Name changes and docstrings added

.github/workflows/test.yml

@@ -23,3 +23,22 @@ jobs:
         with:
           file: lcov.info
           token: ${{ secrets.CODECOV_TOKEN }}
+  test-moonshot:
+    env:
+      JULIA_DEPOT_PATH: /scratch/sshin/github-actions/julia_depot_madnlp
+    runs-on: self-hosted
+    strategy:
+      matrix:
+        julia-version: ['1.9']
+    steps:
+      - uses: actions/checkout@v2
+      - uses: julia-actions/setup-julia@latest
+        with:
+          version: ${{ matrix.julia-version }}
+      - uses: julia-actions/julia-buildpkg@latest
+      - uses: julia-actions/julia-runtest@latest
+      - uses: julia-actions/julia-processcoverage@v1
+      - uses: codecov/codecov-action@v1 
+        with:
+          file: lcov.info
+          token: ${{ secrets.CODECOV_TOKEN }}

ExaModelsExamples/src/ExaModelsExamples.jl

@@ -1,10 +1,9 @@
 module ExaModelsExamples
 
 import ExaModels: ExaModels, NLPModels
+import JuMP, NLPModelsJuMP
 import PowerModels: PowerModels, silence
-using JuMP, NLPModelsJuMP
 
-include("adbenchmarkmodel.jl")
 include("opf.jl")
 include("luksanvlcek.jl")
 include("distillation.jl")
@@ -63,4 +62,10 @@ function parse_log(file)
     end
 end
 
+for name in filter(names(ExaModelsExamples; all=true)) do x
+    endswith(string(x), "model")
+end
+    @eval export $name
+end 
+
 end # module ExaModelsExamples

ExaModelsExamples/src/distillation.jl

@@ -1,4 +1,4 @@
-function distillation_column_model(T, S = Array, device = nothing)
+function distillation_column_model(T, backend = nothing)
 
     NT = 30
     FT = 17
@@ -12,13 +12,13 @@ function distillation_column_model(T, S = Array, device = nothing)
     alpha= 1.6
     dt = 10/T
     xAf = 0.5
-    xA0s = S([(i,0.5) for i in 0:NT+1])
+    xA0s = ExaModels.convert_array([(i,0.5) for i in 0:NT+1], backend)
 
-    itr0 = S(collect(Iterators.product(1:T,1:FT-1)))
-    itr1 = S(collect(Iterators.product(1:T,FT+1:NT)))
-    itr2 = S(collect(Iterators.product(0:T,0:NT+1)))
+    itr0 = ExaModels.convert_array(collect(Iterators.product(1:T,1:FT-1)), backend)
+    itr1 = ExaModels.convert_array(collect(Iterators.product(1:T,FT+1:NT)), backend)
+    itr2 = ExaModels.convert_array(collect(Iterators.product(0:T,0:NT+1)), backend)
 
-    c = ExaModels.Core(S)
+    c = ExaModels.ExaCore(backend)
 
     xA  = ExaModels.variable(c, 0:T, 0:NT+1; start = .5)
     yA  = ExaModels.variable(c, 0:T, 0:NT+1; start = .5)
@@ -60,10 +60,7 @@ function distillation_column_model(T, S = Array, device = nothing)
     ExaModels.constraint(c, L2[t]- u[t] * D - F for t in 0:T)
     ExaModels.constraint(c, yA[t,i] * (1-xA[t,i]) - alpha * xA[t,i] * (1-yA[t,i]) for (t,i) in itr2)
 
-    # Iterators.product
-    return ExaModels.Model(
-        c; device = device
-    )
+    return ExaModels.ExaModel(c)
 end
 
 function jump_distillation_column_model(T)
@@ -86,14 +83,6 @@ function jump_distillation_column_model(T)
     itr1 = collect(Iterators.product(1:T,FT+1:NT))
     itr2 = collect(Iterators.product(0:T,0:NT+1))
 
-    # c = ExaModels.Core()
-
-    # xA  = ExaModels.variable(c, 0:T, 0:NT+1; start = .5)
-    # yA  = ExaModels.variable(c, 0:T, 0:NT+1; start = .5)
-    # u   = ExaModels.variable(c, 0:T; start = 1.)
-    # V   = ExaModels.variable(c, 0:T; start = 1.)
-    # L2  = ExaModels.variable(c, 0:T; start = 1.)
-
     m = JuMP.Model()
 
     JuMP.@variable(m, xA[0:T,0:NT+1], start = .5)

ExaModelsExamples/src/luksanvlcek.jl

@@ -1,6 +1,6 @@
 function luksan_vlcek_model(N, backend = nothing)
 
-    c = ExaModels.Core(backend)
+    c = ExaModels.ExaCore(backend)
     x = ExaModels.variable(
         c, N;
         start = (mod(i,2)==1 ? -1.2 : 1. for i=1:N)
@@ -10,7 +10,7 @@ function luksan_vlcek_model(N, backend = nothing)
         3x[i+1]^3+2*x[i+2]-5+sin(x[i+1]-x[i+2])sin(x[i+1]+x[i+2])+4x[i+1]-x[i]exp(x[i]-x[i+1])-3
         for i in 1:N-2)
     ExaModels.objective(c, 100*(x[i-1]^2-x[i])^2+(x[i-1]-1)^2 for i in 2:N)
-    return ExaModels.Model(c)
+    return ExaModels.ExaModel(c)
 end
 
 function jump_luksan_vlcek_model(N)

ExaModelsExamples/src/opf.jl

@@ -217,7 +217,7 @@ function ac_power_model(
 
     data = parse_ac_power_data(filename, backend) 
 
-    w = ExaModels.Core(T, backend)
+    w = ExaModels.ExaCore(T, backend)
 
     va = ExaModels.variable(
         w, length(data.bus);
@@ -350,6 +350,7 @@ function ac_power_model(
         g.bus =>-qg[g.i]
         for g in data.gen)
 
-    return ExaModels.Model(w)
+    return ExaModels.ExaModel(w)
 
 end
+

ExaModelsExamples/src/quadrotor.jl

@@ -1,5 +1,4 @@
-function quadrotor_model(N,device=nothing)
-    S = Array
+function quadrotor_model(N,backend=nothing)
 
     n = 9
     p = 4
@@ -10,12 +9,12 @@ function quadrotor_model(N,device=nothing)
     Q = [1,0,1,0,1,0,1,1,1]
     Qf= [1,0,1,0,1,0,1,1,1]/dt
 
-    c = ExaModels.Core(device)
+    c = ExaModels.ExaCore(backend)
 
-    x0s = ExaModels.data(c, (i,0.) for i=1:n)
-    itr0 = ExaModels.data(c, (i,j,R[j]) for (i,j) in Base.product(1:N,1:p))
-    itr1 = S([(i,j,Q[j],d(i,j,N)) for (i,j) in Base.product(1:N,1:n)])
-    itr2 = S([(j,Qf[j],d(N+1,j,N)) for j in 1:n])
+    x0s  = ExaModels.convert_array([(i,0.) for i=1:n], backend)
+    itr0 = ExaModels.convert_array([(i,j,R[j]) for (i,j) in Base.product(1:N,1:p)], backend)
+    itr1 = ExaModels.convert_array([(i,j,Q[j],d(i,j,N)) for (i,j) in Base.product(1:N,1:n)], backend)
+    itr2 = ExaModels.convert_array([(j,Qf[j],d(N+1,j,N)) for j in 1:n], backend)
 
     x= ExaModels.variable(c,1:N+1,1:n)
     u= ExaModels.variable(c,1:N,1:p)
@@ -34,7 +33,7 @@ function quadrotor_model(N,device=nothing)
     ExaModels.objective(c, .5*Q*(x[i,j]-d)^2 for (i,j,Q,d) in itr1)
     ExaModels.objective(c, .5*Qf*(x[N+1,j]-d)^2 for (j,Qf,d) in itr2)
 
-    return ExaModels.Model(c)
+    return ExaModels.ExaModel(c)
 end
 
 function jump_quadrotor_model(N)
@@ -49,19 +48,19 @@ function jump_quadrotor_model(N)
     R = ones(4)/10
 
     m = JuMP.Model()
-    @variable(m,x[1:N+1,1:n],start = 0)
-    @variable(m,u[1:N,1:p],start = 0)
-    @constraint(m,[i=1:n],x[1,i]==x0[i])
-    @constraint(m,[i=1:N],x[i+1,1] == x[i,1] + (x[i,2])*dt)
-    @NLconstraint(m,[i=1:N], x[i+1,2] == x[i,2] + (u[i,1]*cos(x[i,7])*sin(x[i,8])*cos(x[i,9])+u[i,1]*sin(x[i,7])*sin(x[i,9]))*dt)
-    @constraint(m,[i=1:N], x[i+1,3] == x[i,3] + (x[i,4])*dt)
-    @NLconstraint(m,[i=1:N], x[i+1,4] == x[i,4] + (u[i,1]*cos(x[i,7])*sin(x[i,8])*sin(x[i,9])-u[i,1]*sin(x[i,7])*cos(x[i,9]))*dt)
-    @constraint(m,[i=1:N], x[i+1,5] == x[i,5] + (x[i,6])*dt)
-    @NLconstraint(m,[i=1:N], x[i+1,6] == x[i,6] + (u[i,1]*cos(x[i,7])*cos(x[i,8])-9.8)*dt)
-    @NLconstraint(m,[i=1:N], x[i+1,7] == x[i,7] + (u[i,2]*cos(x[i,7])/cos(x[i,8])+u[i,3]*sin(x[i,7])/cos(x[i,8]))*dt)
-    @NLconstraint(m,[i=1:N], x[i+1,8] == x[i,8] + (-u[i,2]*sin(x[i,7])+u[i,3]*cos(x[i,7]))*dt)
-    @NLconstraint(m,[i=1:N], x[i+1,9] == x[i,9] + (u[i,2]*cos(x[i,7])*tan(x[i,8])+u[i,3]*sin(x[i,7])*tan(x[i,8])+u[i,4])*dt)
-    @objective(m,Min, .5*sum(Q[j]*(x[i,j]-d(i,j,N))^2 for i=1:N for j=1:n) + .5*sum(R[j]*(u[i,j]^2) for i=1:N for j=1:p)
+    JuMP.@variable(m,x[1:N+1,1:n],start = 0)
+    JuMP.@variable(m,u[1:N,1:p],start = 0)
+    JuMP.@constraint(m,[i=1:n],x[1,i]==x0[i])
+    JuMP.@constraint(m,[i=1:N],x[i+1,1] == x[i,1] + (x[i,2])*dt)
+    JuMP.@NLconstraint(m,[i=1:N], x[i+1,2] == x[i,2] + (u[i,1]*cos(x[i,7])*sin(x[i,8])*cos(x[i,9])+u[i,1]*sin(x[i,7])*sin(x[i,9]))*dt)
+    JuMP.@constraint(m,[i=1:N], x[i+1,3] == x[i,3] + (x[i,4])*dt)
+    JuMP.@NLconstraint(m,[i=1:N], x[i+1,4] == x[i,4] + (u[i,1]*cos(x[i,7])*sin(x[i,8])*sin(x[i,9])-u[i,1]*sin(x[i,7])*cos(x[i,9]))*dt)
+    JuMP.@constraint(m,[i=1:N], x[i+1,5] == x[i,5] + (x[i,6])*dt)
+    JuMP.@NLconstraint(m,[i=1:N], x[i+1,6] == x[i,6] + (u[i,1]*cos(x[i,7])*cos(x[i,8])-9.8)*dt)
+    JuMP.@NLconstraint(m,[i=1:N], x[i+1,7] == x[i,7] + (u[i,2]*cos(x[i,7])/cos(x[i,8])+u[i,3]*sin(x[i,7])/cos(x[i,8]))*dt)
+    JuMP.@NLconstraint(m,[i=1:N], x[i+1,8] == x[i,8] + (-u[i,2]*sin(x[i,7])+u[i,3]*cos(x[i,7]))*dt)
+    JuMP.@NLconstraint(m,[i=1:N], x[i+1,9] == x[i,9] + (u[i,2]*cos(x[i,7])*tan(x[i,8])+u[i,3]*sin(x[i,7])*tan(x[i,8])+u[i,4])*dt)
+    JuMP.@objective(m,Min, .5*sum(Q[j]*(x[i,j]-d(i,j,N))^2 for i=1:N for j=1:n) + .5*sum(R[j]*(u[i,j]^2) for i=1:N for j=1:p)
                + .5*sum(Qf[j]*(x[N+1,j]-d(N+1,j,N))^2 for j=1:n))
     return m
 end

Project.toml

@@ -5,6 +5,7 @@ version = "0.1.0"
 
 [deps]
 NLPModels = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
+SolverCore = "ff4d7338-4cf1-434d-91df-b86cb86fb843"
 
 [weakdeps]
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
@@ -19,18 +20,19 @@ ExaModelsOneAPI = "oneAPI"
 ExaModelsSpecialFunctions = "SpecialFunctions"
 
 [compat]
+julia = "1.9"
 NLPModels = "0.20"
-SpecialFunctions = "2"
-KernelAbstractions = "0.9"
+SolverCore = "0.3"
 CUDA = "4"
+KernelAbstractions = "0.9"
+SpecialFunctions = "2"
 oneAPI = "1"
-julia = "1.9"
 
 [extras]
-NLPModels = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
 ADNLPModels = "54578032-b7ea-4c30-94aa-7cbd1cce6c9a"
-NLPModelsIpopt = "f4238b75-b362-5c4c-b852-0801c9a21d71"
 ExaModelsExaples = "ff8351d9-12a3-4c2d-a61a-51dfbae68567"
+NLPModels = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
+NLPModelsIpopt = "f4238b75-b362-5c4c-b852-0801c9a21d71"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]

docs/make.jl

@@ -23,12 +23,12 @@ for jl_filename in _JL_FILENAMES
 end
 
 
-# makedocs(
-#     sitename = "ExaModels.jl",
-#     authors = "Sungho Shin",
-#     format = Documenter.LaTeX(platform="docker"),
-#     pages = _PAGES
-# )
+makedocs(
+    sitename = "ExaModels.jl",
+    authors = "Sungho Shin",
+    format = Documenter.LaTeX(platform="docker"),
+    pages = _PAGES
+)
 
 makedocs(
     sitename = "ExaModels.jl",