Fix cadence BUCK deps and pyre

Differential Revision: D66553586

Pull Request resolved: pytorch#7116

Author: kirklandsign

backends/cadence/runtime/TARGETS

@@ -18,6 +18,7 @@ python_library(
         "//executorch/devtools/bundled_program:config",
         "//executorch/devtools/bundled_program:core",
         "//executorch/devtools/bundled_program/serialize:lib",
+        "//executorch/devtools:lib",
         "//executorch/exir:lib",
     ],
 )

backends/cadence/runtime/runtime.py

@@ -167,9 +167,7 @@ def run(
 
 
 def compare(
-    # pyre-fixme[2]: Parameter annotation cannot be `Any`.
     outputs: Any,
-    # pyre-fixme[2]: Parameter annotation cannot be `Any`.
     ref_outputs: Any,
     name: str = "",
     eps_error: float = 1e-1,
@@ -223,7 +221,6 @@ def run_and_compare(
     compare(outputs, ref_outputs, eps_error=eps_error, eps_warn=eps_warn)
 
 
-# pyre-fixme[24]: Generic type `np.ndarray` expects 2 type parameters.
 def to_nd_array(v: Union[bool, numbers.Number, ndarray, torch.Tensor]) -> np.ndarray:
     if isinstance(v, np.ndarray):
         return v

backends/cadence/runtime/utils.py

@@ -13,12 +13,11 @@
 import torch
 
 
-# pyre-fixme[24]: Generic type `np.ndarray` expects 2 type parameters.
-def distance(fn: Callable[[np.ndarray, np.ndarray], float]) -> Callable[
+def distance(
+    fn: Callable[[np.ndarray, np.ndarray], float],
+) -> Callable[
     [
-        # pyre-fixme[24]: Generic type `np.ndarray` expects 2 type parameters.
         typing.Union[np.ndarray, torch._tensor.Tensor],
-        # pyre-fixme[24]: Generic type `np.ndarray` expects 2 type parameters.
         typing.Union[np.ndarray, torch._tensor.Tensor],
     ],
     float,
@@ -27,9 +26,7 @@ def distance(fn: Callable[[np.ndarray, np.ndarray], float]) -> Callable[
     # the distance between two N-D tensors given a function. This can be a RMS
     # function, maximum abs diff, or any kind of distance function.
     def wrapper(
-        # pyre-fixme[24]: Generic type `np.ndarray` expects 2 type parameters.
         a: Union[np.ndarray, torch.Tensor],
-        # pyre-fixme[24]: Generic type `np.ndarray` expects 2 type parameters.
         b: Union[np.ndarray, torch.Tensor],
     ) -> float:
         # convert a and b to np.ndarray type fp64
@@ -68,24 +65,20 @@ def wrapper(
 
 
 @distance
-# pyre-fixme[24]: Generic type `np.ndarray` expects 2 type parameters.
 def rms(a: np.ndarray, b: np.ndarray) -> float:
     return ((a - b) ** 2).mean() ** 0.5
 
 
 @distance
-# pyre-fixme[24]: Generic type `np.ndarray` expects 2 type parameters.
 def max_abs_diff(a: np.ndarray, b: np.ndarray) -> float:
     return np.abs(a - b).max()
 
 
 @distance
-# pyre-fixme[24]: Generic type `np.ndarray` expects 2 type parameters.
 def max_rel_diff(x: np.ndarray, x_ref: np.ndarray) -> float:
     return np.abs((x - x_ref) / x_ref).max()
 
 
-# pyre-fixme[24]: Generic type `np.ndarray` expects 2 type parameters.
 def to_np_arr_fp64(x: Union[np.ndarray, torch.Tensor]) -> np.ndarray:
     if isinstance(x, torch.Tensor):
         x = x.detach().cpu().numpy()
@@ -94,11 +87,8 @@ def to_np_arr_fp64(x: Union[np.ndarray, torch.Tensor]) -> np.ndarray:
     return x
 
 
-# pyre-fixme[3]: Return type must be annotated.
 def normalized_rms(
-    # pyre-fixme[24]: Generic type `np.ndarray` expects 2 type parameters.
     predicted: Union[np.ndarray, torch.Tensor],
-    # pyre-fixme[24]: Generic type `np.ndarray` expects 2 type parameters.
     ground_truth: Union[np.ndarray, torch.Tensor],
 ):
     num = rms(predicted, ground_truth)

examples/cadence/operators/test_add_op.py

@@ -13,6 +13,7 @@
 
 
 class ATenOpTestCases(unittest.TestCase):
+    # pyre-fixme[16]: Module `parameterized.parameterized` has no attribute `expand`.
     @parameterized.expand(
         [
             [(7, 5, 6), (7, 5, 6)],
@@ -61,6 +62,7 @@ def forward(self, x: torch.Tensor, y: torch.Tensor):
             model, (X, Y), file_name=self._testMethodName, run_and_compare=False
         )
 
+    # pyre-fixme[16]: Module `parameterized.parameterized` has no attribute `expand`.
     @parameterized.expand(
         [
             [(7, 5, 6), (7, 5, 6)],

extension/llm/export/quantizer_lib.py

@@ -184,14 +184,12 @@ def get_qnn_quantizer(
         )
         qnn_quantizer.set_per_channel_conv_quant(enable=False)
         qnn_quantizer.set_per_channel_linear_quant(enable=False)
-        # pyre-ignore: Undefined attribute [16]: Module `executorch.backends` has no attribute `qualcomm`.
         qnn_quantizer.set_quant_config(
             quant_dtype, is_qat=is_qat, act_observer=MinMaxObserver
         )
     elif quant_config == "16a4w":
         # pyre-ignore: Undefined attribute [16]: Module `executorch.backends` has no attribute `qualcomm`.
         quant_dtype = QuantDtype.use_16a4w
-        # pyre-ignore: Undefined attribute [16]: Module `executorch.backends` has no attribute `qualcomm`.
         qnn_quantizer.set_quant_config(
             quant_dtype, is_qat=is_qat, act_observer=MinMaxObserver
         )