Another mul specialization for diagonals

src/host/linalg.jl

@@ -258,6 +258,21 @@ function LinearAlgebra.mul!(C::Diagonal{<:Any, <:AbstractGPUArray},
     return C
 end
 
+function LinearAlgebra.mul!(C::Diagonal{<:Any, <:AbstractGPUArray},
+                            A::AbstractGPUArray,
+                            B::AbstractGPUArray)
+    dc = C.diag
+    d  = length(dc)
+    m, n   = size(A, 1), size(A, 2)
+    m′, n′ = size(B, 1), size(B, 2)
+    m == d  || throw(DimensionMismatch("left hand side has $m rows but output is $d by $d"))
+    n′ == d || throw(DimensionMismatch("right hand side has $n′ cols but output is $d by $d"))
+    C_ = A * B
+    isdiag(C_) || throw(ErrorException("output matrix must be diagonal"))
+    dc .= diag(C_)
+    return C
+end
+
 function LinearAlgebra.mul!(B::AbstractGPUVecOrMat,
                             D::Diagonal{<:Any, <:AbstractGPUArray},
                             A::AbstractGPUVecOrMat)

test/testsuite/linalg.jl

@@ -250,6 +250,11 @@
                 A = Diagonal(a)
                 mul!(C, A, B)
                 @test collect(C.diag) ≈ collect(A.diag) .* collect(B.diag) 
+                a = AT(diagm(rand(elty, n)))
+                b = AT(diagm(rand(elty, n)))
+                C = Diagonal(d)
+                mul!(C, a, b)
+                @test collect(C) ≈ Diagonal(collect(a) * collect(b)) 
             end
         end