Add recursive_map and base make_zero(!) on it

ext/EnzymeStaticArraysExt.jl

@@ -32,11 +32,15 @@ end
     end
 end
 
-@inline function Enzyme.EnzymeCore.make_zero(x::FT)::FT where {FT<:SArray}
-    return Base.zero(x)
-end
-@inline function Enzyme.EnzymeCore.make_zero(x::FT)::FT where {FT<:MArray}
-    return Base.zero(x)
+# SArrays and MArrays don't need special treatment for `make_zero(!)` to work or be correct,
+# but in case their dedicated `zero` and `fill!` methods are more efficient than
+# `make_zero(!)`s generic recursion, we opt into treating them as leaves when they have
+# isbits eltypes (non-isbits eltypes excluded as the dedicated `zero` and `fill!` methods
+# don't support those).
+@inline function Enzyme.EnzymeCore.isvectortype(
+    ::Type{<:Union{SArray{S,T},MArray{S,T}}}
+) where {S,T}
+    return isbitstype(T) && Enzyme.Compiler.RecursiveMap.isscalartype(T)
 end
 
 end

lib/EnzymeCore/src/EnzymeCore.jl

@@ -501,28 +501,96 @@ function autodiff_thunk end
 function autodiff_deferred_thunk end
 
 """
+    make_zero(prev::T, ::Val{copy_if_inactive}=Val(false))::T
     make_zero(::Type{T}, seen::IdDict, prev::T, ::Val{copy_if_inactive}=Val(false))::T
 
 Recursively make a zero'd copy of the value `prev` of type `T`. The argument `copy_if_inactive` specifies
 what to do if the type `T` is guaranteed to be inactive, use the primal (the default) or still copy the value. 
+
+Extending this method for custom types is rarely needed. For new plain array types like GPU
+arrays, extending [`isvectortype`](@ref) is sufficient as long as the array type implements
+`Base.zero`.
 """
 function make_zero end
 
 """
-    make_zero!(val::T, seen::IdSet{Any}=IdSet())::Nothing
+    make_zero!(val::T, seen::IdDict=IdDict())::Nothing
+
+Recursively set a variables differentiable fields to zero. Only applicable for types `T`
+that are mutable or hold all differentiable values in mutable containers (e.g.,
+`Tuple{Vector{Float64}}`).
 
-Recursively set a variables differentiable fields to zero. Only applicable for mutable types `T`.
+Extending this method for custom types is rarely needed. For new plain mutable array types
+like GPU arrays, extending [`isvectortype`](@ref) is sufficient as long as the array type
+implements `Base.zero` and `Base.fill!`.
 """
 function make_zero! end
 
 """
-    make_zero(prev::T)
+    isvectortype(::Type{T})::Bool
+
+Trait defining types whose values should be considered leaf nodes when [`make_zero`](@ref)
+and [`make_zero!`](@ref) recurse through an object.
+
+By default, `isvectortype(T) == true` for `T` such that `isscalartype(T) == true` or
+`T <: Union{Array{U},GenericMemory{_,U}}` where `isscalartype(U) == true`.
+
+A new plain array type, for example a GPU array, may extend this as follows:
+
+```julia
+@inline EnzymeCore.isvectortype(::Type{<:GPUArray{U}}) where {U} = isscalartype(U)
+```
+
+Such a type should implement `Base.zero` and, if mutable, `Base.fill!`. (If this is not
+feasible, an alternative is to add methods `EnzymeCore.make_zero(arr::T)::T` and, if
+mutable, `EnzymeCore.make_zero!(arr::T)::Nothing`; such methods will also be picked up by
+recursive calls.)
 
-Helper function to recursively make zero.
+Such extensions are mostly relevant for the lowest-level of abstraction of memory at which
+vector space operations like addition and scalar multiplication are supported, the
+prototypical case being `Array`. Regular Julia structs with vector space-like semantics
+should normally not extend `isvectorspace`; `make_zero(!)` will recurse into them and act
+directly on their backing arrays, just like how Enzyme treats them when differentiating. For
+example, structured matrix wrappers and sparse array types that are backed by `Array` should
+not extend `isvectortype`.
+
+See also [`isscalartype`](@ref).
 """
-@inline function make_zero(prev::T, ::Val{copy_if_inactive}=Val(false)) where {T, copy_if_inactive}
-    make_zero(Core.Typeof(prev), IdDict(), prev, Val(copy_if_inactive))
-end
+function isvectortype end
+
+"""
+    isscalartype(::Type{T})::Bool
+
+Trait defining a subset of [`isvectortype`](@ref) types that should not be considered
+composite, such that even if the type is mutable, [`make_zero!`](@ref) will not try to zero
+values of the type in-place. For example, `BigFloat` is a mutable type but does not support
+in-place mutation through any Julia API, and `isscalartype(BigFloat) == true` ensures that
+`make_zero!` will not try to mutate `BigFloat` values.[^BigFloat]
+
+By default, `isscalartype(T) == true` for `T <: AbstractFloat` and
+`T <: Complex{<:AbstractFloat}`.
+
+A hypothetical new real number type with Enzyme support should in most cases simply subtype
+`AbstractFloat` and inherit the `isscalartype` trait that way. If this is not appropriate,
+the function can be extended as follows:
+
+```julia
+@inline EnzymeCore.isscalartype(::Type{<:NewReal}) = true
+@inline EnzymeCore.isscalartype(::Type{<:Complex{<:NewReal}}) = true
+```
+
+In either case, the type should implement `Base.zero`. (If this is not feasible, an
+alternative is to add a method `EnzymeCore.make_zero(x::T)::T`; such a method will also be
+picked up by recursive calls.)
+
+See also [`isvectortype`](@ref).
+
+[^BigFloat]: Enzyme does not support differentiating `BigFloat` as of this writing; it is
+mentioned here only to illustrate that it would be inappropriate to use traits like
+`ismutable` or `isbitstype` to choose between in-place and out-of-place zeroing,
+demonstrating the need for a dedicated `isscalartype` trait.
+"""
+function isscalartype end
 
 function tape_type end
 

src/compiler.jl

@@ -1132,8 +1132,6 @@ struct Tape{TapeTy,ShadowTy,ResT}
     shadow_return::ShadowTy
 end
 
-include("make_zero.jl")
-
 function nested_codegen!(mode::API.CDerivativeMode, mod::LLVM.Module, f, tt, world)
     funcspec = my_methodinstance(typeof(f), tt, world)
     nested_codegen!(mode, mod, funcspec, world)
@@ -7610,6 +7608,7 @@ end
 end
 
 # Recursively return x + f(y), where y is active, otherwise x
+include("recursive_map.jl")
 
 @inline function recursive_add(
     x::T,