chore!: remove annotation of WrappedFunction

ext/LuxDynamicExpressionsExt.jl

@@ -20,7 +20,7 @@ function Lux.DynamicExpressionsLayer(
         Parallel(nothing,
             ntuple(i -> Lux.DynamicExpressionsLayer(operator_enum, expressions[i],
                 _name_fn(i), turbo, bumper), length(expressions))...),
-        WrappedFunction{:direct_call}(Lux.__stack1);
+        Lux.__stack1;
         name="DynamicExpressionsLayer")
     #! format: on
 end

ext/LuxFluxExt.jl

@@ -17,7 +17,7 @@ function __from_flux_adaptor(l::T; preserve_ps_st::Bool=false, kwargs...) where
     return Lux.FluxLayer(l)
 end
 
-__from_flux_adaptor(l::Function; kwargs...) = Lux.WrappedFunction{:direct_call}(l)
+__from_flux_adaptor(l::Function; kwargs...) = Lux.WrappedFunction(l)
 
 function __from_flux_adaptor(l::Flux.Chain; kwargs...)
     fn = x -> __from_flux_adaptor(x; kwargs...)

src/layers/basic.jl

@@ -214,8 +214,7 @@ struct NoOpLayer <: AbstractExplicitLayer end
 @inline (noop::NoOpLayer)(x, ps, st::NamedTuple) = x, st
 
 """
-    WrappedFunction{DC}(f)
-    WrappedFunction(f) -> WrappedFunction{:runtime_check}(f)
+    WrappedFunction(f)
 
 Wraps a stateless and parameter less function. Might be used when a function is added to
 `Chain`. For example, `Chain(x -> relu.(x))` would not work and the right thing to do would
@@ -224,9 +223,6 @@ be `Chain((x, ps, st) -> (relu.(x), st))`. An easier thing to do would be
 
 ## Arguments
 
-  - `DC`: If `:runtime_check`, then we check if the function can be called with the input
-    `x`, `ps`, and `st` using `hasmethod`. If `:direct_call`, we call `f(x)` directly.
-    For all other values, we call `f(x, ps, st)` which must return a tuple.
   - `f`: Some function.
 
 ## Inputs
@@ -238,33 +234,14 @@ be `Chain((x, ps, st) -> (relu.(x), st))`. An easier thing to do would be
   - Output of `f(x)`
   - Empty `NamedTuple()`
 """
-@concrete struct WrappedFunction{DC} <: AbstractExplicitLayer
+@concrete struct WrappedFunction <: AbstractExplicitLayer
     func <: Function
 end
 
-WrappedFunction(f::F) where {F} = WrappedFunction{:runtime_check}(f)
+(wf::WrappedFunction)(x, ps, st::NamedTuple{}) = wf.func(x), st
 
-function (wf::WrappedFunction{:direct_call})(x, ps, st::NamedTuple)
-    return __maybe_direct_call(wf.func, x, ps, st, Val(true))
-end
-
-function (wf::WrappedFunction)(x, ps, st::NamedTuple)
-    return __maybe_direct_call(wf.func, x, ps, st, Val(false))
-end
-
-@generated function (wf::WrappedFunction{:runtime_check, F})(
-        x, ps, st::NamedTuple) where {F}
-    DC = hasmethod(F, Tuple{typeof(x)})
-    return :(__maybe_direct_call(wf.func, x, ps, st, Val($(DC))))
-end
-
-@inline __maybe_direct_call(f, x, ps, st, ::Val{false}) = f(x, ps, st)
-@inline __maybe_direct_call(f, x, ps, st, ::Val{true}) = f(x), st
-
-function Base.show(io::IO, w::WrappedFunction{T}) where {T}
-    print(io, "WrappedFunction{$(Meta.quot(T))}(")
-    show(io, w.func)
-    print(io, ")")
+function Base.show(io::IO, w::WrappedFunction)
+    print(io, "WrappedFunction(", w.func, ")")
 end
 
 """
@@ -332,7 +309,7 @@ end
 function initialparameters(rng::AbstractRNG, d::Dense{use_bias}) where {use_bias}
     if use_bias
         return (weight=d.init_weight(rng, d.out_dims, d.in_dims),
-            bias=d.init_bias(rng, d.out_dims, 1)) #TODO: In v0.6 make it a vector
+            bias=d.init_bias(rng, d.out_dims))
     else
         return (weight=d.init_weight(rng, d.out_dims, d.in_dims),)
     end
@@ -345,10 +322,6 @@ statelength(d::Dense) = 0
 
 outputsize(d::Dense) = (d.out_dims,)
 
-@inline function (d::Dense)(x::AbstractVector, ps, st::NamedTuple)
-    return vec(first(d(reshape(x, :, 1), ps, st))), st
-end
-
 @inline function (d::Dense)(x::AbstractArray, ps, st::NamedTuple)
     return reshape(first(d(reshape(x, size(x, 1), :), ps, st)), :, size(x)[2:end]...), st
 end
@@ -357,7 +330,7 @@ end
     y = match_eltype(d, ps, st, x)
     return (
         fused_dense_bias_activation(
-            d.activation, ps.weight, y, _vec(_getproperty(ps, Val(:bias)))),
+            d.activation, ps.weight, y, _getproperty(ps, Val(:bias))),
         st)
 end
 

src/layers/conv.jl

@@ -504,7 +504,7 @@ function set to `Base.Fix2(pixel_shuffle, r)`
   - Output of size `(r x W, r x H, C, N)` for 4D-arrays, and `(r x W, r x H, ..., C, N)`
     for D-dimensional data, where `D = ndims(x) - 2`
 """
-PixelShuffle(r::Int) = WrappedFunction{:direct_call}(Base.Fix2(pixel_shuffle, r))
+PixelShuffle(r::Int) = WrappedFunction(Base.Fix2(pixel_shuffle, r))
 
 @doc doc"""
     CrossCor(k::NTuple{N,Integer}, (in_chs => out_chs)::Pair{<:Integer,<:Integer},

src/layers/extension.jl

@@ -54,7 +54,7 @@ DynamicExpressionsLayer(
         layer_1 = DynamicExpressionsLayer(DynamicExpressions.OperatorEnumModule.OperatorEnum{Tuple{typeof(+), typeof(-), typeof(*)}, Tuple{typeof(cos)}}((+, -, *), (cos,)), x1 * cos(x2 - 3.2); turbo=Val{false}(), bumper=Val{false}()),  # 1 parameters
         layer_2 = DynamicExpressionsLayer(DynamicExpressions.OperatorEnumModule.OperatorEnum{Tuple{typeof(+), typeof(-), typeof(*)}, Tuple{typeof(cos)}}((+, -, *), (cos,)), ((x2 - (x1 * x2)) + 2.5) - (1.0 * x1); turbo=Val{false}(), bumper=Val{false}()),  # 2 parameters
     ),
-    layer_2 = WrappedFunction{:direct_call}(Lux.__stack1),
+    layer_2 = WrappedFunction(Lux.__stack1),
 )         # Total: 3 parameters,
           #        plus 0 states.
 

test/contrib/share_parameters_tests.jl

@@ -16,9 +16,9 @@
         @test ps_1.d3.weight == ps_1.d2.l1.weight
         @test ps_1.d3.bias == ps_1.d2.l1.bias
 
-        ps_new_1 = (; weight=randn(rng, Float32, 4, 2), bias=randn(rng, Float32, 4, 1)) |>
+        ps_new_1 = (; weight=randn(rng, Float32, 4, 2), bias=randn(rng, Float32, 4)) |>
                    device
-        ps_new_2 = (; weight=randn(rng, Float32, 2, 4), bias=randn(rng, Float32, 2, 1)) |>
+        ps_new_2 = (; weight=randn(rng, Float32, 2, 4), bias=randn(rng, Float32, 2)) |>
                    device
 
         ps_2 = Lux.Experimental.share_parameters(ps, sharing, (ps_new_1, ps_new_2))
@@ -46,9 +46,9 @@
             ps, sharing, (ps_new_1,))
 
         # Parameter Structure Mismatch
-        ps_new_1 = (; weight=randn(rng, Float32, 2, 4), bias=randn(rng, Float32, 4, 1)) |>
+        ps_new_1 = (; weight=randn(rng, Float32, 2, 4), bias=randn(rng, Float32, 4)) |>
                    device
-        ps_new_2 = (; weight=randn(rng, Float32, 2, 4), bias=randn(rng, Float32, 2, 1)) |>
+        ps_new_2 = (; weight=randn(rng, Float32, 2, 4), bias=randn(rng, Float32, 2)) |>
                    device
 
         @test_throws ArgumentError Lux.Experimental.share_parameters(

test/helpers/compact_tests.jl

@@ -104,14 +104,14 @@
             ps, st = Lux.setup(rng, model) |> device
 
             @test size(ps.w1.weight) == (128, 1)
-            @test size(ps.w1.bias) == (128, 1)
+            @test size(ps.w1.bias) == (128,)
             @test length(ps.w2) == nlayers
             for i in 1:nlayers
                 @test size(ps.w2[i].weight) == (128, 128)
-                @test size(ps.w2[i].bias) == (128, 1)
+                @test size(ps.w2[i].bias) == (128,)
             end
             @test size(ps.w3.weight) == (1, 128)
-            @test size(ps.w3.bias) == (1, 1)
+            @test size(ps.w3.bias) == (1,)
 
             x = randn(n_in, 32) |> aType
 

test/layers/basic_tests.jl

@@ -98,26 +98,6 @@
             @jet layer(x, ps, st)
             __f = x -> sum(first(layer(x, ps, st)))
             @eval @test_gradients $__f $x gpu_testing=$ongpu atol=1.0f-3 rtol=1.0f-3
-
-            f11(x) = x .* x
-
-            layer = WrappedFunction{:runtime_check}(f11)
-            display(layer)
-            ps, st = Lux.setup(rng, layer) .|> device
-            x = randn(rng, 2, 3) |> aType
-
-            @test layer(x, ps, st)[1] ≈ x .* x
-            @inferred layer(x, ps, st)
-
-            f12(x, ps, st) = x .+ 1, st
-
-            layer = WrappedFunction{:runtime_check}(f12)
-            display(layer)
-            ps, st = Lux.setup(rng, layer) .|> device
-            x = randn(rng, 2, 3) |> aType
-
-            @test layer(x, ps, st)[1] ≈ x .+ 1
-            @inferred layer(x, ps, st)
         end
 
         @testset "PeriodicEmbedding" begin
@@ -149,7 +129,7 @@ end
             ps, st = Lux.setup(rng, layer) .|> device
 
             @test size(ps.weight) == (100, 10)
-            @test size(ps.bias) == (100, 1)
+            @test size(ps.bias) == (100,)
             @test layer.activation == identity
 
             layer = Dense(10, 100, relu; use_bias=false)

test/layers/containers_tests.jl

@@ -4,8 +4,7 @@
 
     @testset "$mode" for (mode, aType, device, ongpu) in MODES
         @testset "zero sum" begin
-            layer = SkipConnection(
-                WrappedFunction{:direct_call}(Broadcast.BroadcastFunction(zero)), .+)
+            layer = SkipConnection(WrappedFunction(Broadcast.BroadcastFunction(zero)), .+)
             display(layer)
             ps, st = Lux.setup(rng, layer) .|> device
             x = randn(rng, 10, 10, 10, 10) |> aType
@@ -38,8 +37,7 @@ end
     @testset "$mode" for (mode, aType, device, ongpu) in MODES
         @testset "zero sum" begin
             layer = Parallel(
-                +, WrappedFunction{:direct_call}(Broadcast.BroadcastFunction(zero)),
-                NoOpLayer())
+                +, WrappedFunction(Broadcast.BroadcastFunction(zero)), NoOpLayer())
             display(layer)
             ps, st = Lux.setup(rng, layer) .|> device
             x = randn(rng, 10, 10, 10, 10) |> aType
@@ -320,7 +318,7 @@ end
     end
 
     @testset "constructors" begin
-        f1(x, ps, st::NamedTuple) = (x .+ 1, st)
+        f1(x) = x .+ 1
         f2(x) = x .+ 2
         model = Chain((Dense(2 => 3), Dense(3 => 2)), f1, f2, NoOpLayer())
 

test/transform/flux_tests.jl

@@ -503,7 +503,7 @@
         @testset "Functions" begin
             @test tolux(Flux.flatten) isa Lux.FlattenLayer
             @test tolux(identity) isa Lux.NoOpLayer
-            @test tolux(+) isa Lux.WrappedFunction{:direct_call}
+            @test tolux(+) isa Lux.WrappedFunction
         end
 
         @testset "Unsupported Layers" begin