Arm backend: Add docstrings for to_dim_order_copy_support.py (#14537)

Signed-off-by: Sebastian Larsson <[email protected]>

Author: Sebastian-Larsson

backends/arm/operator_support/to_dim_order_copy_support.py

@@ -2,6 +2,13 @@
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
+"""Declare operator support for ``_to_dim_order_copy`` in TOSA.
+
+Provide dtype-compatibility checks for casting when converting to a specific
+dimension order. Supported input/output dtype pairs depend on the active TOSA
+profile (integer and/or float).
+
+"""
 
 # pyre-unsafe
 import copy
@@ -25,6 +32,16 @@
 
 @register_tosa_support_check
 class ToCopySupported(SupportedTOSAOperatorCheck):
+    """Provide TOSA support check for ``_to_dim_order_copy``.
+
+    Attributes:
+        SUPPORTED_INT_PROFILE_DTYPES (dict[torch.dtype, list[torch.dtype]]):
+            Allowed output dtypes for each integer input dtype.
+        SUPPORTED_FP_PROFILE_DTYPES (dict[torch.dtype, list[torch.dtype]]):
+            Allowed output dtypes for each floating input dtype.
+
+    """
+
     targets = [
         exir_ops.edge.dim_order_ops._to_dim_order_copy.default,
     ]
@@ -40,21 +57,31 @@ def _merge_supported_types(
         dtypes1: SupportedTypeDict,
         dtypes2: SupportedTypeDict,
     ) -> SupportedTypeDict:
+        """Return a merged mapping of supported dtype transitions.
+
+        Args:
+            dtypes1 (dict[torch.dtype, list[torch.dtype]]): Base mapping.
+            dtypes2 (dict[torch.dtype, list[torch.dtype]]): Mapping to merge in.
+
+        Returns:
+            dict[torch.dtype, list[torch.dtype]]: Combined mapping.
+
+        """
         merged_dtypes = copy.deepcopy(
             dtypes1
-        )  # Use deepcopy to avoid unintentionally modifying SUPPORTED_INT_TYPES
+        )  # Use deepcopy to avoid unintentionally modifying SUPPORTED_INT_PROFILE_DTYPES
         for k, v in dtypes2.items():
             merged_dtypes[k] = merged_dtypes.get(k, []) + v
         return merged_dtypes
 
-    SUPPORTED_INT_TYPES: SupportedTypeDict = {
+    SUPPORTED_INT_PROFILE_DTYPES: SupportedTypeDict = {
         torch.bool: [torch.bool, torch.int8, torch.int16, torch.int32],
         torch.int8: [torch.bool, torch.int8, torch.int16, torch.int32],
         torch.int16: [torch.bool, torch.int8, torch.int16, torch.int32],
         torch.int32: [torch.bool, torch.int8, torch.int16, torch.int32],
         torch.int64: [torch.bool, torch.int8, torch.int16, torch.int32],
     }
-    SUPPORTED_FLOAT_TYPES: SupportedTypeDict = {
+    SUPPORTED_FP_PROFILE_DTYPES: SupportedTypeDict = {
         torch.int8: [torch.int8, torch.float16, torch.bfloat16, torch.float32],
         torch.int16: [torch.int16, torch.float16, torch.bfloat16, torch.float32],
         torch.int32: [torch.int32, torch.float16, torch.bfloat16, torch.float32],
@@ -92,22 +119,25 @@ def _merge_supported_types(
             torch.float32,
         ],
     }
-    ALL_SUPPORTED_TYPES = _merge_supported_types(
-        SUPPORTED_INT_TYPES, SUPPORTED_FLOAT_TYPES
-    )
 
     def is_node_tosa_supported(
         self, node: fx.Node, tosa_spec: TosaSpecification
     ) -> bool:
+        """Return True if the node is supported by TOSA.
+
+        Check FakeTensor metadata, validate input dtype is supported for the
+        active profile, and ensure the output dtype is allowed for the given
+        input dtype.
 
+        """
         supported_dtypes: SupportedTypeDict = {}
         if tosa_spec.support_integer():
             supported_dtypes = self._merge_supported_types(
-                self.SUPPORTED_INT_TYPES, supported_dtypes
+                self.SUPPORTED_INT_PROFILE_DTYPES, supported_dtypes
             )
         if tosa_spec.support_float():
             supported_dtypes = self._merge_supported_types(
-                self.SUPPORTED_FLOAT_TYPES, supported_dtypes
+                self.SUPPORTED_FP_PROFILE_DTYPES, supported_dtypes
             )
 
         if len(node.all_input_nodes) != 1: