Add INT16 support to rescale operation

Add INT16 support for RequantizeNode rescale operations in ExecutorTorch ARM backend.

This follows the pattern established for linear, mul, sigmoid, tanh, slice, view/transpose, cat, and FCNode operations, extending int16 support to RequantizeNode rescale operations.

Changes:
- Add INT16 dtype validation support in op_rescale.py
- Enable rescale operations for 16A8W quantization configuration

The 16A8W configuration uses 16-bit activations with 8-bit weights, enabling higher precision for activations while maintaining weight efficiency. RequantizeNode rescale operations are essential for proper quantization scaling in the 16A8W pipeline.

Differential Revision: [D80513725](https://our.internmc.facebook.com/intern/diff/D80513725/)

[ghstack-poisoned]

Author: Ninja91

backends/arm/operators/op_rescale.py

@@ -46,13 +46,20 @@ def define_node(
         input_zp = cast(int, node.args[3])
         output_zp = cast(int, node.args[4])
 
-        if input_dtype != map_dtype(torch.int8, self.tosa_spec) and input_zp != 0:
+        if (
+            input_dtype
+            not in [
+                map_dtype(torch.int8, self.tosa_spec),
+                map_dtype(torch.int16, self.tosa_spec),
+            ]
+            and input_zp != 0
+        ):
             raise ValueError(
-                f"If input dtype is not int8, input_zp must be 0. Got input_dtype{input_dtype=}, {input_zp=}"
+                f"If input dtype is not int8 or int16, input_zp must be 0. Got input_dtype{input_dtype=}, {input_zp=}"
             )
-        if output_dtype != torch.int8 and output_zp != 0:
+        if output_dtype not in [torch.int8, torch.int16] and output_zp != 0:
             raise ValueError(
-                f"If output dtype is not int8, output_zp must be 0. Got {ts.DTypeNames[output_dtype]}, {output_zp=}"
+                f"If output dtype is not int8 or int16, output_zp must be 0. Got {ts.DTypeNames[output_dtype]}, {output_zp=}"
             )
 
         build_rescale(