Add forward mode to line search

[tansongchen]

Checklist

• Appropriate tests were added
• Any code changes were done in a way that does not break public API
• All documentation related to code changes were updated
• The new code follows the
  contributor guidelines, in particular the SciML Style Guide and
  COLPRAC.
• Any new documentation only uses public API

Additional context

Added forward mode support to line searches. We prefer forward AD for better performance, however, reverse AD is also supported if user explicitly requests it.

1. If jvp is available, we use forward AD;
2. If reverse type is requested, we use reverse AD;
3. Otherwise, we use forward AD.

[tansongchen]

Getting significant perf improvement for in-place functions like

quadratic_f!(du, u, p) = (du .= u .* u .- p)
u0 = fill(1.0, 1000)
p = 2.0
prob = NonlinearProblem(quadratic_f!, u0, p)
algfwd = NewtonRaphson(; linesearch=LineSearchesJL(; method=LineSearches.HagerZhang()))
algrev = NewtonRaphson(; linesearch=LineSearchesJL(; method=LineSearches.HagerZhang(), autodiff=AutoZygote())) # fallback to finitediff

julia> @btime sol = solve(prob, algfwd)
  27.795 ms (265 allocations: 15.62 MiB)
Julia> @btime sol = solve(prob, algrev)
  50.685 ms (15732 allocations: 133.12 MiB)

[codecov]

Codecov Report

Attention: Patch coverage is 77.77778% with 4 lines in your changes missing coverage. Please review.

Project coverage is 85.54%. Comparing base (a74c321) to head (82944d0).

Files	Patch %	Lines
src/globalization/line_search.jl	77.77%	4 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##           master     #446      +/-   ##
==========================================
- Coverage   86.45%   85.54%   -0.91%     
==========================================
  Files          47       47              
  Lines        2872     2878       +6     
==========================================
- Hits         2483     2462      -21     
- Misses        389      416      +27     

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[tansongchen]

Updated strategy:

0. If FiniteDiff is requested, we use finite diff on $\phi$;
1. If jvp is available, we use jvp;
2. If vjp is available, we use vjp;
3. If reverse type is requested, we use reverse AD;
4. Finally, we use forward AD.