Arm backend: Replace asserts with error handling in upsample operators (pytorch#10577)

Update `op_upsample_bilinear2d.py` and `op_upsample_nearest2d.py`
classes to replace `assert` checks with `ValueError` exceptions for
improved error handling and code robustness.

- Change static shape checks from assertions to conditional raises of
  `ValueError` for clear runtime error communication.
- Replaced `assert` checks for int16 range validation of `scale_n_yx`,
  `scale_d_yx`, and `border_yx` with explicit value range checks that
  raise `ValueError` when bounds are breached.

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

Author: Sebastian-Larsson

backends/arm/operators/op_upsample_bilinear2d.py

@@ -34,9 +34,8 @@ def define_node(
         inputs: List[TosaArg],
         output: TosaArg,
     ) -> None:
-        assert (
-            inputs[0].shape is not None and output.shape is not None
-        ), "Only static shapes are supported"
+        if inputs[0].shape is None or output.shape is None:
+            raise ValueError("Only static shapes are supported")
 
         input_dtype = inputs[0].dtype
 
@@ -55,9 +54,12 @@ def define_node(
         def in_int16_range(x):
             return torch.all(x >= -(2**15)) and torch.all(x <= 2**15 - 1)
 
-        assert in_int16_range(scale_n_yx)
-        assert in_int16_range(scale_d_yx)
-        assert in_int16_range(border_yx)
+        if not in_int16_range(scale_n_yx):
+            raise ValueError("scale_n_yx is out of the int16 range")
+        if not in_int16_range(scale_d_yx):
+            raise ValueError("scale_d_yx is out of the int16 range")
+        if not in_int16_range(border_yx):
+            raise ValueError("border_yx is out of the int16 range")
 
         attr = ts.TosaSerializerAttribute()
         attr.ResizeAttribute(

backends/arm/operators/op_upsample_nearest2d.py

@@ -36,9 +36,8 @@ def define_node(
     ) -> None:
         import tosa_tools.v0_80.serializer.tosa_serializer as ts  # type: ignore
 
-        assert (
-            inputs[0].shape is not None and output.shape is not None
-        ), "Only static shapes are supported"
+        if inputs[0].shape is None or output.shape is None:
+            raise ValueError("Only static shapes are supported")
 
         # tosa_shape output is NHWC, take HW
         input_size_yx = torch.tensor(
@@ -55,9 +54,12 @@ def define_node(
         def in_int16_range(x):
             return torch.all(x >= -(2**15)) and torch.all(x <= 2**15 - 1)
 
-        assert in_int16_range(scale_n_yx)
-        assert in_int16_range(scale_d_yx)
-        assert in_int16_range(border_yx)
+        if not in_int16_range(scale_n_yx):
+            raise ValueError("scale_n_yx is out of the int16 range")
+        if not in_int16_range(scale_d_yx):
+            raise ValueError("scale_d_yx is out of the int16 range")
+        if not in_int16_range(border_yx):
+            raise ValueError("border_yx is out of the int16 range")
 
         attr = ts.TosaSerializerAttribute()
         attr.ResizeAttribute(