MAINT: remove return_on_failure at normalizer

Author: ev-br

torch_np/_funcs.py

@@ -18,6 +18,7 @@
     DTypeLike,
     NDArray,
     SubokLike,
+    normalize_array_like,
     normalizer,
 )
 
@@ -1813,10 +1814,13 @@ def i0(x: ArrayLike):
     return torch.special.i0(x)
 
 
-@normalizer(return_on_failure=False)
-def isscalar(a: ArrayLike):
+def isscalar(a):
     # XXX: this is a stub
-    return a.numel() == 1
+    try:
+        t = normalize_array_like(a)
+        return t.numel() == 1
+    except Exception:
+        return False
 
 
 """

torch_np/_normalizations.py

@@ -1,5 +1,6 @@
 """ "Normalize" arguments: convert array_likes to tensors, dtypes to torch dtypes and so on.
 """
+import functools
 import operator
 import typing
 from typing import Optional, Sequence
@@ -79,21 +80,11 @@ def normalize_ndarray(arg, name=None):
     AxisLike: normalize_axis_like,
 }
 
-import functools
-
-_sentinel = object()
-
 
-def maybe_normalize(arg, parm, return_on_failure=_sentinel):
+def maybe_normalize(arg, parm):
     """Normalize arg if a normalizer is registred."""
     normalizer = normalizers.get(parm.annotation, None)
-    try:
-        return normalizer(arg, parm.name) if normalizer else arg
-    except Exception as exc:
-        if return_on_failure is not _sentinel:
-            return return_on_failure
-        else:
-            raise exc from None
+    return normalizer(arg, parm.name) if normalizer else arg
 
 
 # ### Return value helpers ###
@@ -145,7 +136,7 @@ def array_or_scalar(values, py_type=float, return_scalar=False):
 # ### The main decorator to normalize arguments / postprocess the output ###
 
 
-def normalizer(_func=None, *, return_on_failure=_sentinel, promote_scalar_result=False):
+def normalizer(_func=None, *, promote_scalar_result=False):
     def normalizer_inner(func):
         @functools.wraps(func)
         def wrapped(*args, **kwds):
@@ -154,14 +145,12 @@ def wrapped(*args, **kwds):
             first_param = next(iter(params.values()))
             # NumPy's API does not have positional args before variadic positional args
             if first_param.kind == inspect.Parameter.VAR_POSITIONAL:
-                args = [
-                    maybe_normalize(arg, first_param, return_on_failure) for arg in args
-                ]
+                args = [maybe_normalize(arg, first_param) for arg in args]
             else:
                 # NB: extra unknown arguments: pass through, will raise in func(*args) below
                 args = (
                     tuple(
-                        maybe_normalize(arg, parm, return_on_failure)
+                        maybe_normalize(arg, parm)
                         for arg, parm in zip(args, params.values())
                     )
                     + args[len(params.values()) :]