ENH: cache helper functions

[crusaderky]

Speed up helper functions through caching.

>>> import sys
>>> import array_api_compat.numpy as np
>>> import array_api_compat.torch as torch
>>> from array_api_compat import is_numpy_namespace, is_numpy_array, is_torch_array
>>> a = np.asarray(1)
>>> b = torch.asarray(1)

>>> %timeit is_numpy_namespace(np)
BEFORE 333 ns ± 1.83 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)
AFTER 62 ns ± 0.227 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)

>>> %timeit is_numpy_namespace(sys)
BEFORE 334 ns ± 4.79 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)
AFTER 69.3 ns ± 1.18 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)

>>> %timeit is_numpy_array(a)
BEFORE 382 ns ± 2.01 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)
AFTER 281 ns ± 4.03 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)

>>> %timeit is_numpy_array(1)
BEFORE 272 ns ± 1.37 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)
AFTER 175 ns ± 5.22 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)

>>> %timeit is_numpy_array([1])
BEFORE 288 ns ± 1.16 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)
AFTER 213 ns ± 7.33 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)

>>> %timeit is_torch_array(b)
BEFORE 214 ns ± 0.244 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)
AFTER 126 ns ± 1.05 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)

>>> %timeit is_torch_array(1)
BEFORE 249 ns ± 4.49 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)
AFTER 121 ns ± 0.724 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)

>>> %timeit is_array_api_obj(a)
BEFORE 423 ns ± 1.35 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)
AFTER 99.5 ns ± 1.47 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)

>>> %timeit is_array_api_obj(1)
BEFORE 773 ns ± 2.07 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)
AFTER 142 ns ± 1.52 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)

>>> %timeit is_lazy_array(a)
BEFORE 437 ns ± 7.46 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)
AFTER 381 ns ± 5.56 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)

>>> %timeit is_lazy_array(1)
BEFORE 2.12 μs ± 34.6 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)
AFTER 624 ns ± 8.06 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)

>>> %timeit is_writeable_array(a)
BEFORE 491 ns ± 1.07 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)
AFTER 183 ns ± 0.446 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)

>>> %timeit is_writeable_array(b)
BEFORE 1.15 μs ± 5.57 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)
AFTER 185 ns ± 0.766 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)

>>> %timeit is_writeable_array(1)
BEFORE 1.52 μs ± 13.7 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)
AFTER 189 ns ± 5.21 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)

Note: is_numpy_array and is_jax_array are slower than the other equivalent functions due to the reclassification of JAX zero gradient arrays.

[Copilot]

Copilot reviewed 1 out of 1 changed files in this pull request and generated no comments.

Comments suppressed due to low confidence (1)

array_api_compat/common/_helpers.py:944

• [nitpick] Consider adding a comment to explain why 'cache' is included in _all_ignore to improve clarity for future maintainers.

_all_ignore = ['cache', 'sys', 'math', 'inspect', 'warnings']

[crusaderky]

This in theory could lead to a memory leak for a user that somehow dynamically defines and then forgets a lot of classes. I don't think it's something to worry about in real life?