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Unstrict Zygote #148

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Unstrict Zygote #148

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26 changes: 13 additions & 13 deletions ext/OptimizationZygoteExt.jl
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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))
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))
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))
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 Down Expand Up @@ -143,7 +143,7 @@ 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)
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 +157,7 @@ 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),))
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 +169,7 @@ 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)),))
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 +182,7 @@ 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))
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 +201,7 @@ 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))
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 +294,7 @@ 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))
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 @@ -311,7 +311,7 @@ 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))
extras_grad = prepare_gradient(f.f, adtype.dense_ad, x, Constant(p), strict=Val(false))
end
function fg!(res, θ)
(y, _) = value_and_gradient!(
Expand All @@ -334,7 +334,7 @@ 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))
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 Down Expand Up @@ -458,7 +458,7 @@ 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))
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 +479,7 @@ 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))
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
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