Arm backend: Add docstrings for bmm and conv2d operators

backends/arm/operators/op_bmm.py

@@ -5,6 +5,8 @@
 # LICENSE file in the root directory of this source tree.
 
 # pyre-unsafe
+"""Provide a visitor for lowering batched matmul (BMM) to TOSA."""
+
 from typing import Any, List
 
 import torch
@@ -30,6 +32,13 @@
 
 @register_node_visitor
 class BMMVisitor(NodeVisitor):
+    """Provide a visitor that lowers ``aten.bmm`` to TOSA ``MATMUL``.
+
+    INT8 accumulates into INT32; add a rescale to INT8 using SINGLE_ROUND
+    rounding and output zero-point.
+
+    """
+
     target = "aten.bmm.default"
 
     tosa_specs = [
@@ -47,7 +56,7 @@ def define_node(
         inputs: List[TosaArg],
         output: TosaArg,
     ) -> None:
-
+        """Define the TOSA ``MATMUL`` operator and optional rescale."""
         import serializer.tosa_serializer as ts  # type: ignore
 
         validate_num_inputs(self.target, inputs, 2)

backends/arm/operators/op_conv2d.py

@@ -4,6 +4,8 @@
 # LICENSE file in the root directory of this source tree.
 
 # pyre-unsafe
+"""Provide a visitor for lowering 2D convolution to TOSA (INT/FP)."""
+
 import itertools
 from typing import Any, List
 
@@ -28,6 +30,12 @@
 
 @register_node_visitor
 class Conv2dVisitor(NodeVisitor):
+    """Provide a visitor that lowers ``aten.convolution`` to TOSA.
+
+    Map to ``CONV2D`` or ``DEPTHWISE_CONV2D`` as appropriate.
+
+    """
+
     target = "aten.convolution.default"
 
     tosa_specs = [
@@ -38,13 +46,32 @@ class Conv2dVisitor(NodeVisitor):
     def __init__(self, *args):
         super().__init__(*args)
 
-    # torch.nn.Conv2d does not require the result of
-    # `(input + 2 * pad - dilation * (weight - 1) - 1) / stride`
-    # to be an integer, but tosa currently strictly require this property.
-    # This function adjusts the pad value to meet the requirement.
     def adjust_pad_if_needed(
         self, input_size: int, input_weight: int, stride: int, pad: int, dilation: int
     ) -> int:
+        """Adjust padding to satisfy TOSA's integer output-size requirement.
+
+        Torch ``Conv2d`` does not require the result of
+        ``(input + 2 * pad - dilation * (weight - 1) - 1) / stride`` to be an
+        integer, but TOSA does. This helper reduces the provided padding so
+        that the expression becomes divisible by ``stride``.
+
+        Args:
+            input_size (int): Spatial input size along the dimension (H or W).
+            input_weight (int): Kernel size along the same dimension.
+            stride (int): Stride along the same dimension.
+            pad (int): Padding value to adjust (bottom or right after duplication).
+            dilation (int): Dilation along the same dimension.
+
+        Returns:
+            int: Adjusted padding value that yields an integer output size.
+
+        Raises:
+            RuntimeError: If the required adjustment exceeds the provided
+                padding, which should be handled by the ``SizeAdjustInputPass``
+                pass instead.
+
+        """
         mod_remainder = (
             input_size + 2 * pad - dilation * (input_weight - 1) - 1
         ) % stride
@@ -55,7 +82,8 @@ def adjust_pad_if_needed(
 
         if mod_remainder > pad:
             raise RuntimeError(
-                "This case should be handled by the SizeAdjustConv2d pass, is it enabled?"
+                "This case should be handled by the SizeAdjustInputPass pass, "
+                "is it enabled?"
             )
         return pad - mod_remainder
 
@@ -66,7 +94,7 @@ def define_node(
         inputs: List[TosaArg],
         output: TosaArg,
     ) -> None:
-
+        """Define the TOSA CONV2D/DEPTHWISE_CONV2D operator and post-rescale."""
         import serializer.tosa_serializer as ts  # type: ignore
         from tosa.RoundingMode import RoundingMode  # type: ignore
 
@@ -133,7 +161,7 @@ def define_node(
         in_channels = input.shape[1]
         out_channels = weight.shape[0]
         if (in_channels == group.number) and (out_channels % in_channels) == 0:
-            """Depthwise convolution case"""
+            """Depthwise convolution case."""
             # Reshape torch shape format of weight tensor to tosa required format.
             # https://www.mlplatform.org/tosa/tosa_spec.html#_depthwise_conv2d
             m_length = int(out_channels / in_channels)
@@ -178,7 +206,7 @@ def define_node(
                 acc_type=acc_type,
             )
         else:
-            """Regular convolution case"""
+            """Regular convolution case."""
             tosa_op = ts.TosaOp.Op().CONV2D
             weight_name = weight.name