Merged
merged 3 commits into from
Jul 11, 2020
Merged

Out of place optimizations #107

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925c5be
(fix) hcat non-sparse matrices
kanav99 Jul 10, 2020
e9ad65c
use mapreduce
kanav99 Jul 11, 2020
e890b3d
fix sparsity
kanav99 Jul 11, 2020
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148 changes: 89 additions & 59 deletions src/jacobians.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -171,7 +171,7 @@ function finite_difference_jacobian(
end
vecx = _vec(x)
vecx1 = _vec(x1)
J = jac_prototype isa Nothing ? (sparsity isa Nothing ? false.*vecfx.*vecx' : zeros(eltype(x),size(sparsity))) : zero(jac_prototype)
J = jac_prototype isa Nothing ? (sparsity isa Nothing ? Array{eltype(x),2}(undef, length(vecfx), 0) : zeros(eltype(x),size(sparsity))) : zero(jac_prototype)
nrows, ncols = size(J)

if !(sparsity isa Nothing)
Expand All @@ -181,71 +181,101 @@ function finite_difference_jacobian(
end

if fdtype == Val{:forward}
@inbounds for color_i ∈ 1:maximum(colorvec)
if sparsity isa Nothing
x_save = ArrayInterface.allowed_getindex(vecx,color_i)
epsilon = compute_epsilon(Val{:forward}, x_save, relstep, absstep, dir)
_vecx1 = Base.setindex(vecx,x_save+epsilon,color_i)
_x1 = reshape(_vecx1,size(x))
vecfx1 = _vec(f(_x1))
dx = (vecfx1-vecfx)/epsilon
J = J + _make_Ji(J, eltype(x), dx, color_i, nrows, ncols)
else
tmp = norm(vecx .* (colorvec .== color_i))
epsilon = compute_epsilon(Val{:forward}, sqrt(tmp), relstep, absstep, dir)
_vecx = @. vecx + epsilon * (colorvec == color_i)
_x = reshape(_vecx,size(x))
vecfx1 = _vec(f(_x))
dx = (vecfx1-vecfx)/epsilon
Ji = _make_Ji(J,rows_index,cols_index,dx,colorvec,color_i,nrows,ncols)
J = J + Ji

function calculate_Ji_forward(i)
x_save = ArrayInterface.allowed_getindex(vecx, i)
epsilon = compute_epsilon(Val{:forward}, x_save, relstep, absstep, dir)
_vecx1 = Base.setindex(vecx, x_save+epsilon, i)
_x1 = reshape(_vecx1,size(x))
vecfx1 = _vec(f(_x1))
dx = (vecfx1-vecfx) / epsilon
return dx
end

if jac_prototype isa Nothing && sparsity isa Nothing
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@ChrisRackauckas ChrisRackauckas Jul 11, 2020

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is there a specific file in OrdinaryDiffEq.jl that we should include as a downstream test here?

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@kanav99 kanav99 Jul 11, 2020

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https://github.com/SciML/OrdinaryDiffEq.jl/blob/master/test/interface/sparsediff_tests.jl probably this?

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@ChrisRackauckas ChrisRackauckas Jul 11, 2020

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yes that's a good one. Can you setup an environment which does a downstream test to that? That should hopefully catch any outrageous issues.

J = mapreduce(calculate_Ji_forward, hcat, 1:maximum(colorvec))
else
@inbounds for color_i ∈ 1:maximum(colorvec)
if sparsity isa Nothing
dx = calculate_Ji(color_i)
J = J + _make_Ji(J, eltype(x), dx, color_i, nrows, ncols)
else
tmp = norm(vecx .* (colorvec .== color_i))
epsilon = compute_epsilon(Val{:forward}, sqrt(tmp), relstep, absstep, dir)
_vecx = @. vecx + epsilon * (colorvec == color_i)
_x = reshape(_vecx,size(x))
vecfx1 = _vec(f(_x))
dx = (vecfx1-vecfx)/epsilon
Ji = _make_Ji(J,rows_index,cols_index,dx,colorvec,color_i,nrows,ncols)
J = J + Ji
end
end
end
elseif fdtype == Val{:central}
@inbounds for color_i ∈ 1:maximum(colorvec)
if sparsity isa Nothing
x1_save = ArrayInterface.allowed_getindex(vecx1,color_i)
x_save = ArrayInterface.allowed_getindex(vecx,color_i)
epsilon = compute_epsilon(Val{:forward}, x1_save, relstep, absstep, dir)
_vecx1 = Base.setindex(vecx1,x1_save+epsilon,color_i)
_vecx = Base.setindex(vecx,x_save-epsilon,color_i)
_x1 = reshape(_vecx1,size(x))
_x = reshape(_vecx,size(x))
vecfx1 = _vec(f(_x1))
vecfx = _vec(f(_x))
dx = (vecfx1-vecfx)/(2epsilon)
J = J + _make_Ji(J, eltype(x), dx, color_i, nrows, ncols)
else
tmp = norm(vecx1 .* (colorvec .== color_i))
epsilon = compute_epsilon(Val{:forward}, sqrt(tmp), relstep, absstep, dir)
_vecx1 = @. vecx1 + epsilon * (colorvec == color_i)
_vecx = @. vecx - epsilon * (colorvec == color_i)
_x1 = reshape(_vecx1,size(x))
_x = reshape(_vecx, size(x))
vecfx1 = _vec(f(_x1))
vecfx = _vec(f(_x))
dx = (vecfx1-vecfx)/(2epsilon)
Ji = _make_Ji(J,rows_index,cols_index,dx,colorvec,color_i,nrows,ncols)
J = J + Ji

function calculate_Ji_central(i)
x1_save = ArrayInterface.allowed_getindex(vecx1,i)
x_save = ArrayInterface.allowed_getindex(vecx,i)
epsilon = compute_epsilon(Val{:forward}, x1_save, relstep, absstep, dir)
_vecx1 = Base.setindex(vecx1,x1_save+epsilon,i)
_vecx = Base.setindex(vecx,x_save-epsilon,i)
_x1 = reshape(_vecx1,size(x))
_x = reshape(_vecx,size(x))
vecfx1 = _vec(f(_x1))
vecfx = _vec(f(_x))
dx = (vecfx1-vecfx)/(2epsilon)
return dx
end

if jac_prototype isa Nothing && sparsity isa Nothing
J = mapreduce(calculate_Ji_central, hcat, 1:maximum(colorvec))
else
@inbounds for color_i ∈ 1:maximum(colorvec)
if sparsity isa Nothing
dx = calculate_Ji(color_i)
J = J + _make_Ji(J, eltype(x), dx, color_i, nrows, ncols)
else
tmp = norm(vecx1 .* (colorvec .== color_i))
epsilon = compute_epsilon(Val{:forward}, sqrt(tmp), relstep, absstep, dir)
_vecx1 = @. vecx1 + epsilon * (colorvec == color_i)
_vecx = @. vecx - epsilon * (colorvec == color_i)
_x1 = reshape(_vecx1,size(x))
_x = reshape(_vecx, size(x))
vecfx1 = _vec(f(_x1))
vecfx = _vec(f(_x))
dx = (vecfx1-vecfx)/(2epsilon)
Ji = _make_Ji(J,rows_index,cols_index,dx,colorvec,color_i,nrows,ncols)
J = J + Ji
end
end
end
elseif fdtype == Val{:complex} && returntype <: Real
epsilon = eps(eltype(x))
@inbounds for color_i ∈ 1:maximum(colorvec)
if sparsity isa Nothing
x_save = ArrayInterface.allowed_getindex(vecx,color_i)
_vecx = Base.setindex(complex.(vecx),x_save+im*epsilon,color_i)
_x = reshape(_vecx,size(x))
vecfx = _vec(f(_x))
dx = imag(vecfx)/epsilon
J = J + _make_Ji(J, eltype(x), dx, color_i, nrows, ncols)
else
_vecx = @. vecx + im * epsilon * (colorvec == color_i)
_x = reshape(_vecx,size(x))
vecfx = _vec(f(_x))
dx = imag(vecfx)/epsilon
Ji = _make_Ji(J,rows_index,cols_index,dx,colorvec,color_i,nrows,ncols)
J = J + Ji

function calculate_Ji_complex(i)
x_save = ArrayInterface.allowed_getindex(vecx,i)
_vecx = Base.setindex(complex.(vecx),x_save+im*epsilon,i)
_x = reshape(_vecx,size(x))
vecfx = _vec(f(_x))
dx = imag(vecfx)/epsilon
return dx
end

if jac_prototype isa Nothing && sparsity isa Nothing
J = mapreduce(calculate_Ji_complex, hcat, 1:maximum(colorvec))
else
@inbounds for color_i ∈ 1:maximum(colorvec)
if sparsity isa Nothing
dx = calculate_Ji_complex(color_i)
J = J + _make_Ji(J, eltype(x), dx, color_i, nrows, ncols)
else
_vecx = @. vecx + im * epsilon * (colorvec == color_i)
_x = reshape(_vecx,size(x))
vecfx = _vec(f(_x))
dx = imag(vecfx)/epsilon
Ji = _make_Ji(J,rows_index,cols_index,dx,colorvec,color_i,nrows,ncols)
J = J + Ji
end
end
end
else
Expand Down