fix: take absstep into account for FiniteDiff (#812)

* Add tests

* Fix

* Changelog

Author: gdalle

DifferentiationInterface/CHANGELOG.md

@@ -11,6 +11,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Fixed
 
+- Take `absstep` into account for FiniteDiff ([#812])
 - Make basis work for `CuArray` ([#810])
 
 ## [0.7.0]
@@ -39,6 +40,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 [0.6.54]: https://github.com/JuliaDiff/DifferentiationInterface.jl/compare/DifferentiationInterface-v0.6.53...DifferentiationInterface-v0.6.54
 [0.6.53]: https://github.com/JuliaDiff/DifferentiationInterface.jl/compare/DifferentiationInterface-v0.6.52...DifferentiationInterface-v0.6.53
 
+[#812]: https://github.com/JuliaDiff/DifferentiationInterface.jl/pull/812
 [#810]: https://github.com/JuliaDiff/DifferentiationInterface.jl/pull/810
 [#799]: https://github.com/JuliaDiff/DifferentiationInterface.jl/pull/799
 [#795]: https://github.com/JuliaDiff/DifferentiationInterface.jl/pull/795

DifferentiationInterface/ext/DifferentiationInterfaceFiniteDiffExt/onearg.jl

@@ -27,7 +27,7 @@ function DI.prepare_pushforward_nokwarg(
     absstep = if isnothing(backend.absstep)
         relstep
     else
-        backend.relstep
+        backend.absstep
     end
     dir = backend.dir
     return FiniteDiffOneArgPushforwardPrep(_sig, cache, relstep, absstep, dir)
@@ -144,7 +144,7 @@ function DI.prepare_derivative_nokwarg(
     absstep = if isnothing(backend.absstep)
         relstep
     else
-        backend.relstep
+        backend.absstep
     end
     dir = backend.dir
     return FiniteDiffOneArgDerivativePrep(_sig, cache, relstep, absstep, dir)
@@ -269,7 +269,7 @@ function DI.prepare_gradient_nokwarg(
     absstep = if isnothing(backend.absstep)
         relstep
     else
-        backend.relstep
+        backend.absstep
     end
     dir = backend.dir
     return FiniteDiffGradientPrep(_sig, cache, relstep, absstep, dir)
@@ -359,7 +359,7 @@ function DI.prepare_jacobian_nokwarg(
     absstep = if isnothing(backend.absstep)
         relstep
     else
-        backend.relstep
+        backend.absstep
     end
     dir = backend.dir
     return FiniteDiffOneArgJacobianPrep(_sig, cache, relstep, absstep, dir)
@@ -465,8 +465,16 @@ function DI.prepare_hessian_nokwarg(
     else
         backend.relstep
     end
-    absstep_g = isnothing(backend.absstep) ? relstep_g : backend.absstep
-    absstep_h = isnothing(backend.absstep) ? relstep_h : backend.absstep
+    absstep_g = if isnothing(backend.absstep)
+        relstep_g
+    else
+        backend.absstep
+    end
+    absstep_h = if isnothing(backend.absstep)
+        relstep_h
+    else
+        backend.absstep
+    end
     return FiniteDiffHessianPrep(
         _sig, gradient_cache, hessian_cache, relstep_g, absstep_g, relstep_h, absstep_h
     )

DifferentiationInterface/ext/DifferentiationInterfaceFiniteDiffExt/twoarg.jl

@@ -31,7 +31,7 @@ function DI.prepare_pushforward_nokwarg(
     absstep = if isnothing(backend.absstep)
         relstep
     else
-        backend.relstep
+        backend.absstep
     end
     dir = backend.dir
     return FiniteDiffTwoArgPushforwardPrep(_sig, cache, relstep, absstep, dir)
@@ -175,7 +175,7 @@ function DI.prepare_derivative_nokwarg(
     absstep = if isnothing(backend.absstep)
         relstep
     else
-        backend.relstep
+        backend.absstep
     end
     dir = backend.dir
     return FiniteDiffTwoArgDerivativePrep(_sig, cache, relstep, absstep, dir)
@@ -295,7 +295,7 @@ function DI.prepare_jacobian_nokwarg(
     absstep = if isnothing(backend.absstep)
         relstep
     else
-        backend.relstep
+        backend.absstep
     end
     dir = backend.dir
     return FiniteDiffTwoArgJacobianPrep(_sig, cache, relstep, absstep, dir)

DifferentiationInterface/test/Back/FiniteDiff/test.jl

@@ -72,3 +72,45 @@ end
         logging=LOGGING,
     )
 end;
+
+@testset "Step size" begin  # fix 811
+    backend = AutoFiniteDiff(; absstep=1000, relstep=0.1)
+    preps = [
+        prepare_pushforward(identity, backend, 1.0, (1.0,)),
+        prepare_pushforward(copyto!, [0.0], backend, [1.0], ([1.0],)),
+        prepare_derivative(identity, backend, 1.0),
+        prepare_derivative((y, x) -> y .= x, [0.0], backend, 1.0),
+        prepare_gradient(sum, backend, [1.0]),
+        prepare_jacobian(identity, backend, [1.0]),
+        prepare_jacobian(copyto!, [0.0], backend, [1.0]),
+    ]
+    for prep in preps
+        @test prep.relstep == 0.1
+        @test prep.absstep == 1000
+    end
+    prep = prepare_hessian(sum, backend, [1.0])
+    @test prep.absstep_g == 1000
+    @test prep.absstep_h == 1000
+    @test prep.relstep_g == 0.1
+    @test prep.relstep_h == 0.1
+
+    backend = AutoFiniteDiff(; relstep=0.1)
+    preps = [
+        prepare_pushforward(identity, backend, 1.0, (1.0,)),
+        prepare_pushforward(copyto!, [0.0], backend, [1.0], ([1.0],)),
+        prepare_derivative(identity, backend, 1.0),
+        prepare_derivative((y, x) -> y .= x, [0.0], backend, 1.0),
+        prepare_gradient(sum, backend, [1.0]),
+        prepare_jacobian(identity, backend, [1.0]),
+        prepare_jacobian(copyto!, [0.0], backend, [1.0]),
+    ]
+    for prep in preps
+        @test prep.relstep == 0.1
+        @test prep.absstep == 0.1
+    end
+    prep = prepare_hessian(sum, backend, [1.0])
+    @test prep.absstep_g == 0.1
+    @test prep.absstep_h == 0.1
+    @test prep.relstep_g == 0.1
+    @test prep.relstep_h == 0.1
+end