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remove scalar->vector gradients #160

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remove scalar->vector gradients #160

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60 changes: 0 additions & 60 deletions src/gradients.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -352,63 +352,3 @@ function finite_difference_gradient!(
end
df
end

# vector of derivatives of a scalar->vector map
# this is effectively a vector of partial derivatives, but we still call it a gradient
function finite_difference_gradient!(
df,
f,
x::Number,
cache::GradientCache{T1,T2,T3,T4,fdtype,returntype,inplace};
relstep=default_relstep(fdtype, eltype(x)),
absstep=relstep,
dir=true) where {T1,T2,T3,T4,fdtype,returntype,inplace}

# NOTE: in this case epsilon is a scalar, we need two arrays for fx1 and fx2
# c1 denotes fx1, c2 is fx2, sizes guaranteed by the cache constructor
fx, c1, c2 = cache.fx, cache.c1, cache.c2

if inplace == Val(true)
_c1, _c2 = c1, c2
end

if fdtype == Val(:forward)
epsilon = compute_epsilon(Val(:forward), x, relstep, absstep, dir)
if inplace == Val(true)
f(c1, x + epsilon)
else
_c1 = f(x + epsilon)
end
if typeof(fx) != Nothing
@. df = (_c1 - fx) / epsilon
else
if inplace == Val(true)
f(c2, x)
else
_c2 = f(x)
end
@. df = (_c1 - _c2) / epsilon
end
elseif fdtype == Val(:central)
epsilon = compute_epsilon(Val(:central), x, relstep, absstep, dir)
if inplace == Val(true)
f(c1, x + epsilon)
f(c2, x - epsilon)
else
_c1 = f(x + epsilon)
_c2 = f(x - epsilon)
end
@. df = (_c1 - _c2) / (2 * epsilon)
elseif fdtype == Val(:complex) && returntype <: Real
epsilon_complex = eps(real(eltype(x)))
if inplace == Val(true)
f(c1, x + im * epsilon_complex)
else
_c1 = f(x + im * epsilon_complex)
end
@. df = imag(_c1) / epsilon_complex
else
fdtype_error(returntype)
end
df
end
49 changes: 0 additions & 49 deletions test/finitedifftests.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -261,55 +261,6 @@ central_cache = FiniteDiff.GradientCache(df, x, Val{:central}, eltype(df))
@test err_func(FiniteDiff.finite_difference_gradient!(df, f, x, central_cache), df_ref) < 1e-8
end

f(df, x) = (df[1] = sin(x); df[2] = cos(x); df)
z = x = 2π * rand()
fx = fill(0.0, 2)
f(fx, x)
ff(df, x) = !all(x .<= z) ? error() : f(df, x)
df = fill(0.0, 2)
df_ref = [cos(x), -sin(x)]
forward_cache = FiniteDiff.GradientCache(df, x, Val{:forward})
central_cache = FiniteDiff.GradientCache(df, x, Val{:central})
complex_cache = FiniteDiff.GradientCache(df, x, Val{:complex})


@time @testset "Gradient of f:scalar->vector real-valued tests" begin
@test_broken err_func(FiniteDiff.finite_difference_gradient(f, x, Val{:forward}), df_ref) < 1e-4
@test err_func(FiniteDiff.finite_difference_gradient(f, x, Val{:forward}, eltype(x), Val{true}, fx), df_ref) < 1e-4
@test err_func(FiniteDiff.finite_difference_gradient(ff, x, Val{:forward}, eltype(x), Val{true}, fx, dir=-1), df_ref) < 1e-4
@test_throws Any err_func(FiniteDiff.finite_difference_gradient(ff, x, Val{:forward}), df_ref) < 1e-4
@test err_func(FiniteDiff.finite_difference_gradient(f, x, Val{:central}, eltype(x), Val{true}, fx), df_ref) < 1e-8
@test err_func(FiniteDiff.finite_difference_gradient(f, x, Val{:complex}, eltype(x), Val{true}, fx), df_ref) < 1e-15

@test err_func(FiniteDiff.finite_difference_gradient!(df, f, x, Val{:forward}), df_ref) < 1e-4
@test err_func(FiniteDiff.finite_difference_gradient!(df, f, x, Val{:central}), df_ref) < 1e-8
@test err_func(FiniteDiff.finite_difference_gradient!(df, f, x, Val{:complex}), df_ref) < 1e-15

@test err_func(FiniteDiff.finite_difference_gradient!(df, f, x, forward_cache), df_ref) < 1e-4
@test err_func(FiniteDiff.finite_difference_gradient!(df, f, x, central_cache), df_ref) < 1e-8
@test err_func(FiniteDiff.finite_difference_gradient!(df, f, x, complex_cache), df_ref) < 1e-15
end

f(df, x) = (df[1] = sin(x); df[2] = cos(x); df)
x = (2π * rand()) * (1 + im)
fx = fill(zero(typeof(x)), 2)
f(fx, x)
df = zero(fx)
df_ref = [cos(x), -sin(x)]
forward_cache = FiniteDiff.GradientCache(df, x, Val{:forward})
central_cache = FiniteDiff.GradientCache(df, x, Val{:central})

@time @testset "Gradient of f:vector->scalar complex-valued tests" begin
@test err_func(FiniteDiff.finite_difference_gradient(f, x, Val{:forward}, eltype(x), Val{true}, fx), df_ref) < 1e-4
@test err_func(FiniteDiff.finite_difference_gradient(f, x, Val{:central}, eltype(x), Val{true}, fx), df_ref) < 3e-7

@test err_func(FiniteDiff.finite_difference_gradient!(df, f, x, Val{:forward}), df_ref) < 1e-4
@test err_func(FiniteDiff.finite_difference_gradient!(df, f, x, Val{:central}), df_ref) < 3e-7

@test err_func(FiniteDiff.finite_difference_gradient!(df, f, x, forward_cache), df_ref) < 1e-4
@test err_func(FiniteDiff.finite_difference_gradient!(df, f, x, central_cache), df_ref) < 3e-7
end

function ret_allocs(res, _f, x, cache)
allocs = @allocated FiniteDiff.finite_difference_gradient!(res, _f, x, cache)
allocs
Expand Down