Switch backends to ADTypes.jl

Project.toml

@@ -4,12 +4,15 @@ authors = ["Guillaume Dalle", "Adrian Hill"]
 version = "0.1.0"
 
 [deps]
+ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
 FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 
 [weakdeps]
+AbstractDifferentiation = "c29ec348-61ec-40c8-8164-b8c60e9d9f3d"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
+Diffractor = "9f5e2b26-1114-432f-b630-d3fe2085c51c"
 DiffResults = "163ba53b-c6d8-5494-b064-1a9d43ac40c5"
 Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
 FiniteDiff = "6a86dc24-6348-571c-b903-95158fe2bd41"
@@ -20,15 +23,26 @@ Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [extensions]
 DifferentiationInterfaceChainRulesCoreExt = "ChainRulesCore"
+DifferentiationInterfaceDiffractorExt = [
+ "Diffractor",
+ "AbstractDifferentiation",
+]
 DifferentiationInterfaceEnzymeExt = "Enzyme"
 DifferentiationInterfaceFiniteDiffExt = "FiniteDiff"
 DifferentiationInterfaceForwardDiffExt = ["ForwardDiff", "DiffResults"]
-DifferentiationInterfacePolyesterForwardDiffExt = ["PolyesterForwardDiff", "ForwardDiff", "DiffResults"]
+DifferentiationInterfacePolyesterForwardDiffExt = [
+ "PolyesterForwardDiff",
+ "ForwardDiff",
+ "DiffResults",
+]
 DifferentiationInterfaceReverseDiffExt = ["ReverseDiff", "DiffResults"]
 DifferentiationInterfaceZygoteExt = ["Zygote"]
 
 [compat]
+AbstractDifferentiation = "0.6"
+ADTypes = "0.2.6"
 ChainRulesCore = "1.19"
+Diffractor = "0.2"
 DiffResults = "1.1"
 DocStringExtensions = "0.9"
 Enzyme = "0.11"

README.md

@@ -13,18 +13,21 @@ This package provides a backend-agnostic syntax to differentiate functions `f(x)
 
 It started out as an experimental redesign for [AbstractDifferentiation.jl](https://github.com/JuliaDiff/AbstractDifferentiation.jl).
 
-## Example
+## Compatibility
 
-```jldoctest
-julia> using DifferentiationInterface, Enzyme
+We support some of the backends defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl):
 
-julia> backend = EnzymeReverseBackend();
+- [Enzyme.jl](https://github.com/EnzymeAD/Enzyme.jl) with `AutoEnzyme(Val(:forward))` or `AutoEnzyme(Val(:reverse))`
+- [FiniteDiff.jl](https://github.com/JuliaDiff/FiniteDiff.jl) with `AutoFiniteDiff()`
+- [ForwardDiff.jl](https://github.com/JuliaDiff/ForwardDiff.jl) with `AutoForwardDiff()`
+- [PolyesterForwardDiff.jl](https://github.com/JuliaDiff/PolyesterForwardDiff.jl) with `AutoPolyesterForwardDiff(; chunksize=C)`
+- [ReverseDiff.jl](https://github.com/JuliaDiff/ReverseDiff.jl) with `AutoReverseDiff()`
+- [Zygote.jl](https://github.com/FluxML/Zygote.jl) with `AutoZygote()`
 
-julia> f(x) = sum(abs2, x);
+We also support two more backends which are not yet part of ADTypes.jl:
 
-julia> value_and_gradient(backend, f, [1., 2., 3.])
-(14.0, [2.0, 4.0, 6.0])
-```
+- [ChainRulesCore.jl](https://github.com/JuliaDiff/ChainRulesCore.jl) with `AutoChainRules(ruleconfig)`
+- [Diffractor.jl](https://github.com/JuliaDiff/Diffractor.jl) with `AutoDiffractor()`
 
 ## Design
 
@@ -40,22 +43,15 @@ From these primitives, several utilities are defined, depending on the type of t
 | scalar input | derivative | multiderivative |
 | array input | gradient | jacobian |
 
-## Supported backends
-
-Forward mode:
-
-- [ForwardDiff.jl](https://github.com/JuliaDiff/ForwardDiff.jl)
-- [Enzyme.jl](https://github.com/EnzymeAD/Enzyme.jl)
-- [ChainRulesCore.jl](https://github.com/JuliaDiff/ChainRulesCore.jl)
-- [FiniteDiff.jl](https://github.com/JuliaDiff/FiniteDiff.jl)
+## Example
 
-Reverse mode:
+```jldoctest
+julia> import DifferentiationInterface, ADTypes, ForwardDiff
 
-- [ChainRulesCore.jl](https://github.com/JuliaDiff/ChainRulesCore.jl)
-- [Zygote.jl](https://github.com/FluxML/Zygote.jl)
-- [ReverseDiff.jl](https://github.com/JuliaDiff/ReverseDiff.jl)
+julia> backend = ADTypes.AutoForwardDiff();
 
-Experimental:
+julia> f(x) = sum(abs2, x);
 
-- [PolyesterForwardDiff.jl](https://github.com/JuliaDiff/PolyesterForwardDiff.jl)
-- [Diffractor.jl](https://github.com/JuliaDiff/Diffractor.jl) (currently broken due to [#277](https://github.com/JuliaDiff/Diffractor.jl/issues/277))
+julia> DifferentiationInterface.value_and_gradient(backend, f, [1., 2., 3.])
+(14.0, [2.0, 4.0, 6.0])
+```

benchmark/Project.toml

@@ -1,4 +1,5 @@
 [deps]
+ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
 BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"

benchmark/benchmarks.jl

@@ -1,13 +1,15 @@
+using ADTypes
 using BenchmarkTools
 using DifferentiationInterface
 using LinearAlgebra
 
+using Diffractor: Diffractor
 using Enzyme: Enzyme
 using FiniteDiff: FiniteDiff
 using ForwardDiff: ForwardDiff
 using PolyesterForwardDiff: PolyesterForwardDiff
 using ReverseDiff: ReverseDiff
-using Zygote: Zygote
+using Zygote: Zygote, ZygoteRuleConfig
 
 ## Settings
 
@@ -41,38 +43,18 @@ end
 
 ## Backends
 
-forward_custom_backends = [
- EnzymeForwardBackend(; custom=true),
- FiniteDiffBackend(; custom=true),
- ForwardDiffBackend(; custom=true),
- PolyesterForwardDiffBackend(4; custom=true),
+all_backends = [
+ AutoChainRules(ZygoteRuleConfig()),
+ AutoDiffractor(),
+ AutoEnzyme(Val(:forward)),
+ AutoEnzyme(Val(:reverse)),
+ AutoFiniteDiff(),
+ AutoForwardDiff(),
+ AutoPolyesterForwardDiff(; chunksize=4),
+ AutoReverseDiff(),
+ AutoZygote(),
 ]
 
-forward_fallback_backends = [
- EnzymeForwardBackend(; custom=false),
- FiniteDiffBackend(; custom=false),
- ForwardDiffBackend(; custom=false),
-]
-
-reverse_custom_backends = [
- ZygoteBackend(; custom=true),
- EnzymeReverseBackend(; custom=true),
- ReverseDiffBackend(; custom=true),
-]
-
-reverse_fallback_backends = [
- ZygoteBackend(; custom=false),
- EnzymeReverseBackend(; custom=false),
- ReverseDiffBackend(; custom=false),
-]
-
-all_backends = vcat(
- forward_custom_backends,
- forward_fallback_backends,
- reverse_custom_backends,
- reverse_fallback_backends,
-)
-
 ## Suite
 
 function make_suite()
@@ -83,11 +65,7 @@ function make_suite()
 
  for backend in all_backends
  add_derivative_benchmarks!(SUITE, backend, scalar_to_scalar, 1, 1)
- end
- for backend in forward_fallback_backends
  add_pushforward_benchmarks!(SUITE, backend, scalar_to_scalar, 1, 1)
- end
- for backend in reverse_fallback_backends
  add_pullback_benchmarks!(SUITE, backend, scalar_to_scalar, 1, 1)
  end
 
@@ -97,11 +75,7 @@ function make_suite()
 
  for backend in all_backends
  add_multiderivative_benchmarks!(SUITE, backend, scalar_to_vector, 1, m)
- end
- for backend in forward_fallback_backends
  add_pushforward_benchmarks!(SUITE, backend, scalar_to_vector, 1, m)
- end
- for backend in reverse_fallback_backends
  add_pullback_benchmarks!(SUITE, backend, scalar_to_vector, 1, m)
  end
  end
@@ -112,11 +86,7 @@ function make_suite()
 
  for backend in all_backends
  add_gradient_benchmarks!(SUITE, backend, vector_to_scalar, n, 1)
- end
- for backend in forward_fallback_backends
  add_pushforward_benchmarks!(SUITE, backend, vector_to_scalar, n, 1)
- end
- for backend in reverse_fallback_backends
  add_pullback_benchmarks!(SUITE, backend, vector_to_scalar, n, 1)
  end
  end
@@ -127,11 +97,7 @@ function make_suite()
 
  for backend in all_backends
  add_jacobian_benchmarks!(SUITE, backend, vector_to_vector, n, m)
- end
- for backend in forward_fallback_backends
  add_pushforward_benchmarks!(SUITE, backend, vector_to_vector, n, m)
- end
- for backend in reverse_fallback_backends
  add_pullback_benchmarks!(SUITE, backend, vector_to_vector, n, m)
  end
  end
@@ -144,7 +110,7 @@ include("utils.jl")
 SUITE = make_suite()
 
 # Run benchmarks locally
-# results = BenchmarkTools.run(SUITE; verbose=true)
+results = BenchmarkTools.run(SUITE; verbose=true)
 
 # Compare commits locally
 # using BenchmarkCI; BenchmarkCI.judge(baseline="origin/main"); BenchmarkCI.displayjudgement()