Support for default input transfroms in MBM

[saitcakmak]

Summary:
Input normalization is important to get the best performance out of the BoTorch models we use. The current setup relies on either using UnitX transform from Ax, or manually adding Normalize to ModelConfig.input_transform_classes to achieve input normalization.

• UnitX is not ideal since it only applies to float valued RangeParameters. If we make everything into floats to use UnitX, we're locked into using continuous relaxation for acquisition optimization, which is something we want to move away from.
• Normalize works well, particularly when bounds argument is provided (It's applied at each pass through the model, rather than once to the training data, but that's a separate discussion). However, requiring it as an explicit user input is a bit cumbersome.
  This diff adds the machinery for constructing a default set of input transforms. This implementation retains the previous InputPerturbation transform for robust optimization, and adds Normalize transform if the non-task features of the search space are not normalized.

With this change, we should be able to remove UnitX transform from an MBM model(spec) without losing input normalization.

Other considerations:

• This setup only adds the default transforms if the input_transform_classes argument is left as DEFAULT. If the user supplies input_transform_classes or sets it to None, no defaults will be used. Would we want to add defaults even when the user supplies some transforms? If so, how would we decide whether to append or prepend the defaults?
• As mentioned above, applying Normalize at each pass through the model is not super efficient. A vectorized application of an Ax transform should generally be more efficient. A longer term alternative would be to expand Ax-side UnitX to support more general parameter classes and types, without losing information in the process. This would require additional changes such as support for non-integer valued discrete RangeParameters, and support for non-integer discrete values in the mixed optimizer.

Differential Revision: D65622788

[facebook-github-bot]

This pull request was exported from Phabricator. Differential Revision: D65622788

[codecov-commenter]

Codecov Report

Attention: Patch coverage is 96.07843% with 2 lines in your changes missing coverage. Please review.

Project coverage is 95.42%. Comparing base (5d1e441) to head (59d6980).
Report is 1 commits behind head on main.

Files with missing lines	Patch %	Lines
...rch_modular/input_constructors/input_transforms.py	91.66%	1 Missing ⚠️
ax/models/torch/botorch_modular/surrogate.py	95.65%	1 Missing ⚠️

Additional details and impacted files

@@           Coverage Diff           @@
##             main    #3102   +/-   ##
=======================================
  Coverage   95.41%   95.42%           
=======================================
  Files         495      495           
  Lines       49941    49963   +22     
=======================================
+ Hits        47653    47675   +22     
  Misses       2288     2288           

☔ View full report in Codecov by Sentry.
📢 Have feedback on the report? Share it here.

---

🚨 Try these New Features:

• Flaky Tests Detection - Detect and resolve failed and flaky tests
• JS Bundle Analysis - Avoid shipping oversized bundles