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Stop precomputing chunk sizes #708

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merged 3 commits into from
Aug 19, 2024
Merged

Stop precomputing chunk sizes #708

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f17b7c0
Stop precomputing chunk sizes
Aug 15, 2024
eb6f1bd
Update src/prelude.jl
KristofferC Aug 15, 2024
330440e
unroll the chunk picking
KristofferC Aug 15, 2024
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5 changes: 1 addition & 4 deletions src/prelude.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -7,12 +7,9 @@ const UNARY_PREDICATES = Symbol[:isinf, :isnan, :isfinite, :iseven, :isodd, :isr

struct Chunk{N} end

const CHUNKS = [Chunk{i}() for i in 1:DEFAULT_CHUNK_THRESHOLD]

function Chunk(input_length::Integer, threshold::Integer = DEFAULT_CHUNK_THRESHOLD)
N = pickchunksize(input_length, threshold)
0 < N <= DEFAULT_CHUNK_THRESHOLD && return CHUNKS[N]
return Chunk{N}()
Base.@nif 12 d->(N == d) d->(Chunk{d}()) d->(Chunk{N}())
end

function Chunk(x::AbstractArray, threshold::Integer = DEFAULT_CHUNK_THRESHOLD)
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3 changes: 3 additions & 0 deletions test/GradientTest.jl
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Expand Up @@ -136,6 +136,9 @@ end
@test DiffResults.gradient(sresult1) == DiffResults.gradient(result)
@test DiffResults.gradient(sresult2) == DiffResults.gradient(result)
@test DiffResults.gradient(sresult3) == DiffResults.gradient(result)

# make sure this is not a source of type instability
@inferred ForwardDiff.GradientConfig(f, sx)
end

@testset "exponential function at base zero" begin
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5 changes: 4 additions & 1 deletion test/JacobianTest.jl
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Expand Up @@ -222,6 +222,9 @@ for T in (StaticArrays.SArray, StaticArrays.MArray)
@test DiffResults.jacobian(sresult1) == DiffResults.jacobian(result)
@test DiffResults.jacobian(sresult2) == DiffResults.jacobian(result)
@test DiffResults.jacobian(sresult3) == DiffResults.jacobian(result)

# make sure this is not a source of type instability
@inferred ForwardDiff.JacobianConfig(f, sx)
end

#########
Expand All @@ -237,7 +240,7 @@ end
@testset "eigen" begin
@test ForwardDiff.jacobian(x -> eigvals(SymTridiagonal(x, x[1:end-1])), [1.,2.]) ≈ [(1 - 3/sqrt(5))/2 (1 - 1/sqrt(5))/2 ; (1 + 3/sqrt(5))/2 (1 + 1/sqrt(5))/2]
@test ForwardDiff.jacobian(x -> eigvals(Symmetric(x*x')), [1.,2.]) ≈ [0 0; 2 4]

x0 = [1.0, 2.0];
ev1(x) = eigen(Symmetric(x*x')).vectors[:,1]
@test ForwardDiff.jacobian(ev1, x0) ≈ Calculus.finite_difference_jacobian(ev1, x0)
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