cont.

Author: michel2323

Project.toml

@@ -7,7 +7,10 @@ ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
 Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+ManualNLPModels = "30dfa513-9b2f-4fb3-9796-781eabac1617"
 NLPModels = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
+NLPModelsModifiers = "e01155f1-5c6f-4375-a9d8-616dd036575f"
+NLPModelsTest = "7998695d-6960-4d3a-85c4-e1bceb8cd856"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"

src/ADNLPModels.jl

@@ -7,7 +7,7 @@ using LinearAlgebra, SparseArrays
 using ADTypes: ADTypes, AbstractColoringAlgorithm, AbstractSparsityDetector
 using SparseConnectivityTracer: TracerSparsityDetector
 using SparseMatrixColorings
-using ForwardDiff, ReverseDiff
+using ForwardDiff, ReverseDiff, Enzyme
 
 # JSO
 using NLPModels

src/enzyme.jl

@@ -14,6 +14,12 @@ function EnzymeReverseADGradient(
   return EnzymeReverseADGradient()
 end
 
+function ADNLPModels.gradient(::EnzymeReverseADGradient, f, x)
+  g = similar(x)
+  Enzyme.autodiff(Enzyme.Reverse, f, Enzyme.Duplicated(x, g)) # gradient!(Reverse, g, f, x)
+  return g
+end
+
 function ADNLPModels.gradient!(::EnzymeReverseADGradient, g, f, x)
   Enzyme.autodiff(Enzyme.Reverse, f, Enzyme.Duplicated(x, g)) # gradient!(Reverse, g, f, x)
   return g
@@ -47,11 +53,13 @@ end
 function hessian(::EnzymeReverseADHessian, f, x)
   seed = similar(x)
   hess = zeros(eltype(x), length(x), length(x))
-  fill!(seed, zero(x))
+  fill!(seed, zero(eltype(x)))
+  tmp = similar(x)
   for i in 1:length(x)
-    seed[i] = one(x)
-    Enzyme.hvp!(view(hess, i, :), f, x, seed)
-    seed[i] = zero(x)
+    seed[i] = one(eltype(seed))
+    Enzyme.hvp!(tmp, f, x, seed)
+    hess[:, i] .= tmp
+    seed[i] = zero(eltype(seed))
   end
   return hess
 end
@@ -72,7 +80,9 @@ function EnzymeReverseADJprod(
 end
 
 function Jprod!(b::EnzymeReverseADJprod, Jv, c!, x, v, ::Val)
-  Enzyme.autodiff(Enzyme.Forward, c!, Duplicated(b.x, Jv), Enzyme.Duplicated(x, v))
+  @show c!(x)
+  @show Enzyme.autodiff(Enzyme.Forward, Const(c!), Duplicated(x, v))
+  error("This is BAD")
   return Jv
 end
 
@@ -91,7 +101,7 @@ function EnzymeReverseADJtprod(
   return EnzymeReverseADJtprod(x)
 end
 
-function Jtvprod!(b::EnzymeReverseADJtprod, Jtv, c!, x, v, ::Val)
+function Jtprod!(b::EnzymeReverseADJtprod, Jtv, c!, x, v, ::Val)
   Enzyme.autodiff(Enzyme.Reverse, c!, Duplicated(b.x, Jtv), Enzyme.Duplicated(x, v))
   return Jtv
 end

test/enzyme.jl

@@ -10,8 +10,70 @@ for problem in NLPModelsTest.nls_problems
   include("nls/problems/$(lowercase(problem)).jl")
 end
 
+EnzymeReverseAD() = ADNLPModels.ADModelBackend(
+  ADNLPModels.EnzymeReverseADGradient(),
+  ADNLPModels.EnzymeReverseADHvprod(zeros(1)),
+  ADNLPModels.EnzymeReverseADJprod(zeros(1)),
+  ADNLPModels.EnzymeReverseADJtprod(zeros(1)),
+  ADNLPModels.EnzymeReverseADJacobian(),
+  ADNLPModels.EnzymeReverseADHessian(),
+  ADNLPModels.EnzymeReverseADHvprod(zeros(1)),
+  ADNLPModels.EmptyADbackend(),
+  ADNLPModels.EmptyADbackend(),
+  ADNLPModels.EmptyADbackend(),
+  ADNLPModels.EmptyADbackend(),
+  ADNLPModels.EmptyADbackend(),
+)
+
+function test_autodiff_backend_error()
+  @testset "Error without loading package - $backend" for backend in [:EnzymeReverseAD]
+    adbackend = eval(backend)()
+    # @test_throws ArgumentError gradient(adbackend.gradient_backend, sum, [1.0])
+    # @test_throws ArgumentError gradient!(adbackend.gradient_backend, [1.0], sum, [1.0])
+    # @test_throws ArgumentError jacobian(adbackend.jacobian_backend, identity, [1.0])
+    # @test_throws ArgumentError hessian(adbackend.hessian_backend, sum, [1.0])
+    # @test_throws ArgumentError Jprod!(
+    #   adbackend.jprod_backend,
+    #   [1.0],
+    #   [1.0],
+    #   identity,
+    #   [1.0],
+    #   Val(:c),
+    # )
+    # @test_throws ArgumentError Jtprod!(
+    #   adbackend.jtprod_backend,
+    #   [1.0],
+    #   [1.0],
+    #   identity,
+    #   [1.0],
+    #   Val(:c),
+    # )
+    gradient(adbackend.gradient_backend, sum, [1.0])
+    gradient!(adbackend.gradient_backend, [1.0], sum, [1.0])
+    jacobian(adbackend.jacobian_backend, identity, [1.0])
+    hessian(adbackend.hessian_backend, sum, [1.0])
+    Jprod!(
+      adbackend.jprod_backend,
+      [1.0],
+      identity,
+      [1.0],
+      [1.0],
+      Val(:c),
+    )
+    # Jtprod!(
+    #   adbackend.jtprod_backend,
+    #   [1.0],
+    #   identity,
+    #   [1.0],
+    #   [1.0],
+    #   Val(:c),
+    # )
+  end
+end
+
+test_autodiff_backend_error()
 #=
-ADNLPModels.EmptyADbackend(args...; kwargs...) = ADNLPModels.EmptyADbackend()
+# ADNLPModels.EmptyADbackend(args...; kwargs...) = ADNLPModels.EmptyADbackend()
 
 names = OptimizationProblems.meta[!, :name]
 list_excluded_enzyme = [