ENH: Improve the performance of einsum by using universal simd - #17049

       before           after         ratio
     [00a45b4d]       [47118fb6]
     <master>         <einsum-usimd>
-         149±3μs          104±2μs     0.70  bench_linalg.Einsum.time_einsum_contig_contig(<class 'numpy.float32'>)
-         238±6μs          156±5μs     0.65  bench_linalg.Einsum.time_einsum_contig_contig(<class 'numpy.float64'>)
-         247±8μs          149±6μs     0.60  bench_linalg.Einsum.time_einsum_contig_outstride0(<class 'numpy.float32'>)
-        440±10μs         245±10μs     0.56  bench_linalg.Einsum.time_einsum_contig_outstride0(<class 'numpy.float64'>)

SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.

       before           after         ratio
     [00a45b4d]       [47118fb6]
     <master>         <einsum-usimd>
-         154±8μs          106±5μs     0.69  bench_linalg.Einsum.time_einsum_contig_contig(<class 'numpy.float32'>)
-         239±6μs          160±4μs     0.67  bench_linalg.Einsum.time_einsum_contig_contig(<class 'numpy.float64'>)
-        256±10μs          138±4μs     0.54  bench_linalg.Einsum.time_einsum_contig_outstride0(<class 'numpy.float32'>)
-         450±5μs          225±4μs     0.50  bench_linalg.Einsum.time_einsum_contig_outstride0(<class 'numpy.float64'>)

SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.

       before           after         ratio
     [00a45b4d]       [47118fb6]
     <master>         <einsum-usimd>
-         145±4μs          107±2μs     0.74  bench_linalg.Einsum.time_einsum_contig_contig(<class 'numpy.float32'>)
-         240±2μs          161±6μs     0.67  bench_linalg.Einsum.time_einsum_contig_contig(<class 'numpy.float64'>)
-        448±10μs          247±5μs     0.55  bench_linalg.Einsum.time_einsum_contig_outstride0(<class 'numpy.float64'>)
-        253±10μs          137±6μs     0.54  bench_linalg.Einsum.time_einsum_contig_outstride0(<class 'numpy.float32'>)

       before           after         ratio
     [7aced6e5]       [ad0b3b42]
     <master>         <einsum-usimd>
-      22.0±0.7ms       20.0±0.3ms     0.91  bench_linalg.Einsum.time_einsum_outer(<class 'numpy.float64'>)
-       109±0.9μs       99.1±0.8μs     0.91  bench_linalg.Einsum.time_einsum_sum_mul(<class 'numpy.float64'>)
-         111±1μs          100±1μs     0.91  bench_linalg.Einsum.time_einsum_sum_mul2(<class 'numpy.float64'>)
-       159±0.8μs          142±2μs     0.90  bench_linalg.Einsum.time_einsum_multiply(<class 'numpy.float64'>)
-       112±0.6μs         96.3±2μs     0.86  bench_linalg.Einsum.time_einsum_sum_mul2(<class 'numpy.float32'>)
-       112±0.6μs       95.2±0.3μs     0.85  bench_linalg.Einsum.time_einsum_sum_mul(<class 'numpy.float32'>)
-       136±0.4μs        115±0.5μs     0.85  bench_linalg.Einsum.time_einsum_multiply(<class 'numpy.float32'>)
-         518±5μs          363±2μs     0.70  bench_linalg.Einsum.time_einsum_contig_contig(<class 'numpy.float64'>)
-        773±20μs         479±10μs     0.62  bench_linalg.Einsum.time_einsum_mul(<class 'numpy.float32'>)
-      17.0±0.4ms       9.77±0.2ms     0.57  bench_linalg.Einsum.time_einsum_outer(<class 'numpy.float32'>)
-         873±4μs          350±4μs     0.40  bench_linalg.Einsum.time_einsum_contig_outstride0(<class 'numpy.float64'>)
-         554±2μs          200±2μs     0.36  bench_linalg.Einsum.time_einsum_contig_contig(<class 'numpy.float32'>)
-        1.02±0ms          233±4μs     0.23  bench_linalg.Einsum.time_einsum_contig_outstride0(<class 'numpy.float32'>)

SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.