Fix pyre errors in Kernel Shap (pytorch#1399)

Summary:

Initial work on fixing Pyre errors in KernelShap

Differential Revision: D64677350

captum/attr/_core/kernel_shap.py

@@ -2,7 +2,7 @@
 
 # pyre-strict
 
-from typing import Any, Callable, Generator, Tuple, Union
+from typing import Any, Callable, cast, Generator, Tuple, Union
 
 import torch
 from captum._utils.models.linear_model import SkLearnLinearRegression
@@ -27,8 +27,7 @@ class KernelShap(Lime):
     https://arxiv.org/abs/1705.07874
     """
 
-    # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-    def __init__(self, forward_func: Callable) -> None:
+    def __init__(self, forward_func: Callable[..., Tensor]) -> None:
         r"""
         Args:
 
@@ -50,8 +49,7 @@ def attribute(  # type: ignore
         inputs: TensorOrTupleOfTensorsGeneric,
         baselines: BaselineType = None,
         target: TargetType = None,
-        # pyre-fixme[2]: Parameter annotation cannot be `Any`.
-        additional_forward_args: Any = None,
+        additional_forward_args: object = None,
         feature_mask: Union[None, Tensor, Tuple[Tensor, ...]] = None,
         n_samples: int = 25,
         perturbations_per_eval: int = 1,
@@ -279,10 +277,7 @@ def attribute(  # type: ignore
         )
         num_features_list = torch.arange(num_interp_features, dtype=torch.float)
         denom = num_features_list * (num_interp_features - num_features_list)
-        # pyre-fixme[58]: `/` is not supported for operand types
-        # `int` and `torch._tensor.Tensor`.
-        probs = (num_interp_features - 1) / denom
-        # pyre-fixme[16]: `float` has no attribute `__setitem__`.
+        probs = torch.tensor((num_interp_features - 1)) / denom
         probs[0] = 0.0
         return self._attribute_kwargs(
             inputs=inputs,
@@ -309,8 +304,7 @@ def kernel_shap_similarity_kernel(
         _,
         __,
         interpretable_sample: Tensor,
-        # pyre-fixme[2]: Parameter must be annotated.
-        **kwargs,
+        **kwargs: object,
     ) -> Tensor:
         assert (
             "num_interp_features" in kwargs
@@ -332,8 +326,7 @@ def kernel_shap_similarity_kernel(
     def kernel_shap_perturb_generator(
         self,
         original_inp: Union[Tensor, Tuple[Tensor, ...]],
-        # pyre-fixme[2]: Parameter must be annotated.
-        **kwargs,
+        **kwargs: object,
     ) -> Generator[Tensor, None, None]:
         r"""
         Perturbations are sampled by the following process:
@@ -361,11 +354,13 @@ def kernel_shap_perturb_generator(
             device = original_inp.device
         else:
             device = original_inp[0].device
-        num_features = kwargs["num_interp_features"]
+        num_features = cast(int, kwargs["num_interp_features"])
         yield torch.ones(1, num_features, device=device, dtype=torch.long)
         yield torch.zeros(1, num_features, device=device, dtype=torch.long)
         while True:
-            num_selected_features = kwargs["num_select_distribution"].sample()
+            num_selected_features = cast(
+                Categorical, kwargs["num_select_distribution"]
+            ).sample()
             rand_vals = torch.randn(1, num_features)
             threshold = torch.kthvalue(
                 rand_vals, num_features - num_selected_features