diff --git a/.buildkite/pipeline.yml b/.buildkite/pipeline.yml
index 1a9f70d04c..98b8facf86 100644
--- a/.buildkite/pipeline.yml
+++ b/.buildkite/pipeline.yml
@@ -25,55 +25,55 @@ steps:
     env:
       JULIA_PKG_SERVER_REGISTRY_PREFERENCE: eager
 
-  - label: "AMDGPU Julia v{{matrix.version}}"
-    matrix:
-      setup:
-        version:
-          - "1.10"
-    plugins:
-      - JuliaCI/julia#v1:
-          version: "{{matrix.version}}"
-    agents:
-      queue: "juliagpu"
-      rocm: "*"
-    if: build.message !~ /\[skip tests\]/
-    timeout_in_minutes: 60
-    commands: |
-      echo "--- Setup Julia packages"
-      julia --color=yes -e '
-          using Pkg
-          pkgs = [PackageSpec(; path) for path in (".", "lib/EnzymeCore", "lib/EnzymeTestUtils")]
-          push!(pkgs, PackageSpec(; name="AMDGPU"))
-          Pkg.develop(pkgs)' || exit 3
-
-      echo "+++ Run tests"
-      julia --color=yes test/amdgpu.jl
-    env:
-      JULIA_PKG_SERVER_REGISTRY_PREFERENCE: eager
-
-  - label: "Metal Julia v{{matrix.version}}"
-    matrix:
-      setup:
-        version:
-          - "1.10"
-    plugins:
-      - JuliaCI/julia#v1:
-          version: "{{matrix.version}}"
-    agents:
-      queue: "juliaecosystem"
-      os: "macos"
-      arch: "aarch64"
-    if: build.message !~ /\[skip tests\]/
-    timeout_in_minutes: 60
-    commands: |
-      echo "--- Setup Julia packages"
-      julia --color=yes -e '
-          using Pkg
-          pkgs = [PackageSpec(; path) for path in (".", "lib/EnzymeCore", "lib/EnzymeTestUtils")]
-          push!(pkgs, PackageSpec(; name="Metal"))
-          Pkg.develop(pkgs)' || exit 3
-
-      echo "+++ Run tests"
-      julia --color=yes test/metal.jl
-    env:
-      JULIA_PKG_SERVER_REGISTRY_PREFERENCE: eager
+#   - label: "AMDGPU Julia v{{matrix.version}}"
+#     matrix:
+#       setup:
+#         version:
+#           - "1.10"
+#     plugins:
+#       - JuliaCI/julia#v1:
+#           version: "{{matrix.version}}"
+#     agents:
+#       queue: "juliagpu"
+#       rocm: "*"
+#     if: build.message !~ /\[skip tests\]/
+#     timeout_in_minutes: 60
+#     commands: |
+#       echo "--- Setup Julia packages"
+#       julia --color=yes -e '
+#           using Pkg
+#           pkgs = [PackageSpec(; path) for path in (".", "lib/EnzymeCore", "lib/EnzymeTestUtils")]
+#           push!(pkgs, PackageSpec(; name="AMDGPU"))
+#           Pkg.develop(pkgs)' || exit 3
+# 
+#       echo "+++ Run tests"
+#       julia --color=yes test/amdgpu.jl
+#     env:
+#       JULIA_PKG_SERVER_REGISTRY_PREFERENCE: eager
+# 
+#   - label: "Metal Julia v{{matrix.version}}"
+#     matrix:
+#       setup:
+#         version:
+#           - "1.10"
+#     plugins:
+#       - JuliaCI/julia#v1:
+#           version: "{{matrix.version}}"
+#     agents:
+#       queue: "juliaecosystem"
+#       os: "macos"
+#       arch: "aarch64"
+#     if: build.message !~ /\[skip tests\]/
+#     timeout_in_minutes: 60
+#     commands: |
+#       echo "--- Setup Julia packages"
+#       julia --color=yes -e '
+#           using Pkg
+#           pkgs = [PackageSpec(; path) for path in (".", "lib/EnzymeCore", "lib/EnzymeTestUtils")]
+#           push!(pkgs, PackageSpec(; name="Metal"))
+#           Pkg.develop(pkgs)' || exit 3
+# 
+#       echo "+++ Run tests"
+#       julia --color=yes test/metal.jl
+#     env:
+#       JULIA_PKG_SERVER_REGISTRY_PREFERENCE: eager
diff --git a/test/internal_rules.jl b/test/internal_rules.jl
index fb5926a1d3..67ec233982 100644
--- a/test/internal_rules.jl
+++ b/test/internal_rules.jl
@@ -1,4 +1,3 @@
-module InternalRules
 
 using Enzyme
 using Enzyme.EnzymeRules
@@ -7,753 +6,18 @@ using SparseArrays
 using Test
 import Random
 
-struct TPair
-    a::Float64
-    b::Float64
-end
-
-function sorterrfn(t, x)
-    function lt(a, b)
-        return a.a < b.a
-    end
-    return first(sortperm(t, lt=lt)) * x
-end
-
-@testset "Sort rules" begin
-    function f1(x)
-        a = [1.0, 3.0, x]
-        sort!(a)
-        return a[2]
-    end
-
-    @test autodiff(Forward, f1, Duplicated(2.0, 1.0))[1] == 1
-    @test autodiff(Forward, f1, BatchDuplicated(2.0, (1.0, 2.0)))[1] == (var"1"=1.0, var"2"=2.0)
-    @test autodiff(Reverse, f1, Active, Active(2.0))[1][1] == 1
-    @test autodiff(Forward, f1, Duplicated(4.0, 1.0))[1] == 0
-    @test autodiff(Forward, f1, BatchDuplicated(4.0, (1.0, 2.0)))[1] == (var"1"=0.0, var"2"=0.0)
-    @test autodiff(Reverse, f1, Active, Active(4.0))[1][1] == 0
-
-    function f2(x)
-        a = [1.0, -3.0, -x, -2x, x]
-        sort!(a; rev=true, lt=(x, y) -> abs(x) < abs(y) || (abs(x) == abs(y) && x < y))
-        return sum(a .* [1, 2, 3, 4, 5])
-    end
-
-    @test autodiff(Forward, f2, Duplicated(2.0, 1.0))[1] == -3
-    @test autodiff(Forward, f2, BatchDuplicated(2.0, (1.0, 2.0)))[1] == (var"1"=-3.0, var"2"=-6.0)
-    @test autodiff(Reverse, f2, Active, Active(2.0))[1][1] == -3
-
-    function f3(x)
-        a = [2.0, 2.5, x, 1.0]
-        return partialsort(a, 2)
-    end
-
-    @test autodiff(Forward, f3, Duplicated(1.5, 1.0))[1] == 1.0
-    @test autodiff(Forward, f3, BatchDuplicated(1.5, (1.0, 2.0)))[1] == (var"1"=1.0, var"2"=2.0)
-    @test autodiff(Reverse, f3, Active(1.5))[1][1] == 1.0
-    @test autodiff(Reverse, f3, Active(2.5))[1][1] == 0.0
-
-    function f4(x)
-        a = [2.0, 2.5, x, x / 2]
-        y = partialsort(a, 1:2)
-        return sum(y)
-    end
-
-    @test autodiff(Forward, f4, Duplicated(1.5, 1.0))[1] == 1.5
-    @test autodiff(Forward, f4, BatchDuplicated(1.5, (1.0, 2.0)))[1] == (var"1"=1.5, var"2"=3.0)
-    @test autodiff(Reverse, f4, Active(1.5))[1][1] == 1.5
-    @test autodiff(Reverse, f4, Active(4.0))[1][1] == 0.5
-    @test autodiff(Reverse, f4, Active(6.0))[1][1] == 0.0
-
-    dd = Duplicated([TPair(1, 2), TPair(2, 3), TPair(0, 1)], [TPair(0, 0), TPair(0, 0), TPair(0, 0)])
-    res = Enzyme.autodiff(Reverse, sorterrfn, dd, Active(1.0))
-
-    @test res[1][2] ≈ 3
-    @test dd.dval[1].a ≈ 0
-    @test dd.dval[1].b ≈ 0
-    @test dd.dval[2].a ≈ 0
-    @test dd.dval[2].b ≈ 0
-    @test dd.dval[3].a ≈ 0
-    @test dd.dval[3].b ≈ 0
-end
-
-@testset "Linear Solve" begin
-    A = Float64[2 3; 5 7]
-    dA = zero(A)
-    b = Float64[11, 13]
-    db = zero(b)
-
-    forward, pullback = Enzyme.autodiff_thunk(ReverseSplitNoPrimal, Const{typeof(\)}, Duplicated, Duplicated{typeof(A)}, Duplicated{typeof(b)})
-
-    tape, primal, shadow = forward(Const(\), Duplicated(A, dA), Duplicated(b, db))
-
-    dy = Float64[17, 19]
-    copyto!(shadow, dy)
-
-    pullback(Const(\), Duplicated(A, dA), Duplicated(b, db), tape)
-
-    z = transpose(A) \ dy
-
-    y = A \ b
-    @test dA ≈ (-z * transpose(y))
-    @test db ≈ z
-
-    db = zero(b)
-
-    forward, pullback = Enzyme.autodiff_thunk(ReverseSplitNoPrimal, Const{typeof(\)}, Duplicated, Const{typeof(A)}, Duplicated{typeof(b)})
-
-    tape, primal, shadow = forward(Const(\), Const(A), Duplicated(b, db))
-
-    dy = Float64[17, 19]
-    copyto!(shadow, dy)
-
-    pullback(Const(\), Const(A), Duplicated(b, db), tape)
-
-    z = transpose(A) \ dy
-
-    y = A \ b
-    @test db ≈ z
-
-    dA = zero(A)
-
-    forward, pullback = Enzyme.autodiff_thunk(ReverseSplitNoPrimal, Const{typeof(\)}, Duplicated, Duplicated{typeof(A)}, Const{typeof(b)})
-
-    tape, primal, shadow = forward(Const(\), Duplicated(A, dA), Const(b))
-
-    dy = Float64[17, 19]
-    copyto!(shadow, dy)
-
-    pullback(Const(\), Duplicated(A, dA), Const(b), tape)
-
-    z = transpose(A) \ dy
-
-    y = A \ b
-    @test dA ≈ (-z * transpose(y))
-
-    # Ensure multi dim doesn't crash
-    function test2!(A)
-        A .= A \ [1.0 0;0.0 1.0]
-        return nothing
-    end
-
-    A = rand(2,2)
-    dA = [1.0 0.0; 0.0 0.0]
-
-    Enzyme.autodiff(
-        Enzyme.Reverse,
-        test2!,
-        Enzyme.Duplicated(A,dA),
-    )
-end
-
-function tr_solv(A, B, uplo, trans, diag, idx)
-  B = copy(B)
-  LAPACK.trtrs!(uplo, trans, diag, A, B)
-  return @inbounds B[idx]
-end
-
-
-using FiniteDifferences
-@testset "Reverse triangular solve" begin
-	A = [0.7550523937508613 0.7979976952197996 0.29318222271218364; 0.4416768066117529 0.4335305304334933 0.8895389673238051; 0.07752980210005678 0.05978245503334367 0.4504482683752542]
-	B = [0.10527381151977078 0.5450388247476627 0.3179106723232359 0.43919576779182357 0.20974326586875847; 0.7551160501548224 0.049772782182839426 0.09284926395551141 0.07862188927391855 0.17346407477062986; 0.6258040138863172 0.5928022963567454 0.24251650865340169 0.6626410383247967 0.32752198021506784]
-    for idx in 1:15
-    for uplo in ('L', 'U')
-    for diag in ('N', 'U')
-    for trans in ('N', 'T')
-        dA = zero(A)
-        dB = zero(B)	
-        Enzyme.autodiff(Reverse, tr_solv, Duplicated(A, dA), Duplicated(B, dB), Const(uplo),Const(trans), Const(diag), Const(idx))
-        fA = FiniteDifferences.grad(central_fdm(5, 1), A->tr_solv(A, B, uplo, trans, diag, idx), A)[1]
-        fB = FiniteDifferences.grad(central_fdm(5, 1), B->tr_solv(A, B, uplo, trans, diag, idx), B)[1]
-
-		if max(abs.(dA)...) >= 1e-10 || max(abs.(fA)...) >= 1e-10
-			@test dA ≈ fA
-		end
-		if max(abs.(dB)...) >= 1e-10 || max(abs.(fB)...) >= 1e-10
-			@test dB ≈ fB
-		end
-    end
-    end
-    end
-    end
-end
+Enzyme.Compiler.DumpPostOpt[] = true
 
 function chol_lower0(x)
   c = copy(x)
   C, info = LinearAlgebra.LAPACK.potrf!('L', c)
-  return c[2,1]
-end
-
-function chol_upper0(x)
-  c = copy(x)
-  C, info = LinearAlgebra.LAPACK.potrf!('U', c)
-  return c[1,2]
-end
-
-@testset "Cholesky PotRF" begin
-    x = reshape([1.0, -0.10541615131279458, 0.6219810761363638, 0.293343219811946, -0.10541615131279458, 1.0, -0.05258941747718969, 0.34629296878264443, 0.6219810761363638, -0.05258941747718969, 1.0, 0.4692436399208845, 0.293343219811946, 0.34629296878264443, 0.4692436399208845, 1.0], 4, 4)
-     dL = zero(x)
-     dL[2, 1] = 1.0
- 
-     @test Enzyme.gradient(Reverse, chol_lower0, x)[1] ≈  [0.05270807565639164 0.0 0.0 0.0; 1.0000000000000024 0.0 0.0 0.0; 0.0 0.0 0.0 0.0; 0.0 0.0 0.0 0.0] 
-     
-     @test Enzyme.gradient(Forward, chol_lower0, x)[1] ≈  [0.05270807565639164 0.0 0.0 0.0; 1.0000000000000024 0.0 0.0 0.0; 0.0 0.0 0.0 0.0; 0.0 0.0 0.0 0.0] 
-
-     @test FiniteDifferences.grad(central_fdm(5, 1), chol_lower0, x)[1] ≈ [0.05270807565639164 0.0 0.0 0.0; 1.0000000000000024 0.0 0.0 0.0; 0.0 0.0 0.0 0.0; 0.0 0.0 0.0 0.0]
-     
-     @test Enzyme.gradient(Forward, chol_upper0, x)[1] ≈ [0.05270807565639728 0.9999999999999999 0.0 0.0; 0.0 0.0 0.0 0.0; 0.0 0.0 0.0 0.0; 0.0 0.0 0.0 0.0]
-     @test Enzyme.gradient(Reverse, chol_upper0, x)[1] ≈ [0.05270807565639728 0.9999999999999999 0.0 0.0; 0.0 0.0 0.0 0.0; 0.0 0.0 0.0 0.0; 0.0 0.0 0.0 0.0]
-     @test FiniteDifferences.grad(central_fdm(5, 1), chol_upper0, x)[1] ≈ [0.05270807565639728 0.9999999999999999 0.0 0.0; 0.0 0.0 0.0 0.0; 0.0 0.0 0.0 0.0; 0.0 0.0 0.0 0.0]
-end
-
-
-function tchol_lower(x, row, col)
-    c = copy(x)
-    C, info = LinearAlgebra.LAPACK.potrf!('L', c)
-    return c[row, col]
-end
-function tchol_upper(x, row, col)
-    c = copy(x)
-    C, info = LinearAlgebra.LAPACK.potrf!('U', c)
-    return c[row, col]
-end
-
-@testset "Cholesky PotRF 3x3" begin
-
-    x = [1.0 0.13147601759884564 0.5282944836504488; 0.13147601759884564 1.0 0.18506733179093515; 0.5282944836504488 0.18506733179093515 1.0]
-    for i in 1:size(x, 1)
-        for j in 1:size(x, 2)
-             reverse_grad  = Enzyme.gradient(Reverse, x -> tchol_lower(x, i, j), x)[1]
-             forward_grad  = Enzyme.gradient(Forward, x -> tchol_lower(x, i, j), x)[1]
-             finite_diff = FiniteDifferences.grad(central_fdm(5, 1), x -> tchol_lower(x, i, j), x)[1]
-             @test reverse_grad  ≈ finite_diff 
-             @test forward_grad  ≈ finite_diff 
-             
-             reverse_grad  = Enzyme.gradient(Reverse, x -> tchol_upper(x, i, j), x)[1]
-             forward_grad  = Enzyme.gradient(Forward, x -> tchol_upper(x, i, j), x)[1]
-             finite_diff = FiniteDifferences.grad(central_fdm(5, 1), x -> tchol_upper(x, i, j), x)[1]
-             @test reverse_grad  ≈ finite_diff 
-             @test forward_grad  ≈ finite_diff
-        end
-    end
-end
-
-function tcholsolv_lower(A, B, i)
-    c = copy(B)
-    C, info = LinearAlgebra.LAPACK.potrs!('L', A, c)
-    return c[i]
-end
-function tcholsolv_upper(A, B, i)
-    c = copy(B)
-    C, info = LinearAlgebra.LAPACK.potrs!('U', A, c)
-    return c[i]
-end
-
-
-@testset "Cholesky PotRS 3x5" begin
-
-    x = [1.0 0.13147601759884564 0.5282944836504488; 0.13147601759884564 1.0 0.18506733179093515; 0.5282944836504488 0.18506733179093515 1.0]
-    for i in 1:15
-         B = [3.1 2.7 5.9 2.4 1.6; 7.9 8.2 1.3 9.4 5.5; 4.7 2.9 9.8 7.1 4.3]
-         reverse_grad  = Enzyme.gradient(Reverse, Const(B -> tcholsolv_lower(x, B, i)), B)[1]
-         # forward_grad  = Enzyme.gradient(Forward, B -> tcholsolv_lower(x, B, i), B)[1]
-         finite_diff = FiniteDifferences.grad(central_fdm(5, 1), B -> tcholsolv_lower(x, B, i), B)[1]
-         @test reverse_grad  ≈ finite_diff 
-         # @test forward_grad  ≈ finite_diff 
-         
-         reverse_grad  = Enzyme.gradient(Reverse, Const(B -> tcholsolv_upper(x, B, i)), B)[1]
-         # forward_grad  = Enzyme.gradient(Forward, B -> tcholsolv_upper(x, B, i), B))[1]
-         finite_diff = FiniteDifferences.grad(central_fdm(5, 1), B -> tcholsolv_upper(x, B, i), B)[1]
-         @test reverse_grad  ≈ finite_diff 
-         # @test forward_grad  ≈ finite_diff
-
-         reverse_grad  = Enzyme.gradient(Reverse, Const(x -> tcholsolv_lower(x, B, i)), x)[1]
-         #forward_grad  = Enzyme.gradient(Forward, x -> tcholsolv_lower(x, B, i), x)[1]
-         finite_diff = FiniteDifferences.grad(central_fdm(5, 1), x -> tcholsolv_lower(x, B, i), x)[1]
-         @test reverse_grad  ≈ finite_diff 
-         #@test forward_grad  ≈ finite_diff 
-         # 
-         reverse_grad  = Enzyme.gradient(Reverse, Const(x -> tcholsolv_upper(x, B, i)), x)[1]
-         #forward_grad  = Enzyme.gradient(Forward, x -> tcholsolv_upper(x, B, i), x)[1]
-         finite_diff = FiniteDifferences.grad(central_fdm(5, 1), x -> tcholsolv_upper(x, B, i), x)[1]
-         @test reverse_grad  ≈ finite_diff 
-         #@test forward_grad  ≈ finite_diff
-    end
-end
-
-@testset "Cholesky" begin
-    function symmetric_definite(n :: Int=10)
-        α = one(Float64)
-        A = spdiagm(-1 => α * ones(n-1), 0 => 4 * ones(n), 1 => conj(α) * ones(n-1))
-        b = A * Float64[1:n;]
-        return A, b
-    end
-
-    function divdriver_NC(x, fact, b)
-        res = fact\b
-        x .= res
-        return nothing
-    end
-    
-    function ldivdriver_NC(x, fact, b)
-        ldiv!(fact,b)
-        x .= b
-        return nothing
-    end
-
-    function divdriver(x, A, b)
-        fact = cholesky(A)
-        divdriver_NC(x, fact, b)
-    end
-
-    function divdriver_herm(x, A, b)
-        fact = cholesky(Hermitian(A))
-        divdriver_NC(x, fact, b)
-    end
-
-    function divdriver_sym(x, A, b)
-        fact = cholesky(Symmetric(A))
-        divdriver_NC(x, fact, b)
-    end
-    
-    function ldivdriver(x, A, b)
-        fact = cholesky(A)
-        ldivdriver_NC(x, fact, b)
-    end
-
-    function ldivdriver_herm(x, A, b)
-        fact = cholesky(Hermitian(A))
-        ldivdriver_NC(x, fact, b)
-    end
-
-    function ldivdriver_sym(x, A, b)
-        fact = cholesky(Symmetric(A))
-        ldivdriver_NC(x, fact, b)
-    end
-
-    # Test forward
-    function fwdJdxdb(driver, A, b)
-        adJ = zeros(size(A))
-        dA = Duplicated(A, zeros(size(A)))
-        db = Duplicated(b, zeros(length(b)))
-        dx = Duplicated(zeros(length(b)), zeros(length(b)))
-        for i in 1:length(b)
-            copyto!(dA.val, A)
-            copyto!(db.val, b)
-            fill!(dA.dval, 0.0)
-            fill!(db.dval, 0.0)
-            fill!(dx.dval, 0.0)
-            db.dval[i] = 1.0
-            Enzyme.autodiff(
-                Forward,
-                driver,
-                dx,
-                dA,
-                db
-            )
-            adJ[i, :] = dx.dval
-        end
-        return adJ
-    end
-
-    function const_fwdJdxdb(driver, A, b)
-        adJ = zeros(length(b), length(b))
-        db = Duplicated(b, zeros(length(b)))
-        dx = Duplicated(zeros(length(b)), zeros(length(b)))
-        for i in 1:length(b)
-            copyto!(db.val, b)
-            fill!(db.dval, 0.0)
-            fill!(dx.dval, 0.0)
-            db.dval[i] = 1.0
-            Enzyme.autodiff(
-                Forward,
-                driver,
-                dx,
-                Const(A),
-                db
-            )
-            adJ[i, :] = dx.dval
-        end
-        return adJ
-    end
-
-    function batchedfwdJdxdb(driver, A, b)
-        n = length(b)
-        function seed(i)
-            x = zeros(n)
-            x[i] = 1.0
-            return x
-        end
-        adJ = zeros(size(A))
-        dA = BatchDuplicated(A, ntuple(i -> zeros(size(A)), n))
-        db = BatchDuplicated(b, ntuple(i -> seed(i), n))
-        dx = BatchDuplicated(zeros(length(b)), ntuple(i -> zeros(length(b)), n))
-        Enzyme.autodiff(
-            Forward,
-            driver,
-            dx,
-            dA,
-            db
-        )
-        for i in 1:n
-            adJ[i, :] = dx.dval[i]
-        end
-        return adJ
-    end
-
-    # Test reverse
-    function revJdxdb(driver, A, b)
-        adJ = zeros(size(A))
-        dA = Duplicated(A, zeros(size(A)))
-        db = Duplicated(b, zeros(length(b)))
-        dx = Duplicated(zeros(length(b)), zeros(length(b)))
-        for i in 1:length(b)
-            copyto!(dA.val, A)
-            copyto!(db.val, b)
-            fill!(dA.dval, 0.0)
-            fill!(db.dval, 0.0)
-            fill!(dx.dval, 0.0)
-            dx.dval[i] = 1.0
-            Enzyme.autodiff(
-                Reverse,
-                driver,
-                dx,
-                dA,
-                db
-            )
-            adJ[i, :] = db.dval
-        end
-        return adJ
-    end
-
-    function const_revJdxdb(driver, A, b)
-        adJ = zeros(length(b), length(b))
-        db = Duplicated(b, zeros(length(b)))
-        dx = Duplicated(zeros(length(b)), zeros(length(b)))
-        for i in 1:length(b)
-            copyto!(db.val, b)
-            fill!(db.dval, 0.0)
-            fill!(dx.dval, 0.0)
-            dx.dval[i] = 1.0
-            Enzyme.autodiff(
-                Reverse,
-                driver,
-                dx,
-                Const(A),
-                db
-            )
-            adJ[i, :] = db.dval
-        end
-        return adJ
-    end
-
-    function batchedrevJdxdb(driver, A, b)
-        n = length(b)
-        function seed(i)
-            x = zeros(n)
-            x[i] = 1.0
-            return x
-        end
-        adJ = zeros(size(A))
-        dA = BatchDuplicated(A, ntuple(i -> zeros(size(A)), n))
-        db = BatchDuplicated(b, ntuple(i -> zeros(length(b)), n))
-        dx = BatchDuplicated(zeros(length(b)), ntuple(i -> seed(i), n))
-            Enzyme.autodiff(
-                Reverse,
-                driver,
-                dx,
-                dA,
-                db
-            )
-        for i in 1:n
-            adJ[i, :] .= db.dval[i]
-        end
-        return adJ
-    end
-
-    function Jdxdb(driver, A, b)
-        x = A\b
-        dA = zeros(size(A))
-        db = zeros(length(b))
-        J = zeros(length(b), length(b))
-        for i in 1:length(b)
-            db[i] = 1.0
-            dx = A\db
-            db[i] = 0.0
-            J[i, :] = dx
-        end
-        return J
-    end
-
-    function JdxdA(driver, A, b)
-        db = zeros(length(b))
-        J = zeros(length(b), length(b))
-        for i in 1:length(b)
-            db[i] = 1.0
-            dx = A\db
-            db[i] = 0.0
-            J[i, :] = dx
-        end
-        return J
-    end
-    
-    @testset "Testing $op" for (op, driver, driver_NC) in (
-        (:\, divdriver, divdriver_NC),
-        (:\, divdriver_herm, divdriver_NC),
-        (:\, divdriver_sym, divdriver_NC),
-        (:ldiv!, ldivdriver, ldivdriver_NC),
-        (:ldiv!, ldivdriver_herm, ldivdriver_NC),
-        (:ldiv!, ldivdriver_sym, ldivdriver_NC)
-    )
-        A, b = symmetric_definite(10)
-        n = length(b)
-        A = Matrix(A)
-        x = zeros(n)
-        x = driver(x, A, b)
-        fdm = forward_fdm(2, 1);
-
-        function b_one(b)
-            _x = zeros(length(b))
-            driver(_x,A,b)
-            return _x
-        end
-
-        fdJ = op==:\ ? FiniteDifferences.jacobian(fdm, b_one, copy(b))[1] : nothing
-        fwdJ = fwdJdxdb(driver, A, b)
-        revJ = revJdxdb(driver, A, b)
-        batchedrevJ = batchedrevJdxdb(driver, A, b)
-        batchedfwdJ = batchedfwdJdxdb(driver, A, b)
-        J = Jdxdb(driver, A, b)
-
-        if op == :\
-            @test isapprox(fwdJ, fdJ)
-        end
-
-        @test isapprox(fwdJ, revJ)
-        @test isapprox(fwdJ, batchedrevJ)
-        @test isapprox(fwdJ, batchedfwdJ)
-
-        fwdJ = const_fwdJdxdb(driver_NC, cholesky(A), b)
-        revJ = const_revJdxdb(driver_NC, cholesky(A), b)
-        if op == :\
-            @test isapprox(fwdJ, fdJ)
-        end
-        @test isapprox(fwdJ, revJ)
-
-        function h(A, b)
-            A = copy(A)
-            LinearAlgebra.LAPACK.potrf!('U', A)
-            b2 = copy(b)
-            LinearAlgebra.LAPACK.potrs!('U', A, b2)
-            @inbounds b2[1]
-        end
-
-        A = [1.3 0.5; 0.5 1.5]
-        b = [1., 2.]
-        dA = zero(A)
-        Enzyme.autodiff(Reverse, h, Active, Duplicated(A, dA), Const(b))
-        # dA_fwd  = Enzyme.gradient(Forward, A->h(A, b), A)[1]
-        dA_fd  = FiniteDifferences.grad(central_fdm(5, 1), A->h(A, b), A)[1]
-
-        @test isapprox(dA, dA_fd)
-    end
+  return @inbounds c[2,1]
 end
 
-function chol_upper(x)
-	x = reshape(x, 4, 4)
-	x = parent(cholesky(Hermitian(x)).U)
-	x = convert(typeof(x), UpperTriangular(x))
-	return x[1,2]
-end
-
-@testset "Cholesky upper triangular v1" begin
-	x = [1.0, -0.10541615131279458, 0.6219810761363638, 0.293343219811946, -0.10541615131279458, 1.0, -0.05258941747718969, 0.34629296878264443, 0.6219810761363638, -0.05258941747718969, 1.0, 0.4692436399208845, 0.293343219811946, 0.34629296878264443, 0.4692436399208845, 1.0]
-
-    @test Enzyme.gradient(Forward, chol_upper, x)[1] ≈ [0.05270807565639728, 0.0, 0.0, 0.0, 0.9999999999999999, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
-
-    @test Enzyme.gradient(Reverse, chol_upper, x)[1] ≈ [0.05270807565639728, 0.0, 0.0, 0.0, 0.9999999999999999, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
-end
- 
-using EnzymeTestUtils
-@testset "Linear solve for triangular matrices" begin
-    @testset for T in (UpperTriangular, LowerTriangular, UnitUpperTriangular, UnitLowerTriangular),
-        TE in (Float64, ComplexF64), sizeB in ((3,), (3, 3))
-        n = sizeB[1]
-        M = rand(TE, n, n)
-        B = rand(TE, sizeB...)
-        Y = zeros(TE, sizeB...)
-        A = T(M)
-        @testset "test through constructor" begin
-            _A = T(A)
-            f!(Y, A, B, ::T) where {T} = ldiv!(Y, T(A), B)
-            for TY in (Const, Duplicated, BatchDuplicated),
-                TM in (Const, Duplicated, BatchDuplicated),
-                TB in (Const, Duplicated, BatchDuplicated)
-                are_activities_compatible(Const, TY, TM, TB) || continue
-                test_reverse(f!, TY, (Y, TY), (M, TM), (B, TB), (_A, Const); atol = 1.0e-5, rtol = 1.0e-5)
-            end
-        end
-        @testset "test through `Adjoint` wrapper (regression test for #1306)" begin
-            # Test that we get the same derivative for `M` as for the adjoint of its
-            # (materialized) transpose. It's the same matrix, but represented differently
-            function f!(Y, A, B)
-                ldiv!(Y, A, B)
-                return nothing
-            end
-            A1 = T(M)
-            A2 = T(conj(permutedims(M))')
-            dA1 = make_zero(A1)
-            dA2 = make_zero(A2)
-            dB1 = make_zero(B)
-            dB2 = make_zero(B)
-            dY1 = rand(TE, sizeB...)
-            dY2 = copy(dY1)
-            autodiff(Reverse, f!, Duplicated(Y, dY1), Duplicated(A1, dA1), Duplicated(B, dB1))
-            autodiff(Reverse, f!, Duplicated(Y, dY2), Duplicated(A2, dA2), Duplicated(B, dB2))
-            @test dA1.data ≈ dA2.data
-            @test dB1 ≈ dB2
-        end
-    end
-end
-
-@testset "rand and randn rules" begin
-    # Distributed as x + unit normal + uniform
-    struct MyDistribution
-        x::Float64
-    end
-
-    Random.rand(rng::Random.AbstractRNG, d::MyDistribution) = d.x + randn() + rand()
-    Random.rand(d::MyDistribution) = rand(Random.default_rng(), d)
-
-    # Outer rand should be differentiated through, and inner rand and randn should be ignored.
-    @test autodiff(Enzyme.Reverse, x -> rand(MyDistribution(x)), Active, Active(1.0)) == ((1.0,),)
-end
-
-
-@testset "Ranges" begin
-    function f1(x)
-        x = 25.0x
-        ts = Array(Base.range_start_stop_length(0.0, x, 30))
-        return sum(ts)
-    end
-    function f2(x)
-        x = 25.0x
-        ts = Array(Base.range_start_stop_length(0.0, 0.25, 30))
-        return sum(ts) + x
-    end
-    function f3(x)
-        ts = Array(Base.range_start_stop_length(x, 1.25, 30))
-        return sum(ts)
-    end
-    @test Enzyme.autodiff(Forward, f1, Duplicated(0.1, 1.0)) == (374.99999999999994,)
-    @test Enzyme.autodiff(Forward, f2, Duplicated(0.1, 1.0)) == (25.0,)
-    @test Enzyme.autodiff(Forward, f3, Duplicated(0.1, 1.0)) == (15.0,)
-
-    @test Enzyme.autodiff(Forward, f1, BatchDuplicated(0.1, (1.0, 2.0))) ==
-          ((var"1" = 374.99999999999994, var"2" = 749.9999999999999),)
-    @test Enzyme.autodiff(Forward, f2, BatchDuplicated(0.1, (1.0, 2.0))) ==
-          ((var"1"=25.0, var"2"=50.0),)
-    @test Enzyme.autodiff(Forward, f3, BatchDuplicated(0.1, (1.0, 2.0))) ==
-          ((var"1"=15.0, var"2"=30.0),)
-
-    @test Enzyme.autodiff(Reverse, f1,  Active, Active(0.1)) == ((375.0,),)
-    @test Enzyme.autodiff(Reverse, f2,  Active, Active(0.1)) == ((25.0,),)
-    @test Enzyme.autodiff(Reverse, f3,  Active, Active(0.1)) == ((15.0,),)
-    
-    # Batch active rule isnt setup
-    # @test Enzyme.autodiff(Reverse, (x, y) -> begin y[] = f1(x); nothing end,  Active(0.1), BatchDuplicated(Ref(0.0), (Ref(1.0), Ref(2.0)))) == (((375.0,750.0)),)
-    # @test Enzyme.autodiff(Reverse, (x, y) -> begin y[] = f2(x); nothing end,  Active(0.1), BatchDuplicated(Ref(0.0), (Ref(1.0), Ref(2.0)))) == (((25.0,50.0)),)
-    # @test Enzyme.autodiff(Reverse, (x, y) -> begin y[] = f3(x); nothing end,  Active(0.1), BatchDuplicated(Ref(0.0), (Ref(1.0), Ref(2.0)))) == (((15.0,30.0)),)
-end
+Enzyme.API.printall!(true)
 
-@testset "Ranges 2" begin
-    function f1(x)
-        x = 25.0x
-        ts = Array(0.0:x:3.0)
-        return sum(ts)
-    end
-    function f2(x)
-        x = 25.0x
-        ts = Array(0.0:0.25:3.0)
-        return sum(ts) + x
-    end
-    function f3(x)
-        x = 25.0x
-        ts = Array(x:0.25:3.0)
-        return sum(ts)
-    end
-    function f4(x)
-        x = 25.0x
-        ts = Array(0.0:0.25:x)
-        return sum(ts)
-    end
-    @test Enzyme.autodiff(Forward, f1, Duplicated(0.1, 1.0)) == (25.0,)
-    @test Enzyme.autodiff(Forward, f2, Duplicated(0.1, 1.0)) == (25.0,)
-    @test Enzyme.autodiff(Forward, f3, Duplicated(0.1, 1.0)) == (75.0,)
-    @test Enzyme.autodiff(Forward, f4, Duplicated(0.12, 1.0)) == (0,)
-
-    @test Enzyme.autodiff(Forward, f1, BatchDuplicated(0.1, (1.0, 2.0))) ==
-          ((var"1"=25.0, var"2"=50.0),)
-    @test Enzyme.autodiff(Forward, f2, BatchDuplicated(0.1, (1.0, 2.0))) ==
-          ((var"1"=25.0, var"2"=50.0),)
-    @test Enzyme.autodiff(Forward, f3, BatchDuplicated(0.1, (1.0, 2.0))) ==
-          ((var"1"=75.0, var"2"=150.0),)
-    @test Enzyme.autodiff(Forward, f4, BatchDuplicated(0.12, (1.0, 2.0))) ==
-          ((var"1"=0.0, var"2"=0.0),)
-
-    @test Enzyme.autodiff(Reverse, f1,  Active, Active(0.1)) == ((25.0,),)
-    @test Enzyme.autodiff(Reverse, f2,  Active, Active(0.1)) == ((25.0,),)
-    @test Enzyme.autodiff(Reverse, f3,  Active, Active(0.1)) == ((75.0,),)
-    @test Enzyme.autodiff(Reverse, f4,  Active, Active(0.12)) == ((0.0,),)
-    
-    # Batch active rule isnt setup
-    # @test Enzyme.autodiff(Reverse, (x, y) -> begin y[] = f1(x); nothing end,  Active(1.1), BatchDuplicated(Ref(0.0), (Ref(1.0), Ref(2.0)))) == (((25.0,50.0)),)
-    # @test Enzyme.autodiff(Reverse, (x, y) -> begin y[] = f2(x); nothing end,  Active(0.1), BatchDuplicated(Ref(0.0), (Ref(1.0), Ref(2.0)))) == (((25.0,50.0)),)
-    # @test Enzyme.autodiff(Reverse, (x, y) -> begin y[] = f3(x); nothing end,  Active(0.1), BatchDuplicated(Ref(0.0), (Ref(1.0), Ref(2.0)))) == (((75.0,150.0)),)
-    # @test Enzyme.autodiff(Reverse, (x, y) -> begin y[] = f4(x); nothing end,  Active(0.1), BatchDuplicated(Ref(0.0), (Ref(1.0), Ref(2.0)))) == (((0.0,0.0)),)
-end
-
-@testset "SparseArrays spmatvec reverse rule" begin
-    C = zeros(18)
-    M = sprand(18, 9, 0.1)
-    v = randn(9)
-    α = 2.0
-    β = 1.0
-
-    for Tret in (Duplicated, BatchDuplicated), Tv in (Const, Duplicated, BatchDuplicated), 
-        Tα in (Const, Active), Tβ in (Const, Active)
-
-        are_activities_compatible(Tret, Tret, Tv, Tα, Tβ) || continue
-        test_reverse(LinearAlgebra.mul!, Tret, (C, Tret), (M, Const), (v, Tv), (α, Tα), (β, Tβ))
-
-    end
-
-
-    for Tret in (Duplicated, BatchDuplicated), Tv in (Const, Duplicated, BatchDuplicated), bα in (true, false), bβ in (true, false)
-        are_activities_compatible(Tret, Tret, Tv) || continue
-        test_reverse(LinearAlgebra.mul!, Tret, (C, Tret), (M, Const), (v, Tv), (bα, Const), (bβ, Const))
-    end
-end
-
-@testset "SparseArrays spmatmat reverse rule" begin
-    C = zeros(18, 11)
-    M = sprand(18, 9, 0.1)
-    v = randn(9, 11)
-    α = 2.0
-    β = 1.0
-
-    for Tret in (Duplicated, BatchDuplicated), Tv in (Const, Duplicated, BatchDuplicated), 
-        Tα in (Const, Active), Tβ in (Const, Active)
-
-        are_activities_compatible(Tret, Tv, Tα, Tβ) || continue
-        test_reverse(LinearAlgebra.mul!, Tret, (C, Tret), (M, Const), (v, Tv), (α, Tα), (β, Tβ))
-    end
-
-    for Tret in (Duplicated, BatchDuplicated), Tv in (Const, Duplicated, BatchDuplicated), bα in (true, false), bβ in (true, false)
-        are_activities_compatible(Tret, Tv) || continue
-        test_reverse(LinearAlgebra.mul!, Tret, (C, Tret), (M, Const), (v, Tv), (bα, Const), (bβ, Const))
-    end
-end
+x = reshape([1.0, -0.10541615131279458, 0.6219810761363638, 0.293343219811946, -0.10541615131279458, 1.0, -0.05258941747718969, 0.34629296878264443, 0.6219810761363638, -0.05258941747718969, 1.0, 0.4692436399208845, 0.293343219811946, 0.34629296878264443, 0.4692436399208845, 1.0], 4, 4)
+ dL = zero(x)
+ dL[2, 1] = 1.0
 
-end # InternalRules
+ @test Enzyme.autodiff(Forward, chol_lower0, Duplicated(x, dL))[1] ≈ 0.05270807565639164