Add view Support for Efficient DArray Chunk Slicing

docs/src/datadeps.md

@@ -220,3 +220,81 @@ function Dagger.move!(dep_mod::Any, from_space::Dagger.MemorySpace, to_space::Da
     return
 end
 ```
+
+## Chunk and DTask slicing with `view`
+
+The `view` function allows you to efficiently create a "view" of a `Chunk` or `DTask` that contains an array. This enables operations on specific parts of your distributed data using standard Julia array slicing, without needing to materialize the entire array.
+
+```julia
+    view(c::Chunk, slices...) -> ChunkView
+    view(c::DTask, slices...) -> ChunkView
+```
+
+These methods create a `ChunkView` of a `Chunk` or `DTask`, which may be used as an argument to a `Dagger.@spawn` call in a Datadeps region. You specify the desired view using standard Julia array slicing syntax, identical to how you would slice a regular array.
+
+#### Examples
+
+```julia
+julia> A = rand(64, 64)
+64×64 Matrix{Float64}:
+[...]
+
+julia> DA = DArray(A, Blocks(8,8)) 
+64x64 DMatrix{Float64} with 8x8 partitions of size 8x8:
+[...]
+
+julia> chunk = DA.chunks[1,1] 
+DTask (finished)
+
+julia> view(chunk, :, :) # View the entire 8x8 chunk
+ChunkSlice{2}(Dagger.Chunk(...), (Colon(), Colon()))
+
+julia> view(chunk, 1:4, 1:4) # View the top-left 4x4 sub-region of the chunk
+ChunkSlice{2}(Dagger.Chunk(...), (1:4, 1:4))
+
+julia> view(chunk, 1, :) # View the first row of the chunk
+ChunkSlice{2}(Dagger.Chunk(...), (1, Colon()))
+
+julia> view(chunk, :, 5) # View the fifth column of the chunk
+ChunkSlice{2}(Dagger.Chunk(...), (Colon(), 5))
+
+julia> view(chunk, 1:2:7, 2:2:8) # View with stepped ranges
+ChunkSlice{2}(Dagger.Chunk(...), (1:2:7, 2:2:8))
+```
+
+#### Example Usage: Parallel Row Summation of a DArray using `view`
+
+This example demonstrates how to sum multiple rows of a `DArray` by using `view` to process individual rows within chunks to get a vector of row sums.
+
+```julia
+julia> A = DArray(rand(10, 1000), Blocks(2, 1000))
+10x1000 DMatrix{Float64} with 5x1 partitions of size 2x1000: 
+[...]
+
+# Helper function to sum a single row and store it in a provided array view
+julia> @everywhere function sum_array_row!(row_sum::AbstractArray{Float64}, x::AbstractArray{Float64})
+    row_sum[1] = sum(x)
+end
+
+# Number of rows
+julia> nrows = size(A,1)
+
+# Initialize a zero array in the final row sums
+julia> row_sums = zeros(nrows)
+
+# Spawn tasks to sum each row in parallel using views
+julia> Dagger.spawn_datadeps() do
+           sz = size(A.chunks,1) 
+           nrows_per_chunk = nrows ÷ sz
+           for i in 1:sz
+               for j in 1:nrows_per_chunk
+                   Dagger.@spawn sum_array_row!(Out(view(row_sums, (nrows_per_chunk*(i-1)+j):(nrows_per_chunk*(i-1)+j))),
+                                                In(Dagger.view(A.chunks[i,1], j:j, :)))
+               end
+           end
+       end
+
+# Print the result
+julia> println("Row sums: ", row_sums)
+Row sums: [499.8765, 500.1234, ..., 499.9876]
+```

src/datadeps.jl

@@ -450,6 +450,9 @@ end
 
 # Make a copy of each piece of data on each worker
 # memory_space => {arg => copy_of_arg}
+isremotehandle(x) = false
+isremotehandle(x::DTask) = true
+isremotehandle(x::Chunk) = true
 function generate_slot!(state::DataDepsState, dest_space, data)
     if data isa DTask
         data = fetch(data; raw=true)
@@ -458,22 +461,25 @@ function generate_slot!(state::DataDepsState, dest_space, data)
     to_proc = first(processors(dest_space))
     from_proc = first(processors(orig_space))
     dest_space_args = get!(IdDict{Any,Any}, state.remote_args, dest_space)
-    if orig_space == dest_space
+    if orig_space == dest_space && (data isa Chunk || !isremotehandle(data))
+        # Fast path for local data or data already in a Chunk
         data_chunk = tochunk(data, from_proc)
         dest_space_args[data] = data_chunk
         @assert processor(data_chunk) in processors(dest_space) || data isa Chunk && processor(data) isa Dagger.OSProc
         @assert memory_space(data_chunk) == orig_space
     else
-        w = only(unique(map(get_parent, collect(processors(dest_space))))).pid
+        to_w = root_worker_id(dest_space)
         ctx = Sch.eager_context()
         id = rand(Int)
         timespan_start(ctx, :move, (;thunk_id=0, id, position=0, processor=to_proc), (;f=nothing, data))
-        dest_space_args[data] = remotecall_fetch(w, from_proc, to_proc, data) do from_proc, to_proc, data
+        dest_space_args[data] = remotecall_fetch(to_w, from_proc, to_proc, data) do from_proc, to_proc, data
             data_converted = move(from_proc, to_proc, data)
             data_chunk = tochunk(data_converted, to_proc)
             @assert processor(data_chunk) in processors(dest_space)
             @assert memory_space(data_converted) == memory_space(data_chunk) "space mismatch! $(memory_space(data_converted)) != $(memory_space(data_chunk)) ($(typeof(data_converted)) vs. $(typeof(data_chunk))), spaces ($orig_space -> $dest_space)"
-            @assert orig_space != memory_space(data_chunk) "space preserved! $orig_space != $(memory_space(data_chunk)) ($(typeof(data)) vs. $(typeof(data_chunk))), spaces ($orig_space -> $dest_space)"
+            if orig_space != dest_space
+                @assert orig_space != memory_space(data_chunk) "space preserved! $orig_space != $(memory_space(data_chunk)) ($(typeof(data)) vs. $(typeof(data_chunk))), spaces ($orig_space -> $dest_space)"
+            end
             return data_chunk
         end
         timespan_finish(ctx, :move, (;thunk_id=0, id, position=0, processor=to_proc), (;f=nothing, data=dest_space_args[data]))

src/memory-spaces.jl

@@ -122,6 +122,7 @@ memory_spans(::T) where T<:AbstractAliasing = throw(ArgumentError("Must define `
 memory_spans(x) = memory_spans(aliasing(x))
 memory_spans(x, T) = memory_spans(aliasing(x, T))
 
+
 struct AliasingWrapper <: AbstractAliasing
     inner::AbstractAliasing
     hash::UInt64
@@ -387,3 +388,71 @@ function will_alias(x_span::MemorySpan, y_span::MemorySpan)
     y_end = y_span.ptr + y_span.len - 1
     return x_span.ptr <= y_end && y_span.ptr <= x_end
 end
+
+struct ChunkView{N}
+    chunk::Chunk
+    slices::NTuple{N, Union{Int, AbstractRange{Int}, Colon}}
+end
+
+function Base.view(c::Chunk, slices...)
+    if c.domain isa ArrayDomain
+        nd, sz = ndims(c.domain), size(c.domain)
+        nd == length(slices) || throw(DimensionMismatch("Expected $nd slices, got $(length(slices))"))
+
+        for (i, s) in enumerate(slices)
+            if s isa Int
+                1 ≤ s ≤ sz[i] || throw(ArgumentError("Index $s out of bounds for dimension $i (size $(sz[i]))"))
+            elseif s isa AbstractRange
+                isempty(s) && continue
+                1 ≤ first(s) ≤ last(s) ≤ sz[i] || throw(ArgumentError("Range $s out of bounds for dimension $i (size $(sz[i]))"))
+            elseif s === Colon()
+                continue
+            else
+                throw(ArgumentError("Invalid slice type $(typeof(s)) at dimension $i, Expected Type of Int, AbstractRange, or Colon"))
+            end
+        end
+    end
+
+    return ChunkView(c, slices)
+end
+
+Base.view(c::DTask, slices...) = view(fetch(c; raw=true), slices...)
+
+function aliasing(x::ChunkView{N}) where N
+    remotecall_fetch(root_worker_id(x.chunk.processor), x.chunk, x.slices) do x, slices
+        x = unwrap(x)
+        v = view(x, slices...)
+        return aliasing(v)
+    end
+end
+memory_space(x::ChunkView) = memory_space(x.chunk)
+isremotehandle(x::ChunkView) = true
+
+#=
+function move!(dep_mod, to_space::MemorySpace, from_space::MemorySpace, to::ChunkView, from::ChunkView)
+    to_w = root_worker_id(to_space)
+    @assert to_w == myid()
+    to_raw = unwrap(to.chunk)
+    from_w = root_worker_id(from_space)
+    from_raw = to_w == from_w ? unwrap(from.chunk) : remotecall_fetch(f->copy(unwrap(f)), from_w, from.chunk)
+    from_view = view(from_raw, from.slices...)
+    to_view = view(to_raw, to.slices...)
+    move!(dep_mod, to_space, from_space, to_view, from_view)
+    return
+end
+=#
+
+function move(from_proc::Processor, to_proc::Processor, slice::ChunkView)
+    if from_proc == to_proc
+        return view(unwrap(slice.chunk), slice.slices...)
+    else
+        # Need to copy the underlying data, so collapse the view
+        from_w = root_worker_id(from_proc)
+        data = remotecall_fetch(from_w, slice.chunk, slice.slices) do chunk, slices
+            copy(view(unwrap(chunk), slices...))
+        end
+        return move(from_proc, to_proc, data)
+    end
+end
+
+Base.fetch(slice::ChunkView) = view(fetch(slice.chunk), slice.slices...)

test/datadeps.jl

@@ -1,3 +1,4 @@
+import Dagger: ChunkView, Chunk
 using LinearAlgebra, Graphs
 
 @testset "Memory Aliasing" begin
@@ -17,6 +18,70 @@ using LinearAlgebra, Graphs
     @test s.sz == sizeof(3)
 end
 
+@testset "ChunkView" begin
+    DA = rand(Blocks(8, 8), 64, 64)
+    task1 = DA.chunks[1,1]::DTask
+    chunk1 = fetch(task1; raw=true)::Chunk
+    v1 = view(chunk1, :, :)
+    task2 = DA.chunks[1,2]::DTask
+    chunk2 = fetch(task2; raw=true)::Chunk
+    v2 = view(chunk2, :, :)
+
+    for obj in (chunk1, task1)
+        @testset "Valid Slices" begin
+            @test view(obj, :, :)     isa ChunkView && view(obj, 1:8, 1:8)   isa ChunkView
+            @test view(obj, 1:2:7, :) isa ChunkView && view(obj, :, 2:2:8)   isa ChunkView
+            @test view(obj, 1, :)     isa ChunkView && view(obj, :, 1)       isa ChunkView
+            @test view(obj, 3:3, 5:5) isa ChunkView && view(obj, 5:7, 1:2:4) isa ChunkView
+            @test view(obj, 8, 8)     isa ChunkView
+            @test view(obj, 1:0, :)   isa ChunkView
+        end
+
+        @testset "Dimension Mismatch" begin
+            @test_throws DimensionMismatch view(obj, :)
+            @test_throws DimensionMismatch view(obj, :, :, :)
+        end
+
+        @testset "Int Slice Out of Bounds" begin
+            @test_throws ArgumentError view(obj, 0, :)
+            @test_throws ArgumentError view(obj, :, 9)
+            @test_throws ArgumentError view(obj, 9, 1)
+        end
+
+        @testset "Range Slice Out of Bounds" begin
+            @test_throws ArgumentError view(obj, 0:5, :)
+            @test_throws ArgumentError view(obj, 1:8, 5:10)
+            @test_throws ArgumentError view(obj, 2:2:10, :)
+        end
+
+        @testset "Invalid Slice Types" begin
+            @test_throws DimensionMismatch view(obj, (1:2, :))
+            @test_throws ArgumentError view(obj, :, [1, 2])
+        end
+    end
+
+    @test fetch(v1) == fetch(chunk1)
+
+    @test Dagger.memory_space(v1) == Dagger.memory_space(chunk1)
+    @test Dagger.aliasing(v1) isa Dagger.StridedAliasing
+    ptr = remotecall_fetch(chunk1.handle.owner, chunk1) do chunk
+        UInt(pointer(Dagger.unwrap(chunk)))
+    end
+    @test Dagger.aliasing(v1).base_ptr.addr == ptr
+
+    @testset "Aliasing" begin
+        f! = v1 -> begin
+            @show typeof(v1) v1
+            v1 .= 0
+            return
+        end
+        Dagger.spawn_datadeps() do
+            Dagger.@spawn f!(InOut(v1))
+        end
+        @test collect(DA)[1:8, 1:8] == zeros(8, 8)
+    end
+end
+
 function with_logs(f)
     Dagger.enable_logging!(;taskdeps=true, taskargs=true)
     try