ENH: Improve array function overhead by using vectorcall

This moves dispatching for `__array_function__` into a C-wrapper.  This
helps speed for multiple reasons:
* Avoids one additional dispatching function call to C
* Avoids the use of `*args, **kwargs` which is slower.
* For simple NumPy calls we can stay in the faster "vectorcall" world

This speeds up things generally a little, but can speed things up a lot
when keyword arguments are used on lightweight functions, for example::

    np.can_cast(arr, dtype, casting="same_kind")

is more than twice as fast with this.

There is one alternative in principle to get best speed:  We could inline
the "relevant argument"/dispatcher extraction.  That changes behavior in
an acceptable but larger way (passes default arguments).
Unless the C-entry point seems unwanted, this should be a decent step
in the right direction even if we want to do that eventually, though.

Closes gh-20790
Closes gh-18547  (although not quite sure why)

1 files changed, 2 insertions, 8 deletions

diff --git a/numpy/core/_asarray.py b/numpy/core/_asarray.py
index cbaab8c3f..a9abc5a88 100644
--- a/numpy/core/_asarray.py
+++ b/numpy/core/_asarray.py
@@ -24,10 +24,6 @@ POSSIBLE_FLAGS = {
 }
 
 
-def _require_dispatcher(a, dtype=None, requirements=None, *, like=None):
-    return (like,)
-
-
 @set_array_function_like_doc
 @set_module('numpy')
 def require(a, dtype=None, requirements=None, *, like=None):
@@ -100,10 +96,10 @@ def require(a, dtype=None, requirements=None, *, like=None):
     """
     if like is not None:
         return _require_with_like(
+            like,
             a,
             dtype=dtype,
             requirements=requirements,
-            like=like,
         )
 
     if not requirements:
@@ -135,6 +131,4 @@ def require(a, dtype=None, requirements=None, *, like=None):
     return arr
 
 
-_require_with_like = array_function_dispatch(
-    _require_dispatcher
-)(require)
+_require_with_like = array_function_dispatch()(require)