chunk by partition indexes

src/utils.jl

@@ -62,7 +62,8 @@ Base.show_function(io::IO, u::Base.Fix2{typeof(_unsqueeze)}, ::Bool) = print(io,
 
 Unroll the given `xs` into an array of arrays along the given dimension `dims`.
 
-See also [`stack`](@ref) and [`unbatch`](@ref).
+See also [`stack`](@ref), [`unbatch`](@ref),
+and [`chunk`](@ref).
 
 # Examples
 
@@ -156,6 +157,46 @@ function chunk(x::AbstractArray; size, dims::Int=ndims(x))
     return [_selectdim(x, dims, i) for i in idxs]
 end
 
+
+"""
+    chunk(x, partition_idxs; [npartitions, dims])
+
+Partition the array `x` along the dimension `dims` according to the indexes 
+in `partition_idxs`.
+
+`partition_idxs` must be sorted and contain only positive integers 
+between 1 and the number of partitions. 
+
+If the number of partition `npartitions` is not provided, 
+it is inferred from `partition_idxs`.
+
+If `dims` is not provided, it defaults to the last dimension.
+
+See also [`unbatch`](@ref).
+
+# Examples
+
+```jldoctest
+julia> x = reshape([1:10;], 2, 5)
+2×5 Matrix{Int64}:
+ 1  3  5  7   9
+ 2  4  6  8  10
+
+julia> chunk(x, [1, 2, 2, 3, 3])
+3-element Vector{SubArray{Int64, 2, Matrix{Int64}, Tuple{Base.Slice{Base.OneTo{Int64}}, UnitRange{Int64}}, true}}:
+ [1; 2;;]
+ [3 5; 4 6]
+ [7 9; 8 10]
+```
+"""
+function chunk(x::AbstractArray{T,N}, partition_idxs::AbstractVector; 
+        npartitions=nothing, dims=ndims(x)) where {T, N}
+    @assert issorted(partition_idxs) "partition_idxs must be sorted"
+    m = npartitions === nothing ? maximum(partition_idxs) : npartitions
+    degrees = NNlib.scatter(+, ones_like(partition_idxs), partition_idxs, dstsize=(m,))
+    return chunk(x; size=degrees, dims)
+end
+
 # work around https://github.com/JuliaML/MLUtils.jl/issues/103
 _selectdim(x::AbstractArray, dims::Int, i) = selectdim(x, dims, i)
 _selectdim(x::AbstractArray, dims::Int, i::UnitRange) = _selectdim(x, Val(dims), i)
@@ -349,13 +390,13 @@ end
 Reverse of the [`batch`](@ref) operation,
 unstacking the last dimension of the array `x`.
 
-See also [`unstack`](@ref).
+See also [`unstack`](@ref) and [`chunk`](@ref).
 
 # Examples
 
 ```jldoctest
 julia> unbatch([1 3 5 7;
-                     2 4 6 8])
+                2 4 6 8])
 4-element Vector{Vector{Int64}}:
  [1, 2]
  [3, 4]

test/utils.jl

@@ -134,6 +134,16 @@ end
     idxs = MLUtils._partition_idxs(x, cld(size(x, dims), n), dims)
     test_zygote(MLUtils.∇chunk, dl, x, idxs, Val(dims), check_inferred=false)
 
+
+    if CUDA.functional()
+        # https://github.com/JuliaML/MLUtils.jl/issues/103
+        x = rand(2, 10) |> cu
+        cs = chunk(x, 2)
+        @test length(cs) == 2
+        @test cs[1] isa CuArray
+        @test cs[1] == x[:, 1:5]
+    end
+
     @testset "size collection" begin
         a = reshape(collect(1:10), (5, 2))
         y = chunk(a; dims = 1, size = (1, 4))
@@ -144,13 +154,25 @@ end
         test_zygote(x -> chunk(x; dims = 1, size = (1, 4)), a)
     end
 
-    if CUDA.functional()
-        # https://github.com/JuliaML/MLUtils.jl/issues/103
-        x = rand(2, 10) |> cu
-        cs = chunk(x, 2)
-        @test length(cs) == 2
-        @test cs[1] isa CuArray
-        @test cs[1] == x[:, 1:5]
+    @testset "chunk by partition_idxs" begin
+        x = reshape(collect(1:15), (3, 5))
+        partition_idxs = [1,1,3,3,4]
+
+        y = chunk(x, partition_idxs)
+        @test length(y) == 4
+        @test y[1] == [1 4; 2 5; 3 6]
+        @test size(y[2]) == (3, 0)
+        @test y[3] == [7 10; 8 11; 9 12]
+        @test y[4] == reshape([13, 14, 15], 3, 1)
+
+        y = chunk(x, partition_idxs; npartitions=5)
+        @test length(y) == 5
+        @test size(y[5]) == (3, 0)
+
+        y = chunk(x, [1,1,2]; dims=1)
+        @test length(y) == 2
+        @test y[1] == [1 4 7 10 13; 2 5 8 11 14]
+        @test y[2] == [3 6 9 12 15]
     end
 end