Added simple CuArray patch

Project.toml

@@ -4,6 +4,7 @@ authors = ["Songchen Tan <[email protected]>"]
 version = "0.2.1"
 
 [deps]
+CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 ChainRules = "082447d4-558c-5d27-93f4-14fc19e9eca2"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 ChainRulesOverloadGeneration = "f51149dc-2911-5acf-81fc-2076a2a81d4f"

src/derivative.jl

@@ -1,5 +1,6 @@
 
 export derivative
+using CUDA
 
 """
     derivative(f, x::T, order::Int64)
@@ -23,6 +24,12 @@ end
     derivative(f, x, l, Val{order + 1}())
 end
 
+# add CUDA support
+@inline function derivative(f, x::CuArray, l::CuArray,
+    order::Int64) 
+derivative(f, x, l, Val{order + 1}())
+end
+
 @inline function derivative(f, x::T, ::Val{N}) where {T <: Number, N}
     t = TaylorScalar{T, N}(x, one(x))
     return extract_derivative(f(t), N)

test/cuda.jl

@@ -0,0 +1,19 @@
+using CUDA
+
+# cpu
+f(x,y) = x^2 * y^2
+input_cpu = [1e0,1e0]
+derivative(temp -> f(temp[1],temp[2]),input_cpu,[1e0,0e0],2)
+
+# gpu
+f(x,y) = x^2 * y^2
+input_gpu = CuArray([1e0,1e0])
+direction_gpu = CuArray([1e0,0e0])
+derivative(temp -> f(temp[1],temp[2]),input_gpu,direction_gpu,2)
+
+
+
+
+
+
+