Default enable_cross_tensor_attribution to true (#1643)

Summary:

Internal usage:
- PyPer/APS/MVAI FI at 100% since 4/29
- Fluent2 FI at 100% since 5/22
- NI at 100% since 7/3

Differential Revision: D81948483

Author: sarahtranfb

captum/attr/_core/feature_ablation.py

@@ -114,7 +114,7 @@ def attribute(
         feature_mask: Union[None, Tensor, Tuple[Tensor, ...]] = None,
         perturbations_per_eval: int = 1,
         show_progress: bool = False,
-        enable_cross_tensor_attribution: bool = False,
+        enable_cross_tensor_attribution: bool = True,
         **kwargs: Any,
     ) -> TensorOrTupleOfTensorsGeneric:
         r"""
@@ -704,9 +704,11 @@ def _construct_ablated_input_across_tensors(
                 tensor_mask.append(mask)
 
                 assert baseline is not None, "baseline must be provided"
-                ablated_input[start_idx:end_idx] = input_tensor[start_idx:end_idx] * (
-                    1 - mask
+                ablated_feature = input_tensor[start_idx:end_idx] * (1 - mask).to(
+                    input_tensor.dtype
                 ) + (baseline * mask.to(input_tensor.dtype))
+                ablated_input = ablated_input.to(ablated_feature.dtype)
+                ablated_input[start_idx:end_idx] = ablated_feature
             current_masks.append(torch.stack(tensor_mask, dim=0))
             ablated_inputs.append(ablated_input)
 
@@ -742,7 +744,7 @@ def attribute_future(
         feature_mask: Union[None, Tensor, Tuple[Tensor, ...]] = None,
         perturbations_per_eval: int = 1,
         show_progress: bool = False,
-        enable_cross_tensor_attribution: bool = False,
+        enable_cross_tensor_attribution: bool = True,
         **kwargs: Any,
     ) -> Future[TensorOrTupleOfTensorsGeneric]:
         r"""

captum/attr/_core/feature_permutation.py

@@ -115,7 +115,7 @@ def attribute(  # type: ignore
         feature_mask: Union[None, TensorOrTupleOfTensorsGeneric] = None,
         perturbations_per_eval: int = 1,
         show_progress: bool = False,
-        enable_cross_tensor_attribution: bool = False,
+        enable_cross_tensor_attribution: bool = True,
         **kwargs: Any,
     ) -> TensorOrTupleOfTensorsGeneric:
         r"""
@@ -304,7 +304,7 @@ def attribute_future(
         feature_mask: Union[None, TensorOrTupleOfTensorsGeneric] = None,
         perturbations_per_eval: int = 1,
         show_progress: bool = False,
-        enable_cross_tensor_attribution: bool = False,
+        enable_cross_tensor_attribution: bool = True,
         **kwargs: Any,
     ) -> Future[TensorOrTupleOfTensorsGeneric]:
         """

captum/attr/_core/occlusion.py

@@ -267,7 +267,6 @@ def attribute(  # type: ignore
             shift_counts=tuple(shift_counts),
             strides=strides,
             show_progress=show_progress,
-            enable_cross_tensor_attribution=True,
         )
 
     def attribute_future(self) -> None:

tests/attr/test_feature_permutation.py

@@ -120,14 +120,14 @@ def forward_func(x: Tensor) -> Tensor:
         inp = torch.tensor([[1.0, 2.0]])
         assertTensorAlmostEqual(
             self,
-            feature_importance.attribute(inp),
+            feature_importance.attribute(inp, enable_cross_tensor_attribution=False),
             torch.tensor([[0.0, 0.0]]),
             delta=0.0,
         )
 
         feature_importance._min_examples_per_batch = 1
         with self.assertRaises(AssertionError):
-            feature_importance.attribute(inp)
+            feature_importance.attribute(inp, enable_cross_tensor_attribution=False)
 
     def test_simple_input_with_min_examples_in_group(self) -> None:
         def forward_func(x: Tensor) -> Tensor: