Minor perf improvements for gradients #2.

src/gradients.jl

@@ -33,8 +33,10 @@ function GradientCache(
             if typeof(c2)!=Void && x<:StridedVector
                 warn("c2 cache isn't necessary when x<:StridedVector.")
             end
-            if typeof(c2)==Void || eltype(c2)!=real(eltype(x)) && !(x<:StridedVector)
+            if (typeof(c2)==Void || eltype(c2)!=real(eltype(x))) && !(typeof(x)<:StridedVector)
                 _c2 = zeros(real(eltype(x)), size(x))
+            elseif typeof(x)<:StridedArray
+                _c2 = nothing
             else
                 _c2 = c2
             end
@@ -176,7 +178,7 @@ function finite_difference_gradient!(df::AbstractArray{<:Number}, f, x::Abstract
     end
     df
 end
-#=
+
 function finite_difference_gradient!(df::StridedVector{<:Number}, f, x::StridedVector{<:Number},
     cache::GradientCache{T1,T2,T3,fdtype,returntype,inplace}) where {T1,T2,T3,fdtype,returntype,inplace}
 
@@ -189,24 +191,24 @@ function finite_difference_gradient!(df::StridedVector{<:Number}, f, x::StridedV
     if fdtype == Val{:forward}
         @inbounds for i ∈ eachindex(x)
             epsilon = compute_epsilon(fdtype, x[i], epsilon_factor)
-            c2_old = c2[i]
-            c2[i] += epsilon
+            c1_old = c1[i]
+            c1[i] += epsilon
             if typeof(fx) != Void
-                df[i] = (f(c2) - fx) / epsilon
+                df[i] = (f(c1) - fx) / epsilon
             else
-                df[i]  = (f(c2) - f(x)) / epsilon
+                df[i]  = (f(c1) - f(x)) / epsilon
             end
-            c2[i] = c2_old
+            c1[i] = c1_old
         end
     elseif fdtype == Val{:central}
         @inbounds for i ∈ eachindex(x)
             epsilon = compute_epsilon(fdtype, x[i], epsilon_factor)
-            c2_old = c2[i]
-            c2[i] += epsilon
+            c1_old = c1[i]
+            c1[i] += epsilon
             x_old  = x[i]
             x[i]  -= epsilon
-            df[i]  = (f(c2) - f(x)) / (2*epsilon)
-            c2[i]  = c2_old
+            df[i]  = (f(c1) - f(x)) / (2*epsilon)
+            c1[i]  = c1_old
             x[i]   = x_old
         end
     elseif fdtype == Val{:complex} && returntype <: Real
@@ -224,7 +226,7 @@ function finite_difference_gradient!(df::StridedVector{<:Number}, f, x::StridedV
     end
     df
 end
-=#
+
 # vector of derivatives of a scalar->vector map
 # this is effectively a vector of partial derivatives, but we still call it a gradient
 function finite_difference_gradient!(df::AbstractArray{<:Number}, f, x::Number,