Merge pull request #325 from JuliaGPU/tb/rm_device

Remove AbstractGPUDevice

Author: maleadt

docs/src/interface.md

@@ -9,15 +9,8 @@ one that actually can be instantiated on the device (i.e. in kernels).
 ## Device functionality
 
 Several types and interfaces are related to the device and execution of code on it. First of
-all, you need to provide a type that represents your device and exposes some properties of
-it:
-
-```@docs
-GPUArrays.AbstractGPUDevice
-GPUArrays.threads
-```
-
-Another important set of interfaces relates to executing code on the device:
+all, you need to provide a type that represents your execution back-end and a way to call
+kernels:
 
 ```@docs
 GPUArrays.AbstractGPUBackend
@@ -26,7 +19,7 @@ GPUArrays.gpu_call
 GPUArrays.thread_block_heuristic
 ```
 
-Finally, you need to provide implementations of certain methods that will be executed on the
+You then need to provide implementations of certain methods that will be executed on the
 device itself:
 
 ```@docs

src/GPUArrays.jl

@@ -13,7 +13,6 @@ using AbstractFFTs
 using Adapt
 
 # device functionality
-include("device/device.jl")
 include("device/execution.jl")
 ## executed on-device
 include("device/abstractarray.jl")
@@ -38,5 +37,7 @@ include("host/uniformscaling.jl")
 # CPU reference implementation
 include("reference.jl")
 
+include("deprecated.jl")
+
 
 end # module

src/deprecated.jl

@@ -0,0 +1 @@
+function device end

src/device/device.jl

@@ -15,9 +15,6 @@ const AbstractGPUVector{T} = AbstractGPUArray{T, 1}
 const AbstractGPUMatrix{T} = AbstractGPUArray{T, 2}
 const AbstractGPUVecOrMat{T} = Union{AbstractGPUArray{T, 1}, AbstractGPUArray{T, 2}}
 
-device(::AbstractGPUDevice) = error("Not implemented") # COV_EXCL_LINE
-backend(::Type{<:AbstractGPUDevice}) = error("Not implemented") # COV_EXCL_LINE
-
 
 # convenience aliases for working with wrapped arrays
 

src/host/abstractarray.jl

@@ -16,10 +16,6 @@ using Adapt
 # Device functionality
 #
 
-## device properties
-
-struct JLDevice <: AbstractGPUDevice end
-
 const MAXTHREADS = 256
 
 
@@ -266,7 +262,7 @@ struct JLArrayStyle{N} <: AbstractGPUArrayStyle{N} end
 JLArrayStyle(::Val{N}) where N = JLArrayStyle{N}()
 JLArrayStyle{M}(::Val{N}) where {N,M} = JLArrayStyle{N}()
 
-BroadcastStyle(::Type{<:AnyJLArray{T,N}}) where {T,N} = JLArrayStyle{N}()
+BroadcastStyle(::Type{JLArray{T,N}}) where {T,N} = JLArrayStyle{N}()
 
 # Allocating the output container
 Base.similar(bc::Broadcasted{JLArrayStyle{N}}, ::Type{T}) where {N,T} =
@@ -368,9 +364,7 @@ Random.randn!(A::AnyJLArray) = Random.randn!(GPUArrays.default_rng(JLArray), A)
 
 ## GPUArrays interfaces
 
-GPUArrays.device(x::AnyJLArray) = JLDevice()
-
-GPUArrays.backend(::Type{<:AnyJLArray}) = JLBackend()
+GPUArrays.backend(::Type{<:JLArray}) = JLBackend()
 
 Adapt.adapt_storage(::Adaptor, x::JLArray{T,N}) where {T,N} =
   JLDeviceArray{T,N}(x.data, x.dims)