Format

benchmark/mlp.jl

@@ -1,5 +1,5 @@
 function create_benchmark_mlp(mlp_conf::Tuple{Int, Int}, x::Vector{T},
-    l::Vector{T}) where {T <: Number}
+        l::Vector{T}) where {T <: Number}
     input, hidden = mlp_conf
     W₁, W₂, b₁, b₂ = rand(hidden, input), rand(1, hidden), rand(hidden), rand(1)
     σ = exp

ext/TaylorDiffSFExt.jl

@@ -8,7 +8,7 @@ using ChainRules, ChainRulesCore
 
 dummy = (NoTangent(), 1)
 @variables z
-for func in (erf, )
+for func in (erf,)
     F = typeof(func)
     # base case
     @eval function (op::$F)(t::TaylorScalar{T, 2}) where {T}
@@ -30,4 +30,4 @@ for func in (erf, )
     end
 end
 
-end
+end

src/chainrules.jl

@@ -22,7 +22,7 @@ function rrule(::typeof(value), t::TaylorScalar{T, N}) where {N, T}
 end
 
 function rrule(::typeof(extract_derivative), t::TaylorScalar{T, N},
-    i::Integer) where {N, T}
+        i::Integer) where {N, T}
     function extract_derivative_pullback(d̄)
         NoTangent(), TaylorScalar{T, N}(ntuple(j -> j === i ? d̄ : zero(T), Val(N))),
         NoTangent()
@@ -31,7 +31,7 @@ function rrule(::typeof(extract_derivative), t::TaylorScalar{T, N},
 end
 
 function rrule(::typeof(*), A::AbstractMatrix{S},
-    t::AbstractVector{TaylorScalar{T, N}}) where {N, S, T}
+        t::AbstractVector{TaylorScalar{T, N}}) where {N, S, T}
     project_A = ProjectTo(A)
     function gemv_pullback(x̄)
         x̂ = reinterpret(reshape, T, x̄)
@@ -42,12 +42,14 @@ function rrule(::typeof(*), A::AbstractMatrix{S},
 end
 
 function rrule(::typeof(*), A::AbstractMatrix{S},
-    B::AbstractMatrix{TaylorScalar{T, N}}) where {N, S, T}
+        B::AbstractMatrix{TaylorScalar{T, N}}) where {N, S, T}
     project_A = ProjectTo(A)
     project_B = ProjectTo(B)
     function gemm_pullback(x̄)
         X̄ = unthunk(x̄)
-        NoTangent(), @thunk(project_A(X̄ * transpose(B))), @thunk(project_B(transpose(A) * X̄))
+        NoTangent(),
+        @thunk(project_A(X̄ * transpose(B))),
+        @thunk(project_B(transpose(A) * X̄))
     end
     return A * B, gemm_pullback
 end
@@ -85,8 +87,8 @@ struct TaylorOneElement{T, N, I, A} <: AbstractArray{T, N}
     ind::I
     axes::A
     function TaylorOneElement(val::T, ind::I,
-        axes::A) where {T <: TaylorScalar, I <: NTuple{N, Int},
-        A <: NTuple{N, AbstractUnitRange}} where {N}
+            axes::A) where {T <: TaylorScalar, I <: NTuple{N, Int},
+            A <: NTuple{N, AbstractUnitRange}} where {N}
         new{T, N, I, A}(val, ind, axes)
     end
 end
@@ -125,7 +127,7 @@ function rrule(::typeof(*), x::TaylorScalar, y::TaylorScalar)
 end
 
 function rrule(::typeof(*), x::TaylorScalar, y::TaylorScalar, z::TaylorScalar,
-    more::TaylorScalar...)
+        more::TaylorScalar...)
     Ω2, back2 = rrule(*, x, y)
     Ω3, back3 = rrule(*, Ω2, z)
     Ω4, back4 = rrule(*, Ω3, more...)

src/derivative.jl

@@ -46,7 +46,7 @@ make_taylor(t0::T, t1::S, ::Val{N}) where {T, S, N} = TaylorScalar{T, N}(t0, T(t
 end
 
 @inline function derivative(f, x::AbstractVector{T}, l::AbstractVector{S},
-    vN::Val{N}) where {T <: TN, S <: TN, N}
+        vN::Val{N}) where {T <: TN, S <: TN, N}
     t = map((t0, t1) -> make_taylor(t0, t1, vN), x, l)
     # equivalent to map(TaylorScalar{T, N}, x, l)
     return extract_derivative(f(t), N)
@@ -61,7 +61,7 @@ end
 end
 
 @inline function derivative(f, x::AbstractMatrix{T}, l::AbstractVector{S},
-    vN::Val{N}) where {T <: TN, S <: TN, N}
+        vN::Val{N}) where {T <: TN, S <: TN, N}
     t = make_taylor.(x, l, vN)
     return extract_derivative.(f(t), N)
 end

src/primitive.jl

@@ -148,7 +148,7 @@ end
 end
 
 @generated function raise(f::T, df::TaylorScalar{T, M},
-    t::TaylorScalar{T, N}) where {T, M, N} # M + 1 == N
+        t::TaylorScalar{T, N}) where {T, M, N} # M + 1 == N
     return quote
         $(Expr(:meta, :inline))
         vdf, vt = value(df), value(t)
@@ -162,7 +162,7 @@ end
 raise(::T, df::S, t::TaylorScalar{T, N}) where {S <: Number, T, N} = df * t
 
 @generated function raiseinv(f::T, df::TaylorScalar{T, M},
-    t::TaylorScalar{T, N}) where {T, M, N} # M + 1 == N
+        t::TaylorScalar{T, N}) where {T, M, N} # M + 1 == N
     ex = quote
         vdf, vt = value(df), value(t)
         v1 = vt[2] / vdf[1]

src/scalar.jl

@@ -58,7 +58,7 @@ end
 @inline value(t::TaylorScalar) = t.value
 @inline extract_derivative(t::TaylorScalar, i::Integer) = t.value[i]
 @inline function extract_derivative(v::AbstractArray{T},
-    i::Integer) where {T <: TaylorScalar}
+        i::Integer) where {T <: TaylorScalar}
     map(t -> extract_derivative(t, i), v)
 end
 @inline extract_derivative(r, i::Integer) = false
@@ -73,7 +73,7 @@ adjoint(t::TaylorScalar) = t
 conj(t::TaylorScalar) = t
 
 function promote_rule(::Type{TaylorScalar{T, N}},
-    ::Type{S}) where {T, S, N}
+        ::Type{S}) where {T, S, N}
     TaylorScalar{promote_type(T, S), N}
 end
 

test/derivative.jl

@@ -3,14 +3,14 @@
     g(x) = x^3
     @test derivative(g, 1.0, 1) ≈ 3
 
-    h(x) = x.^3
+    h(x) = x .^ 3
     @test derivative(h, [2.0 3.0], 1) ≈ [12.0 27.0]
 end
 
 @testset "Directional derivative" begin
     g(x) = x[1] * x[1] + x[2] * x[2]
     @test derivative(g, [1.0, 2.0], [1.0, 0.0], 1) ≈ 2.0
 
-    h(x) = sum(x, dims=1)
-    @test derivative(h, [1.0 2.0; 2.0 3.0], [1.0, 1.0], 1) ≈ [2. 2.]
+    h(x) = sum(x, dims = 1)
+    @test derivative(h, [1.0 2.0; 2.0 3.0], [1.0, 1.0], 1) ≈ [2.0 2.0]
 end