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Remove unusued lambda in test file #551

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Merged
devmotion merged 4 commits into from
Oct 26, 2026
+18 −8
Merged

Remove unusued lambda in test file #551

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47723f3
Remove unusued lambda in test file
oxinabox Dec 7, 2021
67ee5ea
Merge branch 'main' into ox/deadvar1
devmotion Oct 25, 2026
9bc167e
Keep matrix function test inputs in the domain under finite differencing
devmotion Oct 26, 2026
d6f43fa
Format code
devmotion Oct 26, 2026
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26 changes: 18 additions & 8 deletions test/rulesets/LinearAlgebra/symmetric.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -283,9 +283,10 @@ using ChainRulesTestUtils: _fdm
U = Matrix(qr(randn(T, n, n)).Q)
if hermout # f(A) will also be a TA
λ = if f in (acos, asin, atanh)
# generate random number between -1 and 0.9. This would be between
# generate random number between -0.9 and 0.9. This would be between
# -1 and 1 but we want to avoid domain errors from numerical error
T(1.9) .* rand(real(T), n) .- 1
# and finite differencing (see `rand_matfun_tangent`)
1.8 .* rand(real(T), n) .- 0.9
elseif f in (log, sqrt)
abs.(randn(real(T), n))
elseif f === acosh
Expand All @@ -294,16 +295,24 @@ using ChainRulesTestUtils: _fdm
randn(real(T), n)
end
else
λ = randn(real(T), n)
λ = if f === atanh
2 .* rand(real(T), n) .- 1
# see above
1.8 .* rand(real(T), n) .- 0.9
else
randn(real(T), n)
end
end
return TA(U * Diagonal(λ) * U', uplo)
end

# generate random tangents of type TA with unit spectral norm
# `_fdm` evaluates `f(A + ε * ΔA)` with `|ε| ≤ max_range = 1e-2`, so by Weyl's
# inequality the eigenvalues of `A` move by at most 0.01 and stay in the domain of `f`
function rand_matfun_tangent(TA, T, n, uplo=:U)
ΔA = TA(randn(T, n, n), uplo)
return TA(parent(ΔA) ./ opnorm(ΔA), uplo)
end

# Adapted From ChainRulesTestUtils._is_inferable
function is_inferable(f, A)
try
Expand All @@ -326,7 +335,8 @@ using ChainRulesTestUtils: _fdm
n = 10
@testset "frule" begin
@testset for uplo in (:L, :U), hermout in (true, false)
A, ΔA = rand_matfun_input(f, TA, T, uplo, n, hermout), TA(randn(T, n, n), uplo)
A, ΔA = rand_matfun_input(f, TA, T, uplo, n, hermout),
rand_matfun_tangent(TA, T, n, uplo)
Y = f(A)
if is_inferable(f, A)
Y_ad, ∂Y_ad = @maybe_inferred frule((ZeroTangent(), ΔA), f, A)
Expand All @@ -351,13 +361,13 @@ using ChainRulesTestUtils: _fdm
m = div(n, 2)
λ[1:m] .= λ[m+1:2m] .+ cbrt(eps(eltype(λ))) / 100
A = TA(U * Diagonal(λ) * U')
ΔA = TA(randn(T, n, n))
ΔA = rand_matfun_tangent(TA, T, n)
_, ∂Y = frule((ZeroTangent(), ΔA), f, A)
@test parent(∂Y) ≈ jvp(_fdm, x -> Matrix{TC}(parent(f(TA(x)))), (A.data, ΔA.data))

λ[1:m] .= λ[m+1:2m]
A2 = TA(U * Diagonal(λ) * U')
ΔA2 = TA(randn(T, n, n))
ΔA2 = rand_matfun_tangent(TA, T, n)
_, ∂Y2 = frule((ZeroTangent(), ΔA2), f, A2)
@test parent(∂Y2) ≈ jvp(_fdm, x -> Matrix{TC}(parent(f(TA(x)))), (A2.data, ΔA2.data))
end
Expand All @@ -366,7 +376,7 @@ using ChainRulesTestUtils: _fdm
λ, U = eigen(rand_matfun_input(f, TA, T, :U, n, true))
λ[2:n] .= 0
A = TA(U * Diagonal(λ) * U')
ΔA = TA(randn(T, n, n))
ΔA = rand_matfun_tangent(TA, T, n)
_, ∂Y = frule((ZeroTangent(), ΔA), f, A)
@test parent(∂Y) ≈ jvp(_fdm, x -> Matrix{TC}(parent(f(TA(x)))), (A.data, ΔA.data))
end
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