Add test coverage for futures in ShapleyValueSampling 2/n (#1490)

Summary:

This diffs adds more testing coverage for attribute_future to ShapleyValueSampling unit tests that handle shapley sampling with boolean inputs

Reviewed By: cyrjano

Differential Revision: D68230069

captum/testing/helpers/basic_models.py

@@ -533,6 +533,22 @@ def forward(
             return result
 
 
+class BasicModelBoolInput_with_Future(nn.Module):
+    def __init__(self) -> None:
+        super().__init__()
+        self.mod = BasicModel_MultiLayer_with_Future()
+
+    # pyre-fixme[3]: Return type must be annotated.
+    def forward(
+        self,
+        x: Tensor,
+        add_input: Optional[Tensor] = None,
+        mult: float = 10.0,
+    ):
+        assert x.dtype is torch.bool, "Input must be boolean"
+        return self.mod(x.float() * mult, add_input)
+
+
 class BasicModelBoolInput(nn.Module):
     def __init__(self) -> None:
         super().__init__()

tests/attr/test_shapley.py

@@ -16,6 +16,7 @@
     BasicModel_MultiLayer_MultiInput,
     BasicModel_MultiLayer_with_Future,
     BasicModelBoolInput,
+    BasicModelBoolInput_with_Future,
 )
 from parameterized import parameterized
 from torch.futures import Future
@@ -66,28 +67,51 @@ def test_simple_shapley_sampling_with_mask(self, use_future) -> None:
                 perturbations_per_eval=(1, 2, 3),
             )
 
-    def test_simple_shapley_sampling_boolean(self) -> None:
-        net = BasicModelBoolInput()
+    @parameterized.expand([True, False])
+    def test_simple_shapley_sampling_boolean(self, use_future) -> None:
         inp = torch.tensor([[True, False, True]])
-        self._shapley_test_assert(
-            net,
-            inp,
-            [[35.0, 35.0, 35.0]],
-            feature_mask=torch.tensor([[0, 0, 1]]),
-            perturbations_per_eval=(1, 2, 3),
-        )
+        if use_future:
+            net_fut = BasicModelBoolInput_with_Future()
+            self._shapley_test_assert_future(
+                net_fut,
+                inp,
+                [[35.0, 35.0, 35.0]],
+                feature_mask=torch.tensor([[0, 0, 1]]),
+                perturbations_per_eval=(1, 2, 3),
+            )
+        else:
+            net = BasicModelBoolInput()
+            self._shapley_test_assert(
+                net,
+                inp,
+                [[35.0, 35.0, 35.0]],
+                feature_mask=torch.tensor([[0, 0, 1]]),
+                perturbations_per_eval=(1, 2, 3),
+            )
 
-    def test_simple_shapley_sampling_boolean_with_baseline(self) -> None:
-        net = BasicModelBoolInput()
+    @parameterized.expand([True, False])
+    def test_simple_shapley_sampling_boolean_with_baseline(self, use_future) -> None:
         inp = torch.tensor([[True, False, True]])
-        self._shapley_test_assert(
-            net,
-            inp,
-            [[-40.0, -40.0, 0.0]],
-            feature_mask=torch.tensor([[0, 0, 1]]),
-            baselines=True,
-            perturbations_per_eval=(1, 2, 3),
-        )
+        if use_future:
+            net_fut = BasicModelBoolInput_with_Future()
+            self._shapley_test_assert_future(
+                net_fut,
+                inp,
+                [[-40.0, -40.0, 0.0]],
+                feature_mask=torch.tensor([[0, 0, 1]]),
+                baselines=True,
+                perturbations_per_eval=(1, 2, 3),
+            )
+        else:
+            net = BasicModelBoolInput()
+            self._shapley_test_assert(
+                net,
+                inp,
+                [[-40.0, -40.0, 0.0]],
+                feature_mask=torch.tensor([[0, 0, 1]]),
+                baselines=True,
+                perturbations_per_eval=(1, 2, 3),
+            )
 
     @parameterized.expand([True, False])
     def test_simple_shapley_sampling_with_baselines(self, use_future) -> None: