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Rewrite all benchmarks with PkgBenchmark.jl conventions
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Lines changed: 114 additions & 161 deletions

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‎benchmark/Project.toml‎

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Original file line numberDiff line numberDiff line change
@@ -4,7 +4,6 @@ ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
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Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
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ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
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Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
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Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
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ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
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TaylorDiff = "b36ab563-344f-407b-a36a-4f200bebf99c"
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TaylorSeries = "6aa5eb33-94cf-58f4-a9d0-e4b2c4fc25ea"

‎benchmark/benchmark.jl‎

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‎benchmark/benchmarks.jl‎

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using BenchmarkTools
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using Random: seed!
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using ForwardDiff, Zygote, Flux
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using TaylorSeries: Taylor1
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using TaylorDiff
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seed!(19260817)
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include("scalar.jl")
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include("mlp.jl")
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include("taylor_expansion.jl")
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include("pinn.jl")
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scalar = create_benchmark_scalar_function(sin, 0.1)
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mlp = create_benchmark_mlp((2, 16), [2.0, 3.0], [1.0, 1.0])
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const SUITE = BenchmarkGroup("scalar" => scalar,
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"mlp" => mlp,
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"taylor_expansion" => taylor_expansion,
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"pinn" => pinn)

‎benchmark/case1.jl‎

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‎benchmark/linearmodel.jl‎

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‎benchmark/mlp.jl‎

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function create_benchmark_mlp(mlp_conf::Tuple{Int, Int}, x::Vector{T},
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l::Vector{T}) where {T <: Number}
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input, hidden = mlp_conf
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W₁, W₂, b₁, b₂ = rand(hidden, input), rand(1, hidden), rand(hidden), rand(1)
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σ = exp
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mlp(x) = first(W₂ * σ.(W₁ * x + b₁) + b₂)
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f1 = z -> ForwardDiff.derivative(t -> mlp(x + t * l), z)
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f2 = x -> ForwardDiff.derivative(f1, x)
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f3 = x -> ForwardDiff.derivative(f2, x)
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f4 = x -> ForwardDiff.derivative(f3, x)
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f5 = x -> ForwardDiff.derivative(f4, x)
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f6 = x -> ForwardDiff.derivative(f5, x)
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f7 = x -> ForwardDiff.derivative(f6, x)
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functions = Function[f1, f2, f3, f4, f5, f6, f7]
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forwarddiff, taylordiff = BenchmarkGroup(), BenchmarkGroup()
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for (index, func) in enumerate(functions)
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forwarddiff[index] = @benchmarkable $func(0)
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end
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Ns = [Val{order + 1}() for order in 1:7]
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for (index, N) in enumerate(Ns)
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taylordiff[index] = @benchmarkable derivative($mlp, $x, $l, $N)
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end
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return BenchmarkGroup("forwarddiff" => forwarddiff,
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"taylordiff" => taylordiff)
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end

‎benchmark/pinn.jl‎

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@@ -1,22 +1,13 @@
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using Flux
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using TaylorDiff
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using Zygote
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using Plots
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const input = 2
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const hidden = 16
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# model = Chain(Dense(input => hidden, exp),
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# Dense(hidden => hidden, exp),
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# Dense(hidden => 1),
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# first)
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# trial(model, x) = x[1] * (1 - x[1]) * x[2] * (1 - x[2]) * model(x)
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model = Chain(Dense(input => 1, exp), first)
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trial(model, x) = model(x)
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model = Chain(Dense(input => hidden, exp),
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Dense(hidden => hidden, exp),
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Dense(hidden => 1),
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first)
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trial(model, x) = x[1] * (1 - x[1]) * x[2] * (1 - x[2]) * model(x)
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M = 100
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data = [rand(Float32, input) for _ in 1:M]
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x = rand(Float32, input)
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function loss_by_finitediff(model, x)
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ε = cbrt(eps(Float32))
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ε₁ = [ε, 0]
@@ -28,24 +19,19 @@ function loss_by_finitediff(model, x)
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end
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function loss_by_taylordiff(model, x)
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f(x) = trial(model, x)
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error = derivative(f, x, Float32[1, 0], Val(3)) + derivative(f, x, Float32[0, 1], Val(3)) +
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error = derivative(f, x, Float32[1, 0], Val(3)) +
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derivative(f, x, Float32[0, 1], Val(3)) +
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sin(π * x[1]) * sin(π * x[2])
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abs2(error)
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end
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opt = Flux.setup(Adam(), model)
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allloss(model, loss) = sum([loss(model, x) for x in data])
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for epoch in 1:1000
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Flux.train!(loss_by_taylordiff, model, data, opt)
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end
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grid = 0:0.01:1
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solution(x, y) = (sin(π * x) * sin(π * y)) / (2π^2)
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u = [trial(model, [x, y]) for x in grid, y in grid]
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utrue = [solution(x, y) for x in grid, y in grid]
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diff_u = abs.(u .- utrue)
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surface(u)
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surface(utrue)
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surface(diff_u)
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pinn = BenchmarkGroup("primal" => BenchmarkGroup("finitediff" => (@benchmarkable loss_by_finitediff($model,
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$x)),
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"taylordiff" => (@benchmarkable loss_by_taylordiff($model,
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$x))),
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"gradient" => BenchmarkGroup("finitediff" => (@benchmarkable gradient($loss_by_finitediff,
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$model,
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$x)),
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"taylordiff" => (@benchmarkable gradient($loss_by_taylordiff,
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$model,
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$x))))

‎benchmark/scalar.jl‎

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function create_benchmark_scalar_function(f::F, x::T) where {F, T <: Number}
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f1 = x -> ForwardDiff.derivative(f, x)
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f2 = x -> ForwardDiff.derivative(f1, x)
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f3 = x -> ForwardDiff.derivative(f2, x)
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f4 = x -> ForwardDiff.derivative(f3, x)
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f5 = x -> ForwardDiff.derivative(f4, x)
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f6 = x -> ForwardDiff.derivative(f5, x)
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f7 = x -> ForwardDiff.derivative(f6, x)
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f8 = x -> ForwardDiff.derivative(f7, x)
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f9 = x -> ForwardDiff.derivative(f8, x)
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functions = Function[f1, f2, f3, f4, f5, f6, f7, f8, f9]
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forwarddiff_group = BenchmarkGroup([index => @benchmarkable $func($(Ref(x))[])
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for (index, func) in enumerate(functions)]...)
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taylordiff_group = BenchmarkGroup()
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Ns = [Val{order + 1}() for order in 1:9]
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for (index, N) in enumerate(Ns)
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taylordiff_group[index] = @benchmarkable derivative($f, $x, $N)
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end
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return BenchmarkGroup("forwarddiff" => forwarddiff_group,
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"taylordiff" => taylordiff_group)
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end

‎benchmark/taylor_expansion.jl‎

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function my_calculation(t, p, α, s)
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x = 1.0 / (1.0 - s * (t + 1) / (t - 1))
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rez = zero(x)
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for i in eachindex(p)
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rez += p[i] * x^α[i]
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end
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return rez * sqrt(2) / (1 - t)
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end
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N, m = 100, 20
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p, α, s = rand(N), rand(N), rand()
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p ./= sum(p)
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t_ts = Taylor1(eltype(p), m)
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t_td = TaylorScalar{eltype(p), m + 1}(0.0, 1.0)
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taylor_expansion = BenchmarkGroup("taylorseries" => (@benchmarkable my_calculation($t_ts,
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$p, $α,
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$s)),
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"taylordiff" => (@benchmarkable my_calculation($t_td, $p,
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$α, $s)))

‎src/chainrules.jl‎

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@@ -39,7 +39,9 @@ end
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function rrule(::typeof(value), t::TaylorScalar{T, N}) where {N, T}
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value_pullback(v̄::NTuple{N, T}) = NoTangent(), TaylorScalar(v̄)
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# for structural tangent, convert to tuple
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value_pullback(v̄::Tangent{P, NTuple{N, T}}) where P = NoTangent(), TaylorScalar{T, N}(backing(v̄))
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function value_pullback(v̄::Tangent{P, NTuple{N, T}}) where {P}
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NoTangent(), TaylorScalar{T, N}(backing(v̄))
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end
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value_pullback(v̄) = NoTangent(), TaylorScalar{T, N}(map(x -> convert(T, x), Tuple(v̄)))
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return value(t), value_pullback
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end

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