CUDA as extension

Project.toml

@@ -27,6 +27,13 @@ NCDatasets = "0.11, 0.12, 0.13, 0.14"
 ThreadsX = "0.1"
 julia = "1"
 
+[weakdeps]
+CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
+
+[extensions]
+CUDAExt = "CUDA"
+
+
 [extras]
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"

ext/CUDAExt.jl

@@ -0,0 +1,20 @@
+module CUDAExt
+
+import DINCAE: interpnd!, interp_adjn!, _to_device
+using CUDA
+using Flux
+
+
+function interpnd!(pos::AbstractVector{<:NTuple{N}},cuA::CuArray,cuvec) where N
+    @cuda interpnd!(pos,cuA,cuvec)
+end
+
+function interp_adjn!(pos::AbstractVector{<:NTuple{N}},cuvalues::CuArray,cuA2) where N
+    @cuda interp_adjn!(pos,cuvalues,cuA2)
+end
+
+@inline function _to_device(::Type{Atype}) where Atype <: CuArray
+    return Flux.gpu
+end
+
+end

src/DINCAE.jl

@@ -29,7 +29,6 @@ The code is available at:
 """
 module DINCAE
 using Base.Threads
-using CUDA
 using Dates
 using Random
 using NCDatasets

src/flux.jl

@@ -42,12 +42,6 @@ weights(m::Chain) = Flux.params(m)
 
 weights(c::Conv) = [c.weight]
 
-
-
-@inline function _to_device(::Type{Atype}) where Atype <: CuArray
-    return Flux.gpu
-end
-
 @inline function _to_device(::Type{Atype}) where Atype <: AbstractArray
     return Flux.cpu
 end

src/points.jl

@@ -25,10 +25,6 @@ function interpnd!(pos::AbstractVector{<:NTuple{N}},A,vec) where N
     return nothing
 end
 
-function interpnd!(pos::AbstractVector{<:NTuple{N}},cuA::CuArray,cuvec) where N
-    @cuda DINCAE.interpnd!(pos,cuA,cuvec)
-end
-
 #interpnd(pos,A) = interpnd!(pos,A,zeros(eltype(A),length(pos)))
 function interpnd(pos,A)
     vec = similar(A,length(pos))
@@ -75,10 +71,6 @@ function interp_adjn!(pos::AbstractVector{<:NTuple{N}},values,A2) where N
     return nothing
 end
 
-function interp_adjn!(pos::AbstractVector{<:NTuple{N}},cuvalues::CuArray,cuA2) where N
-    @cuda interp_adjn!(pos,cuvalues,cuA2)
-end
-
 function interp_adjn(pos::AbstractVector{<:NTuple{N}},values,sz::NTuple{N,Int}) where N
     A2 = similar(values,sz)
     interp_adjn!(pos,values,A2)
@@ -364,7 +356,7 @@ function costfun(
     xrec,xtrue::Vector{NamedTuple{(:pos, :x),Tuple{Tpos,TA}}},truth_uncertain;
     laplacian_penalty = 0,
     laplacian_error_penalty = laplacian_penalty,
-    ) where TA <: Union{Array{T,N},CuArray{T,N}} where Tpos <: AbstractVector{NTuple{N,T}} where {N,T}
+    ) where TA <: Union{AbstractArray{T,N}} where Tpos <: AbstractVector{NTuple{N,T}} where {N,T}
 
     #@show typeof(xin)
     #@show typeof(xrec)
@@ -463,7 +455,7 @@ end
 Mandatory parameters:
 
 * `T`: `Float32` or `Float64`: float-type used by the neural network
-* `Array{T}` or `CuArray{T}`: array-type used by the neural network.
+* `Array{T}`, `CuArray{T}`,...: array-type used by the neural network.
 * `filename`: NetCDF file in the format described below.
 * `varname`: name of the primary variable in the NetCDF file.
 * `grid`: tuple of ranges with the grid in the longitude and latitude direction e.g. `(-180:1:180,-90:1:90)`.