-
Notifications
You must be signed in to change notification settings - Fork 9
Expand file tree
/
Copy pathnep-0031-uarray.html
More file actions
1258 lines (1050 loc) · 96.9 KB
/
nep-0031-uarray.html
File metadata and controls
1258 lines (1050 loc) · 96.9 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
<!DOCTYPE html>
<html lang="en" data-content_root="./" >
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
<title>NEP 31 — Context-local and global overrides of the NumPy API — NumPy Enhancement Proposals</title>
<script data-cfasync="false">
document.documentElement.dataset.mode = localStorage.getItem("mode") || "";
document.documentElement.dataset.theme = localStorage.getItem("theme") || "";
</script>
<!--
this give us a css class that will be invisible only if js is disabled
-->
<noscript>
<style>
.pst-js-only { display: none !important; }
</style>
</noscript>
<!-- Loaded before other Sphinx assets -->
<link href="_static/styles/theme.css?digest=55c26ed414f5f8a31ebb" rel="stylesheet" />
<link href="_static/styles/pydata-sphinx-theme.css?digest=55c26ed414f5f8a31ebb" rel="stylesheet" />
<link rel="stylesheet" type="text/css" href="_static/pygments.css?v=03e43079" />
<!-- So that users can add custom icons -->
<script defer src="_static/scripts/fontawesome.js?digest=55c26ed414f5f8a31ebb"></script>
<!-- Pre-loaded scripts that we'll load fully later -->
<link rel="preload" as="script" href="_static/scripts/bootstrap.js?digest=55c26ed414f5f8a31ebb" />
<link rel="preload" as="script" href="_static/scripts/pydata-sphinx-theme.js?digest=55c26ed414f5f8a31ebb" />
<script src="_static/documentation_options.js?v=7f41d439"></script>
<script src="_static/doctools.js?v=888ff710"></script>
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
<script>DOCUMENTATION_OPTIONS.pagename = 'nep-0031-uarray';</script>
<script>DOCUMENTATION_OPTIONS.search_as_you_type = false;</script>
<link rel="icon" href="_static/favicon.ico"/>
<link rel="index" title="Index" href="genindex.html" />
<link rel="search" title="Search" href="search.html" />
<link rel="next" title="NEP 37 — A dispatch protocol for NumPy-like modules" href="nep-0037-array-module.html" />
<link rel="prev" title="NEP 30 — Duck typing for NumPy arrays - implementation" href="nep-0030-duck-array-protocol.html" />
<meta name="viewport" content="width=device-width, initial-scale=1"/>
<meta name="docsearch:language" content="en"/>
<meta name="docsearch:version" content="" />
<meta name="docbuild:last-update" content="Apr 23, 2026"/>
<script src="_static/searchtools.js"></script>
<script src="_static/language_data.js"></script>
<script src="searchindex.js"></script>
</head>
<body data-default-mode="">
<div id="pst-skip-link" class="skip-link d-print-none"><a href="#main-content">Skip to main content</a></div>
<div id="pst-scroll-pixel-helper"></div>
<button type="button" class="btn rounded-pill" id="pst-back-to-top">
<i class="fa-solid fa-arrow-up"></i>Back to top</button>
<dialog id="pst-search-dialog">
<form class="bd-search d-flex align-items-center"
action="search.html"
method="get">
<i class="fa-solid fa-magnifying-glass"></i>
<input type="search"
class="form-control"
name="q"
placeholder="Search the docs ..."
aria-label="Search the docs ..."
autocomplete="off"
autocorrect="off"
autocapitalize="off"
spellcheck="false"/>
<span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd>K</kbd></span>
</form>
</dialog>
<div class="pst-async-banner-revealer d-none">
<aside id="bd-header-version-warning" class="d-none d-print-none" aria-label="Version warning"></aside>
</div>
<header id="pst-header" class="bd-header navbar navbar-expand-lg bd-navbar d-print-none">
<div class="bd-header__inner bd-page-width">
<button class="pst-navbar-icon sidebar-toggle primary-toggle" aria-label="Site navigation">
<span class="fa-solid fa-bars"></span>
</button>
<div class="col-lg-3 navbar-header-items__start">
<div class="navbar-item">
<a class="navbar-brand logo" href="content.html">
<img src="_static/numpylogo.svg" class="logo__image only-light" alt="NumPy Enhancement Proposals - Home"/>
<img src="_static/numpylogo_dark.svg" class="logo__image only-dark pst-js-only" alt="NumPy Enhancement Proposals - Home"/>
</a></div>
</div>
<div class="col-lg-9 navbar-header-items">
<div class="me-auto navbar-header-items__center">
<div class="navbar-item">
<nav>
<ul class="bd-navbar-elements navbar-nav">
<li class="nav-item current active">
<a class="nav-link nav-internal" href="index.html">
Index
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="scope.html">
The Scope of NumPy
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="roadmap.html">
Current roadmap
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-external" href="https://github.com/numpy/numpy/issues?q=is%3Aopen+is%3Aissue+label%3A%2223+-+Wish+List%22">
Wishlist
</a>
</li>
</ul>
</nav></div>
</div>
<div class="navbar-header-items__end">
<div class="navbar-item navbar-persistent--container">
<button class="btn search-button-field search-button__button pst-js-only" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
<i class="fa-solid fa-magnifying-glass"></i>
<span class="search-button__default-text">Search</span>
<span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd class="kbd-shortcut__modifier">K</kbd></span>
</button>
</div>
<div class="navbar-item">
<button class="btn btn-sm nav-link pst-navbar-icon theme-switch-button pst-js-only" aria-label="Color mode" data-bs-title="Color mode" data-bs-placement="bottom" data-bs-toggle="tooltip">
<i class="theme-switch fa-solid fa-sun fa-lg" data-mode="light" title="Light"></i>
<i class="theme-switch fa-solid fa-moon fa-lg" data-mode="dark" title="Dark"></i>
<i class="theme-switch fa-solid fa-circle-half-stroke fa-lg" data-mode="auto" title="System Settings"></i>
</button></div>
<div class="navbar-item"><ul class="navbar-icon-links"
aria-label="Icon Links">
<li class="nav-item">
<a href="https://github.com/numpy/numpy" title="GitHub" class="nav-link pst-navbar-icon" rel="noopener" target="_blank" data-bs-toggle="tooltip" data-bs-placement="bottom"><i class="fa-brands fa-square-github fa-lg" aria-hidden="true"></i>
<span class="sr-only">GitHub</span></a>
</li>
</ul></div>
</div>
</div>
<div class="navbar-persistent--mobile">
<button class="btn search-button-field search-button__button pst-js-only" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
<i class="fa-solid fa-magnifying-glass"></i>
<span class="search-button__default-text">Search</span>
<span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd class="kbd-shortcut__modifier">K</kbd></span>
</button>
</div>
<button class="pst-navbar-icon sidebar-toggle secondary-toggle" aria-label="On this page">
<span class="fa-solid fa-outdent"></span>
</button>
</div>
</header>
<div class="bd-container">
<div class="bd-container__inner bd-page-width">
<dialog id="pst-primary-sidebar-modal"></dialog>
<div id="pst-primary-sidebar" class="bd-sidebar-primary bd-sidebar">
<div class="sidebar-header-items sidebar-primary__section">
<div class="sidebar-header-items__center">
<div class="navbar-item">
<nav>
<ul class="bd-navbar-elements navbar-nav">
<li class="nav-item current active">
<a class="nav-link nav-internal" href="index.html">
Index
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="scope.html">
The Scope of NumPy
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="roadmap.html">
Current roadmap
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-external" href="https://github.com/numpy/numpy/issues?q=is%3Aopen+is%3Aissue+label%3A%2223+-+Wish+List%22">
Wishlist
</a>
</li>
</ul>
</nav></div>
</div>
<div class="sidebar-header-items__end">
<div class="navbar-item">
<button class="btn btn-sm nav-link pst-navbar-icon theme-switch-button pst-js-only" aria-label="Color mode" data-bs-title="Color mode" data-bs-placement="bottom" data-bs-toggle="tooltip">
<i class="theme-switch fa-solid fa-sun fa-lg" data-mode="light" title="Light"></i>
<i class="theme-switch fa-solid fa-moon fa-lg" data-mode="dark" title="Dark"></i>
<i class="theme-switch fa-solid fa-circle-half-stroke fa-lg" data-mode="auto" title="System Settings"></i>
</button></div>
<div class="navbar-item"><ul class="navbar-icon-links"
aria-label="Icon Links">
<li class="nav-item">
<a href="https://github.com/numpy/numpy" title="GitHub" class="nav-link pst-navbar-icon" rel="noopener" target="_blank" data-bs-toggle="tooltip" data-bs-placement="bottom"><i class="fa-brands fa-square-github fa-lg" aria-hidden="true"></i>
<span class="sr-only">GitHub</span></a>
</li>
</ul></div>
</div>
</div>
<div class="sidebar-primary-items__start sidebar-primary__section">
<div class="sidebar-primary-item pst-sidebar-collapse"><button id="pst-collapse-sidebar-button" aria-expanded="true" aria-controls="pst-primary-sidebar"><svg class="pst-icon svg-inline--fa" role="img" aria-hidden="true" focusable="false" viewBox="0 0 16 16" xmlns="http://www.w3.org/2000/svg">
<path fill="currentColor" d="M3 15.5C2.36232 15.5 1.74874 15.2564 1.28478 14.8189C0.820828 14.3815 0.541576 13.7832 0.504167 13.1467L0.5 13L0.5 3C0.499965 2.36232 0.743605 1.74874 1.18107 1.28478C1.61854 0.820828 2.21676 0.541576 2.85333 0.504167L3 0.5L13 0.5C13.6377 0.499965 14.2513 0.743605 14.7152 1.18107C15.1792 1.61854 15.4584 2.21676 15.4958 2.85333L15.5 3L15.5 13C15.5 13.6377 15.2564 14.2513 14.8189 14.7152C14.3815 15.1792 13.7832 15.4584 13.1467 15.4958L13 15.5L3 15.5ZM3 13.8333L10.5 13.8333L10.5 2.16667L3 2.16667C2.79589 2.16669 2.59889 2.24163 2.44636 2.37726C2.29383 2.5129 2.19638 2.69979 2.1725 2.9025L2.16667 3L2.16667 13C2.16669 13.2041 2.24163 13.4011 2.37726 13.5536C2.5129 13.7062 2.69979 13.8036 2.9025 13.8275L3 13.8333ZM6.65583 10.325L6.5775 10.2558L4.91083 8.58917C4.76735 8.44567 4.68116 8.25476 4.66843 8.05223C4.65569 7.84971 4.71729 7.6495 4.84167 7.48917L4.91083 7.41083L6.5775 5.74417C6.72747 5.59471 6.9287 5.50794 7.14032 5.50148C7.35194 5.49502 7.55809 5.56935 7.7169 5.70937C7.8757 5.8494 7.97525 6.04463 7.99533 6.25539C8.01541 6.46616 7.95451 6.67667 7.825 6.84417L7.75583 6.9225L6.67917 8L7.75583 9.0775C7.89931 9.22099 7.98551 9.41191 7.99824 9.61443C8.01097 9.81695 7.94938 10.0172 7.825 10.1775L7.75583 10.2558C7.61234 10.3993 7.42142 10.4855 7.2189 10.4982C7.01638 10.511 6.81617 10.4494 6.65583 10.325Z"/>
</svg>
<span class="pst-collapse-sidebar-label">Collapse Sidebar</span>
<span class="pst-expand-sidebar-label">Expand Sidebar</span>
</button></div>
<div class="sidebar-primary-item">
<nav class="bd-docs-nav bd-links"
aria-label="Section Navigation">
<p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
<div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
<li class="toctree-l1"><a class="reference internal" href="scope.html">The Scope of NumPy</a></li>
<li class="toctree-l1"><a class="reference internal" href="roadmap.html">Current roadmap</a></li>
</ul>
<ul class="current nav bd-sidenav">
<li class="toctree-l1 has-children"><a class="reference internal" href="meta.html">Meta-NEPs (NEPs about NEPs or active Processes)</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="nep-0000.html">NEP 0 — Purpose and process</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0023-backwards-compatibility.html">NEP 23 — Backwards compatibility and deprecation policy</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0036-fair-play.html">NEP 36 — Fair play</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0045-c_style_guide.html">NEP 45 — C style guide</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0046-sponsorship-guidelines.html">NEP 46 — NumPy sponsorship guidelines</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0048-spending-project-funds.html">NEP 48 — Spending NumPy project funds</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-template.html">NEP X — Template and instructions</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="provisional.html">Provisional NEPs (provisionally accepted; interface may change)</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="simple">
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="accepted.html">Accepted NEPs (implementation in progress)</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="nep-0041-improved-dtype-support.html">NEP 41 — First step towards a new datatype system</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0042-new-dtypes.html">NEP 42 — New and extensible DTypes</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0044-restructuring-numpy-docs.html">NEP 44 — Restructuring the NumPy documentation</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0051-scalar-representation.html">NEP 51 — Changing the representation of NumPy scalars</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0054-simd-cpp-highway.html">NEP 54 — SIMD infrastructure evolution: adopting Google Highway when moving to C++</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="open.html">Open NEPs (under consideration)</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="nep-0043-extensible-ufuncs.html">NEP 43 — Enhancing the extensibility of UFuncs</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0053-c-abi-evolution.html">NEP 53 — Evolving the NumPy C-API for NumPy 2.0</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0057-numpy-platform-support.html">NEP 57 — NumPy platform support</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="finished.html">Finished NEPs</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="nep-0001-npy-format.html">NEP 1 — A simple file format for NumPy arrays</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0005-generalized-ufuncs.html">NEP 5 — Generalized universal functions</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0007-datetime-proposal.html">NEP 7 — A proposal for implementing some date/time types in NumPy</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0010-new-iterator-ufunc.html">NEP 10 — Optimizing iterator/UFunc performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0013-ufunc-overrides.html">NEP 13 — A mechanism for overriding Ufuncs</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0014-dropping-python2.7-proposal.html">NEP 14 — Plan for dropping Python 2.7 support</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0015-merge-multiarray-umath.html">NEP 15 — Merging multiarray and umath</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0018-array-function-protocol.html">NEP 18 — A dispatch mechanism for NumPy's high level array functions</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0019-rng-policy.html">NEP 19 — Random number generator policy</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0020-gufunc-signature-enhancement.html">NEP 20 — Expansion of generalized universal function signatures</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0022-ndarray-duck-typing-overview.html">NEP 22 — Duck typing for NumPy arrays – high level overview</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0027-zero-rank-arrays.html">NEP 27 — Zero rank arrays</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0028-website-redesign.html">NEP 28 — numpy.org website redesign</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0029-deprecation_policy.html">NEP 29 — Recommend Python and NumPy version support as a community policy standard</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0032-remove-financial-functions.html">NEP 32 — Remove the financial functions from NumPy</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0034-infer-dtype-is-object.html">NEP 34 — Disallow inferring ``dtype=object`` from sequences</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0035-array-creation-dispatch-with-array-function.html">NEP 35 — Array creation dispatching with __array_function__</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0038-SIMD-optimizations.html">NEP 38 — Using SIMD optimization instructions for performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0040-legacy-datatype-impl.html">NEP 40 — Legacy datatype implementation in NumPy</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0049-data-allocation-strategies.html">NEP 49 — Data allocation strategies</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0050-scalar-promotion.html">NEP 50 — Promotion rules for Python scalars</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0052-python-api-cleanup.html">NEP 52 — Python API cleanup for NumPy 2.0</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0055-string_dtype.html">NEP 55 — Add a UTF-8 variable-width string DType to NumPy</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0056-array-api-main-namespace.html">NEP 56 — Array API standard support in NumPy's main namespace</a></li>
</ul>
</details></li>
<li class="toctree-l1 current active has-children"><a class="reference internal" href="deferred.html">Deferred and Superseded NEPs</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="nep-0002-warnfix.html">NEP 2 — A proposal to build numpy without warning with a big set of warning flags</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0003-math_config_clean.html">NEP 3 — Cleaning the math configuration of numpy.core</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0004-datetime-proposal3.html">NEP 4 — A (third) proposal for implementing some date/time types in NumPy</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0006-newbugtracker.html">NEP 6 — Replacing Trac with a different bug tracker</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0008-groupby_additions.html">NEP 8 — A proposal for adding groupby functionality to NumPy</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0009-structured_array_extensions.html">NEP 9 — Structured array extensions</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0011-deferred-ufunc-evaluation.html">NEP 11 — Deferred UFunc evaluation</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0012-missing-data.html">NEP 12 — Missing data functionality in NumPy</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0021-advanced-indexing.html">NEP 21 — Simplified and explicit advanced indexing</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0024-missing-data-2.html">NEP 24 — Missing data functionality - alternative 1 to NEP 12</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0025-missing-data-3.html">NEP 25 — NA support via special dtypes</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0026-missing-data-summary.html">NEP 26 — Summary of missing data NEPs and discussion</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0030-duck-array-protocol.html">NEP 30 — Duck typing for NumPy arrays - implementation</a></li>
<li class="toctree-l2 current active"><a class="current reference internal" href="#">NEP 31 — Context-local and global overrides of the NumPy API</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0037-array-module.html">NEP 37 — A dispatch protocol for NumPy-like modules</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0047-array-api-standard.html">NEP 47 — Adopting the array API standard</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="rejected.html">Rejected and Withdrawn NEPs</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="nep-0016-abstract-array.html">NEP 16 — An abstract base class for identifying "duck arrays"</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0017-split-out-maskedarray.html">NEP 17 — Split out masked arrays</a></li>
</ul>
</details></li>
</ul>
</div>
</nav></div>
</div>
<div class="sidebar-primary-items__end sidebar-primary__section">
<div class="sidebar-primary-item">
<div id="ethical-ad-placement"
class="flat"
data-ea-publisher="readthedocs"
data-ea-type="readthedocs-sidebar"
data-ea-manual="true">
</div></div>
</div>
</div>
<main id="main-content" class="bd-main" role="main">
<div class="bd-content">
<div class="bd-article-container">
<div class="bd-header-article d-print-none">
<div class="header-article-items header-article__inner">
<div class="header-article-items__start">
<div class="header-article-item">
<nav aria-label="Breadcrumb" class="d-print-none">
<ul class="bd-breadcrumbs">
<li class="breadcrumb-item breadcrumb-home">
<a href="content.html" class="nav-link" aria-label="Home">
<i class="fa-solid fa-home"></i>
</a>
</li>
<li class="breadcrumb-item"><a href="index.html" class="nav-link">Roadmap & NumPy enhancement proposals</a></li>
<li class="breadcrumb-item"><a href="deferred.html" class="nav-link">Deferred and Superseded NEPs</a></li>
<li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">NEP 31 — Context-local and global overrides of the NumPy API</span></li>
</ul>
</nav>
</div>
</div>
</div>
</div>
<div id="searchbox"></div>
<article class="bd-article">
<section id="nep-31-context-local-and-global-overrides-of-the-numpy-api">
<span id="nep31"></span><h1>NEP 31 — Context-local and global overrides of the NumPy API<a class="headerlink" href="#nep-31-context-local-and-global-overrides-of-the-numpy-api" title="Link to this heading">#</a></h1>
<dl class="field-list simple">
<dt class="field-odd">Author<span class="colon">:</span></dt>
<dd class="field-odd"><p>Hameer Abbasi <<a class="reference external" href="mailto:habbasi%40quansight.com">habbasi<span>@</span>quansight<span>.</span>com</a>></p>
</dd>
<dt class="field-even">Author<span class="colon">:</span></dt>
<dd class="field-even"><p>Ralf Gommers <<a class="reference external" href="mailto:rgommers%40quansight.com">rgommers<span>@</span>quansight<span>.</span>com</a>></p>
</dd>
<dt class="field-odd">Author<span class="colon">:</span></dt>
<dd class="field-odd"><p>Peter Bell <<a class="reference external" href="mailto:pbell%40quansight.com">pbell<span>@</span>quansight<span>.</span>com</a>></p>
</dd>
<dt class="field-even">Status<span class="colon">:</span></dt>
<dd class="field-even"><p>Superseded</p>
</dd>
<dt class="field-odd">Replaced-By<span class="colon">:</span></dt>
<dd class="field-odd"><p><a class="reference internal" href="nep-0056-array-api-main-namespace.html#nep56"><span class="std std-ref">NEP 56 — Array API standard support in NumPy’s main namespace</span></a></p>
</dd>
<dt class="field-even">Type<span class="colon">:</span></dt>
<dd class="field-even"><p>Standards Track</p>
</dd>
<dt class="field-odd">Created<span class="colon">:</span></dt>
<dd class="field-odd"><p>2019-08-22</p>
</dd>
<dt class="field-even">Resolution<span class="colon">:</span></dt>
<dd class="field-even"><p><a class="reference external" href="https://mail.python.org/archives/list/numpy-discussion@python.org/message/Z6AA5CL47NHBNEPTFWYOTSUVSRDGHYPN/">https://mail.python.org/archives/list/numpy-discussion@python.org/message/Z6AA5CL47NHBNEPTFWYOTSUVSRDGHYPN/</a></p>
</dd>
</dl>
<section id="abstract">
<h2>Abstract<a class="headerlink" href="#abstract" title="Link to this heading">#</a></h2>
<p>This NEP proposes to make all of NumPy’s public API overridable via an
extensible backend mechanism.</p>
<p>Acceptance of this NEP means NumPy would provide global and context-local
overrides in a separate namespace, as well as a dispatch mechanism similar
to NEP-18 <a class="footnote-reference brackets" href="#id21" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>. First experiences with <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> show that it
is necessary to be able to override NumPy functions that <em>do not take an
array-like argument</em>, and hence aren’t overridable via
<code class="docutils literal notranslate"><span class="pre">__array_function__</span></code>. The most pressing need is array creation and coercion
functions, such as <code class="docutils literal notranslate"><span class="pre">numpy.zeros</span></code> or <code class="docutils literal notranslate"><span class="pre">numpy.asarray</span></code>; see e.g. NEP-30 <a class="footnote-reference brackets" href="#id28" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>9<span class="fn-bracket">]</span></a>.</p>
<p>This NEP proposes to allow, in an opt-in fashion, overriding any part of the
NumPy API. It is intended as a comprehensive resolution to NEP-22 <a class="footnote-reference brackets" href="#id22" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a>, and
obviates the need to add an ever-growing list of new protocols for each new
type of function or object that needs to become overridable.</p>
</section>
<section id="motivation-and-scope">
<h2>Motivation and scope<a class="headerlink" href="#motivation-and-scope" title="Link to this heading">#</a></h2>
<p>The primary end-goal of this NEP is to make the following possible:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># On the library side</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy.overridable</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">unp</span>
<span class="k">def</span><span class="w"> </span><span class="nf">library_function</span><span class="p">(</span><span class="n">array</span><span class="p">):</span>
<span class="n">array</span> <span class="o">=</span> <span class="n">unp</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
<span class="c1"># Code using unumpy as usual</span>
<span class="k">return</span> <span class="n">array</span>
<span class="c1"># On the user side:</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy.overridable</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">unp</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">uarray</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">ua</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">dask.array</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">da</span>
<span class="n">ua</span><span class="o">.</span><span class="n">register_backend</span><span class="p">(</span><span class="n">da</span><span class="p">)</span> <span class="c1"># Can be done within Dask itself</span>
<span class="n">library_function</span><span class="p">(</span><span class="n">dask_array</span><span class="p">)</span> <span class="c1"># works and returns dask_array</span>
<span class="k">with</span> <span class="n">unp</span><span class="o">.</span><span class="n">set_backend</span><span class="p">(</span><span class="n">da</span><span class="p">):</span>
<span class="n">library_function</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span> <span class="c1"># actually returns a Dask array.</span>
</pre></div>
</div>
<p>Here, <code class="docutils literal notranslate"><span class="pre">backend</span></code> can be any compatible object defined either by NumPy or an
external library, such as Dask or CuPy. Ideally, it should be the module
<code class="docutils literal notranslate"><span class="pre">dask.array</span></code> or <code class="docutils literal notranslate"><span class="pre">cupy</span></code> itself.</p>
<p>These kinds of overrides are useful for both the end-user as well as library
authors. End-users may have written or wish to write code that they then later
speed up or move to a different implementation, say PyData/Sparse. They can do
this simply by setting a backend. Library authors may also wish to write code
that is portable across array implementations, for example <code class="docutils literal notranslate"><span class="pre">sklearn</span></code> may wish
to write code for a machine learning algorithm that is portable across array
implementations while also using array creation functions.</p>
<p>This NEP takes a holistic approach: It assumes that there are parts of
the API that need to be overridable, and that these will grow over time. It
provides a general framework and a mechanism to avoid a design of a new
protocol each time this is required. This was the goal of <code class="docutils literal notranslate"><span class="pre">uarray</span></code>: to
allow for overrides in an API without needing the design of a new protocol.</p>
<p>This NEP proposes the following: That <code class="docutils literal notranslate"><span class="pre">unumpy</span></code> <a class="footnote-reference brackets" href="#id27" id="id4" role="doc-noteref"><span class="fn-bracket">[</span>8<span class="fn-bracket">]</span></a> becomes the
recommended override mechanism for the parts of the NumPy API not yet covered
by <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> or <code class="docutils literal notranslate"><span class="pre">__array_ufunc__</span></code>, and that <code class="docutils literal notranslate"><span class="pre">uarray</span></code> is
vendored into a new namespace within NumPy to give users and downstream
dependencies access to these overrides. This vendoring mechanism is similar
to what SciPy decided to do for making <code class="docutils literal notranslate"><span class="pre">scipy.fft</span></code> overridable (see <a class="footnote-reference brackets" href="#id29" id="id5" role="doc-noteref"><span class="fn-bracket">[</span>10<span class="fn-bracket">]</span></a>).</p>
<p>The motivation behind <code class="docutils literal notranslate"><span class="pre">uarray</span></code> is manyfold: First, there have been several
attempts to allow dispatch of parts of the NumPy API, including (most
prominently), the <code class="docutils literal notranslate"><span class="pre">__array_ufunc__</span></code> protocol in NEP-13 <a class="footnote-reference brackets" href="#id23" id="id6" role="doc-noteref"><span class="fn-bracket">[</span>4<span class="fn-bracket">]</span></a>, and the
<code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> protocol in NEP-18 <a class="footnote-reference brackets" href="#id21" id="id7" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>, but this has shown the need
for further protocols to be developed, including a protocol for coercion (see
<a class="footnote-reference brackets" href="#id24" id="id8" role="doc-noteref"><span class="fn-bracket">[</span>5<span class="fn-bracket">]</span></a>, <a class="footnote-reference brackets" href="#id28" id="id9" role="doc-noteref"><span class="fn-bracket">[</span>9<span class="fn-bracket">]</span></a>). The reasons these overrides are needed have been extensively
discussed in the references, and this NEP will not attempt to go into the
details of why these are needed; but in short: It is necessary for library
authors to be able to coerce arbitrary objects into arrays of their own types,
such as CuPy needing to coerce to a CuPy array, for example, instead of
a NumPy array. In simpler words, one needs things like <code class="docutils literal notranslate"><span class="pre">np.asarray(...)</span></code> or
an alternative to “just work” and return duck-arrays.</p>
</section>
<section id="usage-and-impact">
<h2>Usage and impact<a class="headerlink" href="#usage-and-impact" title="Link to this heading">#</a></h2>
<p>This NEP allows for global and context-local overrides, as well as
automatic overrides a-la <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code>.</p>
<p>Here are some use-cases this NEP would enable, besides the
first one stated in the motivation section:</p>
<p>The first is allowing alternate dtypes to return their
respective arrays.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Returns an XND array</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">unp</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">xnd_dtype</span><span class="p">)</span> <span class="c1"># Or torch dtype</span>
</pre></div>
</div>
<p>The second is allowing overrides for parts of the API.
This is to allow alternate and/or optimized implementations
for <code class="docutils literal notranslate"><span class="pre">np.linalg</span></code>, BLAS, and <code class="docutils literal notranslate"><span class="pre">np.random</span></code>.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">pyfftw</span> <span class="c1"># Or mkl_fft</span>
<span class="c1"># Makes pyfftw the default for FFT</span>
<span class="n">np</span><span class="o">.</span><span class="n">set_global_backend</span><span class="p">(</span><span class="n">pyfftw</span><span class="p">)</span>
<span class="c1"># Uses pyfftw without monkeypatching</span>
<span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">fft</span><span class="p">(</span><span class="n">numpy_array</span><span class="p">)</span>
<span class="k">with</span> <span class="n">np</span><span class="o">.</span><span class="n">set_backend</span><span class="p">(</span><span class="n">pyfftw</span><span class="p">)</span> <span class="c1"># Or mkl_fft, or numpy</span>
<span class="c1"># Uses the backend you specified</span>
<span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">fft</span><span class="p">(</span><span class="n">numpy_array</span><span class="p">)</span>
</pre></div>
</div>
<p>This will allow an official way for overrides to work with NumPy without
monkeypatching or distributing a modified version of NumPy.</p>
<p>Here are a few other use-cases, implied but not already
stated:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">from_zarr</span><span class="p">(</span><span class="s1">'myfile.zarr'</span><span class="p">)</span>
<span class="c1"># result should still be dask, all things being equal</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">library_function</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="n">result</span><span class="o">.</span><span class="n">to_zarr</span><span class="p">(</span><span class="s1">'output.zarr'</span><span class="p">)</span>
</pre></div>
</div>
<p>This second one would work if <code class="docutils literal notranslate"><span class="pre">magic_library</span></code> was built
on top of <code class="docutils literal notranslate"><span class="pre">unumpy</span></code>.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">dask</span><span class="w"> </span><span class="kn">import</span> <span class="n">array</span> <span class="k">as</span> <span class="n">da</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">magic_library</span><span class="w"> </span><span class="kn">import</span> <span class="n">pytorch_predict</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">from_zarr</span><span class="p">(</span><span class="s1">'myfile.zarr'</span><span class="p">)</span>
<span class="c1"># normally here one would use e.g. data.map_overlap</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">pytorch_predict</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="n">result</span><span class="o">.</span><span class="n">to_zarr</span><span class="p">(</span><span class="s1">'output.zarr'</span><span class="p">)</span>
</pre></div>
</div>
<p>There are some backends which may depend on other backends, for example xarray
depending on <cite>numpy.fft</cite>, and transforming a time axis into a frequency axis,
or Dask/xarray holding an array other than a NumPy array inside it. This would
be handled in the following manner inside code:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">ua</span><span class="o">.</span><span class="n">set_backend</span><span class="p">(</span><span class="n">cupy</span><span class="p">),</span> <span class="n">ua</span><span class="o">.</span><span class="n">set_backend</span><span class="p">(</span><span class="n">dask</span><span class="o">.</span><span class="n">array</span><span class="p">):</span>
<span class="c1"># Code that has distributed GPU arrays here</span>
</pre></div>
</div>
</section>
<section id="backward-compatibility">
<h2>Backward compatibility<a class="headerlink" href="#backward-compatibility" title="Link to this heading">#</a></h2>
<p>There are no backward incompatible changes proposed in this NEP.</p>
</section>
<section id="detailed-description">
<h2>Detailed description<a class="headerlink" href="#detailed-description" title="Link to this heading">#</a></h2>
<section id="proposals">
<h3>Proposals<a class="headerlink" href="#proposals" title="Link to this heading">#</a></h3>
<p>The only change this NEP proposes at its acceptance, is to make <code class="docutils literal notranslate"><span class="pre">unumpy</span></code> the
officially recommended way to override NumPy, along with making some submodules
overridable by default via <code class="docutils literal notranslate"><span class="pre">uarray</span></code>. <code class="docutils literal notranslate"><span class="pre">unumpy</span></code> will remain a separate
repository/package (which we propose to vendor to avoid a hard dependency, and
use the separate <code class="docutils literal notranslate"><span class="pre">unumpy</span></code> package only if it is installed, rather than depend
on for the time being). In concrete terms, <code class="docutils literal notranslate"><span class="pre">numpy.overridable</span></code> becomes an
alias for <code class="docutils literal notranslate"><span class="pre">unumpy</span></code>, if available with a fallback to the a vendored version if
not. <code class="docutils literal notranslate"><span class="pre">uarray</span></code> and <code class="docutils literal notranslate"><span class="pre">unumpy</span></code> and will be developed primarily with the input
of duck-array authors and secondarily, custom dtype authors, via the usual
GitHub workflow. There are a few reasons for this:</p>
<ul class="simple">
<li><p>Faster iteration in the case of bugs or issues.</p></li>
<li><p>Faster design changes, in the case of needed functionality.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">unumpy</span></code> will work with older versions of NumPy as well.</p></li>
<li><p>The user and library author opt-in to the override process,
rather than breakages happening when it is least expected.
In simple terms, bugs in <code class="docutils literal notranslate"><span class="pre">unumpy</span></code> mean that <code class="docutils literal notranslate"><span class="pre">numpy</span></code> remains
unaffected.</p></li>
<li><p>For <code class="docutils literal notranslate"><span class="pre">numpy.fft</span></code>, <code class="docutils literal notranslate"><span class="pre">numpy.linalg</span></code> and <code class="docutils literal notranslate"><span class="pre">numpy.random</span></code>, the functions in
the main namespace will mirror those in the <code class="docutils literal notranslate"><span class="pre">numpy.overridable</span></code> namespace.
The reason for this is that there may exist functions in the in these
submodules that need backends, even for <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code> inputs.</p></li>
</ul>
<section id="advantages-of-unumpy-over-other-solutions">
<h4>Advantages of <code class="docutils literal notranslate"><span class="pre">unumpy</span></code> over other solutions<a class="headerlink" href="#advantages-of-unumpy-over-other-solutions" title="Link to this heading">#</a></h4>
<p><code class="docutils literal notranslate"><span class="pre">unumpy</span></code> offers a number of advantages over the approach of defining a new
protocol for every problem encountered: Whenever there is something requiring
an override, <code class="docutils literal notranslate"><span class="pre">unumpy</span></code> will be able to offer a unified API with very minor
changes. For example:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">ufunc</span></code> objects can be overridden via their <code class="docutils literal notranslate"><span class="pre">__call__</span></code>, <code class="docutils literal notranslate"><span class="pre">reduce</span></code> and
other methods.</p></li>
<li><p>Other functions can be overridden in a similar fashion.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">np.asduckarray</span></code> goes away, and becomes <code class="docutils literal notranslate"><span class="pre">np.overridable.asarray</span></code> with a
backend set.</p></li>
<li><p>The same holds for array creation functions such as <code class="docutils literal notranslate"><span class="pre">np.zeros</span></code>,
<code class="docutils literal notranslate"><span class="pre">np.empty</span></code> and so on.</p></li>
</ul>
<p>This also holds for the future: Making something overridable would require only
minor changes to <code class="docutils literal notranslate"><span class="pre">unumpy</span></code>.</p>
<p>Another promise <code class="docutils literal notranslate"><span class="pre">unumpy</span></code> holds is one of default implementations. Default
implementations can be provided for any multimethod, in terms of others. This
allows one to override a large part of the NumPy API by defining only a small
part of it. This is to ease the creation of new duck-arrays, by providing
default implementations of many functions that can be easily expressed in
terms of others, as well as a repository of utility functions that help in the
implementation of duck-arrays that most duck-arrays would require. This would
allow us to avoid designing entire protocols, e.g., a protocol for stacking
and concatenating would be replaced by simply implementing <code class="docutils literal notranslate"><span class="pre">stack</span></code> and/or
<code class="docutils literal notranslate"><span class="pre">concatenate</span></code> and then providing default implementations for everything else
in that class. The same applies for transposing, and many other functions for
which protocols haven’t been proposed, such as <code class="docutils literal notranslate"><span class="pre">isin</span></code> in terms of <code class="docutils literal notranslate"><span class="pre">in1d</span></code>,
<code class="docutils literal notranslate"><span class="pre">setdiff1d</span></code> in terms of <code class="docutils literal notranslate"><span class="pre">unique</span></code>, and so on.</p>
<p>It also allows one to override functions in a manner which
<code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> simply cannot, such as overriding <code class="docutils literal notranslate"><span class="pre">np.einsum</span></code> with the
version from the <code class="docutils literal notranslate"><span class="pre">opt_einsum</span></code> package, or Intel MKL overriding FFT, BLAS
or <code class="docutils literal notranslate"><span class="pre">ufunc</span></code> objects. They would define a backend with the appropriate
multimethods, and the user would select them via a <code class="docutils literal notranslate"><span class="pre">with</span></code> statement, or
registering them as a backend.</p>
<p>The last benefit is a clear way to coerce to a given backend (via the
<code class="docutils literal notranslate"><span class="pre">coerce</span></code> keyword in <code class="docutils literal notranslate"><span class="pre">ua.set_backend</span></code>), and a protocol
for coercing not only arrays, but also <code class="docutils literal notranslate"><span class="pre">dtype</span></code> objects and <code class="docutils literal notranslate"><span class="pre">ufunc</span></code> objects
with similar ones from other libraries. This is due to the existence of actual,
third party dtype packages, and their desire to blend into the NumPy ecosystem
(see <a class="footnote-reference brackets" href="#id25" id="id10" role="doc-noteref"><span class="fn-bracket">[</span>6<span class="fn-bracket">]</span></a>). This is a separate issue compared to the C-level dtype redesign
proposed in <a class="footnote-reference brackets" href="#id26" id="id11" role="doc-noteref"><span class="fn-bracket">[</span>7<span class="fn-bracket">]</span></a>, it’s about allowing third-party dtype implementations to
work with NumPy, much like third-party array implementations. These can provide
features such as, for example, units, jagged arrays or other such features that
are outside the scope of NumPy.</p>
</section>
<section id="mixing-numpy-and-unumpy-in-the-same-file">
<h4>Mixing NumPy and <code class="docutils literal notranslate"><span class="pre">unumpy</span></code> in the same file<a class="headerlink" href="#mixing-numpy-and-unumpy-in-the-same-file" title="Link to this heading">#</a></h4>
<p>Normally, one would only want to import only one of <code class="docutils literal notranslate"><span class="pre">unumpy</span></code> or <code class="docutils literal notranslate"><span class="pre">numpy</span></code>,
you would import it as <code class="docutils literal notranslate"><span class="pre">np</span></code> for familiarity. However, there may be situations
where one wishes to mix NumPy and the overrides, and there are a few ways to do
this, depending on the user’s style:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="kn">import</span> <span class="n">overridable</span> <span class="k">as</span> <span class="n">unp</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
</pre></div>
</div>
<p>or:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="c1"># Use unumpy via np.overridable</span>
</pre></div>
</div>
</section>
</section>
<section id="duck-array-coercion">
<h3>Duck-array coercion<a class="headerlink" href="#duck-array-coercion" title="Link to this heading">#</a></h3>
<p>There are inherent problems about returning objects that are not NumPy arrays
from <code class="docutils literal notranslate"><span class="pre">numpy.array</span></code> or <code class="docutils literal notranslate"><span class="pre">numpy.asarray</span></code>, particularly in the context of C/C++
or Cython code that may get an object with a different memory layout than the
one it expects. However, we believe this problem may apply not only to these
two functions but all functions that return NumPy arrays. For this reason,
overrides are opt-in for the user, by using the submodule <code class="docutils literal notranslate"><span class="pre">numpy.overridable</span></code>
rather than <code class="docutils literal notranslate"><span class="pre">numpy</span></code>. NumPy will continue to work unaffected by anything in
<code class="docutils literal notranslate"><span class="pre">numpy.overridable</span></code>.</p>
<p>If the user wishes to obtain a NumPy array, there are two ways of doing it:</p>
<ol class="arabic simple">
<li><p>Use <code class="docutils literal notranslate"><span class="pre">numpy.asarray</span></code> (the non-overridable version).</p></li>
<li><p>Use <code class="docutils literal notranslate"><span class="pre">numpy.overridable.asarray</span></code> with the NumPy backend set and coercion
enabled</p></li>
</ol>
</section>
<section id="aliases-outside-of-the-numpy-overridable-namespace">
<h3>Aliases outside of the <code class="docutils literal notranslate"><span class="pre">numpy.overridable</span></code> namespace<a class="headerlink" href="#aliases-outside-of-the-numpy-overridable-namespace" title="Link to this heading">#</a></h3>
<p>All functionality in <code class="docutils literal notranslate"><span class="pre">numpy.random</span></code>, <code class="docutils literal notranslate"><span class="pre">numpy.linalg</span></code> and <code class="docutils literal notranslate"><span class="pre">numpy.fft</span></code>
will be aliased to their respective overridable versions inside
<code class="docutils literal notranslate"><span class="pre">numpy.overridable</span></code>. The reason for this is that there are alternative
implementations of RNGs (<code class="docutils literal notranslate"><span class="pre">mkl-random</span></code>), linear algebra routines (<code class="docutils literal notranslate"><span class="pre">eigen</span></code>,
<code class="docutils literal notranslate"><span class="pre">blis</span></code>) and FFT routines (<code class="docutils literal notranslate"><span class="pre">mkl-fft</span></code>, <code class="docutils literal notranslate"><span class="pre">pyFFTW</span></code>) that need to operate on
<code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code> inputs, but still need the ability to switch behaviour.</p>
<p>This is different from monkeypatching in a few different ways:</p>
<ul class="simple">
<li><p>The caller-facing signature of the function is always the same,
so there is at least the loose sense of an API contract. Monkeypatching
does not provide this ability.</p></li>
<li><p>There is the ability of locally switching the backend.</p></li>
<li><p>It has been <a class="reference external" href="https://mail.python.org/archives/list/numpy-discussion@python.org/message/PS7EN3CRT6XERNTCN56MAYOXFFFEC55G/">suggested</a>
that the reason that 1.17 hasn’t landed in the Anaconda defaults channel is
due to the incompatibility between monkeypatching and <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code>,
as monkeypatching would bypass the protocol completely.</p></li>
<li><p>Statements of the form <code class="docutils literal notranslate"><span class="pre">from</span> <span class="pre">numpy</span> <span class="pre">import</span> <span class="pre">x;</span> <span class="pre">x</span></code> and <code class="docutils literal notranslate"><span class="pre">np.x</span></code> would have
different results depending on whether the import was made before or
after monkeypatching happened.</p></li>
</ul>
<p>All this isn’t possible at all with <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> or
<code class="docutils literal notranslate"><span class="pre">__array_ufunc__</span></code>.</p>
<p>It has been formally realized (at least in part) that a backend system is
needed for this, in the <a class="reference external" href="https://numpy.org/neps/roadmap.html#other-functionality">NumPy roadmap</a>.</p>
<p>For <code class="docutils literal notranslate"><span class="pre">numpy.random</span></code>, it’s still necessary to make the C-API fit the one
proposed in <a class="reference internal" href="nep-0019-rng-policy.html#nep19"><span class="std std-ref">NEP-19</span></a>.
This is impossible for <cite>mkl-random</cite>, because then it would need to be
rewritten to fit that framework. The guarantees on stream
compatibility will be the same as before, but if there’s a backend that affects
<code class="docutils literal notranslate"><span class="pre">numpy.random</span></code> set, we make no guarantees about stream compatibility, and it
is up to the backend author to provide their own guarantees.</p>
</section>
<section id="providing-a-way-for-implicit-dispatch">
<h3>Providing a way for implicit dispatch<a class="headerlink" href="#providing-a-way-for-implicit-dispatch" title="Link to this heading">#</a></h3>
<p>It has been suggested that the ability to dispatch methods which do not take
a dispatchable is needed, while guessing that backend from another dispatchable.</p>
<p>As a concrete example, consider the following:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">unumpy</span><span class="o">.</span><span class="n">determine_backend</span><span class="p">(</span><span class="n">array_like</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
<span class="n">unumpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">array_like</span><span class="p">))</span>
</pre></div>
</div>
<p>While this does not exist yet in <code class="docutils literal notranslate"><span class="pre">uarray</span></code>, it is trivial to add it. The need for
this kind of code exists because one might want to have an alternative for the
proposed <code class="docutils literal notranslate"><span class="pre">*_like</span></code> functions, or the <code class="docutils literal notranslate"><span class="pre">like=</span></code> keyword argument. The need for these
exists because there are functions in the NumPy API that do not take a dispatchable
argument, but there is still the need to select a backend based on a different
dispatchable.</p>
</section>
<section id="the-need-for-an-opt-in-module">
<h3>The need for an opt-in module<a class="headerlink" href="#the-need-for-an-opt-in-module" title="Link to this heading">#</a></h3>
<p>The need for an opt-in module is realized because of a few reasons:</p>
<ul class="simple">
<li><p>There are parts of the API (like <cite>numpy.asarray</cite>) that simply cannot be
overridden due to incompatibility concerns with C/Cython extensions, however,
one may want to coerce to a duck-array using <code class="docutils literal notranslate"><span class="pre">asarray</span></code> with a backend set.</p></li>
<li><p>There are possible issues around an implicit option and monkeypatching, such
as those mentioned above.</p></li>
</ul>
<p>NEP 18 notes that this may require maintenance of two separate APIs. However,
this burden may be lessened by, for example, parameterizing all tests over
<code class="docutils literal notranslate"><span class="pre">numpy.overridable</span></code> separately via a fixture. This also has the side-effect
of thoroughly testing it, unlike <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code>. We also feel that it
provides an opportunity to separate the NumPy API contract properly from the
implementation.</p>
</section>
<section id="benefits-to-end-users-and-mixing-backends">
<h3>Benefits to end-users and mixing backends<a class="headerlink" href="#benefits-to-end-users-and-mixing-backends" title="Link to this heading">#</a></h3>
<p>Mixing backends is easy in <code class="docutils literal notranslate"><span class="pre">uarray</span></code>, one only has to do:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Explicitly say which backends you want to mix</span>
<span class="n">ua</span><span class="o">.</span><span class="n">register_backend</span><span class="p">(</span><span class="n">backend1</span><span class="p">)</span>
<span class="n">ua</span><span class="o">.</span><span class="n">register_backend</span><span class="p">(</span><span class="n">backend2</span><span class="p">)</span>
<span class="n">ua</span><span class="o">.</span><span class="n">register_backend</span><span class="p">(</span><span class="n">backend3</span><span class="p">)</span>
<span class="c1"># Freely use code that mixes backends here.</span>
</pre></div>
</div>
<p>The benefits to end-users extend beyond just writing new code. Old code
(usually in the form of scripts) can be easily ported to different backends
by a simple import switch and a line adding the preferred backend. This way,
users may find it easier to port existing code to GPU or distributed computing.</p>
</section>
</section>
<section id="related-work">
<h2>Related work<a class="headerlink" href="#related-work" title="Link to this heading">#</a></h2>
<section id="other-override-mechanisms">
<h3>Other override mechanisms<a class="headerlink" href="#other-override-mechanisms" title="Link to this heading">#</a></h3>
<ul class="simple">
<li><p>NEP-18, the <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> protocol. <a class="footnote-reference brackets" href="#id21" id="id12" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a></p></li>
<li><p>NEP-13, the <code class="docutils literal notranslate"><span class="pre">__array_ufunc__</span></code> protocol. <a class="footnote-reference brackets" href="#id22" id="id13" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a></p></li>
<li><p>NEP-30, the <code class="docutils literal notranslate"><span class="pre">__duck_array__</span></code> protocol. <a class="footnote-reference brackets" href="#id28" id="id14" role="doc-noteref"><span class="fn-bracket">[</span>9<span class="fn-bracket">]</span></a></p></li>
</ul>
</section>
<section id="existing-numpy-like-array-implementations">
<h3>Existing NumPy-like array implementations<a class="headerlink" href="#existing-numpy-like-array-implementations" title="Link to this heading">#</a></h3>
<ul class="simple">
<li><p>Dask: <a class="reference external" href="https://dask.org/">https://dask.org/</a></p></li>
<li><p>CuPy: <a class="reference external" href="https://cupy.chainer.org/">https://cupy.chainer.org/</a></p></li>
<li><p>PyData/Sparse: <a class="reference external" href="https://sparse.pydata.org/">https://sparse.pydata.org/</a></p></li>
<li><p>Xnd: <a class="reference external" href="https://xnd.readthedocs.io/">https://xnd.readthedocs.io/</a></p></li>
<li><p>Astropy’s Quantity: <a class="reference external" href="https://docs.astropy.org/en/stable/units/">https://docs.astropy.org/en/stable/units/</a></p></li>
</ul>
</section>
<section id="existing-and-potential-consumers-of-alternative-arrays">
<h3>Existing and potential consumers of alternative arrays<a class="headerlink" href="#existing-and-potential-consumers-of-alternative-arrays" title="Link to this heading">#</a></h3>
<ul class="simple">
<li><p>Dask: <a class="reference external" href="https://dask.org/">https://dask.org/</a></p></li>
<li><p>scikit-learn: <a class="reference external" href="https://scikit-learn.org/">https://scikit-learn.org/</a></p></li>
<li><p>xarray: <a class="reference external" href="https://xarray.pydata.org/">https://xarray.pydata.org/</a></p></li>
<li><p>TensorLy: <a class="reference external" href="http://tensorly.org/">http://tensorly.org/</a></p></li>
</ul>
</section>
<section id="existing-alternate-dtype-implementations">
<h3>Existing alternate dtype implementations<a class="headerlink" href="#existing-alternate-dtype-implementations" title="Link to this heading">#</a></h3>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">ndtypes</span></code>: <a class="reference external" href="https://ndtypes.readthedocs.io/en/latest/">https://ndtypes.readthedocs.io/en/latest/</a></p></li>
<li><p>Datashape: <a class="reference external" href="https://datashape.readthedocs.io">https://datashape.readthedocs.io</a></p></li>
<li><p>Plum: <a class="reference external" href="https://plum-py.readthedocs.io/">https://plum-py.readthedocs.io/</a></p></li>
</ul>
</section>
<section id="alternate-implementations-of-parts-of-the-numpy-api">
<h3>Alternate implementations of parts of the NumPy API<a class="headerlink" href="#alternate-implementations-of-parts-of-the-numpy-api" title="Link to this heading">#</a></h3>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">mkl_random</span></code>: <a class="github reference external" href="https://github.com/IntelPython/mkl_random">IntelPython/mkl_random</a></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">mkl_fft</span></code>: <a class="github reference external" href="https://github.com/IntelPython/mkl_fft">IntelPython/mkl_fft</a></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">bottleneck</span></code>: <a class="github reference external" href="https://github.com/pydata/bottleneck">pydata/bottleneck</a></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">opt_einsum</span></code>: <a class="github reference external" href="https://github.com/dgasmith/opt_einsum">dgasmith/opt_einsum</a></p></li>
</ul>
</section>
</section>
<section id="implementation">
<h2>Implementation<a class="headerlink" href="#implementation" title="Link to this heading">#</a></h2>
<p>The implementation of this NEP will require the following steps:</p>
<ul class="simple">
<li><p>Implementation of <code class="docutils literal notranslate"><span class="pre">uarray</span></code> multimethods corresponding to the
NumPy API, including classes for overriding <code class="docutils literal notranslate"><span class="pre">dtype</span></code>, <code class="docutils literal notranslate"><span class="pre">ufunc</span></code>
and <code class="docutils literal notranslate"><span class="pre">array</span></code> objects, in the <code class="docutils literal notranslate"><span class="pre">unumpy</span></code> repository, which are usually
very easy to create.</p></li>
<li><p>Moving backends from <code class="docutils literal notranslate"><span class="pre">unumpy</span></code> into the respective array libraries.</p></li>
</ul>
<p>Maintenance can be eased by testing over <code class="docutils literal notranslate"><span class="pre">{numpy,</span> <span class="pre">unumpy}</span></code> via parameterized
tests. If a new argument is added to a method, the corresponding argument
extractor and replacer will need to be updated within <code class="docutils literal notranslate"><span class="pre">unumpy</span></code>.</p>
<p>A lot of argument extractors can be re-used from the existing implementation
of the <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> protocol, and the replacers can be usually
re-used across many methods.</p>
<p>For the parts of the namespace which are going to be overridable by default,
the main method will need to be renamed and hidden behind a <code class="docutils literal notranslate"><span class="pre">uarray</span></code> multimethod.</p>
<p>Default implementations are usually seen in the documentation using the words
“equivalent to”, and thus, are easily available.</p>
<section id="uarray-primer">
<h3><code class="docutils literal notranslate"><span class="pre">uarray</span></code> Primer<a class="headerlink" href="#uarray-primer" title="Link to this heading">#</a></h3>
<p><strong>Note:</strong> <em>This section will not attempt to go into too much detail about
uarray, that is the purpose of the uarray documentation.</em> <a class="footnote-reference brackets" href="#id20" id="id15" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>
<em>However, the NumPy community will have input into the design of
uarray, via the issue tracker.</em></p>
<p><code class="docutils literal notranslate"><span class="pre">unumpy</span></code> is the interface that defines a set of overridable functions
(multimethods) compatible with the numpy API. To do this, it uses the
<code class="docutils literal notranslate"><span class="pre">uarray</span></code> library. <code class="docutils literal notranslate"><span class="pre">uarray</span></code> is a general purpose tool for creating
multimethods that dispatch to one of multiple different possible backend
implementations. In this sense, it is similar to the <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code>
protocol but with the key difference that the backend is explicitly installed
by the end-user and not coupled into the array type.</p>
<p>Decoupling the backend from the array type gives much more flexibility to
end-users and backend authors. For example, it is possible to:</p>
<ul class="simple">
<li><p>override functions not taking arrays as arguments</p></li>
<li><p>create backends out of source from the array type</p></li>
<li><p>install multiple backends for the same array type</p></li>
</ul>
<p>This decoupling also means that <code class="docutils literal notranslate"><span class="pre">uarray</span></code> is not constrained to dispatching
over array-like types. The backend is free to inspect the entire set of
function arguments to determine if it can implement the function e.g. <code class="docutils literal notranslate"><span class="pre">dtype</span></code>
parameter dispatching.</p>
<section id="defining-backends">
<h4>Defining backends<a class="headerlink" href="#defining-backends" title="Link to this heading">#</a></h4>
<p><code class="docutils literal notranslate"><span class="pre">uarray</span></code> consists of two main protocols: <code class="docutils literal notranslate"><span class="pre">__ua_convert__</span></code> and
<code class="docutils literal notranslate"><span class="pre">__ua_function__</span></code>, called in that order, along with <code class="docutils literal notranslate"><span class="pre">__ua_domain__</span></code>.
<code class="docutils literal notranslate"><span class="pre">__ua_convert__</span></code> is for conversion and coercion. It has the signature
<code class="docutils literal notranslate"><span class="pre">(dispatchables,</span> <span class="pre">coerce)</span></code>, where <code class="docutils literal notranslate"><span class="pre">dispatchables</span></code> is an iterable of
<code class="docutils literal notranslate"><span class="pre">ua.Dispatchable</span></code> objects and <code class="docutils literal notranslate"><span class="pre">coerce</span></code> is a boolean indicating whether or
not to force the conversion. <code class="docutils literal notranslate"><span class="pre">ua.Dispatchable</span></code> is a simple class consisting
of three simple values: <code class="docutils literal notranslate"><span class="pre">type</span></code>, <code class="docutils literal notranslate"><span class="pre">value</span></code>, and <code class="docutils literal notranslate"><span class="pre">coercible</span></code>.
<code class="docutils literal notranslate"><span class="pre">__ua_convert__</span></code> returns an iterable of the converted values, or
<code class="docutils literal notranslate"><span class="pre">NotImplemented</span></code> in the case of failure.</p>
<p><code class="docutils literal notranslate"><span class="pre">__ua_function__</span></code> has the signature <code class="docutils literal notranslate"><span class="pre">(func,</span> <span class="pre">args,</span> <span class="pre">kwargs)</span></code> and defines
the actual implementation of the function. It receives the function and its
arguments. Returning <code class="docutils literal notranslate"><span class="pre">NotImplemented</span></code> will cause a move to the default
implementation of the function if one exists, and failing that, the next
backend.</p>
<p>Here is what will happen assuming a <code class="docutils literal notranslate"><span class="pre">uarray</span></code> multimethod is called:</p>
<ol class="arabic simple">
<li><p>We canonicalise the arguments so any arguments without a default
are placed in <code class="docutils literal notranslate"><span class="pre">*args</span></code> and those with one are placed in <code class="docutils literal notranslate"><span class="pre">**kwargs</span></code>.</p></li>
<li><p>We check the list of backends.</p>
<ol class="loweralpha simple">
<li><p>If it is empty, we try the default implementation.</p></li>
</ol>
</li>
<li><p>We check if the backend’s <code class="docutils literal notranslate"><span class="pre">__ua_convert__</span></code> method exists. If it exists:</p>
<ol class="loweralpha simple">
<li><p>We pass it the output of the dispatcher,
which is an iterable of <code class="docutils literal notranslate"><span class="pre">ua.Dispatchable</span></code> objects.</p></li>
<li><p>We feed this output, along with the arguments,
to the argument replacer. <code class="docutils literal notranslate"><span class="pre">NotImplemented</span></code> means we move to 3
with the next backend.</p></li>
<li><p>We store the replaced arguments as the new arguments.</p></li>
</ol>
</li>
<li><p>We feed the arguments into <code class="docutils literal notranslate"><span class="pre">__ua_function__</span></code>, and return the output, and
exit if it isn’t <code class="docutils literal notranslate"><span class="pre">NotImplemented</span></code>.</p></li>
<li><p>If the default implementation exists, we try it with the current backend.</p></li>
<li><p>On failure, we move to 3 with the next backend. If there are no more
backends, we move to 7.</p></li>
<li><p>We raise a <code class="docutils literal notranslate"><span class="pre">ua.BackendNotImplementedError</span></code>.</p></li>
</ol>
</section>
<section id="defining-overridable-multimethods">
<h4>Defining overridable multimethods<a class="headerlink" href="#defining-overridable-multimethods" title="Link to this heading">#</a></h4>
<p>To define an overridable function (a multimethod), one needs a few things:</p>
<ol class="arabic simple">
<li><p>A dispatcher that returns an iterable of <code class="docutils literal notranslate"><span class="pre">ua.Dispatchable</span></code> objects.</p></li>
<li><p>A reverse dispatcher that replaces dispatchable values with the supplied
ones.</p></li>
<li><p>A domain.</p></li>
<li><p>Optionally, a default implementation, which can be provided in terms of
other multimethods.</p></li>
</ol>
<p>As an example, consider the following:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">uarray</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">ua</span>
<span class="k">def</span><span class="w"> </span><span class="nf">full_argreplacer</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">,</span> <span class="n">dispatchables</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">full</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">fill_value</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="s1">'C'</span><span class="p">):</span>
<span class="k">return</span> <span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">fill_value</span><span class="p">),</span> <span class="nb">dict</span><span class="p">(</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">dispatchables</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
<span class="n">order</span><span class="o">=</span><span class="n">order</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">full</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="nd">@ua</span><span class="o">.</span><span class="n">create_multimethod</span><span class="p">(</span><span class="n">full_argreplacer</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="s2">"numpy"</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">full</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">fill_value</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="s1">'C'</span><span class="p">):</span>
<span class="k">return</span> <span class="p">(</span><span class="n">ua</span><span class="o">.</span><span class="n">Dispatchable</span><span class="p">(</span><span class="n">dtype</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">),)</span>
</pre></div>
</div>