Fix pyre errors in Lime (#1400)

Summary:

Initial work on fixing Pyre errors in Lime

Reviewed By: csauper

Differential Revision: D64677340

Author: Vivek Miglani

captum/attr/_core/lime.py

@@ -6,7 +6,7 @@
 import typing
 import warnings
 from collections.abc import Iterator
-from typing import Any, Callable, cast, List, Optional, Tuple, Union
+from typing import Any, Callable, cast, Generator, List, Literal, Optional, Tuple, Union
 
 import torch
 from captum._utils.common import (
@@ -23,12 +23,7 @@
 from captum._utils.models.linear_model import SkLearnLasso
 from captum._utils.models.model import Model
 from captum._utils.progress import progress
-from captum._utils.typing import (
-    BaselineType,
-    Literal,
-    TargetType,
-    TensorOrTupleOfTensorsGeneric,
-)
+from captum._utils.typing import BaselineType, TargetType, TensorOrTupleOfTensorsGeneric
 from captum.attr._utils.attribution import PerturbationAttribution
 from captum.attr._utils.batching import _batch_example_iterator
 from captum.attr._utils.common import (
@@ -73,18 +68,18 @@ class LimeBase(PerturbationAttribution):
 
     def __init__(
         self,
-        # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-        forward_func: Callable,
+        forward_func: Callable[..., Tensor],
         interpretable_model: Model,
-        # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-        similarity_func: Callable,
-        # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-        perturb_func: Callable,
+        similarity_func: Callable[
+            ...,
+            Union[float, Tensor],
+        ],
+        perturb_func: Callable[..., object],
         perturb_interpretable_space: bool,
-        # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-        from_interp_rep_transform: Optional[Callable],
-        # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-        to_interp_rep_transform: Optional[Callable],
+        from_interp_rep_transform: Optional[
+            Callable[..., Union[Tensor, Tuple[Tensor, ...]]]
+        ],
+        to_interp_rep_transform: Optional[Callable[..., Tensor]],
     ) -> None:
         r"""
 
@@ -249,13 +244,11 @@ def attribute(
         self,
         inputs: TensorOrTupleOfTensorsGeneric,
         target: TargetType = None,
-        # pyre-fixme[2]: Parameter annotation cannot be `Any`.
-        additional_forward_args: Any = None,
+        additional_forward_args: object = None,
         n_samples: int = 50,
         perturbations_per_eval: int = 1,
         show_progress: bool = False,
-        # pyre-fixme[2]: Parameter must be annotated.
-        **kwargs,
+        **kwargs: object,
     ) -> Tensor:
         r"""
         This method attributes the output of the model with given target index
@@ -551,7 +544,7 @@ def generate_perturbation() -> (
                     curr_sample, inputs, **kwargs
                 )
 
-            return interpretable_inp, curr_model_input
+            return interpretable_inp, curr_model_input  # type: ignore
 
         return generate_perturbation
 
@@ -568,8 +561,7 @@ def _evaluate_batch(
         self,
         curr_model_inputs: List[TensorOrTupleOfTensorsGeneric],
         expanded_target: TargetType,
-        # pyre-fixme[2]: Parameter annotation cannot be `Any`.
-        expanded_additional_args: Any,
+        expanded_additional_args: object,
         device: torch.device,
     ) -> Tensor:
         model_out = _run_forward(
@@ -630,8 +622,7 @@ def default_from_interp_rep_transform(curr_sample, original_inputs, **kwargs):
 def get_exp_kernel_similarity_function(
     distance_mode: str = "cosine",
     kernel_width: float = 1.0,
-    # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-) -> Callable:
+) -> Callable[..., float]:
     r"""
     This method constructs an appropriate similarity function to compute
     weights for perturbed sample in LIME. Distance between the original
@@ -680,8 +671,9 @@ def default_exp_kernel(original_inp, perturbed_inp, __, **kwargs):
     return default_exp_kernel
 
 
-# pyre-fixme[2]: Parameter must be annotated.
-def default_perturb_func(original_inp, **kwargs) -> Tensor:
+def default_perturb_func(
+    original_inp: TensorOrTupleOfTensorsGeneric, **kwargs: object
+) -> Tensor:
     assert (
         "num_interp_features" in kwargs
     ), "Must provide num_interp_features to use default interpretable sampling function"
@@ -690,25 +682,25 @@ def default_perturb_func(original_inp, **kwargs) -> Tensor:
     else:
         device = original_inp[0].device
 
-    probs = torch.ones(1, kwargs["num_interp_features"]) * 0.5
+    probs = torch.ones(1, cast(int, kwargs["num_interp_features"])) * 0.5
     return torch.bernoulli(probs).to(device=device).long()
 
 
 def construct_feature_mask(
     feature_mask: Union[None, Tensor, Tuple[Tensor, ...]],
     formatted_inputs: Tuple[Tensor, ...],
 ) -> Tuple[Tuple[Tensor, ...], int]:
+    feature_mask_tuple: Tuple[Tensor, ...]
     if feature_mask is None:
-        feature_mask, num_interp_features = _construct_default_feature_mask(
+        feature_mask_tuple, num_interp_features = _construct_default_feature_mask(
             formatted_inputs
         )
     else:
-        feature_mask = _format_tensor_into_tuples(feature_mask)
+        feature_mask_tuple = _format_tensor_into_tuples(feature_mask)
         min_interp_features = int(
             min(
                 torch.min(single_mask).item()
-                # pyre-fixme[16]: `None` has no attribute `__iter__`.
-                for single_mask in feature_mask
+                for single_mask in feature_mask_tuple
                 if single_mask.numel()
             )
         )
@@ -718,14 +710,12 @@ def construct_feature_mask(
                 " start at 0.",
                 stacklevel=2,
             )
-            feature_mask = tuple(
-                single_mask - min_interp_features for single_mask in feature_mask
+            feature_mask_tuple = tuple(
+                single_mask - min_interp_features for single_mask in feature_mask_tuple
             )
 
-        # pyre-fixme[6]: For 1st argument expected `Tuple[Tensor, ...]` but got
-        #  `Optional[typing.Tuple[typing.Any, ...]]`.
-        num_interp_features = _get_max_feature_index(feature_mask) + 1
-    return feature_mask, num_interp_features
+        num_interp_features = _get_max_feature_index(feature_mask_tuple) + 1
+    return feature_mask_tuple, num_interp_features
 
 
 class Lime(LimeBase):
@@ -766,8 +756,7 @@ class Lime(LimeBase):
 
     def __init__(
         self,
-        # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-        forward_func: Callable,
+        forward_func: Callable[..., Tensor],
         interpretable_model: Optional[Model] = None,
         # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
         similarity_func: Optional[Callable] = None,
@@ -887,8 +876,7 @@ 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,
@@ -1133,18 +1121,14 @@ def _attribute_kwargs(  # 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,
         return_input_shape: bool = True,
         show_progress: bool = False,
-        # pyre-fixme[2]: Parameter must be annotated.
-        **kwargs,
+        **kwargs: object,
     ) -> TensorOrTupleOfTensorsGeneric:
-        # pyre-fixme[6]: For 1st argument expected `Tensor` but got
-        #  `TensorOrTupleOfTensorsGeneric`.
         is_inputs_tuple = _is_tuple(inputs)
         formatted_inputs, baselines = _format_input_baseline(inputs, baselines)
         bsz = formatted_inputs[0].shape[0]
@@ -1263,33 +1247,35 @@ def _attribute_kwargs(  # type: ignore
             return coefs
 
     @typing.overload
-    # pyre-fixme[43]: The implementation of `_convert_output_shape` does not accept
-    #  all possible arguments of overload defined on line `1201`.
     def _convert_output_shape(
         self,
         formatted_inp: Tuple[Tensor, ...],
         feature_mask: Tuple[Tensor, ...],
         coefs: Tensor,
         num_interp_features: int,
-        # pyre-fixme[31]: Expression `Literal[True]` is not a valid type.
-        # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
         is_inputs_tuple: Literal[True],
     ) -> Tuple[Tensor, ...]: ...
 
     @typing.overload
-    # pyre-fixme[43]: The implementation of `_convert_output_shape` does not accept
-    #  all possible arguments of overload defined on line `1211`.
     def _convert_output_shape(  # type: ignore
         self,
         formatted_inp: Tuple[Tensor, ...],
         feature_mask: Tuple[Tensor, ...],
         coefs: Tensor,
         num_interp_features: int,
-        # pyre-fixme[31]: Expression `Literal[False]` is not a valid type.
-        # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
         is_inputs_tuple: Literal[False],
     ) -> Tensor: ...
 
+    @typing.overload
+    def _convert_output_shape(
+        self,
+        formatted_inp: Tuple[Tensor, ...],
+        feature_mask: Tuple[Tensor, ...],
+        coefs: Tensor,
+        num_interp_features: int,
+        is_inputs_tuple: bool,
+    ) -> Union[Tensor, Tuple[Tensor, ...]]: ...
+
     def _convert_output_shape(
         self,
         formatted_inp: Tuple[Tensor, ...],