Only explicit imports and add tests with ExplicitImports

Author: devmotion

.github/workflows/CI.yml

@@ -31,13 +31,6 @@ jobs:
           - ubuntu-latest
           - windows-latest
           - macOS-latest
-        exclude:
-          # For Julia 1.6 no aarch64 binary exists
-          - version: 'min'
-            os: macOS-latest
-        include:
-          - version: 'min'
-            os: macOS-13 # uses x64
     steps:
       - uses: actions/checkout@v5
       - uses: julia-actions/setup-julia@v2

Project.toml

@@ -12,13 +12,15 @@ SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 
 [compat]
+ExplicitImports = "1.13.2"
 Primes = "0.4, 0.5"
 Statistics = "<0.0.1, 1"
-julia = "1"
+julia = "1.10"
 
 [extras]
+ExplicitImports = "7d51a73a-1435-4ff3-83d9-f097790105c7"
 SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Test", "SpecialFunctions"]
+test = ["ExplicitImports", "Test", "SpecialFunctions"]

README.md

@@ -33,7 +33,11 @@ julia> import Pkg; Pkg.add("DistributedArrays")
 
 ## Project Status
 
-The package is tested against Julia `0.7`, `1.0` and the nightly builds of the Julia `master` branch on Linux, and macOS.
+The package is tested against
+Julia 1.10.0 (oldest supported Julia version),
+the Julia LTS version,
+the latest stable release of Julia,
+and the pre-release version of Julia.
 
 ## Questions and Contributions
 

src/DistributedArrays.jl

@@ -1,18 +1,16 @@
-__precompile__()
-
 module DistributedArrays
 
-using Distributed
-using Serialization
-using LinearAlgebra
-using Statistics
+using Base: Callable
+using Base.Broadcast: BroadcastStyle, Broadcasted
 
-import Base: +, -, *, div, mod, rem, &, |, xor
-import Base.Callable
-import LinearAlgebra: axpy!, dot, norm, mul!
+using Distributed: Distributed, RemoteChannel, Future, myid, nworkers, procs, remotecall, remotecall_fetch, remotecall_wait, worker_id_from_socket, workers
+using LinearAlgebra: LinearAlgebra, Adjoint, Diagonal, I, Transpose, adjoint, adjoint!, axpy!, dot, lmul!, mul!, norm, rmul!, transpose, transpose!
+using Random: Random, rand!
+using Serialization: Serialization, AbstractSerializer, deserialize, serialize
+using SparseArrays: SparseArrays, nnz
+using Statistics: Statistics
 
-import Primes
-import Primes: factor
+using Primes: factor
 
 # DArray exports
 export DArray, SubDArray, SubOrDArray, @DArray
@@ -22,7 +20,7 @@ export dzeros, dones, dfill, drand, drandn, distribute, localpart, localindices,
 export ddata, gather
 
 # immediate release of localparts
-export close, d_closeall
+export d_closeall
 
 include("darray.jl")
 include("core.jl")

src/broadcast.jl

@@ -2,9 +2,6 @@
 # Distributed broadcast implementation
 ##
 
-using Base.Broadcast
-import Base.Broadcast: BroadcastStyle, Broadcasted
-
 # We define a custom ArrayStyle here since we need to keep track of
 # the fact that it is Distributed and what kind of underlying broadcast behaviour
 # we will encounter.
@@ -13,11 +10,11 @@ DArrayStyle(::S) where {S} = DArrayStyle{S}()
 DArrayStyle(::S, ::Val{N}) where {S,N} = DArrayStyle(S(Val(N)))
 DArrayStyle(::Val{N}) where N = DArrayStyle{Broadcast.DefaultArrayStyle{N}}()
 
-BroadcastStyle(::Type{<:DArray{<:Any, N, A}}) where {N, A} = DArrayStyle(BroadcastStyle(A), Val(N))
+Broadcast.BroadcastStyle(::Type{<:DArray{<:Any, N, A}}) where {N, A} = DArrayStyle(BroadcastStyle(A), Val(N))
 
 # promotion rules
 # TODO: test this
-function BroadcastStyle(::DArrayStyle{AStyle}, ::DArrayStyle{BStyle}) where {AStyle, BStyle}
+function Broadcast.BroadcastStyle(::DArrayStyle{AStyle}, ::DArrayStyle{BStyle}) where {AStyle, BStyle}
     DArrayStyle(BroadcastStyle(AStyle, BStyle))
 end
 
@@ -78,7 +75,7 @@ end
             # create a local version of the broadcast, by constructing views
             # Note: creates copies of the argument
             lbc = bclocal(dbc, dest.indices[lpidx])
-            Base.copyto!(localpart(dest), lbc)
+            copyto!(localpart(dest), lbc)
             return nothing
         end
     end

src/darray.jl

@@ -363,8 +363,7 @@ _localindex(i::AbstractUnitRange, offset) = (first(i)-offset):(last(i)-offset)
 Equivalent to `Array(view(A, I...))` but optimised for the case that the data is local.
 Can return a view into `localpart(A)`
 """
-function makelocal(A::DArray{<:Any, <:Any, AT}, I::Vararg{Any, N}) where {N, AT}
-    Base.@_inline_meta
+@inline function makelocal(A::DArray{<:Any, <:Any, AT}, I::Vararg{Any, N}) where {N, AT}
     J = map(i->Base.unalias(A, i), to_indices(A, I))
     J = map(j-> isa(j, Base.Slice) ? j.indices : j, J)
     @boundscheck checkbounds(A, J...)
@@ -597,17 +596,10 @@ function Base.copyto!(a::Array, s::SubDArray)
     return a
 end
 
-if VERSION < v"1.2"
-    # This is an internal API that has changed
-    reindex(A, I, J) = Base.reindex(A, I, J)
-else
-    reindex(A, I, J) = Base.reindex(I, J)
-end
-
 function DArray(SD::SubArray{T,N}) where {T,N}
     D = SD.parent
     DArray(size(SD), procs(D)) do I
-        lindices = reindex(SD, SD.indices, I)
+        lindices = Base.reindex(SD.indices, I)
         convert(Array, D[lindices...])
     end
 end
@@ -661,9 +653,7 @@ function Base.getindex(d::DArray, i::Int...)
 end
 
 Base.getindex(d::DArray) = d[1]
-if VERSION > v"1.1-"
 Base.getindex(d::SubDArray, I::Int...) = invoke(getindex, Tuple{SubArray{<:Any,N},Vararg{Int,N}} where N, d, I...)
-end
 Base.getindex(d::SubOrDArray, I::Union{Int,UnitRange{Int},Colon,Vector{Int},StepRange{Int,Int}}...) = view(d, I...)
 
 function Base.isassigned(D::DArray, i::Integer...)
@@ -793,13 +783,12 @@ Base.@propagate_inbounds Base.getindex(M::MergedIndices{J,N}, I::Vararg{Int, N})
 const ReshapedMergedIndices{T,N,M<:MergedIndices} = Base.ReshapedArray{T,N,M}
 const SubMergedIndices{T,N,M<:Union{MergedIndices, ReshapedMergedIndices}} = SubArray{T,N,M}
 const MergedIndicesOrSub = Union{MergedIndices, ReshapedMergedIndices, SubMergedIndices}
-import Base: checkbounds_indices
-@inline checkbounds_indices(::Type{Bool}, inds::Tuple{}, I::Tuple{MergedIndicesOrSub,Vararg{Any}}) =
-    checkbounds_indices(Bool, inds, (parent(parent(I[1])).indices..., tail(I)...))
-@inline checkbounds_indices(::Type{Bool}, inds::Tuple{Any}, I::Tuple{MergedIndicesOrSub,Vararg{Any}}) =
-    checkbounds_indices(Bool, inds, (parent(parent(I[1])).indices..., tail(I)...))
-@inline checkbounds_indices(::Type{Bool}, inds::Tuple, I::Tuple{MergedIndicesOrSub,Vararg{Any}}) =
-    checkbounds_indices(Bool, inds, (parent(parent(I[1])).indices..., tail(I)...))
+@inline Base.checkbounds_indices(::Type{Bool}, inds::Tuple{}, I::Tuple{MergedIndicesOrSub,Vararg{Any}}) =
+    Base.checkbounds_indices(Bool, inds, (parent(parent(I[1])).indices..., tail(I)...))
+@inline Base.checkbounds_indices(::Type{Bool}, inds::Tuple{Any}, I::Tuple{MergedIndicesOrSub,Vararg{Any}}) =
+    Base.checkbounds_indices(Bool, inds, (parent(parent(I[1])).indices..., tail(I)...))
+@inline Base.checkbounds_indices(::Type{Bool}, inds::Tuple, I::Tuple{MergedIndicesOrSub,Vararg{Any}}) =
+    Base.checkbounds_indices(Bool, inds, (parent(parent(I[1])).indices..., tail(I)...))
 
 # The tricky thing here is that we want to optimize the accesses into the
 # distributed array, but in doing so, we lose track of which indices in I we
@@ -851,8 +840,6 @@ function Base.fill!(A::DArray, x)
     return A
 end
 
-using Random
-
 function Random.rand!(A::DArray, ::Type{T}) where T
     asyncmap(procs(A)) do p
         remotecall_wait((A, T)->rand!(localpart(A), T), p, A, T)

src/linalg.jl

@@ -21,7 +21,7 @@ const DMatrix{T,A} = DArray{T,2,A}
 
 # Level 1
 
-function axpy!(α, x::DArray, y::DArray)
+function LinearAlgebra.axpy!(α, x::DArray, y::DArray)
     if length(x) != length(y)
         throw(DimensionMismatch("vectors must have same length"))
     end
@@ -34,7 +34,7 @@ function axpy!(α, x::DArray, y::DArray)
     return y
 end
 
-function dot(x::DVector, y::DVector)
+function LinearAlgebra.dot(x::DVector, y::DVector)
     if length(x) != length(y)
         throw(DimensionMismatch(""))
     end
@@ -46,7 +46,7 @@ function dot(x::DVector, y::DVector)
     return reduce(+, results)
 end
 
-function norm(x::DArray, p::Real = 2)
+function LinearAlgebra.norm(x::DArray, p::Real = 2)
     results = []
     @sync begin
         for pp in procs(x)

src/mapreduce.jl

@@ -1,8 +1,5 @@
 ## higher-order functions ##
 
-import Base: +, -, div, mod, rem, &, |, xor
-import SparseArrays: nnz
-
 Base.map(f, d0::DArray, ds::AbstractArray...) = broadcast(f, d0, ds...)
 
 function Base.map!(f::F, dest::DArray, src::DArray{<:Any,<:Any,A}) where {F,A}
@@ -120,7 +117,7 @@ function Base.count(f, A::DArray)
     return sum(B)
 end
 
-function nnz(A::DArray)
+function SparseArrays.nnz(A::DArray)
     B = asyncmap(A.pids) do p
         remotecall_fetch(nnz∘localpart, p, A)
     end
@@ -136,14 +133,10 @@ function Base.extrema(d::DArray)
     return reduce((t,s) -> (min(t[1], s[1]), max(t[2], s[2])), r)
 end
 
-if VERSION < v"1.3"
-    Statistics._mean(A::DArray, region) = sum(A, dims = region) ./ prod((size(A, i) for i in region))
-else
-    Statistics._mean(f, A::DArray, region) = sum(f, A, dims = region) ./ prod((size(A, i) for i in region))
-end
+Statistics._mean(f, A::DArray, region) = sum(f, A, dims = region) ./ prod((size(A, i) for i in region))
 
 # Unary vector functions
-(-)(D::DArray) = map(-, D)
+Base.:(-)(D::DArray) = map(-, D)
 
 
 map_localparts(f::Callable, d::DArray) = DArray(i->f(localpart(d)), d)
@@ -191,12 +184,12 @@ end
 
 for f in (:+, :-, :div, :mod, :rem, :&, :|, :xor)
     @eval begin
-        function ($f)(A::DArray{T}, B::DArray{T}) where T
+        function Base.$f(A::DArray{T}, B::DArray{T}) where T
             B = samedist(A, B)
             map_localparts($f, A, B)
         end
-        ($f)(A::DArray{T}, B::Array{T}) where {T} = map_localparts($f, A, B)
-        ($f)(A::Array{T}, B::DArray{T}) where {T} = map_localparts($f, A, B)
+        Base.$f(A::DArray{T}, B::Array{T}) where {T} = map_localparts($f, A, B)
+        Base.$f(A::Array{T}, B::DArray{T}) where {T} = map_localparts($f, A, B)
     end
 end
 

src/spmd.jl

@@ -1,8 +1,8 @@
 module SPMD
 
-using Distributed
+using Distributed: RemoteChannel, myid, procs, remote_do, remotecall_fetch
+using ..DistributedArrays: DistributedArrays, gather, next_did
 
-import DistributedArrays: gather, next_did, close
 export sendto, recvfrom, recvfrom_any, barrier, bcast, scatter, gather
 export context_local_storage, context, spmd
 
@@ -206,7 +206,7 @@ function scatter(x, pid::Int; tag=nothing, pids=procs())
     end
 end
 
-function gather(x, pid::Int; tag=nothing, pids=procs())
+function DistributedArrays.gather(x, pid::Int; tag=nothing, pids=procs())
     if myid() == pid
         gathered_data = Array{Any}(undef, length(pids))
         myidx = findfirst(isequal(pid), sort(pids))

test/darray.jl

@@ -351,9 +351,7 @@ check_leaks()
 
 unpack(ex::Base.CapturedException) = unpack(ex.ex)
 unpack(ex::Distributed.RemoteException) = unpack(ex.captured)
-if VERSION >= v"1.3.0-alpha.110"
 unpack(ex::Base.TaskFailedException) = unpack(ex.task.exception)
-end
 unpack(ex) = ex
 
 @testset "test sum on DArrays" begin