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May 1, 2024
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DArray: Add automatic partitioning and show array elements #505

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22e7422
tests: Fix load path
jpsamaroo Apr 30, 2024
23c2bd7
DArray: Automatic partitioning, more constructors
jpsamaroo May 1, 2024
7cd588e
DArray: Show array elements
jpsamaroo May 1, 2024
5ad2868
docs: Update DArray docs for show/ctors
jpsamaroo May 1, 2024
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322 changes: 268 additions & 54 deletions docs/src/darray.md
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82 changes: 46 additions & 36 deletions src/array/alloc.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -30,42 +30,71 @@ function stage(ctx, a::AllocateArray)
return DArray(a.eltype, a.domain, a.domainchunks, thunks, a.partitioning)
end

function Base.rand(p::Blocks, eltype::Type, dims)
const BlocksOrAuto = Union{Blocks{N} where N, AutoBlocks}

function Base.rand(p::Blocks, eltype::Type, dims::Dims)
d = ArrayDomain(map(x->1:x, dims))
a = AllocateArray(eltype, (_, x...) -> rand(x...), d, partition(p, d), p)
return _to_darray(a)
end
Base.rand(p::BlocksOrAuto, T::Type, dims::Integer...) = rand(p, T, dims)
Base.rand(p::BlocksOrAuto, T::Type, dims::Dims) = rand(p, T, dims)
Base.rand(p::BlocksOrAuto, dims::Integer...) = rand(p, Float64, dims)
Base.rand(p::BlocksOrAuto, dims::Dims) = rand(p, Float64, dims)
Base.rand(::AutoBlocks, eltype::Type, dims::Dims) =
rand(auto_blocks(dims), eltype, dims)

Base.rand(p::Blocks, t::Type, dims::Integer...) = rand(p, t, dims)
Base.rand(p::Blocks, dims::Integer...) = rand(p, Float64, dims)
Base.rand(p::Blocks, dims::Tuple) = rand(p, Float64, dims)
function Base.randn(p::Blocks, eltype::Type, dims::Dims)
d = ArrayDomain(map(x->1:x, dims))
a = AllocateArray(eltype, (_, x...) -> randn(x...), d, partition(p, d), p)
return _to_darray(a)
end
Base.randn(p::BlocksOrAuto, T::Type, dims::Integer...) = randn(p, T, dims)
Base.randn(p::BlocksOrAuto, T::Type, dims::Dims) = randn(p, T, dims)
Base.randn(p::BlocksOrAuto, dims::Integer...) = randn(p, Float64, dims)
Base.randn(p::BlocksOrAuto, dims::Dims) = randn(p, Float64, dims)
Base.randn(::AutoBlocks, eltype::Type, dims::Dims) =
randn(auto_blocks(dims), eltype, dims)

function Base.randn(p::Blocks, eltype::Type, dims)
function sprand(p::Blocks, eltype::Type, dims::Dims, sparsity::AbstractFloat)
d = ArrayDomain(map(x->1:x, dims))
a = AllocateArray(Float64, (_, x...) -> randn(x...), d, partition(p, d), p)
a = AllocateArray(eltype, (_, T, _dims) -> sprand(T, _dims..., sparsity), d, partition(p, d), p)
return _to_darray(a)
end
Base.randn(p::Blocks, t::Type, dims::Integer...) = randn(p, t, dims)
Base.randn(p::Blocks, dims::Integer...) = randn(p, dims)
Base.randn(p::Blocks, dims::Tuple) = randn(p, Float64, dims)
sprand(p::BlocksOrAuto, T::Type, dims_and_sparsity::Real...) =
sprand(p, T, dims_and_sparsity[1:end-1], dims_and_sparsity[end])
sprand(p::BlocksOrAuto, T::Type, dims::Dims, sparsity::AbstractFloat) =
sprand(p, T, dims, sparsity)
sprand(p::BlocksOrAuto, dims_and_sparsity::Real...) =
sprand(p, Float64, dims_and_sparsity[1:end-1], dims_and_sparsity[end])
sprand(p::BlocksOrAuto, dims::Dims, sparsity::AbstractFloat) =
sprand(p, Float64, dims, sparsity)
sprand(::AutoBlocks, eltype::Type, dims::Dims, sparsity::AbstractFloat) =
sprand(auto_blocks(dims), eltype, dims, sparsity)

function Base.ones(p::Blocks, eltype::Type, dims)
function Base.ones(p::Blocks, eltype::Type, dims::Dims)
d = ArrayDomain(map(x->1:x, dims))
a = AllocateArray(eltype, (_, x...) -> ones(x...), d, partition(p, d), p)
return _to_darray(a)
end
Base.ones(p::Blocks, t::Type, dims::Integer...) = ones(p, t, dims)
Base.ones(p::Blocks, dims::Integer...) = ones(p, Float64, dims)
Base.ones(p::Blocks, dims::Tuple) = ones(p, Float64, dims)
Base.ones(p::BlocksOrAuto, T::Type, dims::Integer...) = ones(p, T, dims)
Base.ones(p::BlocksOrAuto, T::Type, dims::Dims) = ones(p, T, dims)
Base.ones(p::BlocksOrAuto, dims::Integer...) = ones(p, Float64, dims)
Base.ones(p::BlocksOrAuto, dims::Dims) = ones(p, Float64, dims)
Base.ones(::AutoBlocks, eltype::Type, dims::Dims) =
ones(auto_blocks(dims), eltype, dims)

function Base.zeros(p::Blocks, eltype::Type, dims)
function Base.zeros(p::Blocks, eltype::Type, dims::Dims)
d = ArrayDomain(map(x->1:x, dims))
a = AllocateArray(eltype, (_, x...) -> zeros(x...), d, partition(p, d), p)
return _to_darray(a)
end
Base.zeros(p::Blocks, t::Type, dims::Integer...) = zeros(p, t, dims)
Base.zeros(p::Blocks, dims::Integer...) = zeros(p, Float64, dims)
Base.zeros(p::Blocks, dims::Tuple) = zeros(p, Float64, dims)
Base.zeros(p::BlocksOrAuto, T::Type, dims::Integer...) = zeros(p, T, dims)
Base.zeros(p::BlocksOrAuto, T::Type, dims::Dims) = zeros(p, T, dims)
Base.zeros(p::BlocksOrAuto, dims::Integer...) = zeros(p, Float64, dims)
Base.zeros(p::BlocksOrAuto, dims::Dims) = zeros(p, Float64, dims)
Base.zeros(::AutoBlocks, eltype::Type, dims::Dims) =
zeros(auto_blocks(dims), eltype, dims)

function Base.zero(x::DArray{T,N}) where {T,N}
dims = ntuple(i->x.domain.indexes[i].stop, N)
Expand All @@ -83,22 +112,3 @@ function Base.view(A::AbstractArray{T,N}, p::Blocks{N}) where {T,N}
chunks = [tochunk(view(A, x.indexes...)) for x in dc]
return DArray(T, d, dc, chunks, p)
end

function sprand(p::Blocks, m::Integer, n::Integer, sparsity::Real)
s = rand(UInt)
f = function (idx, t,sz)
sprand(MersenneTwister(s+idx), sz...,sparsity)
end
d = ArrayDomain((1:m, 1:n))
a = AllocateArray(Float64, f, d, partition(p, d), p)
return _to_darray(a)
end

function sprand(p::Blocks, n::Integer, sparsity::Real)
s = rand(UInt)
f = function (idx,t,sz)
sprand(MersenneTwister(s+idx), sz...,sparsity)
end
a = AllocateArray(Float64, f, d, partition(p, ArrayDomain((1:n,))), p)
return _to_darray(a)
end
157 changes: 129 additions & 28 deletions src/array/darray.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,10 @@ import Base: ==, fetch
using Serialization
import Serialization: serialize, deserialize

export DArray, DVector, DMatrix, Blocks, AutoBlocks
export distribute


###### Array Domains ######

"""
Expand Down Expand Up @@ -92,18 +96,6 @@ collect(x::Computation) = collect(fetch(x))

Base.fetch(x::Computation) = fetch(stage(Context(global_context()), x))

function Base.show(io::IO, ::MIME"text/plain", x::ArrayOp)
write(io, string(typeof(x)))
write(io, string(size(x)))
end

function Base.show(io::IO, x::ArrayOp)
m = MIME"text/plain"()
show(io, m, x)
end

export BlockPartition, Blocks

abstract type AbstractBlocks{N} end

abstract type AbstractMultiBlocks{N}<:AbstractBlocks{N} end
Expand Down Expand Up @@ -149,11 +141,11 @@ mutable struct DArray{T,N,B<:AbstractBlocks{N},F} <: ArrayOp{T, N}
end
end

WrappedDArray{T,N} = Union{<:DArray{T,N}, Transpose{<:DArray{T,N}}, Adjoint{<:DArray{T,N}}}
WrappedDMatrix{T} = WrappedDArray{T,2}
WrappedDVector{T} = WrappedDArray{T,1}
DMatrix{T} = DArray{T,2}
DVector{T} = DArray{T,1}
const WrappedDArray{T,N} = Union{<:DArray{T,N}, Transpose{<:DArray{T,N}}, Adjoint{<:DArray{T,N}}}
const WrappedDMatrix{T} = WrappedDArray{T,2}
const WrappedDVector{T} = WrappedDArray{T,1}
const DMatrix{T} = DArray{T,2}
const DVector{T} = DArray{T,1}


# mainly for backwards-compatibility
Expand Down Expand Up @@ -196,6 +188,95 @@ function Base.collect(d::DArray; tree=false)
end
end

### show

#= FIXME
@static if isdefined(Base, :AnnotatedString)
# FIXME: Import StyledStrings
struct ColorElement{T}
color::Symbol
value::T
end
function Base.show(io::IO, ::MIME"text/plain", x::ColorElement)
print(io, styled"{(foreground=$(x.color)):$(x.value)}")
end
else
=#
struct ColorElement{T}
color::Symbol
value::Union{Some{T},Nothing}
end
function Base.show(io::IO, ::MIME"text/plain", x::ColorElement)
if x.value !== nothing
printstyled(io, something(x.value); color=x.color)
else
printstyled(io, "..."; color=x.color)
end
end
Base.alignment(io::IO, x::ColorElement) =
Base.alignment(io, something(x.value, "..."))
#end
struct ColorArray{T,N} <: DenseArray{T,N}
A::DArray{T,N}
color_map::Vector{Symbol}
seen_values::Dict{NTuple{N,Int},Union{Some{T},Nothing}}
function ColorArray(A::DArray{T,N}) where {T,N}
colors = [:red, :green, :yellow, :blue, :magenta, :cyan]
color_map = [colors[mod1(idx, length(colors))] for idx in 1:length(A.chunks)]
return new{T,N}(A, color_map, Dict{NTuple{N,Int},Union{Some{T},Nothing}}())
end
end
Base.size(A::ColorArray) = size(A.A)
Base.getindex(A::ColorArray, idx::Integer) = getindex(A, (idx,))
Base.getindex(A::ColorArray, idxs::Integer...) = getindex(A, (idxs...,))
function Base.getindex(A::ColorArray{T,N}, idxs::NTuple{N,Int}) where {T,N}
sd_idx_tuple, _ = partition_for(A.A, idxs)
sd_idx = CartesianIndex(sd_idx_tuple)
sd_idx_linear = LinearIndices(A.A.chunks)[sd_idx]
if !haskey(A.seen_values, idxs)
chunk = A.A.chunks[sd_idx]
if chunk isa Chunk || isready(chunk)
value = A.seen_values[idxs] = Some(getindex(A.A, idxs))
else
# Show a placeholder instead
value = A.seen_values[idxs] = nothing
end
else
value = A.seen_values[idxs]
end
if value !== nothing
color = A.color_map[sd_idx_linear]
else
color = :light_black
end
return ColorElement{T}(color, value)
end
function Base.getindex(A::ColorArray{T,N}, idxs::Dims{S}) where {T,N,S}
if S > N
if all(idxs[(N+1):end] .== 1)
return getindex(A, idxs[1:N])
else
throw(BoundsError(A, idxs))
end
elseif S < N
throw(BoundsError(A, idxs))
end
end
function Base.show(io::IO, ::MIME"text/plain", A::DArray{T,N}) where {T,N}
write(io, string(DArray{T,N}))
write(io, string(size(A)))
write(io, " with $(join(size(A.chunks), 'x')) partitions of size $(join(A.partitioning.blocksize, 'x')):")
pct_complete = 100 * (sum(c->c isa Chunk ? true : isready(c), A.chunks) / length(A.chunks))
if pct_complete < 100
println(io)
printstyled(io, "~$(round(Int, pct_complete))% completed"; color=:yellow)
end
println(io)
with_index_caching(1) do
Base.print_array(IOContext(io, :compact=>true), ColorArray(A))
end
end

function (==)(x::ArrayOp, y::ArrayOp)
x === y || reduce((a,b)->a&&b, map(==, x, y))
end
Expand Down Expand Up @@ -319,8 +400,6 @@ end
Base.@deprecate_binding Cat DArray
Base.@deprecate_binding ComputedArray DArray

export Distribute, distribute

struct Distribute{T,N,B<:AbstractBlocks} <: ArrayOp{T, N}
domainchunks
partitioning::B
Expand Down Expand Up @@ -385,22 +464,44 @@ function stage(ctx::Context, d::Distribute)
d.partitioning)
end

function distribute(x::AbstractArray, dist::Blocks)
_to_darray(Distribute(dist, x))
end
"""
AutoBlocks

Automatically determines the size and number of blocks for a distributed array.
This may construct any kind of `Dagger.AbstractBlocks` partitioning.
"""
struct AutoBlocks end
function auto_blocks(dims::Dims{N}) where N
# TODO: Allow other partitioning schemes
np = num_processors()
p = cld(dims[end], np)
return Blocks(ntuple(i->i == N ? p : dims[i], N))
end
auto_blocks(A::AbstractArray{T,N}) where {T,N} = auto_blocks(size(A))

distribute(A::AbstractArray) = distribute(A, AutoBlocks())
distribute(A::AbstractArray{T,N}, dist::Blocks{N}) where {T,N} =
_to_darray(Distribute(dist, A))
distribute(A::AbstractArray, ::AutoBlocks) = distribute(A, auto_blocks(A))
function distribute(x::AbstractArray{T,N}, n::NTuple{N}) where {T,N}
p = map((d, dn)->ceil(Int, d / dn), size(x), n)
distribute(x, Blocks(p))
end
distribute(x::AbstractVector, n::Int) = distribute(x, (n,))
distribute(x::AbstractVector, n::Vector{<:Integer}) =
distribute(x, DomainBlocks((1,), (cumsum(n),)))

function distribute(x::AbstractVector, n::Int)
distribute(x, (n,))
end
DVector(A::AbstractVector{T}, part::Blocks{1}) where T = distribute(A, part)
DMatrix(A::AbstractMatrix{T}, part::Blocks{2}) where T = distribute(A, part)
DArray(A::AbstractArray{T,N}, part::Blocks{N}) where {T,N} = distribute(A, part)

function distribute(x::AbstractVector, n::Vector{<:Integer})
distribute(x, DomainBlocks((1,), (cumsum(n),)))
end
DVector(A::AbstractVector{T}) where T = DVector(A, AutoBlocks())
DMatrix(A::AbstractMatrix{T}) where T = DMatrix(A, AutoBlocks())
DArray(A::AbstractArray) = DArray(A, AutoBlocks())

DVector(A::AbstractVector{T}, ::AutoBlocks) where T = DVector(A, auto_blocks(A))
DMatrix(A::AbstractMatrix{T}, ::AutoBlocks) where T = DMatrix(A, auto_blocks(A))
DArray(A::AbstractArray, ::AutoBlocks) = DArray(A, auto_blocks(A))

function Base.:(==)(x::ArrayOp{T,N}, y::AbstractArray{S,N}) where {T,S,N}
collect(x) == y
Expand Down
24 changes: 24 additions & 0 deletions src/array/indexing.jl
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Expand Up @@ -95,6 +95,18 @@ Base.getindex(A::DArray, idx::Integer) =
getindex(A, Base._ind2sub(A, idx))
Base.getindex(A::DArray, idx::CartesianIndex) =
getindex(A, Tuple(idx))
function Base.getindex(A::DArray{T,N}, idxs::Dims{S}) where {T,N,S}
if S > N
if all(idxs[(N+1):end] .== 1)
return getindex(A, idxs[1:N])
else
throw(BoundsError(A, idxs))
end
elseif S < N
throw(BoundsError(A, idxs))
end
error()
end

### setindex!

Expand Down Expand Up @@ -126,6 +138,18 @@ Base.setindex!(A::DArray, value, idx::Integer) =
setindex!(A, value, Base._ind2sub(A, idx))
Base.setindex!(A::DArray, value, idx::CartesianIndex) =
setindex!(A, value, Tuple(idx))
function Base.setindex!(A::DArray{T,N}, value, idxs::Dims{S}) where {T,N,S}
if S > N
if all(idxs[(N+1):end] .== 1)
return setindex!(A, value, idxs[1:N])
else
throw(BoundsError(A, idxs))
end
elseif S < N
throw(BoundsError(A, idxs))
end
error()
end

### Allow/disallow scalar indexing

Expand Down
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