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Apply JuliaFormatter to fix code formatting #154

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Apply JuliaFormatter to fix code formatting #154

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20 changes: 13 additions & 7 deletions ext/OptimizationEnzymeExt.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -85,7 +85,7 @@ function set_runtime_activity2(
Enzyme.set_runtime_activity(a, RTA)
end
function_annotation(::Nothing) = Nothing
function_annotation(::AutoEnzyme{<:Any, A}) where A = A
function_annotation(::AutoEnzyme{<:Any, A}) where {A} = A
function OptimizationBase.instantiate_function(f::OptimizationFunction{true}, x,
adtype::AutoEnzyme, p, num_cons = 0;
g = false, h = false, hv = false, fg = false, fgh = false,
Expand Down Expand Up @@ -225,9 +225,12 @@ function OptimizationBase.instantiate_function(f::OptimizationFunction{true}, x,
if func_annot <: Enzyme.Const
basefunc = Enzyme.Const(basefunc)
elseif func_annot <: Enzyme.Duplicated || func_annot <: Enzyme.BatchDuplicated
basefunc = Enzyme.BatchDuplicated(basefunc, Tuple(make_zero(basefunc) for i in 1:length(x)))
elseif func_annot <: Enzyme.DuplicatedNoNeed || func_annot <: Enzyme.BatchDuplicatedNoNeed
basefunc = Enzyme.BatchDuplicatedNoNeed(basefunc, Tuple(make_zero(basefunc) for i in 1:length(x)))
basefunc = Enzyme.BatchDuplicated(basefunc, Tuple(make_zero(basefunc)
for i in 1:length(x)))
elseif func_annot <: Enzyme.DuplicatedNoNeed ||
func_annot <: Enzyme.BatchDuplicatedNoNeed
basefunc = Enzyme.BatchDuplicatedNoNeed(basefunc, Tuple(make_zero(basefunc)
for i in 1:length(x)))
end
# else
# seeds = Enzyme.onehot(zeros(eltype(x), num_cons))
Expand All @@ -241,12 +244,14 @@ function OptimizationBase.instantiate_function(f::OptimizationFunction{true}, x,
Enzyme.make_zero!(jc)
end
Enzyme.make_zero!(y)
if func_annot <: Enzyme.Duplicated || func_annot <: Enzyme.BatchDuplicated || func_annot <: Enzyme.DuplicatedNoNeed || func_annot <: Enzyme.BatchDuplicatedNoNeed
if func_annot <: Enzyme.Duplicated || func_annot <: Enzyme.BatchDuplicated ||
func_annot <: Enzyme.DuplicatedNoNeed ||
func_annot <: Enzyme.BatchDuplicatedNoNeed
for bf in basefunc.dval
Enzyme.make_zero!(bf)
end
end
Enzyme.autodiff(fmode, basefunc , BatchDuplicated(y, Jaccache),
Enzyme.autodiff(fmode, basefunc, BatchDuplicated(y, Jaccache),
BatchDuplicated(θ, seeds), Const(p))
for i in eachindex(θ)
if J isa Vector
Expand Down Expand Up @@ -575,7 +580,8 @@ function OptimizationBase.instantiate_function(f::OptimizationFunction{false}, x
for i in eachindex(Jaccache)
Enzyme.make_zero!(Jaccache[i])
end
Jaccache, y = Enzyme.autodiff(WithPrimal(fmode), f.cons, Duplicated,
Jaccache,
y = Enzyme.autodiff(WithPrimal(fmode), f.cons, Duplicated,
BatchDuplicated(θ, seeds), Const(p))
if size(y, 1) == 1
return reduce(vcat, Jaccache)
Expand Down
53 changes: 34 additions & 19 deletions ext/OptimizationZygoteExt.jl
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,7 @@ function OptimizationBase.instantiate_function(
adtype, soadtype = OptimizationBase.generate_adtype(adtype)

if g == true && f.grad === nothing
prep_grad = prepare_gradient(f.f, adtype, x, Constant(p), strict=Val(false))
prep_grad = prepare_gradient(f.f, adtype, x, Constant(p), strict = Val(false))
function grad(res, θ)
gradient!(f.f, res, prep_grad, adtype, θ, Constant(p))
end
Expand All @@ -47,7 +47,7 @@ function OptimizationBase.instantiate_function(

if fg == true && f.fg === nothing
if g == false
prep_grad = prepare_gradient(f.f, adtype, x, Constant(p), strict=Val(false))
prep_grad = prepare_gradient(f.f, adtype, x, Constant(p), strict = Val(false))
end
function fg!(res, θ)
(y, _) = value_and_gradient!(f.f, res, prep_grad, adtype, θ, Constant(p))
Expand All @@ -68,7 +68,7 @@ function OptimizationBase.instantiate_function(
hess_sparsity = f.hess_prototype
hess_colors = f.hess_colorvec
if h == true && f.hess === nothing
prep_hess = prepare_hessian(f.f, soadtype, x, Constant(p), strict=Val(false))
prep_hess = prepare_hessian(f.f, soadtype, x, Constant(p), strict = Val(false))
function hess(res, θ)
hessian!(f.f, res, prep_hess, soadtype, θ, Constant(p))
end
Expand All @@ -85,13 +85,17 @@ function OptimizationBase.instantiate_function(

if fgh == true && f.fgh === nothing
function fgh!(G, H, θ)
(y, _, _) = value_derivative_and_second_derivative!(
(y,
_,
_) = value_derivative_and_second_derivative!(
f.f, G, H, prep_hess, soadtype, θ, Constant(p))
return y
end
if p !== SciMLBase.NullParameters() && p !== nothing
function fgh!(G, H, θ, p)
(y, _, _) = value_derivative_and_second_derivative!(
(y,
_,
_) = value_derivative_and_second_derivative!(
f.f, G, H, prep_hess, soadtype, θ, Constant(p))
return y
end
Expand Down Expand Up @@ -143,7 +147,7 @@ function OptimizationBase.instantiate_function(
cons_jac_prototype = f.cons_jac_prototype
cons_jac_colorvec = f.cons_jac_colorvec
if cons !== nothing && cons_j == true && f.cons_j === nothing
prep_jac = prepare_jacobian(cons_oop, adtype, x, strict=Val(false))
prep_jac = prepare_jacobian(cons_oop, adtype, x, strict = Val(false))
function cons_j!(J, θ)
jacobian!(cons_oop, J, prep_jac, adtype, θ)
if size(J, 1) == 1
Expand All @@ -157,7 +161,8 @@ function OptimizationBase.instantiate_function(
end

if f.cons_vjp === nothing && cons_vjp == true && cons !== nothing
prep_pullback = prepare_pullback(cons_oop, adtype, x, (ones(eltype(x), num_cons),), strict=Val(false))
prep_pullback = prepare_pullback(
cons_oop, adtype, x, (ones(eltype(x), num_cons),), strict = Val(false))
function cons_vjp!(J, θ, v)
pullback!(cons_oop, (J,), prep_pullback, adtype, θ, (v,))
end
Expand All @@ -169,7 +174,7 @@ function OptimizationBase.instantiate_function(

if cons !== nothing && f.cons_jvp === nothing && cons_jvp == true
prep_pushforward = prepare_pushforward(
cons_oop, adtype, x, (ones(eltype(x), length(x)),), strict=Val(false))
cons_oop, adtype, x, (ones(eltype(x), length(x)),), strict = Val(false))
function cons_jvp!(J, θ, v)
pushforward!(cons_oop, (J,), prep_pushforward, adtype, θ, (v,))
end
Expand All @@ -182,7 +187,8 @@ function OptimizationBase.instantiate_function(
conshess_sparsity = f.cons_hess_prototype
conshess_colors = f.cons_hess_colorvec
if cons !== nothing && cons_h == true && f.cons_h === nothing
prep_cons_hess = [prepare_hessian(cons_oop, soadtype, x, Constant(i), strict=Val(false))
prep_cons_hess = [prepare_hessian(
cons_oop, soadtype, x, Constant(i), strict = Val(false))
for i in 1:num_cons]

function cons_h!(H, θ)
Expand All @@ -201,7 +207,7 @@ function OptimizationBase.instantiate_function(
if f.lag_h === nothing && cons !== nothing && lag_h == true
lag_extras = prepare_hessian(
lagrangian, soadtype, x, Constant(one(eltype(x))),
Constant(ones(eltype(x), num_cons)), Constant(p), strict=Val(false))
Constant(ones(eltype(x), num_cons)), Constant(p), strict = Val(false))
lag_hess_prototype = zeros(Bool, num_cons, length(x))

function lag_h!(H::AbstractMatrix, θ, σ, λ)
Expand Down Expand Up @@ -294,7 +300,8 @@ function OptimizationBase.instantiate_function(
adtype, soadtype = OptimizationBase.generate_sparse_adtype(adtype)

if g == true && f.grad === nothing
extras_grad = prepare_gradient(f.f, adtype.dense_ad, x, Constant(p), strict=Val(false))
extras_grad = prepare_gradient(
f.f, adtype.dense_ad, x, Constant(p), strict = Val(false))
function grad(res, θ)
gradient!(f.f, res, extras_grad, adtype.dense_ad, θ, Constant(p))
end
Expand All @@ -311,16 +318,19 @@ function OptimizationBase.instantiate_function(

if fg == true && f.fg === nothing
if g == false
extras_grad = prepare_gradient(f.f, adtype.dense_ad, x, Constant(p), strict=Val(false))
extras_grad = prepare_gradient(
f.f, adtype.dense_ad, x, Constant(p), strict = Val(false))
end
function fg!(res, θ)
(y, _) = value_and_gradient!(
(y,
_) = value_and_gradient!(
f.f, res, extras_grad, adtype.dense_ad, θ, Constant(p))
return y
end
if p !== SciMLBase.NullParameters() && p !== nothing
function fg!(res, θ, p)
(y, _) = value_and_gradient!(
(y,
_) = value_and_gradient!(
f.f, res, extras_grad, adtype.dense_ad, θ, Constant(p))
return y
end
Expand All @@ -334,7 +344,7 @@ function OptimizationBase.instantiate_function(
hess_sparsity = f.hess_prototype
hess_colors = f.hess_colorvec
if h == true && f.hess === nothing
prep_hess = prepare_hessian(f.f, soadtype, x, Constant(p), strict=Val(false))
prep_hess = prepare_hessian(f.f, soadtype, x, Constant(p), strict = Val(false))
function hess(res, θ)
hessian!(f.f, res, prep_hess, soadtype, θ, Constant(p))
end
Expand All @@ -354,14 +364,18 @@ function OptimizationBase.instantiate_function(

if fgh == true && f.fgh === nothing
function fgh!(G, H, θ)
(y, _, _) = value_derivative_and_second_derivative!(
(y,
_,
_) = value_derivative_and_second_derivative!(
f.f, G, H, θ, prep_hess, soadtype, Constant(p))
return y
end

if p !== SciMLBase.NullParameters() && p !== nothing
function fgh!(G, H, θ, p)
(y, _, _) = value_derivative_and_second_derivative!(
(y,
_,
_) = value_derivative_and_second_derivative!(
f.f, G, H, θ, prep_hess, soadtype, Constant(p))
return y
end
Expand Down Expand Up @@ -458,7 +472,8 @@ function OptimizationBase.instantiate_function(
conshess_sparsity = f.cons_hess_prototype
conshess_colors = f.cons_hess_colorvec
if cons !== nothing && f.cons_h === nothing && cons_h == true
prep_cons_hess = [prepare_hessian(cons_oop, soadtype, x, Constant(i), strict=Val(false))
prep_cons_hess = [prepare_hessian(
cons_oop, soadtype, x, Constant(i), strict = Val(false))
for i in 1:num_cons]
colores = getfield.(prep_cons_hess, :coloring_result)
conshess_sparsity = getfield.(colores, :A)
Expand All @@ -479,7 +494,7 @@ function OptimizationBase.instantiate_function(
if cons !== nothing && f.lag_h === nothing && lag_h == true
lag_extras = prepare_hessian(
lagrangian, soadtype, x, Constant(one(eltype(x))),
Constant(ones(eltype(x), num_cons)), Constant(p), strict=Val(false))
Constant(ones(eltype(x), num_cons)), Constant(p), strict = Val(false))
lag_hess_prototype = lag_extras.coloring_result.A
lag_hess_colors = lag_extras.coloring_result.color

Expand Down
16 changes: 12 additions & 4 deletions src/OptimizationDIExt.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -78,13 +78,17 @@ function instantiate_function(

if fgh == true && f.fgh === nothing
function fgh!(G, H, θ)
(y, _, _) = value_derivative_and_second_derivative!(
(y,
_,
_) = value_derivative_and_second_derivative!(
f.f, G, H, prep_hess, soadtype, θ, Constant(p))
return y
end
if p !== SciMLBase.NullParameters() && p !== nothing
function fgh!(G, H, θ, p)
(y, _, _) = value_derivative_and_second_derivative!(
(y,
_,
_) = value_derivative_and_second_derivative!(
f.f, G, H, prep_hess, soadtype, θ, Constant(p))
return y
end
Expand Down Expand Up @@ -338,13 +342,17 @@ function instantiate_function(

if fgh == true && f.fgh === nothing
function fgh!(θ)
(y, G, H) = value_derivative_and_second_derivative(
(y,
G,
H) = value_derivative_and_second_derivative(
f.f, prep_hess, adtype, θ, Constant(p))
return y, G, H
end
if p !== SciMLBase.NullParameters() && p !== nothing
function fgh!(θ, p)
(y, G, H) = value_derivative_and_second_derivative(
(y,
G,
H) = value_derivative_and_second_derivative(
f.f, prep_hess, adtype, θ, Constant(p))
return y, G, H
end
Expand Down
22 changes: 16 additions & 6 deletions src/OptimizationDISparseExt.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -41,13 +41,15 @@ function instantiate_function(
prep_grad = prepare_gradient(f.f, adtype.dense_ad, x, Constant(p))
end
function fg!(res, θ)
(y, _) = value_and_gradient!(
(y,
_) = value_and_gradient!(
f.f, res, prep_grad, adtype.dense_ad, θ, Constant(p))
return y
end
if p !== SciMLBase.NullParameters()
function fg!(res, θ, p)
(y, _) = value_and_gradient!(
(y,
_) = value_and_gradient!(
f.f, res, prep_grad, adtype.dense_ad, θ, Constant(p))
return y
end
Expand Down Expand Up @@ -81,13 +83,17 @@ function instantiate_function(

if fgh == true && f.fgh === nothing
function fgh!(G, H, θ)
(y, _, _) = value_derivative_and_second_derivative!(
(y,
_,
_) = value_derivative_and_second_derivative!(
f.f, G, H, prep_hess, soadtype.dense_ad, θ, Constant(p))
return y
end
if p !== SciMLBase.NullParameters() && p !== nothing
function fgh!(G, H, θ, p)
(y, _, _) = value_derivative_and_second_derivative!(
(y,
_,
_) = value_derivative_and_second_derivative!(
f.f, G, H, prep_hess, soadtype.dense_ad, θ, Constant(p))
return y
end
Expand Down Expand Up @@ -336,14 +342,18 @@ function instantiate_function(

if fgh == true && f.fgh === nothing
function fgh!(θ)
(y, G, H) = value_derivative_and_second_derivative(
(y,
G,
H) = value_derivative_and_second_derivative(
f.f, prep_hess, soadtype, θ, Constant(p))
return y, G, H
end

if p !== SciMLBase.NullParameters() && p !== nothing
function fgh!(θ, p)
(y, G, H) = value_derivative_and_second_derivative(
(y,
G,
H) = value_derivative_and_second_derivative(
f.f, prep_hess, soadtype, θ, Constant(p))
return y, G, H
end
Expand Down
3 changes: 2 additions & 1 deletion test/matrixvalued.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,8 @@ using Test, ReverseDiff
# 1. Matrix Factorization
@show adtype
function matrix_factorization_objective(X, A)
U, V = @view(X[1:size(A, 1), 1:Int(size(A, 2) / 2)]),
U,
V = @view(X[1:size(A, 1), 1:Int(size(A, 2) / 2)]),
@view(X[1:size(A, 1), (Int(size(A, 2) / 2) + 1):size(A, 2)])
return norm(A - U * V')
end
Expand Down
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