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ChrisRackauckasChrisRackauckas
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‎.github/workflows/Downstream.yml‎

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@@ -17,6 +17,8 @@ jobs:
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os: [ubuntu-latest]
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package:
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- {user: SciML, repo: OrdinaryDiffEq.jl, group: InterfaceII}
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- {user: SciML, repo: ModelingToolkit.jl, group: All}
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- {user: SciML, repo: SciMLSensitivity.jl, group: Core1}
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steps:
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- uses: actions/checkout@v2

‎src/factorization.jl‎

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@@ -70,98 +70,6 @@ function init_cacheval(alg::Union{LUFactorization, GenericLUFactorization}, A, b
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ArrayInterfaceCore.lu_instance(convert(AbstractMatrix, A))
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end
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# This could be a GenericFactorization perhaps?
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Base.@kwdef struct UMFPACKFactorization <: AbstractFactorization
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reuse_symbolic::Bool = true
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end
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function init_cacheval(alg::UMFPACKFactorization, A, b, u, Pl, Pr, maxiters, abstol, reltol,
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verbose)
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A = convert(AbstractMatrix, A)
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#=
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zerobased = SparseArrays.getcolptr(A)[1] == 0
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res = SuiteSparse.UMFPACK.UmfpackLU(C_NULL, C_NULL, size(A, 1), size(A, 2),
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zerobased ? copy(SparseArrays.getcolptr(A)) :
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SuiteSparse.decrement(SparseArrays.getcolptr(A)),
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zerobased ? copy(rowvals(A)) :
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SuiteSparse.decrement(rowvals(A)),
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copy(nonzeros(A)), 0)
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finalizer(SuiteSparse.UMFPACK.umfpack_free_symbolic, res)
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=#
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res = lu(A; check = false)
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@assert res isa SuiteSparse.UMFPACK.UmfpackLU
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res
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end
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function do_factorization(::UMFPACKFactorization, A, b, u)
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A = convert(AbstractMatrix, A)
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if A isa SparseMatrixCSC
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return lu(A)
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else
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error("Sparse LU is not defined for $(typeof(A))")
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end
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end
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function SciMLBase.solve(cache::LinearCache, alg::UMFPACKFactorization; kwargs...)
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A = cache.A
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A = convert(AbstractMatrix, A)
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if cache.isfresh
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if cache.cacheval !== nothing && alg.reuse_symbolic
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# If we have a cacheval already, run umfpack_symbolic to ensure the symbolic factorization exists
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# This won't recompute if it does.
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SuiteSparse.UMFPACK.umfpack_symbolic!(cache.cacheval)
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fact = lu!(cache.cacheval, A)
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else
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fact = do_factorization(alg, A, cache.b, cache.u)
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end
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cache = set_cacheval(cache, fact)
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end
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y = ldiv!(cache.u, cache.cacheval, cache.b)
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SciMLBase.build_linear_solution(alg, y, nothing, cache)
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end
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Base.@kwdef struct KLUFactorization <: AbstractFactorization
125-
reuse_symbolic::Bool = true
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end
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function init_cacheval(alg::KLUFactorization, A, b, u, Pl, Pr, maxiters, abstol, reltol,
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verbose)
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return KLU.KLUFactorization(convert(AbstractMatrix, A)) # this takes care of the copy internally.
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end
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function do_factorization(::KLUFactorization, A, b, u)
134-
A = convert(AbstractMatrix, A)
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if A isa SparseMatrixCSC
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return klu(A)
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else
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error("KLU is not defined for $(typeof(A))")
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end
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end
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function SciMLBase.solve(cache::LinearCache, alg::KLUFactorization; kwargs...)
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A = cache.A
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A = convert(AbstractMatrix, A)
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if cache.isfresh
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if cache.cacheval !== nothing && alg.reuse_symbolic
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# If we have a cacheval already, run umfpack_symbolic to ensure the symbolic factorization exists
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# This won't recompute if it does.
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KLU.klu_analyze!(cache.cacheval)
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copyto!(cache.cacheval.nzval, A.nzval)
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if cache.cacheval._numeric === C_NULL # We MUST have a numeric factorization for reuse, unlike UMFPACK.
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KLU.klu_factor!(cache.cacheval)
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end
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fact = KLU.klu!(cache.cacheval, A)
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else
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fact = do_factorization(alg, A, cache.b, cache.u)
157-
end
158-
cache = set_cacheval(cache, fact)
159-
end
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y = ldiv!(cache.u, cache.cacheval, cache.b)
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SciMLBase.build_linear_solution(alg, y, nothing, cache)
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end
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## QRFactorization
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struct QRFactorization{P} <: AbstractFactorization
@@ -331,6 +239,76 @@ function init_cacheval(alg::Union{GenericFactorization,
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do_factorization(alg, newA, b, u)
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end
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################################## Factorizations which require solve overloads
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Base.@kwdef struct UMFPACKFactorization <: AbstractFactorization
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reuse_symbolic::Bool = true
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end
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function init_cacheval(alg::UMFPACKFactorization, A, b, u, Pl, Pr, maxiters, abstol, reltol,
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verbose)
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A = convert(AbstractMatrix, A)
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zerobased = SparseArrays.getcolptr(A)[1] == 0
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res = SuiteSparse.UMFPACK.UmfpackLU(C_NULL, C_NULL, size(A, 1), size(A, 2),
253+
zerobased ? copy(SparseArrays.getcolptr(A)) :
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SuiteSparse.decrement(SparseArrays.getcolptr(A)),
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zerobased ? copy(rowvals(A)) :
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SuiteSparse.decrement(rowvals(A)),
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copy(nonzeros(A)), 0)
258+
finalizer(SuiteSparse.UMFPACK.umfpack_free_symbolic, res)
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res
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end
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function SciMLBase.solve(cache::LinearCache, alg::UMFPACKFactorization; kwargs...)
263+
A = cache.A
264+
A = convert(AbstractMatrix, A)
265+
if cache.isfresh
266+
if cache.cacheval !== nothing && alg.reuse_symbolic
267+
# Caches the symbolic factorization: https://github.com/JuliaLang/julia/pull/33738
268+
println("factorize"); display(A)
269+
fact = lu!(cache.cacheval, A)
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else
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fact = do_factorization(alg, A, cache.b, cache.u)
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end
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cache = set_cacheval(cache, fact)
274+
end
275+
276+
y = ldiv!(cache.u, cache.cacheval, cache.b)
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SciMLBase.build_linear_solution(alg, y, nothing, cache)
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end
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Base.@kwdef struct KLUFactorization <: AbstractFactorization
281+
reuse_symbolic::Bool = true
282+
end
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284+
function init_cacheval(alg::KLUFactorization, A, b, u, Pl, Pr, maxiters, abstol, reltol,
285+
verbose)
286+
return KLU.KLUFactorization(convert(AbstractMatrix, A)) # this takes care of the copy internally.
287+
end
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function SciMLBase.solve(cache::LinearCache, alg::KLUFactorization; kwargs...)
290+
A = cache.A
291+
A = convert(AbstractMatrix, A)
292+
if cache.isfresh
293+
if cache.cacheval !== nothing && alg.reuse_symbolic
294+
# If we have a cacheval already, run umfpack_symbolic to ensure the symbolic factorization exists
295+
# This won't recompute if it does.
296+
KLU.klu_analyze!(cache.cacheval)
297+
copyto!(cache.cacheval.nzval, A.nzval)
298+
if cache.cacheval._numeric === C_NULL # We MUST have a numeric factorization for reuse, unlike UMFPACK.
299+
KLU.klu_factor!(cache.cacheval)
300+
end
301+
fact = KLU.klu!(cache.cacheval, A)
302+
else
303+
fact = do_factorization(alg, A, cache.b, cache.u)
304+
end
305+
cache = set_cacheval(cache, fact)
306+
end
307+
308+
y = ldiv!(cache.u, cache.cacheval, cache.b)
309+
SciMLBase.build_linear_solution(alg, y, nothing, cache)
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end
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334312
## RFLUFactorization
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336314
struct RFLUFactorization{P, T} <: AbstractFactorization

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