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inelegant solution to #1821 #1934

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inelegant solution to #1821 #1934

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c8df9b7
inelegant solution to #1821
ExpandingMan Oct 1, 2024
e69d1e9
be sure to still use similar
ExpandingMan Oct 1, 2024
11429b0
Merge github.com:EnzymeAD/Enzyme.jl into dev2
ExpandingMan Oct 2, 2024
2606cd1
add unit tests
ExpandingMan Oct 2, 2024
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25 changes: 25 additions & 0 deletions src/Enzyme.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -1566,6 +1566,31 @@ end
return (one(x),)
end

function _is_symmed_index(a::CartesianIndex, b::CartesianIndex)
(a[1] == b[1] && a[2] == b[2]) || (a[1] == b[2] && a[2] == b[1])
end

@inline function _herm_sym_onehot(type, x::AbstractMatrix, start=1, endl=length(x))
idxs = CartesianIndices(x)
ntuple(Val(endl - start + 1)) do i0
Base.@_inline_meta
i = start + i0 - 1
idx = idxs[i]
res = similar(parent(x))
for idx2 ∈ CartesianIndices(x)
@inbounds res[idx2] = _is_symmed_index(idx, idx2) ? 1 : 0
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why do we need the check if symmed?

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This is checking if it's either the index idx or its transpose. In my testing enzyme would return the wrong result if I did not set both elements (as if I had not wrapped it in Symmetric even though I had). I don't understand why that is.

end
type(res)
end
end

@inline onehot(x::Hermitian) = _herm_sym_onehot(Hermitian, x)
@inline onehot(x::Symmetric) = _herm_sym_onehot(Symmetric, x)

@inline onehot(x::Hermitian, start, endl) = _herm_sym_onehot(Hermitian, x, start, endl)
@inline onehot(x::Symmetric, start, endl) = _herm_sym_onehot(Symmetric, x, start, endl)


"""
gradient(::ReverseMode, f, args...)

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17 changes: 17 additions & 0 deletions test/runtests.jl
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Expand Up @@ -3191,6 +3191,23 @@ mkarray(sz, args...) = reshape(vcat(args...), sz)
@test_broken Enzyme.gradient(Enzyme.Reverse, x -> OutStruct(x.i1 * x.i2, cos(x.i3) + x.i1, exp(x.i2)), istruct)[1]
@test_broken Enzyme.jacobian(Enzyme.Forward, x -> OutStruct(x.i1 * x.i2, cos(x.i3) + x.i1, exp(x.i2)), istruct)[1]
@test_broken Enzyme.jacobian(Enzyme.Reverse, x -> OutStruct(x.i1 * x.i2, cos(x.i3) + x.i1, exp(x.i2)), istruct)[1]

f0 = x -> x[1,1]^2 + 2*x[1,2]^2 + 3*x[2,1]^2 - x[2,2]^2
f1 = x -> [x[1,1]^2, 2*x[1,2]^2 + 3*x[2,1]^2, x[1,2]^2 + x[2,1]^2, x[2,2]^2]

for x ∈ (Float64[1 2; 0 3], Float64[1 2; 2 3]) # both are [1 2; 2 3]
for T ∈ (Hermitian, Symmetric)
x = T(x)
df = Enzyme.gradient(Enzyme.Forward, f0, x)[1]
@test df ≈ Float64[2 20; 20 -6]

df = Enzyme.gradient(Enzyme.Forward, f1, x)[1]
@test df[:,1,1] ≈ [2,0,0,0]
@test df[:,1,2] ≈ [0,20,8,0]
@test df[:,2,1] ≈ [0,20,8,0]
@test df[:,2,2] ≈ [0,0,0,6]
end
end
end

@testset "Simple Jacobian" begin
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