Add ScaleRBFLinearKernel covar module

[saitcakmak]

Summary:
Following the ScaleMaternKernel pattern in Ax botorch_modular, this adds ScaleRBFLinearKernel — a sum of ScaleKernel(RBF-ARD) and LinearKernel. It is the kernel-side analog of a linear mean: the LinearKernel models linear-in-input correlations (e.g. a scaling-law trend) through the covariance rather than the mean, while the ScaleKernel-wrapped ARD RBF captures local flexibility and lets the model learn the relative amplitude of the local vs. global signal. This is the kernel-only counterpart of the RBFLinearGP model studied in D108386392, designed to plug into a plain SingleTaskGP via ModelConfig(covar_module_class=...).

ScaleRBFLinearKernel subclasses GPyTorch's AdditiveKernel so that isinstance checks and storage serialization work cleanly. Its __init__ mirrors ScaleMaternKernel, adding a variance_prior for the linear component and an active_dims argument.

active_dims is applied to both component kernels: it is set on the inner RBFKernel and on the LinearKernel, and the ScaleKernel inherits active_dims from the kernel it wraps. Because GPyTorch subsets inputs once at the outermost kernel in a call chain (and ScaleKernel.forward calls base_kernel.forward directly), the input is subset exactly once rather than twice. Threading active_dims through the input constructor also enables the remove_task_features workflow (excluding a task feature from the kernel for a SingleTaskGP on a MultiTaskDataset).

Changes:

• kernels.py: new ScaleRBFLinearKernel(AdditiveKernel) with active_dims support.
• input_constructors/covar_modules.py: register _covar_module_argparse_scale_rbf_linear, reusing _get_default_ard_num_dims_and_batch_shape to infer ard_num_dims/batch_shape/active_dims from the dataset and model class.
• botorch_modular_registry.py: add ScaleRBFLinearKernel to KERNEL_REGISTRY (this automatically wires up JSON/SQA encode+decode).
• Tests in test_kernels.py (incl. an active-dims single-subsetting check) and test_covar_modules_argparse.py (incl. active-dims and remove_task_features cases).

Differential Revision: D109044330

[meta-codesync]

@saitcakmak has exported this pull request. If you are a Meta employee, you can view the originating Diff in D109044330.

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 96.56%. Comparing base (9bd6ec8) to head (36a5255).

Additional details and impacted files

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  Coverage   96.56%   96.56%           
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  Files         619      619           
  Lines       70168    70255   +87     
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+ Hits        67756    67843   +87     
  Misses       2412     2412           

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