Merge pull request #210 from j-fu/access_fx

Allow to access fx from jac_cache via public API

Author: ChrisRackauckas

docs/src/index.md

@@ -183,6 +183,10 @@ forwarddiff_color_jacobian!(J::AbstractMatrix{<:Number},
 `dx` is a pre-allocated output vector which is used to declare the output size,
 and thus allows for specifying a non-square Jacobian.
 
+Also, it is possible retrieve the function value via `value(jac_cache)` or 
+`value!(result, jac_cache)`
+
+
 If one is using an out-of-place function `f(x)`, then the alternative form
 ca be used:
 

src/SparseDiffTools.jl

@@ -42,7 +42,8 @@ export contract_color,
        autonum_hesvec, autonum_hesvec!,
        num_hesvecgrad, num_hesvecgrad!,
        auto_hesvecgrad, auto_hesvecgrad!,
-       JacVec, HesVec, HesVecGrad
+       JacVec, HesVec, HesVecGrad,
+       value!
 
 include("coloring/high_level.jl")
 include("coloring/backtracking_coloring.jl")

src/differentiation/compute_jacobian_ad.jl

@@ -8,6 +8,9 @@ struct ForwardColorJacCache{T, T2, T3, T4, T5, T6}
     chunksize::Int
 end
 
+ForwardDiff.value(cache::ForwardColorJacCache) = ForwardDiff.value.(cache.fx)
+value!(fx, cache::ForwardColorJacCache) = fx .= ForwardDiff.value.(cache.fx)
+
 getsize(::Val{N}) where {N} = N
 getsize(N::Integer) = N
 void_setindex!(args...) = (setindex!(args...); return)

test/test_ad.jl

@@ -1,5 +1,5 @@
 using SparseDiffTools
-using ForwardDiff: Dual, jacobian
+using ForwardDiff: Dual, jacobian, value
 using SparseArrays, Test
 using LinearAlgebra
 using BlockBandedMatrices, ArrayInterfaceBlockBandedMatrices
@@ -212,6 +212,12 @@ jac_cache = ForwardColorJacCache(f, x, colorvec = repeat(1:3, 10), sparsity = _J
 forwarddiff_color_jacobian!(_J1, f, x, jac_cache)
 @test _J1 ≈ J
 @test fcalls == 1
+dx = similar(x)
+f(dx, x)
+@test value(jac_cache) == dx
+dx1 = similar(dx)
+value!(dx1, jac_cache)
+@test dx1 == dx
 
 fcalls = 0
 _J1 = similar(_J)
@@ -227,6 +233,12 @@ jac_cache = ForwardColorJacCache(f, x, colorvec = repeat(1:3, 10), sparsity = _J
 forwarddiff_color_jacobian!(_denseJ1, f, x, jac_cache)
 @test _denseJ1 ≈ J
 @test fcalls == 1
+dx = similar(x)
+f(dx, x)
+@test value(jac_cache) == dx
+dx1 = similar(dx)
+value!(dx1, jac_cache)
+@test dx1 == dx
 
 _Jt = similar(Tridiagonal(J))
 forwarddiff_color_jacobian!(_Jt, f, x, colorvec = repeat(1:3, 10), sparsity = _Jt)