Microoptimizations of contraction logic code

[mtfishman]

This adds some microoptimizations to the contraction logic code. The main strategy is minimizing the number of getfield calls to the ContractionProperties mutable struct by avoiding things like props.Acstart in loops. Here is a benchmark:

using BenchmarkTools
using ITensors

N = 6 
d = 1 
i = Index(QN(0, 2) => d, QN(1, 2) => d)
is = IndexSet(n -> settags(i, "i$n"), N)
A = randomITensor(is'..., dag(is)...)
B = randomITensor(is'..., dag(is)...)

@btime $A' * $B samples = 5

on this branch gives:

julia> include("time.jl");
  115.340 ms (868058 allocations: 138.49 MiB)

while on master:

julia> include("time.jl");
  170.253 ms (1261274 allocations: 141.49 MiB)

This case is pretty extreme since it involves contracting many very small blocks, so the contraction logic really dominates.

Most of the rest of the time spent in this example is block sparse logic like creating the block-offset list for the output tensor.

[codecov-io]

Codecov Report

Merging #53 (6170aa5) into master (649930a) will increase coverage by 0.55%. The diff coverage is 50.79%.

@@            Coverage Diff             @@
##           master      #53      +/-   ##
==========================================
+ Coverage   34.87%   35.43%   +0.55%     
==========================================
  Files          22       22              
  Lines        2523     2588      +65     
==========================================
+ Hits          880      917      +37     
- Misses       1643     1671      +28     

Impacted Files	Coverage Δ
src/blocksparse/blocksparsetensor.jl	47.81% <0.00%> (ø)
src/contraction_logic.jl	51.17% <51.61%> (+1.59%)	:arrow_up:

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