in a similar vein to #61426, we
can speed up `allequal` by unrolling the loop (up to a cap, 32 chosen by
convention)

I suppose this is not particularly a super common bottleneck but we may
as well be faster where possible.

master:
```
julia> @benchmark allequal(t) setup=(t=ntuple(i->rand((1.0, 2)), 5))
BenchmarkTools.Trial: 10000 samples with 998 evaluations per sample.
 Range (min … max):  13.861 ns …   8.303 μs  ┊ GC (min … max): 0.00% … 99.17%
 Time  (median):     18.412 ns               ┊ GC (median):    0.00%
 Time  (mean ± σ):   33.582 ns ± 122.345 ns  ┊ GC (mean ± σ):  6.08% ±  1.71%

  ▅▇█▇▅▂        ▁▄▄▄▃▃▁  ▁▄▅▄▃▃▂▁   ▃▄▄▃▂▁▁▂▂▁ ▁▂▂▁   ▁▁▃▂     ▂
  ██████▅▅▃▅▃▁▄▅███████▇▆████████▇▇▇███████████████▇▆▆█████▆▇▆ █
  13.9 ns       Histogram: log(frequency) by time      83.2 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @benchmark allequal(t) setup=(t=ntuple(i->rand((1.0, 2)), 12))
BenchmarkTools.Trial: 624 samples with 997 evaluations per sample.
 Range (min … max):  16.090 ns … 42.490 μs  ┊ GC (min … max): 0.00% … 73.54%
 Time  (median):     10.193 μs              ┊ GC (median):    0.00%
 Time  (mean ± σ):    8.034 μs ±  4.193 μs  ┊ GC (mean ± σ):  0.62% ±  2.94%

  █                                                    ▆▅▆     
  █▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▁▁▁▁▁▁▁▁▁▁▁▆▅█▃▂▁▁▁▁▁▁▁▆███▆▃▃ ▃
  16.1 ns         Histogram: frequency by time        11.3 μs <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @benchmark allequal(t) setup=(t=ntuple(i->rand((1.0, 2)), 56))
BenchmarkTools.Trial: 480 samples with 1 evaluation per sample.
 Range (min … max):   9.840 ms … 48.062 ms  ┊ GC (min … max): 0.00% … 76.38%
 Time  (median):     10.312 ms              ┊ GC (median):    0.00%
 Time  (mean ± σ):   10.399 ms ±  1.744 ms  ┊ GC (mean ± σ):  0.74% ±  3.49%

       ▁▇ ▁▆▄▁▂▃▂▃▃▆█▆▃▄▂▁▁▁ ▁                                 
  ▄▄▃▅▇██▇██████████████████▇█▄▄▄▃▂▂▁▃▃▃▂▁▂▂▁▁▁▂▁▁▂▁▂▃▂▁▁▁▁▁▂ ▄
  9.84 ms         Histogram: frequency by time        11.5 ms <

 Memory estimate: 1.45 MiB, allocs estimate: 27954.
```

PR
```
julia> @benchmark allequal(t) setup=(t=ntuple(i->rand((1.0, 2)), 5))
BenchmarkTools.Trial: 10000 samples with 998 evaluations per sample.
 Range (min … max):  14.445 ns … 91.516 ns  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     16.868 ns              ┊ GC (median):    0.00%
 Time  (mean ± σ):   16.809 ns ±  1.603 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%

                     ▅▃▁       █▁▁▂▁                           
  ▁▂▄▅▄▃▄▄▄▃▂▂▂▁▂▄▇█████▇▅▃▃▂▃▇█████▇▄▃▂▂▃▃▃▄▄▄▅▄▄▃▂▂▁▁▁▁▁▁▁▁ ▃
  14.4 ns         Histogram: frequency by time        19.6 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @benchmark allequal(t) setup=(t=ntuple(i->rand((1.0, 2)), 12))
BenchmarkTools.Trial: 952 samples with 998 evaluations per sample.
 Range (min … max):  15.697 ns … 20.862 μs  ┊ GC (min … max): 0.00% … 62.59%
 Time  (median):      6.387 μs              ┊ GC (median):    0.00%
 Time  (mean ± σ):    5.256 μs ±  3.257 μs  ┊ GC (mean ± σ):  0.48% ±  2.84%

  █                                                            
  █▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▂▂▂▂▃▂▂▃▃▃▃▄▄▃▄▄▄▃▃▄▄▅▄▄▃▃▃▄▃▃▃▃▃▂ ▃
  15.7 ns         Histogram: frequency by time        9.37 μs <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @benchmark allequal(t) setup=(t=ntuple(i->rand((1.0, 2)), 56))
BenchmarkTools.Trial: 645 samples with 1 evaluation per sample.
 Range (min … max):  6.847 ms …  23.438 ms  ┊ GC (min … max): 0.00% … 62.03%
 Time  (median):     7.830 ms               ┊ GC (median):    0.00%
 Time  (mean ± σ):   7.730 ms ± 827.062 μs  ┊ GC (mean ± σ):  0.29% ±  2.44%

    ▁▂▃▁                   ▅█▄▁▁                               
  ▃▇████▆█▇▆▄▄▄▄▃▄▃▃▃▃▃▄▄▄▇█████▇▇▆▇▄▅▄▃▄▅▄▃▄▃▃▄▃▃▃▄▃▃▃▃▂▃▁▃▂ ▄
  6.85 ms         Histogram: frequency by time        9.08 ms <

 Memory estimate: 488.16 KiB, allocs estimate: 9482.
```