fix: static vector input to dense

Project.toml

@@ -1,7 +1,7 @@
 name = "Lux"
 uuid = "b2108857-7c20-44ae-9111-449ecde12c47"
 authors = ["Avik Pal <avikpal@mit.edu> and contributors"]
-version = "1.0.4"
+version = "1.0.5"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"

src/utils.jl

@@ -9,6 +9,7 @@ using ForwardDiff: Dual
 using Functors: fmapstructure
 using Random: AbstractRNG
 using Static: Static, StaticBool, StaticInteger, StaticSymbol
+using StaticArraysCore: SMatrix, SVector
 
 using LuxCore: LuxCore, AbstractLuxLayer
 using MLDataDevices: get_device
@@ -199,10 +200,12 @@ function named_tuple_layers(layers::Vararg{AbstractLuxLayer, N}) where {N}
 end
 
 make_abstract_matrix(x::AbstractVector) = reshape(x, :, 1)
+make_abstract_matrix(x::SVector{L, T}) where {L, T} = SMatrix{L, 1, T}(x)
 make_abstract_matrix(x::AbstractMatrix) = x
 make_abstract_matrix(x::AbstractArray{T, N}) where {T, N} = reshape(x, Base.size(x, 1), :)
 
 matrix_to_array(x::AbstractMatrix, ::AbstractVector) = vec(x)
+matrix_to_array(x::SMatrix{L, 1, T}, ::AbstractVector) where {L, T} = SVector{L, T}(x)
 matrix_to_array(x::AbstractMatrix, ::AbstractMatrix) = x
 matrix_to_array(x::AbstractMatrix, y::AbstractArray) = reshape(x, :, size(y)[2:end]...)
 

test/Project.toml

@@ -32,6 +32,7 @@ Setfield = "efcf1570-3423-57d1-acb7-fd33fddbac46"
 SimpleChains = "de6bee2f-e2f4-4ec7-b6ed-219cc6f6e9e5"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 Static = "aedffcd0-7271-4cad-89d0-dc628f76c6d3"
+StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
@@ -71,6 +72,7 @@ Setfield = "1.1.1"
 SimpleChains = "0.4.7"
 StableRNGs = "1.0.2"
 Static = "1"
+StaticArrays = "1.9"
 Statistics = "1.11.1"
 Test = "1.10"
 Tracker = "0.2.34"

test/layers/basic_tests.jl

@@ -170,6 +170,25 @@ end
     end
 end
 
+@testitem "Dense StaticArrays" setup=[SharedTestSetup] tags=[:core_layers] begin
+    using StaticArrays, Enzyme, ForwardDiff, ComponentArrays
+
+    N = 8
+    d = Lux.Dense(N => N)
+    ps = (;
+        weight=randn(SMatrix{N, N, Float64}),
+        bias=randn(SVector{N, Float64})
+    )
+    x = randn(SVector{N, Float64})
+
+    fun = let d = d, x = x
+        ps -> sum(d(x, ps, (;))[1])
+    end
+    grad1 = ForwardDiff.gradient(fun, ComponentVector(ps))
+    grad2 = Enzyme.gradient(Enzyme.Reverse, fun, ps)
+    @test maximum(abs, grad1 .- ComponentVector(grad2)) < 1e-6
+end
+
 @testitem "Scale" setup=[SharedTestSetup] tags=[:core_layers] begin
     rng = StableRNG(12345)