upgrade pyre version in fbcode/executorch - batch 1

Differential Revision: D64977682

Author: Pyre Bot Jr.

backends/cadence/aot/quantizer/fusion_pass.py

@@ -331,6 +331,7 @@ def __init__(self, patterns) -> None:
         # pyre-ignore[4]: Parameter `patterns` of class `QuantFusion` has no type specified
         self.patterns = patterns
 
+    # pyre-fixme[7]: Expected `PassResult` but got implicit return value of `None`.
     def call(self, graph_module: fx.GraphModule) -> PassResult:  # noqa: C901
         for pattern in self.patterns:
             fused_partitions = find_sequential_partitions_aten(
@@ -453,7 +454,6 @@ def call(self, graph_module: fx.GraphModule) -> PassResult:  # noqa: C901
 
             legalize_graph(graph_module)
             graph_module.graph.eliminate_dead_code()
-            # pyre-fixme[7]: Incompatible return type
             graph_module.recompile()
 
     @classmethod
@@ -463,7 +463,7 @@ def is_fused(cls, nodes) -> bool:
 
     @classmethod
     # pyre-ignore[2]: Parameter `nodes` has no type specified
+    # pyre-fixme[7]: Expected `bool` but got implicit return value of `None`.
     def mark_fused(cls, nodes) -> bool:
         for n in nodes:
-            # pyre-fixme[7]: Incompatible return type
             n.meta["QuantFusion"] = True

backends/vulkan/_passes/insert_prepack_nodes.py

@@ -64,8 +64,9 @@ def is_non_weight_param_tensor(node: torch.fx.Node) -> bool:
 
         for user in node.users:
             if user.op == "call_function" and (
-                # pyre-ignore [16]
                 user.target in USES_WEIGHTS
+                # pyre-fixme[16]: Item `Callable` of `(...) -> Any | str` has no
+                #  attribute `name`.
                 or user.target.name() in USES_WEIGHTS
             ):
                 return False

backends/vulkan/runtime/gen_vulkan_spv.py

@@ -520,6 +520,7 @@ def preprocess(
         last_indent = input_indent
 
     while blank_lines != 0:
+        # pyre-fixme[61]: `python_indent` is undefined, or not always defined.
         python_lines.append(python_indent + "print(file=OUT_STREAM)")
         blank_lines -= 1
 

backends/vulkan/test/test_vulkan_delegate.py

@@ -33,6 +33,7 @@
 
 
 class TestBackends(unittest.TestCase):
+    # pyre-fixme[11]: Annotation `EdgeCompileConfig` is not defined as a type.
     _edge_compile_config: EdgeCompileConfig = EdgeCompileConfig(
         _skip_dim_order=True,  # TODO(T182928844): Delegate dim order op to backend.
     )

backends/xnnpack/operators/quant_params.py

@@ -113,10 +113,12 @@ def quantize_tensor(self, tensor: torch.Tensor) -> torch.Tensor:
             ), f"Not expecting per channel group quantization, got q dtype: {self.dtype}, tensor.dtype {tensor.dtype}"
             assert (
                 tensor.shape[self.axis] == cast(torch.Tensor, self.scale).shape[0]
+            # pyre-fixme[16]: Item `float` of `float | Tensor` has no attribute `shape`.
             ), f"Invalid size of per channel quantization scales, axis: {self.axis}, scale size: {self.scale.shape}, tensor shape: {tensor.shape}"
 
             assert (
                 tensor.shape[self.axis] == cast(torch.Tensor, self.zp).shape[0]
+            # pyre-fixme[16]: Item `float` of `float | Tensor` has no attribute `shape`.
             ), f"Invalid size of per channel quantization zero-points, axis: {self.axis}, zp size: {self.zp.shape}, tensor shape: {tensor.shape}"
 
             # Assuming folded quant weights

backends/xnnpack/test/models/mobilebert.py

@@ -12,7 +12,6 @@
 
 
 class TestMobilebert(unittest.TestCase):
-    # pyre-ignore
     mobilebert = MobileBertModel(MobileBertConfig()).eval()
     example_inputs = (torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]]),)
     supported_ops = {

backends/xnnpack/test/test_xnnpack_utils.py

@@ -240,7 +240,6 @@ def forward(self, *args):
 
         # Test the model with executor
         executorch_module = _load_for_executorch_from_buffer(executorch_program.buffer)
-        # pyre-fixme[16]: Module `pytree` has no attribute `tree_flatten`.
         inputs_flattened, _ = tree_flatten(sample_inputs)
 
         model_output = executorch_module.run_method("forward", tuple(inputs_flattened))
@@ -452,7 +451,6 @@ def forward(self, x):
             )
 
         executorch_module = _load_for_executorch_from_buffer(executorch_program.buffer)
-        # pyre-fixme[16]: Module `pytree` has no attribute `tree_flatten`.
         inputs_flattened, _ = tree_flatten(example_inputs)
 
         model_output = executorch_module.run_method("forward", tuple(inputs_flattened))

backends/xnnpack/test/tester/tester.py

@@ -208,6 +208,7 @@ def graph_module(self) -> str:
 
 @register_stage
 class ToEdge(Stage):
+    # pyre-fixme[11]: Annotation `EdgeCompileConfig` is not defined as a type.
     def __init__(self, edge_compile_config: Optional[EdgeCompileConfig] = None):
         self.edge_compile_conf = (
             edge_compile_config or get_xnnpack_edge_compile_config()
@@ -262,6 +263,7 @@ def run(
             if self.pass_list:
                 assert isinstance(self.pass_list, list)
                 for pass_ in self.pass_list:
+                    # pyre-fixme[45]: `Callable` cannot be instantiated.
                     transformed_ep = _transform(transformed_ep, pass_())
 
             if self.pass_functions:
@@ -337,6 +339,7 @@ def graph_module(self) -> str:
 class ToExecutorch(Stage):
     def __init__(
         self,
+        # pyre-fixme[11]: Annotation `ExecutorchBackendConfig` is not defined as a type.
         config: Optional[ExecutorchBackendConfig] = None,
     ):
         self.config = config or ExecutorchBackendConfig(

backends/xnnpack/utils/configs.py

@@ -14,6 +14,7 @@
 ### XNNPACK Configs ###
 def get_xnnpack_edge_compile_config(
     skip_dim_order: bool = False,
+# pyre-fixme[11]: Annotation `EdgeCompileConfig` is not defined as a type.
 ) -> exir.EdgeCompileConfig:
     return exir.EdgeCompileConfig(
         _check_ir_validity=False, _skip_dim_order=skip_dim_order
@@ -27,6 +28,7 @@ def get_transform_passes(additional_passes=None) -> List[PassType]:
 
 def get_xnnpack_executorch_backend_config(
     additional_passes=None,
+# pyre-fixme[11]: Annotation `ExecutorchBackendConfig` is not defined as a type.
 ) -> exir.ExecutorchBackendConfig:
     additional_passes = additional_passes if additional_passes else []
     return exir.ExecutorchBackendConfig(

devtools/bundled_program/core.py

@@ -323,6 +323,8 @@ def _assert_valid_bundle(
                         ), "The input tensor {} dtype shall be {}, but now is {}".format(
                             cur_plan_test_inputs[j],
                             self._get_input_dtype(program, program_plan_id, j),
+                            # pyre-fixme[16]: Item `bool` of `bool | float | int |
+                            #  Tensor` has no attribute `dtype`.
                             cur_plan_test_inputs[j].dtype,
                         )
                     elif type(cur_plan_test_inputs[j]) in (
@@ -354,6 +356,8 @@ def _assert_valid_bundle(
                     ), "The label tensor {} dtype shall be {}, but now is {}".format(
                         cur_plan_test_expected_outputs[j],
                         self._get_output_dtype(program, program_plan_id, j),
+                        # pyre-fixme[16]: Item `bool` of `bool | float | int |
+                        #  Tensor` has no attribute `dtype`.
                         cur_plan_test_expected_outputs[j].dtype,
                     )