test from discourse, random seed

test/GradientTest.jl

@@ -175,8 +175,8 @@ end
     @test ForwardDiff.gradient(f, [0.2,25.0]) == [7875.0, 0.0]
 end
 
-# Issue 197
 @testset "det with branches" begin
+    # Issue 197
     det2(A) = return (
         A[1,1]*(A[2,2]*A[3,3]-A[2,3]*A[3,2]) -
         A[1,2]*(A[2,1]*A[3,3]-A[2,3]*A[3,1]) +
@@ -193,6 +193,12 @@ end
 
     # And issue 407
     @test ForwardDiff.hessian(det, A) ≈ ForwardDiff.hessian(det2, A)
+
+    # https://discourse.julialang.org/t/forwarddiff-and-zygote-return-wrong-jacobian-for-log-det-l/77961
+    S = [1.0 0.8; 0.8 1.0]
+    L = cholesky(S).L
+    @test ForwardDiff.gradient(L -> log(det(L)), Matrix(L)) ≈ [1.0 -1.3333333333333337; 0.0 1.666666666666667]
+    @test ForwardDiff.gradient(L -> logdet(L), Matrix(L)) ≈ [1.0 -1.3333333333333337; 0.0 1.666666666666667]
 end
 
 @testset "branches in mul!" begin

test/JacobianTest.jl

@@ -226,6 +226,10 @@ for T in (StaticArrays.SArray, StaticArrays.MArray)
     @test DiffResults.jacobian(sresult3) == DiffResults.jacobian(result)
 end
 
+#########
+# misc. #
+#########
+
 @testset "dimension errors for jacobian" begin
     @test_throws DimensionMismatch ForwardDiff.jacobian(identity, 2pi) # input
     @test_throws DimensionMismatch ForwardDiff.jacobian(sum, fill(2pi, 2)) # vector_mode_jacobian

test/runtests.jl

@@ -1,4 +1,8 @@
-using ForwardDiff, Test
+using ForwardDiff, Test, Random
+
+SEED = trunc(Int, time())
+println("Testing with Random.seed!($SEED)")
+Random.seed!(SEED)
 
 @testset "ForwardDiff.jl" begin
     t0 = time()