@fastmath maximum

[mcabbott]

This implements #48082.

julia> fast_maximum(x::AbstractArray{T}; dims=:) where T = @fastmath reduce(max, x; dims, init=typemin(T));

julia> let x = randn(Float32, 100, 1000)
           @btime maximum($x; dims=1)
           @btime fast_maximum($x; dims=1)
           @btime @fastmath maximum($x; dims=1)  # this PR
       end;
  min 708.375 μs, mean 713.129 μs (4 allocations, 4.17 KiB)
  min 12.375 μs, mean 13.942 μs (1 allocation, 4.06 KiB)
  min 14.791 μs, mean 15.820 μs (3 allocations, 4.12 KiB)

julia> let x = randn(Float32, 100, 1000)
           @btime maximum($x)
           @btime fast_maximum($x)
           @btime @fastmath maximum($x)  # this PR
       end;
  min 104.250 μs, mean 105.658 μs (0 allocations)
  min 12.708 μs, mean 13.205 μs (0 allocations)
  min 9.791 μs, mean 10.284 μs (0 allocations)

The need for this would be reduced if #45581 happened soon.

Currently fails because of an order of imports problem I think.

[N5N3]

We define Fastmath much later than reduce so perhaps it's easier to make reducedim_init(f, op_fast, A, region) fallback to reducedim_init(f, op, A, region)
And perhaps we also want to support @fastmath maximum!/minimum!.