Add Polyester approximate sparsity detection

Author: avik-pal

Project.toml

@@ -1,7 +1,7 @@
 name = "SparseDiffTools"
 uuid = "47a9eef4-7e08-11e9-0b38-333d64bd3804"
 authors = ["Pankaj Mishra <pankajmishra1511@gmail.com>", "Chris Rackauckas <contact@chrisrackauckas.com>"]
-version = "2.16.0"
+version = "2.17.0"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
@@ -27,12 +27,14 @@ VertexSafeGraphs = "19fa3120-7c27-5ec5-8db8-b0b0aa330d6f"
 
 [weakdeps]
 Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
+Polyester = "f517fe37-dbe3-4b94-8317-1923a5111588"
 PolyesterForwardDiff = "98d1487c-24ca-40b6-b7ab-df2af84e126b"
 Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [extensions]
 SparseDiffToolsEnzymeExt = "Enzyme"
+SparseDiffToolsPolyesterExt = "Polyester"
 SparseDiffToolsPolyesterForwardDiffExt = "PolyesterForwardDiff"
 SparseDiffToolsSymbolicsExt = "Symbolics"
 SparseDiffToolsZygoteExt = "Zygote"
@@ -49,6 +51,7 @@ ForwardDiff = "0.10"
 Graphs = "1"
 LinearAlgebra = "<0.0.1, 1"
 PackageExtensionCompat = "1"
+Polyester = "0.7.9"
 PolyesterForwardDiff = "0.1.1"
 Random = "1.6"
 Reexport = "1"
@@ -70,6 +73,7 @@ BlockBandedMatrices = "ffab5731-97b5-5995-9138-79e8c1846df0"
 Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
 IterativeSolvers = "42fd0dbc-a981-5370-80f2-aaf504508153"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
+Polyester = "f517fe37-dbe3-4b94-8317-1923a5111588"
 PolyesterForwardDiff = "98d1487c-24ca-40b6-b7ab-df2af84e126b"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"

ext/SparseDiffToolsPolyesterExt.jl

@@ -0,0 +1,3 @@
+module SparseDiffToolsPolyesterExt
+    
+end

ext/SparseDiffToolsPolyesterForwardDiffExt.jl

@@ -1,6 +1,6 @@
 module SparseDiffToolsPolyesterForwardDiffExt
 
-using ADTypes, SparseDiffTools, PolyesterForwardDiff
+using ADTypes, SparseDiffTools, PolyesterForwardDiff, UnPack, Random, SparseArrays
 import ForwardDiff
 import SparseDiffTools: AbstractMaybeSparseJacobianCache, AbstractMaybeSparsityDetection,
                         ForwardColorJacCache, NoMatrixColoring, sparse_jacobian_cache,
@@ -17,10 +17,8 @@ struct PolyesterForwardDiffJacobianCache{CO, CA, J, FX, X} <:
 end
 
 function sparse_jacobian_cache(
-        ad::Union{AutoSparsePolyesterForwardDiff,
-            AutoPolyesterForwardDiff},
-        sd::AbstractMaybeSparsityDetection, f::F, x;
-        fx = nothing) where {F}
+        ad::Union{AutoSparsePolyesterForwardDiff, AutoPolyesterForwardDiff},
+        sd::AbstractMaybeSparsityDetection, f::F, x; fx = nothing) where {F}
     coloring_result = sd(ad, f, x)
     fx = fx === nothing ? similar(f(x)) : fx
     if coloring_result isa NoMatrixColoring
@@ -39,10 +37,8 @@ function sparse_jacobian_cache(
 end
 
 function sparse_jacobian_cache(
-        ad::Union{AutoSparsePolyesterForwardDiff,
-            AutoPolyesterForwardDiff},
-        sd::AbstractMaybeSparsityDetection, f!::F, fx,
-        x) where {F}
+        ad::Union{AutoSparsePolyesterForwardDiff, AutoPolyesterForwardDiff},
+        sd::AbstractMaybeSparsityDetection, f!::F, fx, x) where {F}
     coloring_result = sd(ad, f!, fx, x)
     if coloring_result isa NoMatrixColoring
         cache = __chunksize(ad, x)
@@ -79,4 +75,38 @@ function sparse_jacobian!(J::AbstractMatrix, _, cache::PolyesterForwardDiffJacob
     return J
 end
 
+## Approximate Sparsity Detection
+function (alg::ApproximateJacobianSparsity)(
+        ad::AutoSparsePolyesterForwardDiff, f::F, x; fx = nothing, kwargs...) where {F}
+    @unpack ntrials, rng = alg
+    fx = fx === nothing ? f(x) : fx
+    ck = __chunksize(ad, x)
+    J = fill!(similar(fx, length(fx), length(x)), 0)
+    J_cache = similar(J)
+    x_ = similar(x)
+    for _ in 1:ntrials
+        randn!(rng, x_)
+        PolyesterForwardDiff.threaded_jacobian!(f, J_cache, x_, ck)
+        @. J += abs(J_cache)
+    end
+    return (JacPrototypeSparsityDetection(; jac_prototype = sparse(J), alg.alg))(ad, f, x;
+        fx, kwargs...)
+end
+
+function (alg::ApproximateJacobianSparsity)(ad::AutoSparsePolyesterForwardDiff, f::F, fx, x;
+        kwargs...) where {F}
+    @unpack ntrials, rng = alg
+    ck = __chunksize(ad, x)
+    J = fill!(similar(fx, length(fx), length(x)), 0)
+    J_cache = similar(J)
+    x_ = similar(x)
+    for _ in 1:ntrials
+        randn!(rng, x_)
+        PolyesterForwardDiff.threaded_jacobian!(f, fx, J_cache, x_, ck)
+        @. J += abs(J_cache)
+    end
+    return (JacPrototypeSparsityDetection(; jac_prototype = sparse(J), alg.alg))(ad, f, x;
+        fx, kwargs...)
+end
+
 end

src/highlevel/coloring.jl

@@ -36,7 +36,11 @@ function (alg::ApproximateJacobianSparsity)(
         ad::AbstractSparseADType, f::F, x; fx = nothing,
         kwargs...) where {F}
     if !(ad isa AutoSparseForwardDiff)
-        @warn "$(ad) support for approximate jacobian not implemented. Using ForwardDiff instead." maxlog=1
+        if ad isa AutoSparsePolyesterForwardDiff
+            @warn "$(ad) is only supported if `PolyesterForwardDiff` is explicitly loaded. Using ForwardDiff instead." maxlog=1
+        else
+            @warn "$(ad) support for approximate jacobian not implemented. Using ForwardDiff instead." maxlog=1
+        end
     end
     @unpack ntrials, rng = alg
     fx = fx === nothing ? f(x) : fx
@@ -56,7 +60,11 @@ end
 function (alg::ApproximateJacobianSparsity)(ad::AbstractSparseADType, f::F, fx, x;
         kwargs...) where {F}
     if !(ad isa AutoSparseForwardDiff)
-        @warn "$(ad) support for approximate jacobian not implemented. Using ForwardDiff instead." maxlog=1
+        if ad isa AutoSparsePolyesterForwardDiff
+            @warn "$(ad) is only supported if `PolyesterForwardDiff` is explicitly loaded. Using ForwardDiff instead." maxlog=1
+        else
+            @warn "$(ad) support for approximate jacobian not implemented. Using ForwardDiff instead." maxlog=1
+        end
     end
     @unpack ntrials, rng = alg
     cfg = ForwardDiff.JacobianConfig(f, fx, x)