How can @tensor macro be used in variable length systems? about tensoroperations.jl

[Jutho]

There is some speed advantage with using @tensor, but it is not that substantial. It arises specifically from the fact that @tensor only works if the contraction is exactly known at compile time, and the different steps can therefore be hardcoded. That's what the macro does, it does some code rewriting at compile time.

If the contraction requires dynamic information, which is not known at compile time, you cannot use @tensor for that.

[sents]

If you really need the speed and you don't have a lot of different contractions you can call the macro from a generated function. The generated function can dynamically dispatch by handing it a Val type.

Generated functions have to compile a function for every call of different type they get. This means that each contraction with a different signature would have to be recompiled. So using this approach with rapidly changing contraction orders will introduce a lot of compilation overhead.

Here is an example, handing the operators the indices (as Tuples of Ints):

using TensorOperations
@generated function contract_operator!(S,
                                       A,
                                       B,
                                       op,
                                       order_A::Val{K}) where {K}
    leftside = Expr(:call, :*,
                    :(A[$(K.a...)]),
                    :(B[$(K.b...)]),
                    :(op[$(K.op...)]))
    return :(@tensor S[:] = $leftside)
end
S = rand(5,5,5,2,5,5,5,2)
a = rand(5,5,5,5,2)
b = rand(5,5,5,5,2)
op = rand(2,2,2,2)
contraction_order = Val((a = (1,-1,-2,-3,2),
                     b = (-5,-6,1,-7,3),
                     op = (2,3,-4,-8)))
contract_operator!(S,a,b,op, contraction_order);