Add type error suppressions for upcoming upgrade

captum/attr/_core/dataloader_attr.py

@@ -369,6 +369,9 @@ def attribute(
             assert len(input_roles) == len(inputs), (
                 "input_roles must have the same size as the return of the dataloader,",
                 f"length of input_roles is {len(input_roles)} ",
+                # pyre-fixme[6]: For 1st argument expected
+                #  `pyre_extensions.ReadOnly[Sized]` but got
+                #  `Optional[typing.Tuple[typing.Any, ...]]`.
                 f"whereas the length of dataloader return is {len(inputs)}",
             )
 
@@ -395,6 +398,9 @@ def attribute(
             "Baselines must have the same size as the return of the dataloader ",
             "that need attribution",
             f"length of baseline is {len(baselines)} ",
+            # pyre-fixme[6]: For 1st argument expected
+            #  `pyre_extensions.ReadOnly[Sized]` but got
+            #  `Optional[typing.Tuple[typing.Any, ...]]`.
             f'whereas the length of dataloader return with role "0" is {len(inputs)}',
         )
 
@@ -413,6 +419,9 @@ def attribute(
             "Feature mask must have the same size as the return of the dataloader ",
             "that need attribution",
             f"length of feature_mask is {len(feature_mask)} ",
+            # pyre-fixme[6]: For 1st argument expected
+            #  `pyre_extensions.ReadOnly[Sized]` but got
+            #  `Optional[typing.Tuple[typing.Any, ...]]`.
             f'whereas the length of dataloader return with role "0" is {len(inputs)}',
         )
 

captum/attr/_core/deep_lift.py

@@ -833,6 +833,9 @@ def attribute(  # type: ignore
             " with more than one example but found: {}."
             " If baselines are provided in shape of scalars or with a single"
             " baseline example, `DeepLift`"
+            # pyre-fixme[16]: Item `Callable` of `Union[(...) ->
+            #  TensorOrTupleOfTensorsGeneric, TensorOrTupleOfTensorsGeneric]` has no
+            #  attribute `__getitem__`.
             " approach can be used instead.".format(baselines[0])
         )
 

captum/attr/_core/gradient_shap.py

@@ -275,6 +275,9 @@ def attribute(
         #  attribute `__getitem__`.
         assert isinstance(baselines[0], torch.Tensor), (
             "Baselines distribution has to be provided in a form "
+            # pyre-fixme[16]: Item `Callable` of `Union[(...) ->
+            #  TensorOrTupleOfTensorsGeneric, TensorOrTupleOfTensorsGeneric]` has no
+            #  attribute `__getitem__`.
             "of a torch.Tensor {}.".format(baselines[0])
         )
 

captum/attr/_core/layer/layer_gradient_shap.py

@@ -308,6 +308,8 @@ def attribute(
         #  TensorOrTupleOfTensorsGeneric]` has no attribute `__getitem__`.
         assert isinstance(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])
         )
 

captum/attr/_utils/attribution.py

@@ -43,6 +43,7 @@ def __init__(self, forward_func: Callable) -> None:
         self.forward_func = forward_func
 
     # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
+    # pyre-fixme[13]: Attribute `attribute` is never initialized.
     attribute: Callable
     r"""
     This method computes and returns the attribution values for each input tensor.
@@ -74,6 +75,7 @@ def __init__(self, forward_func: Callable) -> None:
     """
 
     # pyre-fixme[24] Generic type `Callable` expects 2 type parameters.
+    # pyre-fixme[13]: Attribute `attribute_future` is never initialized.
     attribute_future: Callable
 
     r"""
@@ -126,6 +128,7 @@ def has_convergence_delta(self) -> bool:
         return False
 
     # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
+    # pyre-fixme[13]: Attribute `compute_convergence_delta` is never initialized.
     compute_convergence_delta: Callable
     r"""
     The attribution algorithms which derive `Attribution` class and provide
@@ -504,6 +507,7 @@ def __init__(
         InternalAttribution.__init__(self, forward_func, layer, device_ids)
 
     # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
+    # pyre-fixme[13]: Attribute `attribute` is never initialized.
     attribute: Callable
     r"""
     This method computes and returns the neuron attribution values for each

captum/concept/_core/concept.py

@@ -74,6 +74,7 @@ def __init__(self, model: Module) -> None:
         self.model = model
 
     # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
+    # pyre-fixme[13]: Attribute `interpret` is never initialized.
     interpret: Callable
     r"""
     An abstract interpret method that performs concept-based model interpretability

captum/robust/_core/metrics/min_param_perturbation.py

@@ -21,8 +21,12 @@ def drange(
     min_val: Union[int, float], max_val: Union[int, float], step_val: Union[int, float]
 ) -> Generator[Union[int, float], None, None]:
     curr = min_val
+    # pyre-fixme[58]: `>` is not supported for operand types `Union[float, int]` and
+    #  `int`.
     while curr < max_val:
         yield curr
+        # pyre-fixme[58]: `+` is not supported for operand types `Union[float, int]`
+        #  and `Union[float, int]`.
         curr += step_val
 
 

captum/robust/_core/perturbation.py

@@ -12,6 +12,7 @@ class Perturbation:
     """
 
     # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
+    # pyre-fixme[13]: Attribute `perturb` is never initialized.
     perturb: Callable
     r"""
     This method computes and returns the perturbed input for each input tensor.

tests/attr/test_llm_attr.py

@@ -156,7 +156,9 @@ def device(self) -> torch.device:
 # pyre-fixme[13]: Attribute `device` is never initialized.
 # pyre-fixme[13]: Attribute `use_cached_outputs` is never initialized.
 class TestLLMAttr(BaseTest):
+    # pyre-fixme[13]: Attribute `device` is never initialized.
     device: str
+    # pyre-fixme[13]: Attribute `use_cached_outputs` is never initialized.
     use_cached_outputs: bool
 
     # pyre-fixme[56]: Pyre was not able to infer the type of argument `comprehension
@@ -377,6 +379,7 @@ def test_futures_not_implemented(self) -> None:
 )
 # pyre-fixme[13]: Attribute `device` is never initialized.
 class TestLLMGradAttr(BaseTest):
+    # pyre-fixme[13]: Attribute `device` is never initialized.
     device: str
 
     @parameterized.expand(

tests/attr/test_llm_attr_gpu.py

@@ -149,7 +149,9 @@ def device(self) -> torch._C.device:
 # pyre-fixme[13]: Attribute `use_cached_outputs` is declared in class `TestLlmAttrGpu`
 # to have type `bool` but is never initialized.
 class TestLlmAttrGpu(BaseTest):
+    # pyre-fixme[13]: Attribute `device` is never initialized.
     device: str
+    # pyre-fixme[13]: Attribute `use_cached_outputs` is never initialized.
     use_cached_outputs: bool
 
     @parameterized.expand([(FeatureAblation,), (ShapleyValueSampling,)])
@@ -235,6 +237,7 @@ def test_llm_attr_without_token_gpu(
 # pyre-fixme[13]: Attribute `device` is declared in class `TestLLMGradAttrGPU`
 # to have type `str` but is never initialized.
 class TestLLMGradAttrGPU(BaseTest):
+    # pyre-fixme[13]: Attribute `device` is never initialized.
     device: str
 
     def test_llm_attr(self) -> None: