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Add tests for Utilities/sparse_matrix.jl #1369
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,176 @@ | ||
| module TestSparseMatrix | ||
|
|
||
| import SparseArrays | ||
| using Test | ||
|
|
||
| import MathOptInterface | ||
| const MOI = MathOptInterface | ||
|
|
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| function runtests() | ||
| for name in names(@__MODULE__; all = true) | ||
| if startswith("$(name)", "test_") | ||
| @testset "$(name)" begin | ||
| getfield(@__MODULE__, name)() | ||
| end | ||
| end | ||
| end | ||
| return | ||
| end | ||
|
|
||
| """ | ||
| test_empty_size() | ||
|
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| Julia 1.7 is going to outlaw constructing empty sparse matrices. Test that we | ||
| can still do so. | ||
| """ | ||
| function test_empty_size() | ||
| A = MOI.Utilities.MutableSparseMatrixCSC{ | ||
| Float64, | ||
| Int, | ||
| MOI.Utilities.ZeroBasedIndexing, | ||
| }() | ||
| MOI.empty!(A) | ||
| @test A.rowval == Int[] | ||
| @test A.nzval == Float64[] | ||
| @test A.colptr == Int[0] | ||
| end | ||
|
|
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| function test_empty_large() | ||
| A = MOI.Utilities.MutableSparseMatrixCSC{ | ||
| Float64, | ||
| Int, | ||
| MOI.Utilities.ZeroBasedIndexing, | ||
| }() | ||
| MOI.empty!(A) | ||
| MOI.Utilities.add_column(A) | ||
| MOI.Utilities.add_column(A) | ||
| MOI.Utilities.set_number_of_rows(A, 5) | ||
| B = convert(SparseArrays.SparseMatrixCSC{Float64,Int}, A) | ||
| @test size(B) == (5, 2) | ||
| end | ||
|
|
||
| function test_VectorAffine_ZeroBased() | ||
| A = MOI.Utilities.MutableSparseMatrixCSC{ | ||
| Float64, | ||
| Int, | ||
| MOI.Utilities.ZeroBasedIndexing, | ||
| }() | ||
| MOI.empty!(A) | ||
| x = MOI.VariableIndex.(1:3) | ||
| f = MOI.VectorAffineFunction( | ||
| vcat( | ||
| MOI.VectorAffineTerm.(1, MOI.ScalarAffineTerm.(1.0, x)), | ||
| MOI.VectorAffineTerm.(2, MOI.ScalarAffineTerm.([2.0, 3.0], x[2:3])), | ||
| ), | ||
| [0.5, 1.2], | ||
| ) | ||
|
|
||
| index_map = MOI.Utilities.IndexMap() | ||
| for i in 1:3 | ||
| MOI.Utilities.add_column(A) | ||
| index_map[x[i]] = x[i] | ||
| end | ||
| MOI.Utilities.allocate_terms(A, index_map, f) | ||
| MOI.Utilities.set_number_of_rows(A, 2) | ||
| MOI.Utilities.load_terms(A, index_map, f, 0) | ||
| MOI.Utilities.final_touch(A) | ||
| B = convert(SparseArrays.SparseMatrixCSC{Float64,Int}, A) | ||
| @test B == [1.0 1.0 1.0; 0.0 2.0 3.0] | ||
| @test A.rowval == [0, 0, 1, 0, 1] | ||
| @test A.nzval == [1.0, 1.0, 2.0, 1.0, 3.0] | ||
| @test A.colptr == [0, 1, 3, 5] | ||
| end | ||
|
|
||
| function test_VectorAffine_OneBased() | ||
| A = MOI.Utilities.MutableSparseMatrixCSC{ | ||
| Float64, | ||
| Int, | ||
| MOI.Utilities.OneBasedIndexing, | ||
| }() | ||
| MOI.empty!(A) | ||
| x = MOI.VariableIndex.(1:3) | ||
| f = MOI.VectorAffineFunction( | ||
| vcat( | ||
| MOI.VectorAffineTerm.(1, MOI.ScalarAffineTerm.(1.0, x)), | ||
| MOI.VectorAffineTerm.(2, MOI.ScalarAffineTerm.([2.0, 3.0], x[2:3])), | ||
| ), | ||
| [0.5, 1.2], | ||
| ) | ||
|
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||
| index_map = MOI.Utilities.IndexMap() | ||
| for i in 1:3 | ||
| MOI.Utilities.add_column(A) | ||
| index_map[x[i]] = x[i] | ||
| end | ||
| MOI.Utilities.allocate_terms(A, index_map, f) | ||
| MOI.Utilities.set_number_of_rows(A, 2) | ||
| MOI.Utilities.load_terms(A, index_map, f, 0) | ||
| MOI.Utilities.final_touch(A) | ||
| B = convert(SparseArrays.SparseMatrixCSC{Float64,Int}, A) | ||
| @test B == [1.0 1.0 1.0; 0.0 2.0 3.0] | ||
| @test A.rowval == [1, 1, 2, 1, 2] | ||
| @test A.nzval == [1.0, 1.0, 2.0, 1.0, 3.0] | ||
| @test A.colptr == [1, 2, 4, 6] | ||
| end | ||
|
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||
| function test_ScalarAffine_ZeroBased() | ||
| A = MOI.Utilities.MutableSparseMatrixCSC{ | ||
| Float64, | ||
| Int, | ||
| MOI.Utilities.ZeroBasedIndexing, | ||
| }() | ||
| MOI.empty!(A) | ||
| x = MOI.VariableIndex.(1:3) | ||
| f1 = MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.(1.0, x), 0.5) | ||
| f2 = | ||
| MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.([2.0, 3.0], x[2:3]), 1.2) | ||
| index_map = MOI.Utilities.IndexMap() | ||
| for i in 1:3 | ||
| MOI.Utilities.add_column(A) | ||
| index_map[x[i]] = x[i] | ||
| end | ||
| MOI.Utilities.allocate_terms(A, index_map, f1) | ||
| MOI.Utilities.allocate_terms(A, index_map, f2) | ||
| MOI.Utilities.set_number_of_rows(A, 2) | ||
| MOI.Utilities.load_terms(A, index_map, f1, 0) | ||
| MOI.Utilities.load_terms(A, index_map, f2, 1) | ||
| MOI.Utilities.final_touch(A) | ||
| B = convert(SparseArrays.SparseMatrixCSC{Float64,Int}, A) | ||
| @test B == [1.0 1.0 1.0; 0.0 2.0 3.0] | ||
| @test A.rowval == [0, 0, 1, 0, 1] | ||
| @test A.nzval == [1.0, 1.0, 2.0, 1.0, 3.0] | ||
| @test A.colptr == [0, 1, 3, 5] | ||
| end | ||
|
|
||
| function test_ScalarAffine_OneBased() | ||
| A = MOI.Utilities.MutableSparseMatrixCSC{ | ||
| Float64, | ||
| Int, | ||
| MOI.Utilities.OneBasedIndexing, | ||
| }() | ||
| MOI.empty!(A) | ||
| x = MOI.VariableIndex.(1:3) | ||
| f1 = MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.(1.0, x), 0.5) | ||
| f2 = | ||
| MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.([2.0, 3.0], x[2:3]), 1.2) | ||
| index_map = MOI.Utilities.IndexMap() | ||
| for i in 1:3 | ||
| MOI.Utilities.add_column(A) | ||
| index_map[x[i]] = x[i] | ||
| end | ||
| MOI.Utilities.allocate_terms(A, index_map, f1) | ||
| MOI.Utilities.allocate_terms(A, index_map, f2) | ||
| MOI.Utilities.set_number_of_rows(A, 2) | ||
| MOI.Utilities.load_terms(A, index_map, f1, 0) | ||
| MOI.Utilities.load_terms(A, index_map, f2, 1) | ||
| MOI.Utilities.final_touch(A) | ||
| B = convert(SparseArrays.SparseMatrixCSC{Float64,Int}, A) | ||
| @test B == [1.0 1.0 1.0; 0.0 2.0 3.0] | ||
| @test A.rowval == [1, 1, 2, 1, 2] | ||
| @test A.nzval == [1.0, 1.0, 2.0, 1.0, 3.0] | ||
| @test A.colptr == [1, 2, 4, 6] | ||
| end | ||
|
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| end | ||
|
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| TestSparseMatrix.runtests() |
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@blegat what was the motivation to have these
_allocate_termsfunction barriers?There was a problem hiding this comment.
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It dates back to jump-dev/SCS.jl#192 where I benchmarked the two approaches and the function barrier seemed to be faster even if I didn't really get why.
It's maybe not the case anymore in Julia v1.6 or maybe I was mistaken when writing this.
We can revisit later. An easier speedup would be obtained by using
@inboundsI guess.The good thing with MatrixOfConstraints is that we will be able to spend time optimizing these pieces at once in MOI and it will affect all solver's
copy_toperformance so it might start being worth playing with@inbounds, etc...There was a problem hiding this comment.
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Given the massive amount of new complexity we're introducing, I'd rather that we merged a simplified version first, got it working and debugged, then wrote the various solvers, and only then revisited performance implications with benchmarks.
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Yes, definitely