Validate perturbation baselines before attribution (#1920)

[craymichael]

Summary:

Summary

Feature Ablation and Shapley formatted baselines but did not validate tuple arity or tensor shape before running the model. _tensorize_baseline also paired inputs and baselines with zip, silently discarding extra baselines.

Problem

For two inputs, a one-element baseline tuple reached the model with a missing argument, while a three-element tuple silently ignored its final baseline. A baseline pool with shape [2, 1] for a three-row input was neither a per-example baseline nor a singleton baseline and failed later through opaque broadcasting errors.

Fix

• Validate baselines in both synchronous and future Feature Ablation and Shapley entry points.
• Defensively reject arity mismatches in _tensorize_baseline.
• Require Shapley tensor baselines to match the input or use a singleton leading dimension with matching trailing dimensions.
• Preserve Feature Ablation’s documented support for any tensor shape that broadcasts exactly to the input, including [F] and 0-D tensors.

Test Plan

Before: the new regressions failed through late IndexError, missing-forward-argument, and broadcast errors; the extra-baseline case did not raise at all.

After:

• buck test fbcode//pytorch/captum/tests/attr:test_common fbcode//pytorch/captum/tests/attr:test_feature_ablation fbcode//pytorch/captum/tests/attr:test_shapley — Pass 131, Fail 0.
• arc lint -a on changed Python files — no source lint issues; focused autodeps updates applied.
• arc lint -a --engine extra --take CITRINEAGENT on changed Captum implementation files — no issues.
• arc pyre check-owning-targets on changed files — no type errors.

Differential Revision: D117601314

[meta-codesync]

@craymichael has exported this pull request. If you are a Meta employee, you can view the originating Diff in D117601314.