Fix layer gradient shap pyre fixme issues (pytorch#1471)

Summary:

Fixing unresolved pyre fixme issues in corresponding file

Reviewed By: cyrjano

Differential Revision: D67705670

captum/attr/_core/layer/layer_gradient_shap.py

@@ -61,8 +61,7 @@ class LayerGradientShap(LayerAttribution, GradientAttribution):
 
     def __init__(
         self,
-        # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-        forward_func: Callable,
+        forward_func: Callable[..., Tensor],
         layer: Module,
         device_ids: Union[None, List[int]] = None,
         multiply_by_inputs: bool = True,
@@ -104,13 +103,12 @@ def __init__(
         self._multiply_by_inputs = multiply_by_inputs
 
     @typing.overload
-    # pyre-fixme[43]: The implementation of `attribute` does not accept all possible
-    #  arguments of overload defined on line `106`.
     def attribute(
         self,
         inputs: TensorOrTupleOfTensorsGeneric,
-        # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-        baselines: Union[TensorOrTupleOfTensorsGeneric, Callable],
+        baselines: Union[
+            TensorOrTupleOfTensorsGeneric, Callable[..., TensorOrTupleOfTensorsGeneric]
+        ],
         n_samples: int = 5,
         stdevs: Union[float, Tuple[float, ...]] = 0.0,
         target: TargetType = None,
@@ -121,13 +119,12 @@ def attribute(
     ) -> Tuple[Union[Tensor, Tuple[Tensor, ...]], Tensor]: ...
 
     @typing.overload
-    # pyre-fixme[43]: The implementation of `attribute` does not accept all possible
-    #  arguments of overload defined on line `120`.
     def attribute(
         self,
         inputs: TensorOrTupleOfTensorsGeneric,
-        # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-        baselines: Union[TensorOrTupleOfTensorsGeneric, Callable],
+        baselines: Union[
+            TensorOrTupleOfTensorsGeneric, Callable[..., TensorOrTupleOfTensorsGeneric]
+        ],
         n_samples: int = 5,
         stdevs: Union[float, Tuple[float, ...]] = 0.0,
         target: TargetType = None,
@@ -137,11 +134,14 @@ def attribute(
     ) -> Union[Tensor, Tuple[Tensor, ...]]: ...
 
     @log_usage()
+    # pyre-fixme[43]: This definition does not have the same decorators as the
+    #  preceding overload(s).
     def attribute(
         self,
         inputs: TensorOrTupleOfTensorsGeneric,
-        # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-        baselines: Union[TensorOrTupleOfTensorsGeneric, Callable],
+        baselines: Union[
+            TensorOrTupleOfTensorsGeneric, Callable[..., TensorOrTupleOfTensorsGeneric]
+        ],
         n_samples: int = 5,
         stdevs: Union[float, Tuple[float, ...]] = 0.0,
         target: TargetType = None,
@@ -294,17 +294,10 @@ def attribute(
         """
         # since `baselines` is a distribution, we can generate it using a function
         # rather than passing it as an input argument
-        # pyre-fixme[9]: baselines has type `Union[typing.Callable[..., typing.Any],
-        #  Variable[TensorOrTupleOfTensorsGeneric <: [Tensor, typing.Tuple[Tensor,
-        #  ...]]]]`; used as `Tuple[Tensor, ...]`.
-        baselines = _format_callable_baseline(baselines, inputs)
-        # pyre-fixme[16]: Item `Callable` of `Union[(...) -> Any,
-        #  TensorOrTupleOfTensorsGeneric]` has no attribute `__getitem__`.
-        assert isinstance(baselines[0], torch.Tensor), (
+        formatted_baselines = _format_callable_baseline(baselines, inputs)
+        assert isinstance(formatted_baselines[0], torch.Tensor), (
             "Baselines distribution has to be provided in a form "
-            # pyre-fixme[16]: Item `Callable` of `Union[(...) -> Any,
-            #  TensorOrTupleOfTensorsGeneric]` has no attribute `__getitem__`.
-            "of a torch.Tensor {}.".format(baselines[0])
+            "of a torch.Tensor {}.".format(formatted_baselines[0])
         )
 
         input_min_baseline_x_grad = LayerInputBaselineXGradient(
@@ -323,7 +316,7 @@ def attribute(
             nt_samples=n_samples,
             stdevs=stdevs,
             draw_baseline_from_distrib=True,
-            baselines=baselines,
+            baselines=formatted_baselines,
             target=target,
             additional_forward_args=additional_forward_args,
             return_convergence_delta=return_convergence_delta,
@@ -343,8 +336,7 @@ def multiplies_by_inputs(self) -> bool:
 class LayerInputBaselineXGradient(LayerAttribution, GradientAttribution):
     def __init__(
         self,
-        # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-        forward_func: Callable,
+        forward_func: Callable[..., Tensor],
         layer: Module,
         device_ids: Union[None, List[int]] = None,
         multiply_by_inputs: bool = True,
@@ -436,7 +428,7 @@ def attribute(  # type: ignore
         )
         grads, _ = compute_layer_gradients_and_eval(
             self.forward_func,
-            self.layer,
+            cast(Module, self.layer),
             input_baseline_scaled,
             target,
             additional_forward_args,
@@ -448,7 +440,7 @@ def attribute(  # type: ignore
         attr_baselines = _forward_layer_eval(
             self.forward_func,
             baselines,
-            self.layer,
+            cast(Module, self.layer),
             additional_forward_args=additional_forward_args,
             device_ids=self.device_ids,
             attribute_to_layer_input=attribute_to_layer_input,
@@ -457,19 +449,15 @@ def attribute(  # type: ignore
         attr_inputs = _forward_layer_eval(
             self.forward_func,
             inputs,
-            self.layer,
+            cast(Module, self.layer),
             additional_forward_args=additional_forward_args,
             device_ids=self.device_ids,
             attribute_to_layer_input=attribute_to_layer_input,
         )
-
+        attributions: Tuple[Tensor, ...]
         if self.multiplies_by_inputs:
             input_baseline_diffs = tuple(
-                # pyre-fixme[58]: `-` is not supported for operand types
-                # `typing.Tuple[torch._tensor.Tensor, ...]` and
-                # `typing.Tuple[torch._tensor.Tensor, ...]`.
-                input - baseline
-                for input, baseline in zip(attr_inputs, attr_baselines)
+                input - baseline for input, baseline in zip(attr_inputs, attr_baselines)
             )
             attributions = tuple(
                 input_baseline_diff * grad
@@ -481,8 +469,6 @@ def attribute(  # type: ignore
         return _compute_conv_delta_and_format_attrs(
             self,
             return_convergence_delta,
-            # pyre-fixme[6]: For 3rd argument expected `Tuple[Tensor, ...]` but got
-            #  `Union[List[typing.Tuple[Tensor, ...]], tuple[Tensor]]`.
             attributions,
             baselines,
             inputs,