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Use the Julia wrappers in CUTEst.jl -- Part I #333

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Aug 17, 2024
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Use the Julia wrappers in CUTEst.jl -- Part I #333

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c40217a
Use the Julia wrappers in CUTEst.jl
amontoison Aug 17, 2024
c8011e8
First step to use Julia wrappers
amontoison Aug 17, 2024
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1 change: 0 additions & 1 deletion gen/wrapper.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -32,7 +32,6 @@ function main()
code = replace(code, "Ptr{ip_}" => "Ptr{Cint}")
code = replace(code, "Ptr{ipc_}" => "Ptr{Cint}")
write(path, code)

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:)

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I wanted it without the space ;)

format_file(path, YASStyle())

code = read(path, String)
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327 changes: 5 additions & 322 deletions src/CUTEst.jl
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Expand Up @@ -13,59 +13,17 @@ using Libdl
using NLPModels
import Libdl.dlsym

global cutest_lib_path = ""
global cutest_lib = C_NULL
include("libcutest.jl")

# Only one problem can be interfaced at any given time.
global cutest_instances = 0
global cutest_lib = C_NULL

export CUTEstModel, sifdecoder, set_mastsif

mutable struct CUTEstModel <: AbstractNLPModel{Float64, Vector{Float64}}
meta::NLPModelMeta{Float64, Vector{Float64}}
counters::Counters
hrows::Vector{Int32}
hcols::Vector{Int32}

jac_structure_reliable::Bool
jrows::Vector{Int32}
jcols::Vector{Int32}

lin_structure_reliable::Bool
blin::Vector{Float64}
clinrows::Vector{Int32}
clincols::Vector{Int32}
clinvals::Vector{Float64}

work::Vector{Float64}
Jval::Vector{Cdouble}
Jvar::Vector{Cint}
end
export CUTEstModel, sifdecoder, build_libsif, set_mastsif

const funit = convert(Int32, 42)
@static Sys.isapple() ? (const linker = "gfortran") : (const linker = "ld")
@static Sys.isapple() ? (const sh_flags = ["-dynamiclib", "-undefined", "dynamic_lookup"]) :
(const sh_flags = ["-shared"])

struct CUTEstException <: Exception
info::Int32
msg::String

function CUTEstException(info::Int32)
if info == 1
msg = "memory allocation error"
elseif info == 2
msg = "array bound error"
elseif info == 3
msg = "evaluation error"
else
msg = "unknown error"
end
return new(info, msg)
end
end

const cutest_problems_path = joinpath(dirname(@__FILE__), "..", "deps", "files")
isdir(cutest_problems_path) || mkpath(cutest_problems_path)

Expand All @@ -90,290 +48,15 @@ function __init__()
push!(Libdl.DL_LOAD_PATH, cutest_problems_path)
end

CUTEstException(info::Integer) = CUTEstException(convert(Int32, info))

macro cutest_error() # Handle nonzero exit codes.
esc(:(io_err[1] > 0 && throw(CUTEstException(io_err[1]))))
end

# to allow view inputs with stride one
StrideOneVector{T} =
Union{Vector{T}, SubArray{T, 1, Vector{T}, Tuple{UnitRange{U}}, true} where {U <: Integer}}
Union{Ref{T}, Vector{T}, SubArray{T, 1, Vector{T}, Tuple{UnitRange{U}}, true} where {U <: Integer}}

include("libcutest.jl")
include("model.jl")
include("sifdecoder.jl")
include("core_interface.jl")
include("julia_interface.jl")
include("classification.jl")

"""
set_mastsif(set::String="sifcollection")

Set the MASTSIF environment variable to point to a set of SIF problems.
The supported sets are:
- "sifcollection": the CUTEst NLP test set;
- "maros-meszaros": the Maros-Meszaros QP test set;
- "netlib-lp": the Netlib LP test set.

"""
function set_mastsif(set::String = "sifcollection")
if set == "sifcollection"
ENV["MASTSIF"] = joinpath(artifact"sifcollection", "optrove-sif-229e00b81891")
elseif set == "maros-meszaros"
ENV["MASTSIF"] = joinpath(artifact"maros-meszaros", "optrove-maros-meszaros-9adfb5707b1e")
elseif set == "netlib-lp"
ENV["MASTSIF"] = joinpath(artifact"netlib-lp", "optrove-netlib-lp-f83996fca937")
else
error("The set $set is not supported.")
end
@info "using full SIF collection located at" ENV["MASTSIF"]
return nothing
end

"""
nlp = CUTEstModel(name, args...; kwargs...)

Creates a CUTEst model following the NLPModels API.
This model needs to be finalized before a new one is created (e.g., calling `finalize(nlp)`).

## Optional arguments

Any extra arguments will be passed to `sifdecoder`.
You can, for instance, change parameters of the model:

```jldoctest
using CUTEst

nlp = CUTEstModel("CHAIN", "-param", "NH=50")
println(nlp.meta.nnzh)
finalize(nlp)
nlp = CUTEstModel("CHAIN", "-param", "NH=100")
println(nlp.meta.nnzh)
finalize(nlp)

# output

153
303
```

## Keyword arguments

- `decode::Bool = true`: Whether to call sifdecoder.
- `verbose::Bool = false`: Passed to sifdecoder.
- `efirst`::Bool = true`: Equalities first?
- `lfirst`::Bool = true`: Linear (or affine) constraints first?
- `lvfirst::Bool = true`: Nonlinear variables should appear first?
"""
function CUTEstModel(
name::AbstractString,
args...;
decode::Bool = true,
verbose::Bool = false,
efirst::Bool = true,
lfirst::Bool = true,
lvfirst::Bool = true,
)
sifname = (length(name) < 4 || name[(end - 3):end] != ".SIF") ? "$name.SIF" : name
if isfile(sifname)
# This file is local so make sure the full path is maintained for sifdecoder
path_sifname = joinpath(pwd(), sifname)
else
path_sifname = joinpath(ENV["MASTSIF"], sifname)
if !isfile(path_sifname)
error("$name not found")
end
end

pname, sif = basename(name) |> splitext
outsdif = "OUTSDIF_$pname.d"
global cutest_instances
cutest_instances > 0 && error("CUTEst: call finalize on current model first")
io_err = Cint[0]
global cutest_lib
cd(cutest_problems_path) do
if !decode
isfile(outsdif) || error("CUTEst: no decoded problem found")
libname = "lib$pname"
isfile("$libname.$(Libdl.dlext)") || error("CUTEst: lib not found; decode problem first")
cutest_lib = Libdl.dlopen(libname, Libdl.RTLD_NOW | Libdl.RTLD_DEEPBIND | Libdl.RTLD_GLOBAL)
else
sifdecoder(path_sifname, args..., verbose = verbose, outsdif = outsdif, precision = :double)
build_libsif(path_sifname, precision = :double)
end
ccall(
dlsym(cutest_lib, :fortran_open_),
Nothing,
(Ref{Int32}, Ptr{UInt8}, Ptr{Int32}),
[funit],
outsdif,
io_err,
)
@cutest_error
end

# Obtain problem size.
nvar = Cint[0]
ncon = Cint[0]

cdimen(io_err, [funit], nvar, ncon)
@cutest_error
nvar = nvar[1]
ncon = ncon[1]

x = Vector{Float64}(undef, nvar)
bl = Vector{Float64}(undef, nvar)
bu = Vector{Float64}(undef, nvar)
v = Vector{Float64}(undef, ncon)
cl = Vector{Float64}(undef, ncon)
cu = Vector{Float64}(undef, ncon)
equatn = Vector{Int32}(undef, ncon)
linear = Vector{Int32}(undef, ncon)

if ncon > 0
e_order = efirst ? Cint[1] : Cint[0]
l_order = lfirst ? Cint[1] : Cint[0]
v_order = lvfirst ? Cint[1] : Cint[0]
# Equality constraints first, linear constraints first, nonlinear variables first.
csetup(
io_err,
[funit],
Cint[0],
Cint[6],
[nvar],
[ncon],
x,
bl,
bu,
v,
cl,
cu,
equatn,
linear,
e_order,
l_order,
v_order,
)
else
usetup(io_err, [funit], Cint[0], Cint[6], [nvar], x, bl, bu)
end
@cutest_error

for lim in Any[bl, bu, cl, cu]
I = findall(abs.(lim) .>= 1e20)
lim[I] = Inf * lim[I]
end

lin = findall(linear .!= 0)
nlin = length(lin)

nnzh = Cint[0]
nnzj = Cint[0]

if ncon > 0
cdimsh(io_err, nnzh)
cdimsj(io_err, nnzj)
nnzj[1] -= nvar # nnzj also counts the nonzeros in the objective gradient.
else
udimsh(io_err, nnzh)
end
@cutest_error

nnzh = Int(nnzh[1])
nnzj = Int(nnzj[1])

ccall(dlsym(cutest_lib, :fortran_close_), Nothing, (Ref{Int32}, Ptr{Int32}), funit, io_err)
@cutest_error

ncon = Int(ncon)
nvar = Int(nvar)

lin_nnzj = min(nvar * nlin, nnzj)
nln_nnzj = min(nvar * (ncon - nlin), nnzj)

meta = NLPModelMeta(
nvar,
x0 = x,
lvar = bl,
uvar = bu,
ncon = ncon,
y0 = v,
lcon = cl,
ucon = cu,
nnzj = nnzj,
nnzh = nnzh,
lin = lin,
lin_nnzj = lin_nnzj,
nln_nnzj = nln_nnzj,
name = splitext(name)[1],
)

hrows = Vector{Int32}(undef, nnzh)
hcols = Vector{Int32}(undef, nnzh)

jac_structure_reliable = false
jrows = Vector{Int32}(undef, nnzj)
jcols = Vector{Int32}(undef, nnzj)
work = Vector{Int32}(undef, ncon)

lin_structure_reliable = false
blin = Vector{Float64}(undef, nlin)
clinrows = Vector{Int32}(undef, lin_nnzj)
clincols = Vector{Int32}(undef, lin_nnzj)
clinvals = Vector{Float64}(undef, lin_nnzj)

Jval = Array{Cdouble}(undef, nvar)
Jvar = Array{Cint}(undef, nvar)

nlp = CUTEstModel(
meta,
Counters(),
hrows,
hcols,
jac_structure_reliable,
jrows,
jcols,
lin_structure_reliable,
blin,
clinrows,
clincols,
clinvals,
work,
Jval,
Jvar,
)

cutest_instances += 1
finalizer(cutest_finalize, nlp)

return nlp
end

function cutest_finalize(nlp::CUTEstModel)
global cutest_instances
cutest_instances == 0 && return
global cutest_lib
io_err = Cint[0]
if nlp.meta.ncon > 0
cterminate(io_err)
else
uterminate(io_err)
end
@cutest_error
Libdl.dlclose(cutest_lib)
cutest_instances -= 1
cutest_lib = C_NULL
return
end

# Displaying CUTEstModel instances.

import Base.show, Base.print
function show(io::IO, nlp::CUTEstModel)
show(io, nlp.meta)
end

function print(io::IO, nlp::CUTEstModel)
print(io, nlp.meta)
end

end # module CUTEst.
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