Arm backend: Add docstrings for quantizer/quantization_config.py

backends/arm/quantizer/quantization_config.py

@@ -3,6 +3,13 @@
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
+"""Provide quantization configuration helpers for the Arm backend.
+
+Define a small dataclass to carry activation/weight/bias specs and helper
+accessors that validate specs before use. Use this module to build and validate
+quantization specs consumed by the annotator.
+
+"""
 
 # pyre-unsafe
 
@@ -19,13 +26,38 @@
 
 @dataclass(eq=True, frozen=True)
 class QuantizationConfig:
+    """Provide a container for quantization specs.
+
+    Hold optional specs for input/output activations, weights, and bias, and
+    expose validated accessors.
+
+    Attributes:
+        input_activation (QuantizationSpec | None): Spec for input activations.
+        output_activation (QuantizationSpec | None): Spec for output activations.
+        weight (QuantizationSpec | None): Spec for weights.
+        bias (QuantizationSpec | None): Spec for bias values.
+
+    """
+
     input_activation: QuantizationSpec | None
     output_activation: QuantizationSpec | None
     weight: QuantizationSpec | None
     bias: QuantizationSpec | None
 
     def get_input_act_qspec(self) -> QuantizationSpec | None:
-        """Returns QuantizationSpec 'input_activation' after asserting that input_activation.qscheme is valid."""
+        """Get the validated input activation spec.
+
+        Validate that the input activation qscheme is supported before
+        returning the spec.
+
+        Returns:
+            QuantizationSpec | None: Input activation spec, or ``None`` when
+                unset.
+
+        Raises:
+            ValueError: If the qscheme is not per-tensor affine or symmetric.
+
+        """
         if self.input_activation is None:
             return None
         # Validate that input_activation uses a supported qscheme
@@ -39,7 +71,19 @@ def get_input_act_qspec(self) -> QuantizationSpec | None:
         return self.input_activation
 
     def get_output_act_qspec(self) -> QuantizationSpec | None:
-        """Returns QuantizationSpec 'output_activation' after asserting that output_activation.qscheme is valid."""
+        """Get the validated output activation spec.
+
+        Validate that the output activation qscheme is supported before
+        returning the spec.
+
+        Returns:
+            QuantizationSpec | None: Output activation spec, or ``None`` when
+                unset.
+
+        Raises:
+            ValueError: If the qscheme is not per-tensor affine or symmetric.
+
+        """
         if self.output_activation is None:
             return None
         # Validate that output_activation uses a supported qscheme
@@ -53,7 +97,18 @@ def get_output_act_qspec(self) -> QuantizationSpec | None:
         return self.output_activation
 
     def get_weight_qspec(self) -> QuantizationSpec | None:
-        """Returns QuantizationSpec 'weight' after asserting that weight.qscheme is valid."""
+        """Get the validated weight spec.
+
+        Validate that the weight qscheme is supported (per-tensor or
+        per-channel symmetric) before returning the spec.
+
+        Returns:
+            QuantizationSpec | None: Weight spec, or ``None`` when unset.
+
+        Raises:
+            ValueError: If the qscheme is not a supported symmetric scheme.
+
+        """
         if self.weight is None:
             return None
         # Validate that weight uses a supported qscheme
@@ -65,11 +120,46 @@ def get_weight_qspec(self) -> QuantizationSpec | None:
         return self.weight
 
     def get_bias_qspec(self, node: torch.fx.Node) -> QuantizationSpec | None:
-        """Returns QuantizationSpec 'bias' after asserting that bias.dtype is torch.float."""
+        """Get the derived or validated bias spec.
+
+        For conv/linear ops, derive bias qparams from the input/weight observers.
+        Otherwise, validate a user-provided floating-point bias spec.
+
+        Args:
+            node (torch.fx.Node): Node whose bias spec is requested.
+
+        Returns:
+            QuantizationSpec | None: Derived or provided bias spec, or ``None``
+                when unset.
+
+        Raises:
+            ValueError: If deriving qparams sees an unexpected number of
+                observers/fake-quantizers, or if a provided bias dtype is not
+                floating-point.
+
+        """
 
         def _derive_qparams_fn(
             obs_or_fqs: list[ObserverOrFakeQuantize],
         ) -> tuple[torch.Tensor, torch.Tensor]:
+            """Compute bias scale/zero-point from activation/weight observers.
+
+            Expect two observers or fake-quantize modules: one for the input
+            activation and one for the weight. The bias scale is the product of
+            input and weight scales, and the zero-point is a tensor of zeros.
+
+            Args:
+                obs_or_fqs (list[ObserverOrFakeQuantize]): Observers/fake-quant
+                    in order ``[act, weight]``.
+
+            Returns:
+                Tuple[torch.Tensor, torch.Tensor]: Bias scale tensor and
+                    integer zero-point tensor.
+
+            Raises:
+                ValueError: If the list does not contain exactly two items.
+
+            """
             # Validate expected number of observers/fake-quantizes
             if len(obs_or_fqs) != 2:
                 raise ValueError(