Daniel
effd477558
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66 lines
1.7 KiB
Julia
66 lines
1.7 KiB
Julia
using CUDA
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using .ExpressionProcessing
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using .Transpiler
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expressions = Vector{Expr}(undef, 3)
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variables = Matrix{Float32}(undef, 5, 4)
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parameters = Vector{Vector{Float32}}(undef, 3)
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expressions[1] = :(1 + 3 * 5 / 7 - sqrt(4))
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expressions[2] = :(5 + x1 + 1 * x2 + p1 + p2 + x1^x3)
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expressions[3] = :(log(x1) / x2 * sqrt(p1) + x3^x4 - exp(x5))
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variables[1,1] = 2.0
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variables[2,1] = 3.0
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variables[3,1] = 0.0
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variables[4,1] = 1.0
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variables[5,1] = 0.0
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variables[1,2] = 2.0
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variables[2,2] = 5.0
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variables[3,2] = 3.0
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variables[4,2] = 0.0
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variables[5,2] = 0.0
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variables[1,3] = 6.0
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variables[2,3] = 2.0
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variables[3,3] = 2.0
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variables[4,3] = 4.0
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variables[5,3] = 2.0
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variables[1,4] = 1.0
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variables[2,4] = 2.0
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variables[3,4] = 3.0
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variables[4,4] = 4.0
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variables[5,4] = 5.0
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parameters[1] = Vector{Float32}(undef, 0)
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parameters[2] = Vector{Float32}(undef, 2)
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parameters[3] = Vector{Float32}(undef, 1)
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parameters[2][1] = 5.0
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parameters[2][2] = 0.0
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parameters[3][1] = 16.0
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@testset "Test transpiler evaluation" begin
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results = Transpiler.evaluate(expressions, variables, parameters)
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# dump(expressions[3]; maxdepth=10)
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# Expr 1:
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@test isapprox(results[1,1], 1.14286)
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@test isapprox(results[2,1], 1.14286)
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@test isapprox(results[3,1], 1.14286)
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@test isapprox(results[4,1], 1.14286)
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#Expr 2:
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@test isapprox(results[1,2], 16.0)
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@test isapprox(results[2,2], 25.0)
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@test isapprox(results[3,2], 54.0)
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@test isapprox(results[4,2], 14.0)
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#Expr3:
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@test isapprox(results[1,3], -0.07580)
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@test isapprox(results[2,3], 0.55452)
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@test isapprox(results[3,3], 12.19446)
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@test isapprox(results[4,3], -67.41316)
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end
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# TODO: test performance of transpiler PTX generation when doing "return String(take!(buffer))" vs "return take!(buffer)" |