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Provide CUDA support for the binarize() operation on sparse matrices #601

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Merged
merged 6 commits into from
Jun 21, 2025
Merged

Provide CUDA support for the binarize() operation on sparse matrices #601

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df734e1
Add binarize function for CuSparseMatrixCSC and update its usage
dferre97 Jun 14, 2025
4c0c38e
Refactor CUDA binarize function to use Bool as the CPU version
dferre97 Jun 15, 2025
ccd9058
Revert unrelated changes, make coherent PR on binarize() and nothing …
dferre97 Jun 16, 2025
7e6694d
Removed debug prints
dferre97 Jun 17, 2025
adc6cf9
Unify binarize function signature
dferre97 Jun 19, 2025
bef10aa
Remove debug print statement from binarize function
dferre97 Jun 19, 2025
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13 changes: 13 additions & 0 deletions GNNGraphs/ext/GNNGraphsCUDAExt.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@ using CUDA
using Random, Statistics, LinearAlgebra
using GNNGraphs
using GNNGraphs: COO_T, ADJMAT_T, SPARSE_T
using SparseArrays

const CUMAT_T = Union{CUDA.AnyCuMatrix, CUDA.CUSPARSE.CuSparseMatrix}

Expand All @@ -20,6 +21,18 @@ GNNGraphs.dense_zeros_like(a::CUMAT_T, T::Type, sz = size(a)) = CUDA.zeros(T, sz

GNNGraphs.iscuarray(x::AnyCuArray) = true

function GNNGraphs.binarize(Mat::CUSPARSE.CuSparseMatrixCSC)
@debug "Binarizing CuSparseMatrixCSC"
bin_vals = fill!(similar(nonzeros(Mat), Bool), true)
return CUSPARSE.CuSparseMatrixCSC(Mat.colPtr, rowvals(Mat), bin_vals, size(Mat))
end
function GNNGraphs.binarize(Mat::CUSPARSE.CuSparseMatrixCSC, T::DataType)
@debug "Binarizing CuSparseMatrixCSC with type $(T)"
bin_vals = fill!(similar(nonzeros(Mat)), one(T))
# Binarize a CuSparseMatrixCSC by setting all nonzero values to one(T)
return CUSPARSE.CuSparseMatrixCSC(Mat.colPtr, rowvals(Mat), bin_vals, size(Mat))
end


function sort_edge_index(u::AnyCuArray, v::AnyCuArray)
dev = get_device(u)
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4 changes: 2 additions & 2 deletions GNNGraphs/src/query.jl
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Expand Up @@ -235,7 +235,7 @@ function Graphs.adjacency_matrix(g::GNNGraph{<:ADJMAT_T}, T::DataType = eltype(g
@assert dir ∈ [:in, :out]
A = g.graph
if !weighted
A = binarize(A)
A = binarize(A, T)
end
A = T != eltype(A) ? T.(A) : A
return dir == :out ? A : A'
Expand Down Expand Up @@ -377,7 +377,7 @@ end

function _degree(A::AbstractMatrix, T::Type, dir::Symbol, edge_weight::Bool, num_nodes::Int)
if edge_weight === false
A = binarize(A)
A = binarize(A, T)
end
A = eltype(A) != T ? T.(A) : A
return dir == :out ? vec(sum(A, dims = 2)) :
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2 changes: 2 additions & 0 deletions GNNGraphs/src/utils.jl
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Expand Up @@ -296,6 +296,8 @@ function _rand_edges(rng, (n1, n2), m)
end

binarize(x) = map(>(0), x)
# here just to allow CUDA extension to overload this function with correct type casting
binarize(x, T::DataType) = binarize(x)

CRC.@non_differentiable binarize(x...)
CRC.@non_differentiable edge_encoding(x...)
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