Merge pull request #235 from gaurav-arya/ag-ci

Fix CI

Author: ChrisRackauckas

.github/workflows/CI.yml

@@ -13,7 +13,7 @@ jobs:
       fail-fast: false
       matrix:
         group:
-          - Core
+          - All
         version:
           - '1' # Latest Release
           - '~1.6' # Current LTS

Project.toml

@@ -25,7 +25,7 @@ VertexSafeGraphs = "19fa3120-7c27-5ec5-8db8-b0b0aa330d6f"
 [compat]
 ADTypes = "0.1"
 Adapt = "3.0"
-ArrayInterface = "7"
+ArrayInterface = "7.4.2"
 Compat = "4"
 DataStructures = "0.18"
 FiniteDiff = "2.8.1"

ext/SparseDiffToolsZygote.jl

@@ -41,27 +41,27 @@ function SparseDiffTools.numback_hesvec(f, x, v)
 end
 
 function SparseDiffTools.autoback_hesvec!(dy, f, x, v,
-                          cache1 = Dual{typeof(ForwardDiff.Tag(DeivVecTag, eltype(x))),
+                          cache1 = Dual{typeof(ForwardDiff.Tag(DeivVecTag(), eltype(x))),
                                         eltype(x), 1
                                         }.(x,
-                                           ForwardDiff.Partials.(Tuple.(reshape(v, size(x))))),
-                          cache2 = Dual{typeof(ForwardDiff.Tag(DeivVecTag, eltype(x))),
+                                           ForwardDiff.Partials.(tuple.(reshape(v, size(x))))),
+                          cache2 = Dual{typeof(ForwardDiff.Tag(DeivVecTag(), eltype(x))),
                                         eltype(x), 1
                                         }.(x,
-                                           ForwardDiff.Partials.(Tuple.(reshape(v, size(x))))))
+                                           ForwardDiff.Partials.(tuple.(reshape(v, size(x))))))
     g = let f = f
         (dx, x) -> dx .= first(Zygote.gradient(f, x))
     end
-    cache1 .= Dual{typeof(ForwardDiff.Tag(DeivVecTag, eltype(x))), eltype(x), 1
-                   }.(x, ForwardDiff.Partials.(Tuple.(reshape(v, size(x)))))
+    # Reset each dual number in cache1 to primal = dual = 1.
+    cache1 .= eltype(cache1).(x, ForwardDiff.Partials.(tuple.(reshape(v, size(x)))))
     g(cache2, cache1)
     dy .= partials.(cache2, 1)
 end
 
 function SparseDiffTools.autoback_hesvec(f, x, v)
     g = x -> first(Zygote.gradient(f, x))
     y = Dual{typeof(ForwardDiff.Tag(DeivVecTag, eltype(x))), eltype(x), 1
-             }.(x, ForwardDiff.Partials.(Tuple.(reshape(v, size(x)))))
+             }.(x, ForwardDiff.Partials.(tuple.(reshape(v, size(x)))))
     ForwardDiff.partials.(g(y), 1)
 end
 

src/differentiation/jaches_products.jl

@@ -277,7 +277,7 @@ function HesVec(f, u::AbstractArray, p = nothing, t = nothing; autodiff = AutoFo
         cache1 = Dual{
                       typeof(ForwardDiff.Tag(DeivVecTag(),eltype(u))), eltype(u), 1
                      }.(u, ForwardDiff.Partials.(tuple.(u)))
-        cache2 = copy(u)
+        cache2 = copy(cache1)
 
         (cache1, cache2), autoback_hesvec, autoback_hesvec!
     else

test/runtests.jl

@@ -17,12 +17,12 @@ if GROUP == "All"
     @time @safetestset "Greedy star coloring" begin include("test_greedy_star.jl") end
     @time @safetestset "Acyclic coloring" begin include("test_acyclic.jl") end
     @time @safetestset "Matrix to graph conversion" begin include("test_matrix2graph.jl") end
-    @time @safetestset "AD using colorvec vector" begin include("test_ad.jl") end
     @time @safetestset "Hessian colorvecs" begin include("test_sparse_hessian.jl") end
     @time @safetestset "Integration test" begin include("test_integration.jl") end
     @time @safetestset "Special matrices" begin include("test_specialmatrices.jl") end
     @time @safetestset "Jac Vecs and Hes Vecs" begin include("test_jaches_products.jl") end
     @time @safetestset "Vec Jac Products" begin include("test_vecjac_products.jl") end
+    @time @safetestset "AD using colorvec vector" begin include("test_ad.jl") end
 end
 
 if GROUP == "GPU"

test/test_jaches_products.jl

@@ -20,8 +20,8 @@ function h(dy, x)
 end
 
 cache1 = ForwardDiff.Dual{typeof(ForwardDiff.Tag(SparseDiffTools.DeivVecTag(), eltype(x))),
-                          eltype(x), 1}.(x, ForwardDiff.Partials.(Tuple.(v)))
-cache2 = ForwardDiff.Dual{typeof(ForwardDiff.Tag(SparseDiffTools.DeivVecTag(), eltype(x))), eltype(x), 1}.(x, ForwardDiff.Partials.(Tuple.(v)))
+                          eltype(x), 1}.(x, ForwardDiff.Partials.(tuple.(v)))
+cache2 = ForwardDiff.Dual{typeof(ForwardDiff.Tag(SparseDiffTools.DeivVecTag(), eltype(x))), eltype(x), 1}.(x, ForwardDiff.Partials.(tuple.(v)))
 @test num_jacvec!(dy, f, x, v)≈ForwardDiff.jacobian(f, similar(x), x) * v rtol=1e-6
 @test num_jacvec!(dy, f, x, v, similar(v),
                   similar(v))≈ForwardDiff.jacobian(f, similar(x), x) * v rtol=1e-6
@@ -50,9 +50,9 @@ cache2 = ForwardDiff.Dual{typeof(ForwardDiff.Tag(SparseDiffTools.DeivVecTag(), e
 @test numback_hesvec(g, x, v)≈ForwardDiff.hessian(g, x) * v rtol=1e-8
 
 cache3 = ForwardDiff.Dual{typeof(ForwardDiff.Tag(Nothing, eltype(x))), eltype(x), 1
-                          }.(x, ForwardDiff.Partials.(Tuple.(v)))
+                          }.(x, ForwardDiff.Partials.(tuple.(v)))
 cache4 = ForwardDiff.Dual{typeof(ForwardDiff.Tag(Nothing, eltype(x))), eltype(x), 1
-                          }.(x, ForwardDiff.Partials.(Tuple.(v)))
+                          }.(x, ForwardDiff.Partials.(tuple.(v)))
 @test autoback_hesvec!(dy, g, x, v)≈ForwardDiff.hessian(g, x) * v rtol=1e-8
 @test autoback_hesvec!(dy, g, x, v, cache3, cache4)≈ForwardDiff.hessian(g, x) * v rtol=1e-8
 @test autoback_hesvec(g, x, v)≈ForwardDiff.hessian(g, x) * v rtol=1e-8
@@ -135,8 +135,8 @@ gmres!(out, L, v)
 x = rand(N)
 v = rand(N)
 L = HesVecGrad(h, x, autodiff = AutoFiniteDiff())
-@test L * x ≈ num_hesvec(g, x, x)
-@test L * v ≈ num_hesvec(g, x, v)
+@test L * x ≈ num_hesvec(g, x, x) rtol=1e-2
+@test L * v ≈ num_hesvec(g, x, v) rtol=1e-2
 @test mul!(dy, L, v)≈num_hesvec(g, x, v) rtol=1e-2
 dy=rand(N);_dy=copy(dy);@test mul!(dy,L,v,a,b)≈a*num_hesvec(g,x,v)+b*_dy rtol=1e-2
 update_coefficients!(L, v, nothing, 0.0)

test/test_vecjac_products.jl

@@ -21,14 +21,4 @@ update_coefficients!(L, v, nothing, 0.0)
 L = VecJac(f, x; autodiff = AutoFiniteDiff())
 update_coefficients!(L, v, nothing, 0.0)
 @test L * v ≈ actual_vjp
-
-@info "ZygoteVecJac"
-
-L = ZygoteVecJac(f, x)
-actual_vjp = Zygote.jacobian(x -> f(x, nothing, 0.0), x)[1]' * v
-update_coefficients!(L, v, nothing, 0.0)
-@test L * v ≈ actual_vjp
-L = ZygoteVecJac(f, x; autodiff = AutoFiniteDiff())
-update_coefficients!(L, v, nothing, 0.0)
-@test L * v ≈ actual_vjp
-#
+#