Factor out _get_feature_idx_to_tensor_idx from FeatureAblation (#1645)

Summary:

Still need to keep the instance method since it's overriden by occlusion:
https://www.internalfb.com/code/fbsource/[8178d370745cf77ea10b992de23e6add428711fd]/fbcode/pytorch/captum/captum/attr/_core/occlusion.py?lines=399

Need to use function later in the stack for within-group SVS.

Differential Revision: D82169482

Author: sarahtranfb

captum/_utils/common.py

@@ -908,6 +908,22 @@ def _get_max_feature_index(feature_mask: Tuple[Tensor, ...]) -> int:
     return int(max(torch.max(mask).item() for mask in feature_mask if mask.numel()))
 
 
+def _get_feature_idx_to_tensor_idx(
+    formatted_feature_mask: Tuple[Tensor, ...],
+) -> Dict[int, List[int]]:
+    """
+    For a given tuple of tensors, return dict of tensor values to list of tensor
+    indices they appear in.
+    """
+    feature_idx_to_tensor_idx: Dict[int, List[int]] = {}
+    for i, mask in enumerate(formatted_feature_mask):
+        for feature_idx in torch.unique(mask):
+            if feature_idx.item() not in feature_idx_to_tensor_idx:
+                feature_idx_to_tensor_idx[feature_idx.item()] = []
+            feature_idx_to_tensor_idx[feature_idx.item()].append(i)
+    return feature_idx_to_tensor_idx
+
+
 def _maybe_expand_parameters(
     perturbations_per_eval: int,
     formatted_inputs: Tuple[Tensor, ...],

captum/attr/_core/feature_ablation.py

@@ -24,6 +24,7 @@
     _format_additional_forward_args,
     _format_feature_mask,
     _format_output,
+    _get_feature_idx_to_tensor_idx,
     _is_tuple,
     _maybe_expand_parameters,
     _run_forward,
@@ -616,17 +617,7 @@ def _attribute_with_cross_tensor_feature_masks(
     def _get_feature_idx_to_tensor_idx(
         self, formatted_feature_mask: Tuple[Tensor, ...], **kwargs: Any
     ) -> Dict[int, List[int]]:
-        """
-        For a given tuple of tensors, return dict of tensor values to list of tensor
-        indices they appear in.
-        """
-        feature_idx_to_tensor_idx: Dict[int, List[int]] = {}
-        for i, mask in enumerate(formatted_feature_mask):
-            for feature_idx in torch.unique(mask):
-                if feature_idx.item() not in feature_idx_to_tensor_idx:
-                    feature_idx_to_tensor_idx[feature_idx.item()] = []
-                feature_idx_to_tensor_idx[feature_idx.item()].append(i)
-        return feature_idx_to_tensor_idx
+        return _get_feature_idx_to_tensor_idx(formatted_feature_mask)
 
     def _should_skip_inputs_and_warn(
         self,