Try sparsity aware AD in Enzyme

SciML · Jan 26, 2024 · 9d23251 · 9d23251
1 parent 4da7756
commit 9d23251
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Showing 2 changed files with 39 additions and 11 deletions.
diff --git a/ext/OptimizationEnzymeExt.jl b/ext/OptimizationEnzymeExt.jl
@@ -1,6 +1,6 @@
 module OptimizationEnzymeExt
 
-import Optimization, Optimization.ArrayInterface
+import Optimization, Optimization.ArrayInterface, Optimization.SparseArrays
 import Optimization.SciMLBase: OptimizationFunction
 import Optimization.LinearAlgebra: I
 import Optimization.ADTypes: AutoEnzyme
@@ -47,11 +47,27 @@ function Optimization.instantiate_function(f::OptimizationFunction{true}, x,
             return nothing
         end
         function hess(res, θ, args...)
-            vdθ = Tuple((Array(r) for r in eachrow(I(length(θ)) * 1.0)))
-
-            bθ = zeros(length(θ))
-            vdbθ = Tuple(zeros(length(θ)) for i in eachindex(θ))
-
+            if f.hess_prototype === nothing
+                vdθ = Tuple((similar(r) for r in eachrow(I(length(θ)) * 1.0)))
+                bθ = zeros(length(θ))
+                @show bθ
+                @show typeof(bθ)
+                vdbθ = Tuple(zeros(length(θ)) for i in eachindex(θ))
+                @show vdbθ
+                @show typeof(vdbθ)
+            else
+                θ = SparseArrays.sparse(θ)
+                @show θ
+                vdθ = Tuple((similar(SparseArrays.sparse(r)) for r in eachrow(I(length(θ)) * 1.0)))
+                @show vdθ
+                @show typeof(vdθ)
+                bθ = SparseArrays.similar(θ)
+                @show bθ
+                @show typeof(bθ)
+                vdbθ = Tuple(similar(i) for i in eachrow(f.hess_prototype))
+                @show vdbθ
+                @show typeof(vdbθ)
+            end
             Enzyme.autodiff(Enzyme.Forward,
                 g,
                 Enzyme.BatchDuplicated(θ, vdθ),

diff --git a/test/ADtests.jl b/test/ADtests.jl
@@ -150,8 +150,8 @@ H3 == [[2.0 0.0; 0.0 2.0], [-0.0 1.0; 1.0 0.0]]
 G2 = Array{Float64}(undef, 2)
 H2 = Array{Float64}(undef, 2, 2)
 
-optf = OptimizationFunction(rosenbrock, Optimization.AutoZygote(), cons = con2_c)
-optprob = Optimization.instantiate_function(optf, x0, Optimization.AutoZygote(),
+optf = OptimizationFunction(rosenbrock, Optimization.AutoEnzyme(), cons = con2_c)
+optprob = Optimization.instantiate_function(optf, x0, Optimization.AutoEnzyme(),
     nothing, 2)
 optprob.grad(G2, x0)
 @test G1 == G2
@@ -167,15 +167,27 @@ H3 = [Array{Float64}(undef, 2, 2), Array{Float64}(undef, 2, 2)]
 optprob.cons_h(H3, x0)
 H3 == [[2.0 0.0; 0.0 2.0], [-0.0 1.0; 1.0 0.0]]
 
-optf = OptimizationFunction(rosenbrock, Optimization.AutoModelingToolkit(true, true),
+optf = OptimizationFunction(rosenbrock, Optimization.AutoEnzyme(),
     cons = con2_c)
 optprob = Optimization.instantiate_function(optf, x0,
-    Optimization.AutoModelingToolkit(true, true),
+    Optimization.AutoEnzyme(),
     nothing, 2)
 using SparseArrays
 sH = sparse([1, 1, 2, 2], [1, 2, 1, 2], zeros(4))
-@test findnz(sH)[1:2] == findnz(optprob.hess_prototype)[1:2]
+# @test findnz(sH)[1:2] == findnz(optprob.hess_prototype)[1:2]
 optprob.hess(sH, x0)
+
+sH = sparse([1, 1, 2, 2], [1, 2, 1, 2], zeros(4))
+optf = OptimizationFunction(rosenbrock, Optimization.AutoEnzyme(),
+    cons = con2_c, hess_prototype = sH)
+optprob = Optimization.instantiate_function(optf, x0,
+    Optimization.AutoEnzyme(),
+    nothing, 2)
+using SparseArrays
+
+# @test findnz(sH)[1:2] == findnz(optprob.hess_prototype)[1:2]
+optprob.hess(sH, x0)
+
 @test sH == H2
 res = Array{Float64}(undef, 2)
 optprob.cons(res, x0)