StaticArrays

[cortner]

This must have been asked before but I couldn’t find a reference: the package doesn’t seem to support StaticArrays, is there a plan / possibility to do so?

[Jutho]

This package is mostly intended for big arrays with strided memory layout. In principle, the @tensor parsing and lowering part is reusable and can be made to work with any array or tensor class, provided a few basic methods are implemented. But within this package, those methods are only implemented for StridedArray.

[andyferris]

It is an interesting question - the syntax provided by TensorOperations is very convenient, even if the array is small (and there's also block matrix structures).

[cortner]

I am using Einsum.jl for now, which works in-place (MArrays) but not with SArrays, which is ok for now. My concern still is that Einsum.jl kind of seems semi-abandoned with TensorOperations becoming the dominant package for this?

[andyferris]

What's the error for StaticArrays here? Is it permutedims?

[Jutho]

What should the ideal implementation for StaticArrays look like? Just plain loops, since sizes are typically small? I haven't used StaticArrays yet.

[andyferris]

All the code in StaticArrays.jl is manually unrolled by generated functions, which tend to also generate linear (not Cartesian) indices statically (all of this lets LLVM use SIMD).

I generally assume or the O(length) operations can be completely unrolled (that would be the equivalent of permutedims and reshape here, but I haven't implemented permutedims in StaticArrays yet but am happy to provide). The matrix multiplication stuff is a bit complex since complete unrolling isn't optimal for larger (more than 50-100) elements, so it has code to unroll 2 of the 3 loops for multiplication of larger matrices.

Is there a generic AbstractArray path in this package? I could provide fast permutedims, reshape and *. One catch is that fast permutedims and reshape need special argument singleton types for permutations and sizes for the static compilation (perm::Val and size::Size).

[Jutho]

No it's really only StridedArray. Permutations are not stored in compile time information (at least not in the latest master version), since they are dealt with using stride calculations at run time. I always assumed that approach to be faster than having to compile a new function for every new permutation.

Note that this package does not use permutedims at all, the reason being that I find permutedims from Julia Base way to slow, even for plain arrays. I have my own permute function, which is actually more general and is just called add!.

Over at https://github.com/Jutho/Strided.jl I am actually experimenting with having a multithreaded implementation of my own permute function, which is even much more general (but still assumes a strided memory layout).

[andyferris]

Multithreaded - cool!

Did you ever consider contributing your faster code to Base, and putting it in permutedims!(::StridedArray, perm) in base/multidimensional.jl?

Obviously StaticArrays is the complete opposite end of the spectrum here, and has annoying interface problems as well...

[cortner]

What's the error for StaticArrays here? Is it permutedims?

using TensorOperations, StaticArrays
a = @SVector rand(3)
b = @SMatrix rand(3, 3)
c = @MVector zeros(3)
@tensor c[i] = b[i, j] * a[j]

gives the output:

ERROR: MethodError: no method matching contract!(::Int64, ::StaticArrays.SArray{Tuple{3,3},Float64,2,9}, ::Type{Val{:N}}, ::SVector{3,Float64}, ::Type{Val{:N}}, ::Int64, ::MVector{3,Float64}, ::Tuple{Int64}, ::Tuple{Int64}, ::Tuple{}, ::Tuple{Int64}, ::Tuple{Int64})
Closest candidates are:
  contract!(::Any, ::Union{Base.ReshapedArray{T,N,A,MI} where MI<:Tuple{Vararg{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},N} where N} where A<:Union{DenseArray, SubArray{T,N,P,I,true} where I<:Tuple{Union{Base.Slice, UnitRange},Vararg{Any,N} where N} where P where N where T}, DenseArray{T,N}, SubArray{T,N,A,I,L} where L} where I<:Tuple{Vararg{Union{Base.AbstractCartesianIndex, Int64, Range{Int64}},N} where N} where A<:Union{Base.ReshapedArray{T,N,A,MI} where MI<:Tuple{Vararg{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},N} where N} where A<:Union{DenseArray, SubArray{T,N,P,I,true} where I<:Tuple{Union{Base.Slice, UnitRange},Vararg{Any,N} where N} where P where N where T} where N where T, DenseArray} where N where T, ::Type{Val{CA}}, ::Union{Base.ReshapedArray{T,N,A,MI} where MI<:Tuple{Vararg{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},N} where N} where A<:Union{DenseArray, SubArray{T,N,P,I,true} where I<:Tuple{Union{Base.Slice, UnitRange},Vararg{Any,N} where N} where P where N where T}, DenseArray{T,N}, SubArray{T,N,A,I,L} where L} where I<:Tuple{Vararg{Union{Base.AbstractCartesianIndex, Int64, Range{Int64}},N} where N} where A<:Union{Base.ReshapedArray{T,N,A,MI} where MI<:Tuple{Vararg{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},N} where N} where A<:Union{DenseArray, SubArray{T,N,P,I,true} where I<:Tuple{Union{Base.Slice, UnitRange},Vararg{Any,N} where N} where P where N where T} where N where T, DenseArray} where N where T, ::Type{Val{CB}}, ::Any, ::Union{Base.ReshapedArray{TC<:Union{Complex{Float32}, Complex{Float64}, Float32, Float64},N,A,MI} where MI<:Tuple{Vararg{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},N} where N} where A<:Union{DenseArray, SubArray{T,N,P,I,true} where I<:Tuple{Union{Base.Slice, UnitRange},Vararg{Any,N} where N} where P where N where T}, DenseArray{TC<:Union{Complex{Float32}, Complex{Float64}, Float32, Float64},N}, SubArray{TC<:Union{Complex{Float32}, Complex{Float64}, Float32, Float64},N,A,I,L} where L} where I<:Tuple{Vararg{Union{Base.AbstractCartesianIndex, Int64, Range{Int64}},N} where N} where A<:Union{Base.ReshapedArray{T,N,A,MI} where MI<:Tuple{Vararg{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},N} where N} where A<:Union{DenseArray, SubArray{T,N,P,I,true} where I<:Tuple{Union{Base.Slice, UnitRange},Vararg{Any,N} where N} where P where N where T} where N where T, DenseArray} where N, ::Any, ::Any, ::Any, ::Any, ::Any) where {CA, CB, TC<:Union{Complex{Float32}, Complex{Float64}, Float32, Float64}} at /Users/ortner/.julia/v0.6/TensorOperations/src/implementation/stridedarray.jl:101
  contract!(::Any, ::Union{Base.ReshapedArray{T,N,A,MI} where MI<:Tuple{Vararg{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},N} where N} where A<:Union{DenseArray, SubArray{T,N,P,I,true} where I<:Tuple{Union{Base.Slice, UnitRange},Vararg{Any,N} where N} where P where N where T}, DenseArray{T,N}, SubArray{T,N,A,I,L} where L} where I<:Tuple{Vararg{Union{Base.AbstractCartesianIndex, Int64, Range{Int64}},N} where N} where A<:Union{Base.ReshapedArray{T,N,A,MI} where MI<:Tuple{Vararg{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},N} where N} where A<:Union{DenseArray, SubArray{T,N,P,I,true} where I<:Tuple{Union{Base.Slice, UnitRange},Vararg{Any,N} where N} where P where N where T} where N where T, DenseArray} where N where T, ::Type{Val{CA}}, ::Union{Base.ReshapedArray{T,N,A,MI} where MI<:Tuple{Vararg{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},N} where N} where A<:Union{DenseArray, SubArray{T,N,P,I,true} where I<:Tuple{Union{Base.Slice, UnitRange},Vararg{Any,N} where N} where P where N where T}, DenseArray{T,N}, SubArray{T,N,A,I,L} where L} where I<:Tuple{Vararg{Union{Base.AbstractCartesianIndex, Int64, Range{Int64}},N} where N} where A<:Union{Base.ReshapedArray{T,N,A,MI} where MI<:Tuple{Vararg{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},N} where N} where A<:Union{DenseArray, SubArray{T,N,P,I,true} where I<:Tuple{Union{Base.Slice, UnitRange},Vararg{Any,N} where N} where P where N where T} where N where T, DenseArray} where N where T, ::Type{Val{CB}}, ::Any, ::Union{Base.ReshapedArray{TC<:Union{Complex{Float32}, Complex{Float64}, Float32, Float64},N,A,MI} where MI<:Tuple{Vararg{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},N} where N} where A<:Union{DenseArray, SubArray{T,N,P,I,true} where I<:Tuple{Union{Base.Slice, UnitRange},Vararg{Any,N} where N} where P where N where T}, DenseArray{TC<:Union{Complex{Float32}, Complex{Float64}, Float32, Float64},N}, SubArray{TC<:Union{Complex{Float32}, Complex{Float64}, Float32, Float64},N,A,I,L} where L} where I<:Tuple{Vararg{Union{Base.AbstractCartesianIndex, Int64, Range{Int64}},N} where N} where A<:Union{Base.ReshapedArray{T,N,A,MI} where MI<:Tuple{Vararg{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},N} where N} where A<:Union{DenseArray, SubArray{T,N,P,I,true} where I<:Tuple{Union{Base.Slice, UnitRange},Vararg{Any,N} where N} where P where N where T} where N where T, DenseArray} where N, ::Any, ::Any, ::Any, ::Any, ::Any, ::Type{Val{:BLAS}}) where {CA, CB, TC<:Union{Complex{Float32}, Complex{Float64}, Float32, Float64}} at /Users/ortner/.julia/v0.6/TensorOperations/src/implementation/stridedarray.jl:101
  contract!(::Any, ::Union{Base.ReshapedArray{T,N,A,MI} where MI<:Tuple{Vararg{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},N} where N} where A<:Union{DenseArray, SubArray{T,N,P,I,true} where I<:Tuple{Union{Base.Slice, UnitRange},Vararg{Any,N} where N} where P where N where T}, DenseArray{T,N}, SubArray{T,N,A,I,L} where L} where I<:Tuple{Vararg{Union{Base.AbstractCartesianIndex, Int64, Range{Int64}},N} where N} where A<:Union{Base.ReshapedArray{T,N,A,MI} where MI<:Tuple{Vararg{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},N} where N} where A<:Union{DenseArray, SubArray{T,N,P,I,true} where I<:Tuple{Union{Base.Slice, UnitRange},Vararg{Any,N} where N} where P where N where T} where N where T, DenseArray} where N where T, ::Type{Val{CA}}, ::Union{Base.ReshapedArray{T,N,A,MI} where MI<:Tuple{Vararg{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},N} where N} where A<:Union{DenseArray, SubArray{T,N,P,I,true} where I<:Tuple{Union{Base.Slice, UnitRange},Vararg{Any,N} where N} where P where N where T}, DenseArray{T,N}, SubArray{T,N,A,I,L} where L} where I<:Tuple{Vararg{Union{Base.AbstractCartesianIndex, Int64, Range{Int64}},N} where N} where A<:Union{Base.ReshapedArray{T,N,A,MI} where MI<:Tuple{Vararg{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},N} where N} where A<:Union{DenseArray, SubArray{T,N,P,I,true} where I<:Tuple{Union{Base.Slice, UnitRange},Vararg{Any,N} where N} where P where N where T} where N where T, DenseArray} where N where T, ::Type{Val{CB}}, ::Any, ::Union{Base.ReshapedArray{T,N,A,MI} where MI<:Tuple{Vararg{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},N} where N} where A<:Union{DenseArray, SubArray{T,N,P,I,true} where I<:Tuple{Union{Base.Slice, UnitRange},Vararg{Any,N} where N} where P where N where T}, DenseArray{T,N}, SubArray{T,N,A,I,L} where L} where I<:Tuple{Vararg{Union{Base.AbstractCartesianIndex, Int64, Range{Int64}},N} where N} where A<:Union{Base.ReshapedArray{T,N,A,MI} where MI<:Tuple{Vararg{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},N} where N} where A<:Union{DenseArray, SubArray{T,N,P,I,true} where I<:Tuple{Union{Base.Slice, UnitRange},Vararg{Any,N} where N} where P where N where T} where N where T, DenseArray} where N where T, ::Any, ::Any, ::Any, ::Any, ::Any) where {CA, CB} at /Users/ortner/.julia/v0.6/TensorOperations/src/implementation/stridedarray.jl:198
  ...
Stacktrace:
 [1] macro expansion at /Users/ortner/.julia/v0.6/TensorOperations/src/indexnotation/product.jl:69 [inlined]
 [2] deindexify!(::MVector{3,Float64}, ::TensorOperations.ProductOfIndexedObjects{(:i, :j),(:j,),:N,:N,StaticArrays.SArray{Tuple{3,3},Float64,2,9},SVector{3,Float64},Int64,Int64}, ::TensorOperations.Indices{(:i,)}, ::Int64) at /Users/ortner/.julia/v0.6/TensorOperations/src/indexnotation/product.jl:63

[cortner]

and the allocating version:

@tensor d[i] := b[i, j] * a[j]

leads to

ERROR: MethodError: no method matching similar_from_indices(::Type{Float64}, ::Tuple{Int64}, ::StaticArrays.SArray{Tuple{3,3},Float64,2,9}, ::SVector{3,Float64}, ::Type{Val{:N}}, ::Type{Val{:N}})
Closest candidates are:
  similar_from_indices(::Type{T}, ::Tuple{Vararg{Int64,N}}, ::Union{Base.ReshapedArray{T,N,A,MI} where MI<:Tuple{Vararg{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},N} where N} where A<:Union{DenseArray, SubArray{T,N,P,I,true} where I<:Tuple{Union{Base.Slice, UnitRange},Vararg{Any,N} where N} where P where N where T}, DenseArray{T,N}, SubArray{T,N,A,I,L} where L} where I<:Tuple{Vararg{Union{Base.AbstractCartesianIndex, Int64, Range{Int64}},N} where N} where A<:Union{Base.ReshapedArray{T,N,A,MI} where MI<:Tuple{Vararg{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},N} where N} where A<:Union{DenseArray, SubArray{T,N,P,I,true} where I<:Tuple{Union{Base.Slice, UnitRange},Vararg{Any,N} where N} where P where N where T} where N where T, DenseArray} where N where T, ::Union{Base.ReshapedArray{T,N,A,MI} where MI<:Tuple{Vararg{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},N} where N} where A<:Union{DenseArray, SubArray{T,N,P,I,true} where I<:Tuple{Union{Base.Slice, UnitRange},Vararg{Any,N} where N} where P where N where T}, DenseArray{T,N}, SubArray{T,N,A,I,L} where L} where I<:Tuple{Vararg{Union{Base.AbstractCartesianIndex, Int64, Range{Int64}},N} where N} where A<:Union{Base.ReshapedArray{T,N,A,MI} where MI<:Tuple{Vararg{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},N} where N} where A<:Union{DenseArray, SubArray{T,N,P,I,true} where I<:Tuple{Union{Base.Slice, UnitRange},Vararg{Any,N} where N} where P where N where T} where N where T, DenseArray} where N where T, ::Type{Val{CA}}, ::Type{Val{CB}}) where {T, N, CA, CB} at /Users/ortner/.julia/v0.6/TensorOperations/src/auxiliary/stridedarray.jl:41
  similar_from_indices(::Type{T}, ::Tuple{Vararg{Int64,N}}, ::Union{Base.ReshapedArray{T,N,A,MI} where MI<:Tuple{Vararg{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},N} where N} where A<:Union{DenseArray, SubArray{T,N,P,I,true} where I<:Tuple{Union{Base.Slice, UnitRange},Vararg{Any,N} where N} where P where N where T}, DenseArray{T,N}, SubArray{T,N,A,I,L} where L} where I<:Tuple{Vararg{Union{Base.AbstractCartesianIndex, Int64, Range{Int64}},N} where N} where A<:Union{Base.ReshapedArray{T,N,A,MI} where MI<:Tuple{Vararg{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},N} where N} where A<:Union{DenseArray, SubArray{T,N,P,I,true} where I<:Tuple{Union{Base.Slice, UnitRange},Vararg{Any,N} where N} where P where N where T} where N where T, DenseArray} where N where T, ::Union{Base.ReshapedArray{T,N,A,MI} where MI<:Tuple{Vararg{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},N} where N} where A<:Union{DenseArray, SubArray{T,N,P,I,true} where I<:Tuple{Union{Base.Slice, UnitRange},Vararg{Any,N} where N} where P where N where T}, DenseArray{T,N}, SubArray{T,N,A,I,L} where L} where I<:Tuple{Vararg{Union{Base.AbstractCartesianIndex, Int64, Range{Int64}},N} where N} where A<:Union{Base.ReshapedArray{T,N,A,MI} where MI<:Tuple{Vararg{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},N} where N} where A<:Union{DenseArray, SubArray{T,N,P,I,true} where I<:Tuple{Union{Base.Slice, UnitRange},Vararg{Any,N} where N} where P where N where T} where N where T, DenseArray} where N where T, ::Type{Val{CA}}) where {T, N, CA} at /Users/ortner/.julia/v0.6/TensorOperations/src/auxiliary/stridedarray.jl:41
  similar_from_indices(::Type{T}, ::Tuple{Vararg{Int64,N}}, ::Union{Base.ReshapedArray{T,N,A,MI} where MI<:Tuple{Vararg{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},N} where N} where A<:Union{DenseArray, SubArray{T,N,P,I,true} where I<:Tuple{Union{Base.Slice, UnitRange},Vararg{Any,N} where N} where P where N where T}, DenseArray{T,N}, SubArray{T,N,A,I,L} where L} where I<:Tuple{Vararg{Union{Base.AbstractCartesianIndex, Int64, Range{Int64}},N} where N} where A<:Union{Base.ReshapedArray{T,N,A,MI} where MI<:Tuple{Vararg{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},N} where N} where A<:Union{DenseArray, SubArray{T,N,P,I,true} where I<:Tuple{Union{Base.Slice, UnitRange},Vararg{Any,N} where N} where P where N where T} where N where T, DenseArray} where N where T) where {T, N} at /Users/ortner/.julia/v0.6/TensorOperations/src/auxiliary/stridedarray.jl:27
  ...
Stacktrace:
 [1] macro expansion at /Users/ortner/.julia/v0.6/TensorOperations/src/indexnotation/product.jl:58 [inlined]
 [2] deindexify at /Users/ortner/.julia/v0.6/TensorOperations/src/indexnotation/product.jl:51 [inlined] (repeats 2 times)

[cortner]

with Einsum, the allocating version creates an Array instead of a SArray (ideally) or MArray

[Jutho]

Did you ever consider contributing your faster code to Base, and putting it in permutedims!(::StridedArray, perm) in base/multidimensional.jl?

The problem is that it's actually quite a bit of code, but once you have it it can immediately do more general things. It's basically a recursive blocking strategy (like the transpose! in Base, which I contributed long time ago), but then for arbitrary ranks and strides and permutations.

I certainly think that the new code in Base (since the introduction of PermutedDimsArray) has actually had a negative impact on the speed. My favorite (but worst case) example is (timing both allocating and non-allocating versions)

julia> A=randn(ntuple(n->10,8));
julia> @time B=permutedims(A,(8,7,6,5,4,3,2,1));
  3.814514 seconds (80.71 k allocations: 766.801 MiB, 1.30% gc time)
julia> @time permutedims!(B,A,(8,7,6,5,4,3,2,1));
  4.298002 seconds (8 allocations: 416 bytes)

(this used to be around 2 seconds) whereas

julia> @time @tensor B[:] := A[-8,-7,-6,-5,-4,-3,-2,-1];
  0.715068 seconds (25 allocations: 762.941 MiB, 0.30% gc time)
julia> @time @tensor B[:] = A[-8,-7,-6,-5,-4,-3,-2,-1];
  0.312276 seconds (19 allocations: 1.516 KiB)

[Jutho]

@cortner, similar_from_indices and contract! are two of the functions one needs to implement to make TensorOperations work on custom array types, together with add! and maybe trace!.

[cortner]

ok - so what I ask is feasible in principle and possibly not even too difficult? It is not urgent for me, but if you are willing leave this issue open then at some point I might break down and have a go at it.

[Jutho]

Sure, no problem to leave this open.

I think it's certainly feasible, I am not sure whether you will be able to get optimal code for StaticArrays (i.e. completely/partially unrolled as described by @andyferris ), as the necessary information about the permutations won't be available at compile time.

Not sure where the implementation needs to go. I haven't worked with conditional dependencies yet, and I would prefer not to require StaticArrays for TensorOperations. But I guess this is already possible in Julia?