BUG: torch: fix up clip

array_api_compat/torch/_aliases.py

@@ -4,6 +4,7 @@
 from functools import reduce as _reduce, wraps as _wraps
 from builtins import all as _builtin_all, any as _builtin_any
 from typing import Any, Literal
+import math
 
 import torch
 
@@ -857,13 +858,24 @@ def _isscalar(a: object):
     min_is_scalar = _isscalar(min)
     max_is_scalar = _isscalar(max)
 
-    if min is not None and max is not None:
-        if min_is_scalar and not max_is_scalar:
-            min = torch.as_tensor(min, dtype=x.dtype, device=x.device)
-        if max_is_scalar and not min_is_scalar:
-            max = torch.as_tensor(max, dtype=x.dtype, device=x.device)
+    if min_is_scalar and max_is_scalar:
+        if (min is not None and math.isnan(min)) or (max is not None and math.isnan(max)):
+            # edge case: torch.clamp(torch.zeros(1), float('nan')) -> tensor(0.)
+            # https://github.com/pytorch/pytorch/issues/172067
+            return torch.full_like(x, fill_value=torch.nan)
+        return torch.clamp(x, min, max, **kwargs)
 
-    return torch.clamp(x, min, max, **kwargs)
+    # pytorch has (tensor, tensor, tensor) and (tensor, scalar, scalar) signatures,
+    # but does not accept (tensor, scalar, tensor)
+    a_min = min
+    if min is not None and min_is_scalar:
+        a_min = torch.as_tensor(min, dtype=x.dtype, device=x.device)
+
+    a_max = max
+    if max is not None and max_is_scalar:
+        a_max = torch.as_tensor(max, dtype=x.dtype, device=x.device)
+
+    return torch.clamp(x, a_min, a_max, **kwargs)
 
 
 def sign(x: Array, /) -> Array: