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mateuszbaran merged 2 commits into from
Dec 5, 2020
Merged

Add a work-around on Julia 1.0 for #857 #859

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b48d39d
Add a work-around on Julia 1.0 for #857
odow Dec 3, 2020
314f3bb
Remove Julia 1.0 branch in favor of single implementation
odow Dec 5, 2020
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48 changes: 40 additions & 8 deletions src/matrix_multiply_add.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -218,13 +218,45 @@ const StaticVecOrMatLikeForFiveArgMulDest{T} = Union{
α::Number, β::Number) = _mul!(TSize(dest), mul_parent(dest), Size(A), Size(B), A, B,
AlphaBeta(α,β))

@inline function LinearAlgebra.mul!(dest::StaticVecOrMatLike{TDest}, A::StaticVecOrMatLike{TA},
B::StaticVecOrMatLike{TB}) where {TDest,TA,TB}
TMul = promote_op(matprod, TA, TB)
return _mul!(TSize(dest), mul_parent(dest), Size(A), Size(B), A, B, NoMulAdd{TMul, TDest}())
if VERSION < v"1.1"
# See https://github.com/JuliaArrays/StaticArrays.jl/issues/857.
# `StaticVecOrMatLike` is a very large union, and this caused Julia 1.0.x to
# use a large amount of memory when compiling StaticArrays in the presence
# of other packages that also extend `LinearAlgebra.mul!` in a similar
# fashion. As an example, this led to JuMP using more than 4 GB of RAM when
# running `using JuMP`, causing its CI to crash.
#
# Computing the eltypes within the function, rather than specifying them in
# the type arguments greatly improves the issue, with `using JuMP` going
# from:
# julia> @time using JuMP
# [ Info: Precompiling JuMP [4076af6c-e467-56ae-b986-b466b2749572]
# Killed
# to:
# julia> @time using JuMP
# [ Info: Precompiling JuMP [4076af6c-e467-56ae-b986-b466b2749572]
# 97.868305 seconds (12.75 M allocations: 805.413 MiB, 0.30% gc time)
@inline _eltype(::StaticVecOrMatLike{T}) where {T} = T
@inline function LinearAlgebra.mul!(
dest::StaticVecOrMatLike,
A::StaticVecOrMatLike,
B::StaticVecOrMatLike,
)
TDest, TA, TB = _eltype(dest), _eltype(A), _eltype(B)
TMul = promote_op(matprod, TA, TB)
return _mul!(TSize(dest), mul_parent(dest), Size(A), Size(B), A, B, NoMulAdd{TMul, TDest}())
end
else
@inline function LinearAlgebra.mul!(
dest::StaticVecOrMatLike{TDest},
A::StaticVecOrMatLike{TA},
B::StaticVecOrMatLike{TB},
) where {TDest,TA,TB}
TMul = promote_op(matprod, TA, TB)
return _mul!(TSize(dest), mul_parent(dest), Size(A), Size(B), A, B, NoMulAdd{TMul, TDest}())
end
end


"Calculate the product of the dimensions being multiplied. Useful as a heuristic for unrolling."
@inline multiplied_dimension(A::Type{<:StaticVecOrMatLike}, B::Type{<:StaticVecOrMatLike}) =
prod(size(A)) * size(B,2)
Expand Down Expand Up @@ -310,7 +342,7 @@ function uplo_access(sa, asym, k, j, uplo)
elseif uplo == :unit_lower_triangular
if k > j
return :($asym[$(LinearIndices(sa)[k, j])])
elseif k == j
elseif k == j
return :(oneunit($TAsym))
else
return :(zero($TAsym))
Expand Down Expand Up @@ -439,7 +471,7 @@ end
else
ab = [:(a[$i] * transpose(b[$j])) for i = 1:sa[1], j = 1:sb[2]]
end

exprs = _muladd_expr(lhs, ab, _add)

return quote
Expand Down Expand Up @@ -558,7 +590,7 @@ end
@inbounds $(Expr(:block, exprs...))
end
end

return quote
@_inline_meta
α = alpha(_add)
Expand Down