Add hoist_invariants() directive for semiring reductions

[alexreinking]

This PR adds a scheduling directive similar to rfactor that hoists invariant factors according to a detected distributive (semiring) law into the write-back part of the accumulation step. This enables writing straightforward quantized kernels in the algorithm language and factoring them into efficient kernels.

This is meant to work towards optimizing quantized mat-mul and mat-vec kernels (especially those from GGML).

Checklist

• Tests added or updated (not required for docs, CI config, or typo fixes)
• Documentation updated (if public API changed)
• Python bindings updated (if public API changed)
• Benchmarks are included here if the change is intended to affect performance.
• Commits include AI attribution where applicable (see Code of Conduct)

[codecov]

Codecov Report

❌ Patch coverage is 84.76821% with 23 lines in your changes missing coverage. Please review.
✅ Project coverage is 70.37%. Comparing base (db7edbe) to head (c8dc9c6).

Files with missing lines	Patch %	Lines
src/Func.cpp	84.76%	8 Missing and 15 partials ⚠️

Additional details and impacted files

@@                     Coverage Diff                      @@
##           alexreinking/change-type    #9190      +/-   ##
============================================================
+ Coverage                     70.25%   70.37%   +0.11%     
============================================================
  Files                           258      258              
  Lines                         79605    79714     +109     
  Branches                      19111    19141      +30     
============================================================
+ Hits                          55926    56097     +171     
+ Misses                        17939    17914      -25     
+ Partials                       5740     5703      -37     

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[abadams]

Please add a test that turns a gaussian blur implemented as this:

f(x, y) += kernel(r.x) * kernel(r.y) * input(x + r.x, y + r.y)

into a separable implementation

[abadams]

Also it would be good to demo turning a one-step downsample into two-stage separable downsample (basically the same as the blur example but there's a stride) - it can be a lot faster to separate a downsample into a first stage that just downsamples in y, followed by a second that just downsamples in x.