Update on "Arm backend: Add 16A8W support and test for mul operation"

Add 16A8W quantization support and test for the mul operation in ExecutorTorch ARM backend.

This follows the pattern established for linear operations, extending int16 support to mul operations.

Changes:
- Add INT16 dtype validation support in op_mul.py
- Add test_mul_tensor_16a8w_tosa_INT test function
- Enable test_mul.py in test targets configuration

The 16A8W configuration uses 16-bit activations with 8-bit weights, enabling higher precision for activations while maintaining weight efficiency.

Differential Revision: [D80510628](https://our.internmc.facebook.com/intern/diff/D80510628/)

cc digantdesai freddan80 per zingo oscarandersson8218

[ghstack-poisoned]

Author: Ninja91

backends/arm/tosa/quant_utils.py

@@ -5,7 +5,7 @@
 
 # pyre-unsafe
 
-# Utiliy functions for TOSA quantized lowerings
+# Utility functions for TOSA quantized lowerings
 
 import math
 
@@ -29,11 +29,11 @@ def insert_rescale_ops_to_int32_maxscale(
     tosa_graph: Any, inputs: list[TosaArg], node: Node, tosa_spec=None
 ) -> tuple[list[Any], float]:
     """For ADD and SUB, we rescale to int32 using a different common scale(2*max(left scale,right scale))
-    compared to all the other cases. We also multply the left and right scales by 1<<20 giving us extra precision
+    compared to all the other cases. We also multiply the left and right scales by 1<<20 giving us extra precision
     for the computation without overflowing.
 
     Returns a list of the rescaled nodes and the scale factor used,
-    needed by rescale_node_back_to_int8.
+    needed by insert_rescale_op_to_int8.
     """
 
     if len(inputs) > 2:
@@ -88,7 +88,7 @@ def insert_rescale_ops_to_int32(
     The scales are adjusted using the smallest scale of all 'nodes'.
 
     Returns a list of the rescaled nodes and the scale factor used,
-    needed by rescale_node_back_to_int8.
+    needed by insert_rescale_op_to_int8.
 
     This functions is used in serialization to TOSA for target ops that are
     handled by the DQ/D folding pass, which stores the quantization parameters
@@ -136,7 +136,7 @@ def insert_rescale_op_to_int8(
     Parameters:
         node: The original node that is being handled by the rescales.
         last_tensor:the tosa tensor to rescale back.
-        scale: the scaling factor used to rescale to int32, from the function 'insert_rescale_op_to_int32'
+        scale: the scaling factor used to rescale to int32, from the function 'insert_rescale_ops_to_int32'
         compute_rescale: boolean indicating whether we need to divide the output scale by the original scale.
         tosa_graph: the tosa_graph to manipulate.
 
@@ -161,7 +161,7 @@ def insert_rescale_op_to_int16(
     Parameters:
         node: The original node that is being handled by the rescales.
         last_tensor:the tosa tensor to rescale back.
-        scale: the scaling factor used to rescale to int32, from the function 'insert_rescale_op_to_int32'
+        scale: the scaling factor used to rescale to int32, from the function 'insert_rescale_ops_to_int32'
         compute_rescale: boolean indicating whether we need to divide the output scale by the original scale.
         tosa_graph: the tosa_graph to manipulate.
 
@@ -187,7 +187,7 @@ def _insert_rescale_op_to_dtype(
     Parameters:
         node: The original node that is being handled by the rescales.
         last_tensor:the tosa tensor to rescale back.
-        scale: the scaling factor used to rescale to int32, from the function 'insert_rescale_op_to_int32'
+        scale: the scaling factor used to rescale to int32, from the function 'insert_rescale_ops_to_int32'
         output_dtype: The target dtype (ts.DType.INT8 or ts.DType.INT16)
         compute_rescale: boolean indicating whether we need to divide the output scale by the original scale.
         tosa_graph: the tosa_graph to manipulate.
@@ -224,7 +224,7 @@ def _insert_rescale_op_to_dtype(
 
 # TOSA uses the RESCALE operation to scale between values with differing precision.
 # The RESCALE operator is defined using an integer multiply, add, and shift.
-# This utility function is for calculating the multier and shift given a scale.
+# This utility function is for calculating the multiplier and shift given a scale.
 # Ref: https://www.mlplatform.org/tosa/tosa_spec.html#_precision_scaling
 def compute_multiplier_and_shift(
     scales: list[float], scaleWidth: int = 32
@@ -269,7 +269,7 @@ def compute_multiplier_and_shift(
     return multipliers, shifts
 
 
-# For TOSA spec v1.0 RESCALE operator requires multipler, shifts, input_zp and output_zp to be
+# For TOSA spec v1.0 RESCALE operator requires multiplier, shifts, input_zp and output_zp to be
 # const inputs. Create constant operators from the data already initialized.
 def create_const_ops_for_rescale(
     tosa_fb,