feat: support generators + force calling into interp

src/ConcreteRArray.jl

@@ -650,7 +650,7 @@ function Base.mapreduce(
     @nospecialize(op),
     @nospecialize(A::AbstractConcreteArray{T,N});
     dims=:,
-    init=nothing,
+    init=Base._InitialValue(),
 ) where {T,N}
     fn = compile(CallMapReduce(f, op, dims, init), (A,))
     return fn(A)

src/Ops.jl

@@ -46,7 +46,11 @@ macro opcall(expr)
 
     # Generate location info at the callsite
     location_expr = :($(mlir_stacktrace)(
-        joinpath(string(var"#self#"), $(string(func))),
+        if @isdefined(var"#self#")
+            joinpath(string(var"#self#"), $(string(func)))
+        else
+            $(string(func))
+        end,
         $(string(__source__.file)),
         $(__source__.line),
     ))
@@ -2575,7 +2579,7 @@ end
     seen_cache = Reactant.OrderedIdDict()
     Reactant.make_tracer(
         seen_cache,
-        args,
+        fnwrapped ? (f, args) : args,
         (), # we have to insert something here, but we remove it immediately below.
         Reactant.TracedTrack;
         toscalar=false,

src/Overlay.jl

@@ -156,7 +156,10 @@ end
 end
 
 @reactant_overlay @noinline function Base.mapreduce(
-    f, op, A::Union{AbstractArray,Base.Iterators.Zip,Base.Iterators.Enumerate}; kwargs...
+    f,
+    op,
+    A::Union{AbstractArray,Base.Iterators.Zip,Base.Iterators.Enumerate,Base.Generator};
+    kwargs...,
 )
     if use_overlayed_version(A)
         return TracedRArrayOverrides.overloaded_mapreduce(f, op, A; kwargs...)

src/Reactant.jl

@@ -60,6 +60,7 @@ function _parent end
 _parent_type(::Type{Array}) = Array
 _parent_type(::Type{Array{T}}) where {T} = Array{T}
 _parent_type(::Type{Array{T,N}}) where {T,N} = Array{T,N}
+_parent_type(::Type{<:Slices{P}}) where {P} = P
 
 include("accelerators/Accelerators.jl")
 
@@ -179,10 +180,15 @@ include("TracedRArray.jl")
 include("ConcreteRArray.jl")
 
 use_overlayed_version(x) = false
-use_overlayed_version(x::Base.Iterators.Zip) = any(use_overlayed_version, x.is)
+function use_overlayed_version(x::F) where {F<:Function}
+    return use_overlayed_version(getfield.(Ref(x), fieldnames(F)))
+end
+use_overlayed_version(x::Base.Generator) = use_overlayed_version((x.f, x.iter))
+use_overlayed_version(x::Base.Iterators.Zip) = use_overlayed_version(x.is)
 use_overlayed_version(x::Base.Iterators.Enumerate) = use_overlayed_version(x.itr)
-use_overlayed_version(iter::Tuple) = any(use_overlayed_version, iter)
-use_overlayed_version(iter::NamedTuple) = any(use_overlayed_version, values(iter))
+use_overlayed_version(x::Vector) = looped_any(use_overlayed_version, x)
+use_overlayed_version(iter::Tuple) = looped_any(use_overlayed_version, iter)
+use_overlayed_version(iter::NamedTuple) = looped_any(use_overlayed_version, values(iter))
 use_overlayed_version(::TracedRArray) = true
 use_overlayed_version(::TracedRNumber) = true
 use_overlayed_version(::Number) = false
@@ -195,6 +201,14 @@ function use_overlayed_version(x::AbstractArray)
     return use_overlayed_version(a)
 end
 
+## We avoid calling into `any` to avoid triggering the `any` overlay
+function looped_any(f::F, itr) where {F}
+    @inbounds for x in itr
+        f(x) && return true
+    end
+    return false
+end
+
 # StdLib Overloads
 include("stdlibs/LinearAlgebra.jl")
 include("stdlibs/Random.jl")

src/TracedRArray.jl

@@ -551,7 +551,7 @@ function __default_init(T::Type{<:Reactant.ReactantFloat8}, op::F) where {F}
 end
 
 function overloaded_mapreduce(
-    @nospecialize(f), @nospecialize(op), @nospecialize(A); dims=:, init=nothing
+    @nospecialize(f), @nospecialize(op), @nospecialize(A); dims=:, init=Base._InitialValue()
 )
     res = unwrapped_broadcast(f, A)
     # This means we are unable to use the optimized dispatches. For now we will
@@ -568,7 +568,7 @@ function overloaded_mapreduce(
     @nospecialize(op),
     @nospecialize(A::AnyTracedRArray{T,N});
     dims=:,
-    init=nothing,
+    init=Base._InitialValue(),
 ) where {T,N}
     A = materialize_traced_array(A)
 
@@ -589,7 +589,7 @@ function overloaded_mapreduce(
 
     res = @opcall reduce(reduce_input, reduce_init, dims, op)
 
-    init !== nothing && (res = op.(res, init))
+    (init isa Base._InitialValue || init === nothing) || (res = op.(res, init))
 
     if original_dims isa Colon
         @assert size(res) == () "expected size of result to be (), got $(size(res))"
@@ -677,6 +677,8 @@ function Broadcast.copy(bc::Broadcasted{<:AbstractReactantArrayStyle})
     # Special case a union{} return so we can see the better error message
     if ElType === Union{}
         fn(map(first_scalar, bc.args)...)
+    elseif ElType == Any
+        ElType = eltype(fn(map(first_scalar, bc.args)...))
     end
     @assert ElType != Any && ElType != Union{}
     sim = similar(bc, ElType)
@@ -1231,16 +1233,25 @@ function overloaded_map(f, x::AbstractArray, xs::AbstractArray...)
     @assert allequal((axes(x), axes.(xs)...)) "Expected axes of all inputs to map to be \
                                                equal"
 
+    needs_unrolling = falses(length(xs) + 1)
     inputs = ()
-    for input in (x, xs...)
+    for (i, input) in enumerate((x, xs...))
         if input isa AnyTracedRArray
             input = Reactant.materialize_traced_array(input)
-        else
+        elseif eltype(input) <: Reactant.ReactantPrimitive
             input = Reactant.promote_to(TracedRArray{eltype(input),ndims(input)}, input)
+        else
+            needs_unrolling[i] = true
         end
         inputs = (inputs..., input)
     end
 
+    @assert allequal(needs_unrolling) "All inputs to `overloaded_map` must be \
+                                       unrolled or none of them. Open an issue."
+    if needs_unrolling[1]
+        length(inputs) == 1 && return unrolled_map(f, only(inputs))
+        return unrolled_map(splat(f), zip(inputs...))
+    end
     return TracedUtils.elem_apply(f, inputs...)
 end
 
@@ -1321,14 +1332,14 @@ function scan_impl!(
     output::AnyTracedRArray{T,N},
     input::AnyTracedRArray{T,N};
     dims::Integer,
-    init=nothing,
+    init=Base._InitialValue(),
 ) where {T,N}
     @assert dims > 0 "dims must be a positive integer"
     @assert axes(output) == axes(input) "output and input must have the same shape"
 
     dims > ndims(input) && return copyto!(output, input)
 
-    if init === nothing
+    if init isa Base._InitialValue
         op_in_T = Core.Compiler.return_type(op, Tuple{T,T})
         op_in_T === Union{} && (op_in_T = T)
         init = __default_init(T, op)
@@ -1494,27 +1505,44 @@ struct BroadcastIterator{F}
     f::F
 end
 
-(fn::BroadcastIterator)(args...) = Reactant.call_with_reactant(fn.f, (args...,))
+(fn::BroadcastIterator)(args...) = fn.f((args...,))
 
 function unwrapped_broadcast(f::F, x::Base.Iterators.Zip) where {F}
     min_length = Base.inferencebarrier(minimum)(length, x.is)
     itrs = [length(itr) > min_length ? itr[1:min_length] : itr for itr in x.is]
-    if any(Base.Fix2(isa, AnyTracedRArray), itrs)
-        return (BroadcastIterator(f)).(itrs...)
-    else
-        fn = BroadcastIterator(f)
-        return [fn(Base.Fix2(getindex, i).(itrs)...) for i in 1:min_length]
-    end
+    any(Base.Fix2(isa, AnyTracedRArray), itrs) || return unrolled_map(f, x)
+    return broadcast(BroadcastIterator(f), itrs...)
 end
 
 function unwrapped_broadcast(f::F, x::Base.Iterators.Enumerate) where {F}
-    if x.itr isa AnyTracedRArray
-        return (BroadcastIterator(f)).(1:length(x.itr), x.itr)
-    else
-        return [f((i, x.itr[i])) for i in 1:length(x.itr)]
-    end
+    x.itr isa AnyTracedRArray || return unrolled_map(f, x)
+    return broadcast(
+        BroadcastIterator(f), Reactant.promote_to(TracedRArray, 1:length(x.itr)), x.itr
+    )
 end
 
-unwrapped_broadcast(f::F, xs::Vector) where {F} = [f(x) for x in xs]
+unwrapped_broadcast(f::F, xs) where {F} = unrolled_map(f, xs)
+
+# TODO: once traced_call supports internal mutations, we can use traced_call here
+# TODO: we should overload this for Slices and use mapslices instead
+function unrolled_map(f::F, itr) where {F}
+    y = Reactant.call_with_reactant(iterate, itr)
+    y === nothing && return []
+
+    first, state = y
+    res_first = Reactant.call_with_reactant(f, first)
+    result = [res_first]
+
+    while true
+        y = Reactant.call_with_reactant(iterate, itr, state)
+        y === nothing && break
+
+        val, state = y
+        res = Reactant.call_with_reactant(f, val)
+        push!(result, res)
+    end
+
+    return result
+end
 
 end

src/TracedUtils.jl

@@ -660,7 +660,7 @@ function finalize_mlir_fn(
     skipped_results = Reactant.TracedType[]
     for (k, v) in seen_results
         v isa Reactant.TracedType || continue
-        if any(Base.Fix1(===, k), skipped_args)
+        if Reactant.looped_any(Base.Fix1(===, k), skipped_args)
             push!(skipped_results, v)
 
             _, argpath = get_argidx(v, argprefix)

src/utils.jl

@@ -1,4 +1,4 @@
-struct CallWithReactant{F}
+struct CallWithReactant{F} <: Function
     f::F
 end
 

test/autodiff.jl

@@ -132,7 +132,7 @@ end
 
 @testset "Forward Gradient" begin
     x = Reactant.to_rarray(3.1 * ones(2, 2))
-    res = @test_warn r"`Adapt.parent_type` is not implemented for" @jit gw(x)
+    res = @jit gw(x)
     # TODO we should probably override https://github.com/EnzymeAD/Enzyme.jl/blob/5e6a82dd08e74666822b9d7b2b46c36b075668ca/src/Enzyme.jl#L2132
     # to make sure this gets merged as a tracedrarray
     @test res isa Tuple{<:Enzyme.TupleArray{<:ConcreteRNumber{Float64},(2, 2),4,2}}

test/basic.jl

@@ -926,7 +926,7 @@ end
 
     ra = Reactant.to_rarray(x)
     @jit dip!(ra)
-    ra[:a] ≈ (2.7 * 2) * ones(4)
+    @test ra[:a] ≈ (2.7 * 3.1) * ones(4)
 end
 
 @testset "@code_xla" begin
@@ -1429,7 +1429,10 @@ end
 end
 
 zip_iterator(a, b) = mapreduce(splat(*), +, zip(a, b))
+zip_iterator2(a, b) = mapreduce(splat(.-), +, zip(a, b))
 enumerate_iterator(a) = mapreduce(splat(*), +, enumerate(a))
+enumerate_iterator2(a) = mapreduce(splat(.-), +, enumerate(a))
+mapreduce_vector(a) = mapreduce(-, +, a)
 
 function nested_mapreduce_zip(x, y)
     return mapreduce(+, zip(eachcol(x), eachcol(y)); init=0.0f0) do (x, y)
@@ -1445,44 +1448,65 @@ function nested_mapreduce_hcat(x, y)
     end
 end
 
+function f_generator(points, params)
+    return sum(params * point for point in points)
+end
+
 @testset "Base.Iterators" begin
     @testset "zip" begin
         N = 10
-        a = range(1.0, 5.0; length=N)
-        x = range(10.0, 15.0; length=N + 2)
+        a = collect(range(1.0, 5.0; length=N))
+        x = collect(range(10.0, 15.0; length=N + 2))
         x_ra = Reactant.to_rarray(x)
 
         @test @jit(zip_iterator(a, x_ra)) ≈ zip_iterator(a, x)
+
+        a = [rand(Float32, 2, 3) for _ in 1:10]
+        x = [rand(Float32, 2, 3) for _ in 1:10]
+        a_ra = Reactant.to_rarray(a)
+        x_ra = Reactant.to_rarray(x)
+
+        @test @jit(zip_iterator2(a_ra, x_ra)) ≈ zip_iterator2(a, x)
     end
 
     @testset "enumerate" begin
-        x = range(1.0, 5.0; length=10)
+        x = collect(range(1.0, 5.0; length=10))
         x_ra = Reactant.to_rarray(x)
 
         @test @jit(enumerate_iterator(x_ra)) ≈ enumerate_iterator(x)
+
+        x = [rand(Float32, 2, 3) for _ in 1:10]
+        x_ra = Reactant.to_rarray(x)
+
+        @test @jit(enumerate_iterator2(x_ra)) ≈ enumerate_iterator2(x)
     end
 
     @testset "nested mapreduce" begin
         x = rand(Float32, 4, 3)
         y = rand(Float32, 4, 3)
-
         x_ra = Reactant.to_rarray(x)
         y_ra = Reactant.to_rarray(y)
-
         @test @jit(nested_mapreduce_zip(x_ra, y_ra)) ≈ nested_mapreduce_zip(x, y)
     end
-
     @testset "nested mapreduce hcat" begin
         x = rand(Float32, 4, 3)
         y = rand(Float32, 4, 3)
-
         x_ra = Reactant.to_rarray(x)
         y_ra = Reactant.to_rarray(y)
 
         @test @jit(nested_mapreduce_hcat(x_ra, y_ra)) ≈ nested_mapreduce_hcat(x, y)
     end
 end
 
+@testset "Base.Generator" begin
+    points = eachcol(rand(Float32, 2, 6))
+    params = rand(Float32, 4, 2)
+    points_ra = Reactant.to_rarray(points)
+    params_ra = Reactant.to_rarray(params)
+
+    @test @jit(f_generator(points_ra, params_ra)) ≈ f_generator(points, params)
+end
+
 @testset "compilation cache" begin
     if Reactant.PersistentCompileCache.autotune_cache_enabled() &&
         contains(string(Reactant.devices()[1]), "CUDA")
@@ -1574,3 +1598,31 @@ end
     x_ra = Reactant.to_rarray(x)
     @test @jit(clamp!(x_ra, 0.5, Inf32)) ≈ clamp!(x, 0.5, Inf32)
 end
+
+mapped_sub(xs...) = stack(map(-, xs...))
+
+@testset "map of slices" begin
+    # We shouldn't be using `elem_apply` in this case and instead unroll the map
+    # our passes will fuse them backup if needed.
+    @testset "Vector of Slices" begin
+        x_full = rand(Float32, 10, 5, 3)
+        y_full = rand(Float32, 10, 5, 3)
+        x = [view(x_full, :, i, :) for i in 1:size(x_full, 2)]
+        y = [view(y_full, :, i, :) for i in 1:size(y_full, 2)]
+        x_ra = Reactant.to_rarray(x)
+        y_ra = Reactant.to_rarray(y)
+
+        @test @jit(mapped_sub(x_ra, y_ra)) ≈ mapped_sub(x, y) atol = 1e-5 rtol = 1e-5
+    end
+
+    @testset "Slices" begin
+        x_full = rand(Float32, 10, 5)
+
+        @testset "ColumnSlices" begin
+            x_sliced = eachcol(x_full)
+            x_ra = Reactant.to_rarray(x_sliced)
+
+            @test @jit(mapped_sub(x_ra)) ≈ mapped_sub(x_sliced) atol = 1e-5 rtol = 1e-5
+        end
+    end
+end