Lintrunner: Enable mypy testing on backends/arm

Migration from pyre to mypy in the lintrunner by enabling mypy for
backends/arm.

But, choosing to ignore the directory backends/arm/test.

Adding ignores all over the place. These needs to be fixed properly in
the future, but now we will start to catch new things trying to sneak
in.

Change-Id: Ie7f73d5688aaec3b32dca9f0cd042da94c06f487

Author: perheld

.lintrunner.toml

@@ -291,6 +291,7 @@ code = 'MYPY'
 include_patterns = [
     # TODO(https://github.com/pytorch/executorch/issues/7441): Gradually start enabling all folders.
     # 'backends/**/*.py',
+    'backends/arm/**/*.py',
     'build/**/*.py',
     'codegen/**/*.py',
     # 'devtools/**/*.py',
@@ -312,6 +313,7 @@ exclude_patterns = [
     '**/third-party/**',
     'scripts/check_binary_dependencies.py',
     'profiler/test/test_profiler_e2e.py',
+    'backends/arm/test/**',
 ]
 command = [
     'python',

backends/arm/_passes/annotate_channels_last_dim_order_pass.py

@@ -255,7 +255,7 @@ def call(self, graph_module: torch.fx.GraphModule):
                     # dim_order = (2, 3, 0, 1) (https://www.mlplatform.org/tosa/tosa_spec.html#_depthwise_conv2d).
                     dim_order = self.HWCM_order
             else:
-                dim_order = tuple(range(node_data.dim()))
+                dim_order = tuple(range(node_data.dim()))  # type: ignore[assignment]
             node.meta["tosa_dim_order"] = dim_order
         # Take care of cases when:
         # 4D (NHWC) -> >4D (NCH)

backends/arm/_passes/arm_pass_manager.py

@@ -27,7 +27,9 @@
 )
 from executorch.backends.arm._passes.decompose_linear_pass import DecomposeLinearPass
 from executorch.backends.arm._passes.decompose_meandim_pass import DecomposeMeanDimPass
-from executorch.backends.arm._passes.decompose_select import DecomposeSelectPass
+from executorch.backends.arm._passes.decompose_select import (  # type: ignore[import-not-found]
+    DecomposeSelectPass,
+)
 from executorch.backends.arm._passes.decompose_softmaxes_pass import (
     DecomposeSoftmaxesPass,
 )
@@ -37,18 +39,20 @@
     QuantizeFullArgument,
     RetraceFoldedDtypesPass,
 )
-from executorch.backends.arm._passes.fuse_quantized_activation_pass import (
+from executorch.backends.arm._passes.fuse_quantized_activation_pass import (  # type: ignore[import-not-found]
     FuseQuantizedActivationPass,
 )
 from executorch.backends.arm._passes.insert_table_ops import InsertTableOpsPass
 from executorch.backends.arm._passes.keep_dims_false_to_squeeze_pass import (
     KeepDimsFalseToSqueezePass,
 )
 from executorch.backends.arm._passes.match_arg_ranks_pass import MatchArgRanksPass
-from executorch.backends.arm._passes.meandim_to_averagepool_pass import (
+from executorch.backends.arm._passes.meandim_to_averagepool_pass import (  # type: ignore[attr-defined]
     ConvertMeanDimToAveragePoolPass,
 )
-from executorch.backends.arm._passes.mm_to_bmm_pass import ConvertMmToBmmPass
+from executorch.backends.arm._passes.mm_to_bmm_pass import (  # type: ignore[import-not-found]
+    ConvertMmToBmmPass,
+)
 from executorch.backends.arm._passes.remove_clone_pass import RemoveClonePass
 from executorch.backends.arm._passes.scalars_to_attribute_pass import (
     ScalarsToAttributePass,
@@ -86,7 +90,7 @@ def _tosa_080_BI_pipeline(self, exported_program: ExportedProgram) -> GraphModul
 
         self.add_pass(AnnotateDecomposedMatmulPass())
         self.add_pass(QuantizeFullArgument())
-        self.add_pass(FoldAndAnnotateQParamsPass())
+        self.add_pass(FoldAndAnnotateQParamsPass())  # type: ignore[call-arg]
         self.add_pass(RetraceFoldedDtypesPass())
         self.add_pass(InsertTableOpsPass(exported_program))
 
@@ -121,7 +125,7 @@ def _tosa_080_MI_pipeline(self, exported_program: ExportedProgram) -> GraphModul
 
         self.add_pass(AnnotateDecomposedMatmulPass())
         self.add_pass(QuantizeFullArgument())
-        self.add_pass(FoldAndAnnotateQParamsPass())
+        self.add_pass(FoldAndAnnotateQParamsPass())  # type: ignore[call-arg]
         self.add_pass(RetraceFoldedDtypesPass())
         self.add_pass(InsertTableOpsPass(exported_program))
 

backends/arm/_passes/arm_pass_utils.py

@@ -1,5 +1,5 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates.
-# Copyright 2024 Arm Limited and/or its affiliates.
+# Copyright 2024-2025 Arm Limited and/or its affiliates.
 # All rights reserved.
 #
 # This source code is licensed under the BSD-style license found in the
@@ -58,9 +58,9 @@ def get_param_tensor(
     elif is_get_attr_node(node):
         # This is a hack to support both lifted and unlifted graph
         try:
-            return getattr(node.graph.owning_module, node.target)
+            return getattr(node.graph.owning_module, node.target)  # type: ignore[arg-type]
         except AttributeError:
-            return getattr(exp_prog.graph_module, node.target)
+            return getattr(exp_prog.graph_module, node.target)  # type: ignore[arg-type]
     raise RuntimeError(f"unsupported param type, {node.op}.")
 
 
@@ -156,7 +156,7 @@ def get_node_arg(args: list | dict, key: int | str | type, default_value=None):
                 f"Out of bounds index {key} for getting value in args (of size {len(args)})"
             )
     elif isinstance(key, str):
-        return args.get(key, default_value)  # pyre-ignore[16]
+        return args.get(key, default_value)  # type: ignore[union-attr]  # pyre-ignore[16]
     elif isclass(key):
         for arg in args:
             if isinstance(arg, key):

backends/arm/_passes/fold_qdq_with_annotated_qparams_pass.py

@@ -122,7 +122,7 @@ def fold_and_annotate_arg(
             node.meta["input_qparams"][i] = input_qparams
             for n in nodes_to_remove:
                 assert n.target == dq_op
-                n.replace_all_uses_with(n.args[0])
+                n.replace_all_uses_with(n.args[0])  # type: ignore[arg-type]
                 graph_module.graph.erase_node(n)
 
     def call(self, graph_module: GraphModule) -> PassResult:

backends/arm/_passes/keep_dims_false_to_squeeze_pass.py

@@ -1,4 +1,4 @@
-# Copyright 2024 Arm Limited and/or its affiliates.
+# Copyright 2024-2025 Arm Limited and/or its affiliates.
 # All rights reserved.
 #
 # This source code is licensed under the BSD-style license found in the
@@ -66,15 +66,15 @@ def call(self, graph_module: torch.fx.GraphModule):
             sum_node = cast(torch.fx.Node, node)
             keep_dim = get_node_arg(
                 # pyre-ignore[6]
-                sum_node.args,
+                sum_node.args,  # type: ignore[arg-type]
                 keep_dim_index,
                 False,
             )
 
             if keep_dim:
                 continue
 
-            dim_list = get_node_arg(sum_node.args, 1, [0])  # pyre-ignore[6]
+            dim_list = get_node_arg(sum_node.args, 1, [0])  # type: ignore[arg-type]  # pyre-ignore[6]
 
             # Add keep_dim = True arg to sum node.
             set_node_arg(sum_node, 2, True)

backends/arm/_passes/scalars_to_attribute_pass.py

@@ -1,4 +1,4 @@
-# Copyright 2024 Arm Limited and/or its affiliates.
+# Copyright 2024-2025 Arm Limited and/or its affiliates.
 # All rights reserved.
 #
 # This source code is licensed under the BSD-style license found in the
@@ -54,7 +54,7 @@ def call(self, graph_module: GraphModule) -> PassResult:
                 if isinstance(arg, int) and not torch.is_floating_point(
                     get_first_fake_tensor(n)
                 ):
-                    new_args.append(arg)
+                    new_args.append(arg)  # type: ignore[arg-type]
                     continue
 
                 prefix = "_tensor_constant_"

backends/arm/arm_backend.py

@@ -15,7 +15,7 @@
 import os
 from typing import cast, final, List, Optional
 
-import serializer.tosa_serializer as ts
+import serializer.tosa_serializer as ts  # type: ignore
 from executorch.backends.arm.arm_vela import vela_compile
 from executorch.backends.arm.operators.node_visitor import get_node_visitors
 
@@ -229,7 +229,7 @@ def preprocess(  # noqa: C901
         # Converted output for this subgraph, serializer needs path early as it emits
         # const data directly. Path created and data written only in debug builds.
         tosa_graph = ts.TosaSerializer(artifact_path)
-        graph_module = ArmPassManager(tosa_spec).transform_to_backend_pipeline(
+        graph_module = ArmPassManager(tosa_spec).transform_to_backend_pipeline(  # type: ignore
             exported_program=edge_program
         )
 

backends/arm/arm_partitioner.py

@@ -10,7 +10,7 @@
 from typing import Callable, final, List, Optional, Tuple
 
 import torch
-from executorch.backends.arm.arm_backend import (
+from executorch.backends.arm.arm_backend import (  # type: ignore[attr-defined]
     ArmBackend,
 )  # usort: skip
 from executorch.backends.arm.operator_support.tosa_supported_operators import (

backends/arm/arm_vela.py

@@ -1,4 +1,4 @@
-# Copyright 2023-2024 Arm Limited and/or its affiliates.
+# Copyright 2023-2025 Arm Limited and/or its affiliates.
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
@@ -12,7 +12,7 @@
 from typing import List
 
 import numpy as np
-from ethosu.vela import vela
+from ethosu.vela import vela  # type: ignore
 
 
 # Pack either input or output tensor block, compose the related arrays into
@@ -96,13 +96,13 @@ def vela_compile(tosa_graph, args: List[str], shape_order=None):
                 block_name = block_name + b"\x00" * (16 - len(block_name))
 
                 # We need the acual unpadded block lengths for hw setup
-                block_length = struct.pack("<iiii", len(bin_blocks[key]), 0, 0, 0)
+                block_length = struct.pack("<iiii", len(bin_blocks[key]), 0, 0, 0)  # type: ignore[assignment]
 
                 # Pad block data to multiple of 16 bytes
                 block_data = bin_blocks[key]
                 block_data = block_data + b"\x00" * (15 - (len(block_data) - 1) % 16)
 
-                block = block_name + block_length + block_data
+                block = block_name + block_length + block_data  # type: ignore[operator]
                 blocks = blocks + block
 
         return blocks