Add vector-vector and matrix-matrix Kronecker product

.gitignore

@@ -12,3 +12,5 @@ Manifest.toml
 
 # MacOS generated files
 *.DS_Store
+
+/.vscode

src/host/linalg.jl

@@ -736,3 +736,80 @@ function Base.isone(x::AbstractGPUMatrix{T}) where {T}
 
     Array(y)[]
 end
+
+## Kronecker product
+
+function LinearAlgebra.kron!(z::AbstractGPUVector{T1}, x::AbstractGPUVector{T2}, y::AbstractGPUVector{T3}) where {T1,T2,T3}
+    @assert length(z) == length(x) * length(y)
+
+    @kernel function kron_kernel!(z, @Const(x), @Const(y))
+        i, j = @index(Global, NTuple)
+
+        @inbounds z[(i - 1) * length(y) + j] = x[i] * y[j]
+    end
+
+    backend = KernelAbstractions.get_backend(z)
+    kernel = kron_kernel!(backend)
+
+    kernel(z, x, y, ndrange=(length(x), length(y)))
+
+    return z
+end
+
+function LinearAlgebra.kron(x::AbstractGPUVector{T1}, y::AbstractGPUVector{T2}) where {T1,T2}
+    T = promote_type(T1, T2)
+    z = similar(x, T, length(x) * length(y))
+    return LinearAlgebra.kron!(z, x, y)
+end
+
+trans_adj_wrappers = ((T -> :(AbstractGPUMatrix{$T}), T -> 'N', identity),
+               (T -> :(Transpose{$T, <:AbstractGPUMatrix{$T}}), T -> 'T', A -> :(parent($A))),
+               (T -> :(Adjoint{$T, <:AbstractGPUMatrix{$T}}), T -> T <: Real ? 'T' : 'C', A -> :(parent($A))))
+
+for (wrapa, transa, unwrapa) in trans_adj_wrappers, (wrapb, transb, unwrapb) in trans_adj_wrappers
+    TypeA = wrapa(:(T1))
+    TypeB = wrapb(:(T2))
+    TypeC = :(AbstractGPUMatrix{T3})
+
+    @eval function LinearAlgebra.kron!(C::$TypeC, A::$TypeA, B::$TypeB) where {T1,T2,T3}
+        @assert size(C, 1) == size(A, 1) * size(B, 1)
+        @assert size(C, 2) == size(A, 2) * size(B, 2)
+
+        ta = $transa(T1)
+        tb = $transb(T2)
+
+        @kernel function kron_kernel!(C, @Const(A), @Const(B))
+            ai, aj = @index(Global, NTuple)  # Indices in the result matrix
+
+            # lb1, lb2 = size(B)  # Dimensions of B
+            lb1, lb2 = tb == 'N' ? size(B) : reverse(size(B))
+
+            # Map global indices (ai, aj) to submatrices of the Kronecker product
+            i_a = (ai - 1) ÷ lb1 + 1  # Corresponding row index in A
+            i_b = (ai - 1) % lb1 + 1  # Corresponding row index in B
+            j_a = (aj - 1) ÷ lb2 + 1  # Corresponding col index in A
+            j_b = (aj - 1) % lb2 + 1  # Corresponding col index in B
+
+            @inbounds begin
+                a_ij = ta == 'N' ? A[i_a, j_a] : (ta == 'T' ? A[j_a, i_a] : conj(A[j_a, i_a]))
+                b_ij = tb == 'N' ? B[i_b, j_b] : (tb == 'T' ? B[j_b, i_b] : conj(B[j_b, i_b]))
+
+                C[ai, aj] = a_ij * b_ij
+            end
+        end
+
+        backend = KernelAbstractions.get_backend(C)
+        kernel = kron_kernel!(backend)
+
+        kernel(C, $(unwrapa(:A)), $(unwrapb(:B)), ndrange=(size(C, 1), size(C, 2)))
+
+        return C
+    end
+
+    @eval function LinearAlgebra.kron(A::$TypeA, B::$TypeB) where {T1, T2}
+        T = promote_type(T1, T2)
+        size_C = (size(A, 1) * size(B, 1), size(A, 2) * size(B, 2))
+        C = similar(A, T, size_C...)
+        return kron!(C, A, B)
+    end
+end

test/testsuite/linalg.jl

@@ -310,6 +310,15 @@
         @test iszero(A)
         @test isone(A) == false
     end
+
+    @testset "kron" begin
+        for T in eltypes
+            @test compare(kron, AT, rand(T, 32), rand(T, 64))
+            for opa in (identity, transpose, adjoint), opb in (identity, transpose, adjoint)
+                @test compare(kron, AT, opa(rand(T, 32, 64)), opb(rand(T, 128, 16)))
+            end
+        end
+    end
 end
 
 @testsuite "linalg/mul!/vector-matrix" (AT, eltypes)->begin