Update the code for Enzyme backends

test/Project.toml

@@ -1,5 +1,4 @@
 [deps]
-Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 ManualNLPModels = "30dfa513-9b2f-4fb3-9796-781eabac1617"

test/enzyme.jl

@@ -57,26 +57,6 @@ end
 
 test_autodiff_backend_error()
 
-push!(
-  ADNLPModels.predefined_backend,
-  :enzyme_backend => Dict(
-    :gradient_backend => ADNLPModels.EnzymeReverseADGradient,
-    :jprod_backend => ADNLPModels.EnzymeReverseADJprod,
-    :jtprod_backend => ADNLPModels.EnzymeReverseADJtprod,
-    :hprod_backend => ADNLPModels.EnzymeReverseADHvprod,
-    :jacobian_backend => ADNLPModels.SparseEnzymeADJacobian,
-    :hessian_backend => ADNLPModels.SparseEnzymeADHessian,
-    :ghjvprod_backend => ADNLPModels.ForwardDiffADGHjvprod,
-    :jprod_residual_backend => ADNLPModels.EnzymeReverseADJprod,
-    :jtprod_residual_backend => ADNLPModels.EnzymeReverseADJtprod,
-    :hprod_residual_backend => ADNLPModels.EnzymeReverseADHvprod,
-    :jacobian_residual_backend => ADNLPModels.SparseEnzymeADJacobian,
-    :hessian_residual_backend => ADNLPModels.SparseEnzymeADHessian,
-  ),
-)
-
-const test_enzyme = true
-
 include("sparse_jacobian.jl")
 include("sparse_jacobian_nls.jl")
 include("sparse_hessian.jl")
@@ -124,18 +104,18 @@ include("nls/basic.jl")
 include("nlp/nlpmodelstest.jl")
 include("nls/nlpmodelstest.jl")
 
-@testset "Basic NLP tests using enzyme_backend" begin
-  test_autodiff_model("enzyme_backend", backend = :enzyme_backend)
+@testset "Basic NLP tests using enzyme" begin
+  test_autodiff_model("enzyme", backend = :enzyme)
 end
 
-@testset "Basic NLS tests using enzyme_backend" begin
-  autodiff_nls_test("enzyme_backend", backend = :enzyme_backend)
+@testset "Basic NLS tests using enzyme" begin
+  autodiff_nls_test("enzyme", backend = :enzyme)
 end
 
-@testset "Checking NLPModelsTest (NLP) tests with enzyme_backend" begin
-  nlp_nlpmodelstest(:enzyme_backend)
+@testset "Checking NLPModelsTest (NLP) tests with enzyme" begin
+  nlp_nlpmodelstest(:enzyme)
 end
 
-@testset "Checking NLPModelsTest (NLS) tests with enzyme_backend" begin
-  nls_nlpmodelstest(:enzyme_backend)
+@testset "Checking NLPModelsTest (NLS) tests with enzyme" begin
+  nls_nlpmodelstest(:enzyme)
 end

test/nlp/nlpmodelstest.jl

@@ -18,7 +18,7 @@ function nlp_nlpmodelstest(backend)
     @testset "Check multiple precision" begin
       multiple_precision_nlp(nlp_from_T, exclude = [], linear_api = true)
     end
-    if backend != :enzyme && backend != :enzyme_backend
+    if backend != :enzyme
       @testset "Check view subarray" begin
         view_subarray_nlp(nlp_ad, exclude = [])
       end

test/nls/nlpmodelstest.jl

@@ -42,7 +42,7 @@ function nls_nlpmodelstest(backend)
     @testset "Check multiple precision" begin
       multiple_precision_nls(nls_from_T, exclude = exclude, linear_api = true)
     end
-    if backend != :enzyme && backend != :enzyme_backend
+    if backend != :enzyme
       @testset "Check view subarray" begin
         view_subarray_nls.(nlss, exclude = exclude)
       end

test/sparse_hessian.jl

@@ -1,5 +1,6 @@
 function sparse_hessian(backend, info, kw)
   @testset "Basic Hessian derivative with backend=$(backend) -- $info -- T=$(T)" for T in (
+    Float32,
     Float64,
   )
     c!(cx, x) = begin
@@ -33,7 +34,6 @@ function sparse_hessian(backend, info, kw)
 
     # Test also the implementation of the backends
     b = nlp.adbackend.hessian_backend
-    @show b
     obj_weight = 0.5
     @test nlp.meta.nnzh == ADNLPModels.get_nln_nnzh(b, nvar)
     ADNLPModels.hess_structure!(b, nlp, rows, cols)
@@ -70,17 +70,8 @@ function sparse_hessian(backend, info, kw)
     function f(x)
       n = length(x)
       sum(100 * (x[i + 1] - x[i]^2)^2 + (x[i] - 1)^2 for i = 1:(n - 1))
-      # res = 0
-      # n = length(x)
-      # for i in 1:(n-1)
-      #   res += 100 * (x[i + 1] - x[i]^2)^2 + (x[i] - 1)^2
-      # end
-      # res
     end
     nlp = ADNLPModel(
-      # x -> sum(100 * (x[i + 1] - x[i]^2)^2 + (x[i] - 1)^2 for i = 1:(n - 1)),
-      # x -> sum(100 * (x[i + 1] - x[i]^2)^2 + (x[i] - 1)^2 for i = 1:3),
-      # x -> 100 * (x[2] - x[1]^2)^2 + (x[1] - 1)^2,
       f,
       x0,
       hessian_backend = backend,