Enable pyre for Captum open source part- 2/2

captum/_utils/av.py

@@ -1,5 +1,7 @@
 #!/usr/bin/env python3
 
+# pyre-strict
+
 import glob
 import os
 import re
@@ -66,12 +68,14 @@ def __init__(
                     which the activation vectors are computed
             """
 
+            # pyre-fixme[4]: Attribute must be annotated.
             self.av_filesearch = AV._construct_file_search(
                 path, model_id, identifier, layer, num_id
             )
 
             files = glob.glob(self.av_filesearch)
 
+            # pyre-fixme[4]: Attribute must be annotated.
             self.files = AV.sort_files(files)
 
         def __getitem__(self, idx: int) -> Union[Tensor, Tuple[Tensor, ...]]:
@@ -346,6 +350,7 @@ def _compute_and_save_activations(
         inputs: Union[Tensor, Tuple[Tensor, ...]],
         identifier: str,
         num_id: str,
+        # pyre-fixme[2]: Parameter annotation cannot be `Any`.
         additional_forward_args: Any = None,
         load_from_disk: bool = True,
     ) -> None:
@@ -395,6 +400,8 @@ def _compute_and_save_activations(
             AV.save(path, model_id, identifier, unsaved_layers, new_activations, num_id)
 
     @staticmethod
+    # pyre-fixme[3]: Return annotation cannot be `Any`.
+    # pyre-fixme[2]: Parameter annotation cannot be `Any`.
     def _unpack_data(data: Union[Any, Tuple[Any, Any]]) -> Any:
         r"""
         Helper to extract input from labels when getting items from a Dataset. Assumes
@@ -490,6 +497,8 @@ def sort_files(files: List[str]) -> List[str]:
         lexigraphical sort.
         """
 
+        # pyre-fixme[3]: Return type must be annotated.
+        # pyre-fixme[2]: Parameter must be annotated.
         def split_alphanum(s):
             r"""
             Splits string into a list of strings and numbers

captum/_utils/common.py

@@ -1,4 +1,6 @@
 #!/usr/bin/env python3
+
+# pyre-strict
 import typing
 from enum import Enum
 from functools import reduce
@@ -68,10 +70,18 @@ def safe_div(
 
 
 @typing.overload
+# pyre-fixme[43]: The return type of overloaded function `_is_tuple` (`Literal[]`)
+#  is incompatible with the return type of the implementation (`bool`).
+# pyre-fixme[31]: Expression `Literal[False]` is not a valid type.
+# pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
 def _is_tuple(inputs: Tensor) -> Literal[False]: ...
 
 
 @typing.overload
+# pyre-fixme[43]: The return type of overloaded function `_is_tuple` (`Literal[]`)
+#  is incompatible with the return type of the implementation (`bool`).
+# pyre-fixme[31]: Expression `Literal[True]` is not a valid type.
+# pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
 def _is_tuple(inputs: Tuple[Tensor, ...]) -> Literal[True]: ...
 
 
@@ -230,6 +240,8 @@ def _format_tensor_into_tuples(
     return inputs
 
 
+# pyre-fixme[3]: Return annotation cannot be `Any`.
+# pyre-fixme[2]: Parameter annotation cannot be `Any`.
 def _format_inputs(inputs: Any, unpack_inputs: bool = True) -> Any:
     return (
         inputs
@@ -257,16 +269,21 @@ def _format_additional_forward_args(additional_forward_args: None) -> None: ...
 
 @overload
 def _format_additional_forward_args(
+    # pyre-fixme[24]: Generic type `tuple` expects at least 1 type parameter.
     additional_forward_args: Union[Tensor, Tuple]
+    # pyre-fixme[24]: Generic type `tuple` expects at least 1 type parameter.
 ) -> Tuple: ...
 
 
 @overload
 def _format_additional_forward_args(
+    # pyre-fixme[2]: Parameter annotation cannot be `Any`.
     additional_forward_args: Any,
+    # pyre-fixme[24]: Generic type `tuple` expects at least 1 type parameter.
 ) -> Union[None, Tuple]: ...
 
 
+# pyre-fixme[24]: Generic type `tuple` expects at least 1 type parameter.
 def _format_additional_forward_args(additional_forward_args: Any) -> Union[None, Tuple]:
     if additional_forward_args is not None and not isinstance(
         additional_forward_args, tuple
@@ -276,9 +293,11 @@ def _format_additional_forward_args(additional_forward_args: Any) -> Union[None,
 
 
 def _expand_additional_forward_args(
+    # pyre-fixme[2]: Parameter annotation cannot be `Any`.
     additional_forward_args: Any,
     n_steps: int,
     expansion_type: ExpansionTypes = ExpansionTypes.repeat,
+    # pyre-fixme[24]: Generic type `tuple` expects at least 1 type parameter.
 ) -> Union[None, Tuple]:
     def _expand_tensor_forward_arg(
         additional_forward_arg: Tensor,
@@ -343,9 +362,12 @@ def _expand_target(
     return target
 
 
+# pyre-fixme[3]: Return type must be annotated.
 def _expand_feature_mask(
     feature_mask: Union[Tensor, Tuple[Tensor, ...]], n_samples: int
 ):
+    # pyre-fixme[6]: For 1st argument expected `Tensor` but got `Union[Tensor,
+    #  typing.Tuple[Tensor, ...]]`.
     is_feature_mask_tuple = _is_tuple(feature_mask)
     feature_mask = _format_tensor_into_tuples(feature_mask)
     feature_mask_new = tuple(
@@ -359,12 +381,17 @@ def _expand_feature_mask(
     return _format_output(is_feature_mask_tuple, feature_mask_new)
 
 
+# pyre-fixme[3]: Return type must be annotated.
 def _expand_and_update_baselines(
     inputs: Tuple[Tensor, ...],
     n_samples: int,
+    # pyre-fixme[24]: Generic type `dict` expects 2 type parameters, use
+    #  `typing.Dict[<key type>, <value type>]` to avoid runtime subscripting errors.
     kwargs: dict,
     draw_baseline_from_distrib: bool = False,
 ):
+    # pyre-fixme[3]: Return type must be annotated.
+    # pyre-fixme[2]: Parameter must be annotated.
     def get_random_baseline_indices(bsz, baseline):
         num_ref_samples = baseline.shape[0]
         return np.random.choice(num_ref_samples, n_samples * bsz).tolist()
@@ -404,6 +431,9 @@ def get_random_baseline_indices(bsz, baseline):
     kwargs["baselines"] = baselines
 
 
+# pyre-fixme[3]: Return type must be annotated.
+# pyre-fixme[24]: Generic type `dict` expects 2 type parameters, use
+#  `typing.Dict[<key type>, <value type>]` to avoid runtime subscripting errors.
 def _expand_and_update_additional_forward_args(n_samples: int, kwargs: dict):
     if "additional_forward_args" not in kwargs:
         return
@@ -420,6 +450,9 @@ def _expand_and_update_additional_forward_args(n_samples: int, kwargs: dict):
     kwargs["additional_forward_args"] = additional_forward_args
 
 
+# pyre-fixme[3]: Return type must be annotated.
+# pyre-fixme[24]: Generic type `dict` expects 2 type parameters, use
+#  `typing.Dict[<key type>, <value type>]` to avoid runtime subscripting errors.
 def _expand_and_update_target(n_samples: int, kwargs: dict):
     if "target" not in kwargs:
         return
@@ -431,6 +464,9 @@ def _expand_and_update_target(n_samples: int, kwargs: dict):
     kwargs["target"] = target
 
 
+# pyre-fixme[3]: Return type must be annotated.
+# pyre-fixme[24]: Generic type `dict` expects 2 type parameters, use
+#  `typing.Dict[<key type>, <value type>]` to avoid runtime subscripting errors.
 def _expand_and_update_feature_mask(n_samples: int, kwargs: dict):
     if "feature_mask" not in kwargs:
         return
@@ -444,14 +480,24 @@ def _expand_and_update_feature_mask(n_samples: int, kwargs: dict):
 
 
 @typing.overload
+# pyre-fixme[43]: The implementation of `_format_output` does not accept all
+#  possible arguments of overload defined on line `449`.
 def _format_output(
-    is_inputs_tuple: Literal[True], output: Tuple[Tensor, ...]
+    # 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],
+    output: Tuple[Tensor, ...],
 ) -> Tuple[Tensor, ...]: ...
 
 
 @typing.overload
+# pyre-fixme[43]: The implementation of `_format_output` does not accept all
+#  possible arguments of overload defined on line `455`.
 def _format_output(
-    is_inputs_tuple: Literal[False], output: Tuple[Tensor, ...]
+    # 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],
+    output: Tuple[Tensor, ...],
 ) -> Tensor: ...
 
 
@@ -474,18 +520,30 @@ def _format_output(
         "The input is a single tensor however the output isn't."
         "The number of output tensors is: {}".format(len(output))
     )
+    # pyre-fixme[7]: Expected `Union[Tensor, typing.Tuple[Tensor, ...]]` but got
+    #  `Union[tuple[Tensor], Tensor]`.
     return output if is_inputs_tuple else output[0]
 
 
 @typing.overload
+# pyre-fixme[43]: The implementation of `_format_outputs` does not accept all
+#  possible arguments of overload defined on line `483`.
 def _format_outputs(
-    is_multiple_inputs: Literal[False], outputs: List[Tuple[Tensor, ...]]
+    # pyre-fixme[31]: Expression `Literal[False]` is not a valid type.
+    # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
+    is_multiple_inputs: Literal[False],
+    outputs: List[Tuple[Tensor, ...]],
 ) -> Union[Tensor, Tuple[Tensor, ...]]: ...
 
 
 @typing.overload
+# pyre-fixme[43]: The implementation of `_format_outputs` does not accept all
+#  possible arguments of overload defined on line `489`.
 def _format_outputs(
-    is_multiple_inputs: Literal[True], outputs: List[Tuple[Tensor, ...]]
+    # pyre-fixme[31]: Expression `Literal[True]` is not a valid type.
+    # pyre-fixme[24]: Non-generic type `typing.Literal` cannot take parameters.
+    is_multiple_inputs: Literal[True],
+    outputs: List[Tuple[Tensor, ...]],
 ) -> List[Union[Tensor, Tuple[Tensor, ...]]]: ...
 
 
@@ -512,9 +570,12 @@ def _format_outputs(
 
 
 def _run_forward(
+    # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
     forward_func: Callable,
+    # pyre-fixme[2]: Parameter annotation cannot be `Any`.
     inputs: Any,
     target: TargetType = None,
+    # pyre-fixme[2]: Parameter annotation cannot be `Any`.
     additional_forward_args: Any = None,
 ) -> Union[Tensor, Future[Tensor]]:
     forward_func_args = signature(forward_func).parameters
@@ -529,6 +590,8 @@ def _run_forward(
 
     output = forward_func(
         *(
+            # pyre-fixme[60]: Concatenation not yet support for multiple variadic
+            #  tuples: `*inputs, *additional_forward_args`.
             (*inputs, *additional_forward_args)
             if additional_forward_args is not None
             else inputs
@@ -606,6 +669,8 @@ def _select_targets(output: Tensor, target: TargetType) -> Tensor:
         elif isinstance(target[0], tuple):
             return torch.stack(
                 [
+                    # pyre-fixme[24]: Generic type `tuple` expects at least 1 type
+                    #  parameter.
                     output[(i,) + cast(Tuple, targ_elem)]
                     for i, targ_elem in enumerate(target)
                 ]
@@ -639,9 +704,11 @@ def _verify_select_column(
 
 def _verify_select_neuron(
     layer_output: Tuple[Tensor, ...],
+    # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
     selector: Union[int, Tuple[Union[int, slice], ...], Callable],
 ) -> Tensor:
     if callable(selector):
+        # pyre-fixme[7]: Expected `Tensor` but got `object`.
         return selector(layer_output if len(layer_output) > 1 else layer_output[0])
 
     assert len(layer_output) == 1, (
@@ -688,6 +755,9 @@ def _extract_device(
 
 def _reduce_list(
     val_list: Sequence[TupleOrTensorOrBoolGeneric],
+    # pyre-fixme[2]: Parameter annotation cannot contain `Any`.
+    # pyre-fixme[24]: Generic type `list` expects 1 type parameter, use
+    #  `typing.List[<element type>]` to avoid runtime subscripting errors.
     red_func: Callable[[List], Any] = torch.cat,
 ) -> TupleOrTensorOrBoolGeneric:
     """
@@ -702,21 +772,28 @@ def _reduce_list(
     """
     assert len(val_list) > 0, "Cannot reduce empty list!"
     if isinstance(val_list[0], torch.Tensor):
+        # pyre-fixme[16]: `bool` has no attribute `device`.
         first_device = val_list[0].device
+        # pyre-fixme[16]: `bool` has no attribute `to`.
         return red_func([elem.to(first_device) for elem in val_list])
     elif isinstance(val_list[0], bool):
+        # pyre-fixme[7]: Expected `TupleOrTensorOrBoolGeneric` but got `bool`.
         return any(val_list)
     elif isinstance(val_list[0], tuple):
         final_out = []
+        # pyre-fixme[6]: For 1st argument expected `pyre_extensions.ReadOnly[Sized]`
+        #  but got `TupleOrTensorOrBoolGeneric`.
         for i in range(len(val_list[0])):
             final_out.append(
+                # pyre-fixme[16]: `bool` has no attribute `__getitem__`.
                 _reduce_list([val_elem[i] for val_elem in val_list], red_func)
             )
     else:
         raise AssertionError(
             "Elements to be reduced can only be"
             "either Tensors or tuples containing Tensors."
         )
+    # pyre-fixme[7]: Expected `TupleOrTensorOrBoolGeneric` but got `Tuple[Any, ...]`.
     return tuple(final_out)
 
 
@@ -756,6 +833,7 @@ def _flatten_tensor_or_tuple(inp: TensorOrTupleOfTensorsGeneric) -> Tensor:
     return torch.cat([single_inp.flatten() for single_inp in inp])
 
 
+# pyre-fixme[3]: Return annotation cannot be `Any`.
 def _get_module_from_name(model: Module, layer_name: str) -> Any:
     r"""
     Returns the module (layer) object, given its (string) name
@@ -772,7 +850,11 @@ def _get_module_from_name(model: Module, layer_name: str) -> Any:
 
 
 def _register_backward_hook(
-    module: Module, hook: Callable, attr_obj: Any
+    module: Module,
+    # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
+    hook: Callable,
+    # pyre-fixme[2]: Parameter annotation cannot be `Any`.
+    attr_obj: Any,
 ) -> List[torch.utils.hooks.RemovableHandle]:
     grad_out: Dict[device, Tensor] = {}
 
@@ -784,6 +866,7 @@ def forward_hook(
         nonlocal grad_out
         grad_out = {}
 
+        # pyre-fixme[53]: Captured variable `grad_out` is not annotated.
         def output_tensor_hook(output_grad: Tensor) -> None:
             grad_out[output_grad.device] = output_grad
 
@@ -795,7 +878,11 @@ def output_tensor_hook(output_grad: Tensor) -> None:
         else:
             out.register_hook(output_tensor_hook)
 
+    # pyre-fixme[3]: Return type must be annotated.
+    # pyre-fixme[2]: Parameter must be annotated.
     def pre_hook(module, inp):
+        # pyre-fixme[53]: Captured variable `module` is not annotated.
+        # pyre-fixme[3]: Return type must be annotated.
         def input_tensor_hook(input_grad: Tensor):
             if len(grad_out) == 0:
                 return
@@ -820,6 +907,7 @@ def input_tensor_hook(input_grad: Tensor):
     ]
 
 
+# pyre-fixme[3]: Return type must be annotated.
 def _get_max_feature_index(feature_mask: Tuple[Tensor, ...]):
     """
     Returns the max feature mask index