-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathfunc.ux_limitations.html
1120 lines (904 loc) · 74.4 KB
/
func.ux_limitations.html
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>
<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
<head>
<meta name="robots" content="noindex">
<meta charset="utf-8">
<meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>UX Limitations — PyTorch 2.0 documentation</title>
<link rel="canonical" href="https://pytorch.org/docs/stable/func.ux_limitations.html"/>
<link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
<!-- <link rel="stylesheet" href="_static/pygments.css" type="text/css" /> -->
<link rel="stylesheet" href="_static/pygments.css" type="text/css" />
<link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
<link rel="stylesheet" href="_static/copybutton.css" type="text/css" />
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/katex.min.css" type="text/css" />
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/katex.min.css" type="text/css" />
<link rel="stylesheet" href="_static/katex-math.css" type="text/css" />
<link rel="stylesheet" href="_static/sphinx-dropdown.css" type="text/css" />
<link rel="stylesheet" href="_static/panels-bootstrap.min.css" type="text/css" />
<link rel="stylesheet" href="_static/css/jit.css" type="text/css" />
<link rel="index" title="Index" href="genindex.html" />
<link rel="search" title="Search" href="search.html" />
<link rel="next" title="Migrating from functorch to torch.func" href="func.migrating.html" />
<link rel="prev" title="Patching Batch Norm" href="func.batch_norm.html" />
<!-- Google Analytics -->
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-117752657-2"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-117752657-2');
</script>
<!-- End Google Analytics -->
<script src="_static/js/modernizr.min.js"></script>
<!-- Preload the theme fonts -->
<link rel="preload" href="_static/fonts/FreightSans/freight-sans-book.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="_static/fonts/FreightSans/freight-sans-medium.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="_static/fonts/IBMPlexMono/IBMPlexMono-Medium.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="_static/fonts/FreightSans/freight-sans-bold.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="_static/fonts/FreightSans/freight-sans-medium-italic.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="_static/fonts/IBMPlexMono/IBMPlexMono-SemiBold.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<!-- Preload the katex fonts -->
<link rel="preload" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/fonts/KaTeX_Math-Italic.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/fonts/KaTeX_Main-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/fonts/KaTeX_Main-Bold.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/fonts/KaTeX_Size1-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/fonts/KaTeX_Size4-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/fonts/KaTeX_Size2-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/fonts/KaTeX_Size3-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/fonts/KaTeX_Caligraphic-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.15.2/css/all.css" integrity="sha384-vSIIfh2YWi9wW0r9iZe7RJPrKwp6bG+s9QZMoITbCckVJqGCCRhc+ccxNcdpHuYu" crossorigin="anonymous">
</head>
<div class="container-fluid header-holder tutorials-header" id="header-holder">
<div class="container">
<div class="header-container">
<a class="header-logo" href="https://pytorch.org/" aria-label="PyTorch"></a>
<div class="main-menu">
<ul>
<li>
<a href="https://pytorch.org/get-started">Get Started</a>
</li>
<li>
<a href="https://pytorch.org/ecosystem">Ecosystem</a>
</li>
<li>
<a href="https://pytorch.org/mobile">Mobile</a>
</li>
<li>
<a href="https://pytorch.org/blog/">Blog</a>
</li>
<li>
<a href="https://pytorch.org/tutorials">Tutorials</a>
</li>
<li class="active docs-active">
<div id="resourcesDropdownButton" data-toggle="resources-dropdown" class="resources-dropdown">
<a class="resource-option with-down-orange-arrow">
Docs
</a>
<div class="resources-dropdown-menu">
<a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/docs/stable/index.html">
<span class="dropdown-title">PyTorch</span>
<p></p>
</a>
<a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/audio/stable/index.html">
<span class="dropdown-title">torchaudio</span>
<p></p>
</a>
<a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/text/stable/index.html">
<span class="dropdown-title">torchtext</span>
<p></p>
</a>
<a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/vision/stable/index.html">
<span class="dropdown-title">torchvision</span>
<p></p>
</a>
<a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/torcharrow">
<span class="dropdown-title">torcharrow</span>
<p></p>
</a>
<a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/data">
<span class="dropdown-title">TorchData</span>
<p></p>
</a>
<a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/torchrec">
<span class="dropdown-title">TorchRec</span>
<p></p>
</a>
<a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/serve/">
<span class="dropdown-title">TorchServe</span>
<p></p>
</a>
<a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/torchx/">
<span class="dropdown-title">TorchX</span>
<p></p>
</a>
<a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/xla">
<span class="dropdown-title">PyTorch on XLA Devices</span>
<p></p>
</a>
</div>
</li>
<li>
<div id="resourcesDropdownButton" data-toggle="resources-dropdown" class="resources-dropdown">
<a class="resource-option with-down-arrow">
Resources
</a>
<div class="resources-dropdown-menu">
<a class="nav-dropdown-item" href="https://pytorch.org/features">
<span class="dropdown-title">About</span>
<p>Learn about PyTorch’s features and capabilities</p>
</a>
<a class="nav-dropdown-item" href="https://pytorch.org/foundation">
<span class="dropdown-title">PyTorch Foundation</span>
<p>Learn about the PyTorch foundation</p>
</a>
<a class="nav-dropdown-item" href="https://pytorch.org/#community-module">
<span class="dropdown-title">Community</span>
<p>Join the PyTorch developer community to contribute, learn, and get your questions answered.</p>
</a>
<a class="nav-dropdown-item" href="https://pytorch.org/community-stories">
<span class="dropdown-title">Community Stories</span>
<p>Learn how our community solves real, everyday machine learning problems with PyTorch.</p>
</a>
<a class="nav-dropdown-item" href="https://pytorch.org/resources">
<span class="dropdown-title">Developer Resources</span>
<p>Find resources and get questions answered</p>
</a>
<a class="nav-dropdown-item" href="https://pytorch.org/events">
<span class="dropdown-title">Events</span>
<p>Find events, webinars, and podcasts</p>
</a>
<a class="nav-dropdown-item" href="https://discuss.pytorch.org/" target="_blank">
<span class="dropdown-title">Forums</span>
<p>A place to discuss PyTorch code, issues, install, research</p>
</a>
<a class="nav-dropdown-item" href="https://pytorch.org/hub">
<span class="dropdown-title">Models (Beta)</span>
<p>Discover, publish, and reuse pre-trained models</p>
</a>
</div>
</div>
</li>
<li>
<a href="https://github.com/pytorch/pytorch">GitHub</a>
</li>
</ul>
</div>
<a class="main-menu-open-button" href="#" data-behavior="open-mobile-menu"></a>
</div>
</div>
</div>
<body class="pytorch-body">
<div class="table-of-contents-link-wrapper">
<span>Table of Contents</span>
<a href="#" class="toggle-table-of-contents" data-behavior="toggle-table-of-contents"></a>
</div>
<nav data-toggle="wy-nav-shift" class="pytorch-left-menu" id="pytorch-left-menu">
<div class="pytorch-side-scroll">
<div class="pytorch-menu pytorch-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
<div class="pytorch-left-menu-search">
<div class="version">
<a href='https://pytorch.org/docs/versions.html'>2.0 ▼</a>
</div>
<div role="search">
<form id="rtd-search-form" class="wy-form" action="search.html" method="get">
<input type="text" name="q" placeholder="Search Docs" />
<input type="hidden" name="check_keywords" value="yes" />
<input type="hidden" name="area" value="default" />
</form>
</div>
</div>
<p class="caption" role="heading"><span class="caption-text">Community</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="community/build_ci_governance.html">PyTorch Governance | Build + CI</a></li>
<li class="toctree-l1"><a class="reference internal" href="community/contribution_guide.html">PyTorch Contribution Guide</a></li>
<li class="toctree-l1"><a class="reference internal" href="community/design.html">PyTorch Design Philosophy</a></li>
<li class="toctree-l1"><a class="reference internal" href="community/governance.html">PyTorch Governance | Mechanics</a></li>
<li class="toctree-l1"><a class="reference internal" href="community/persons_of_interest.html">PyTorch Governance | Maintainers</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Developer Notes</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="notes/amp_examples.html">CUDA Automatic Mixed Precision examples</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/autograd.html">Autograd mechanics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/broadcasting.html">Broadcasting semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/cpu_threading_torchscript_inference.html">CPU threading and TorchScript inference</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/cuda.html">CUDA semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/ddp.html">Distributed Data Parallel</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/extending.html">Extending PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/extending.func.html">Extending torch.func with autograd.Function</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/faq.html">Frequently Asked Questions</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/gradcheck.html">Gradcheck mechanics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/hip.html">HIP (ROCm) semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/large_scale_deployments.html">Features for large-scale deployments</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/modules.html">Modules</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/mps.html">MPS backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/multiprocessing.html">Multiprocessing best practices</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/numerical_accuracy.html">Numerical accuracy</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/randomness.html">Reproducibility</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/serialization.html">Serialization semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/windows.html">Windows FAQ</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">torch.compile</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="dynamo/index.html">TorchDynamo Overview</a></li>
<li class="toctree-l1"><a class="reference internal" href="dynamo/installation.html">Installing TorchDynamo</a></li>
<li class="toctree-l1"><a class="reference internal" href="dynamo/get-started.html">Getting Started</a></li>
<li class="toctree-l1"><a class="reference internal" href="dynamo/guards-overview.html">Guards Overview</a></li>
<li class="toctree-l1"><a class="reference internal" href="dynamo/custom-backends.html">Custom Backends</a></li>
<li class="toctree-l1"><a class="reference internal" href="dynamo/deep-dive.html">TorchDynamo Deeper Dive</a></li>
<li class="toctree-l1"><a class="reference internal" href="dynamo/troubleshooting.html">TorchDynamo Troubleshooting</a></li>
<li class="toctree-l1"><a class="reference internal" href="dynamo/faq.html">Frequently Asked Questions</a></li>
<li class="toctree-l1"><a class="reference internal" href="ir.html">IRs</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Language Bindings</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="cpp_index.html">C++</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/javadoc/">Javadoc</a></li>
<li class="toctree-l1"><a class="reference internal" href="deploy.html">torch::deploy</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Python API</span></p>
<ul class="current">
<li class="toctree-l1"><a class="reference internal" href="torch.html">torch</a></li>
<li class="toctree-l1"><a class="reference internal" href="nn.html">torch.nn</a></li>
<li class="toctree-l1"><a class="reference internal" href="nn.functional.html">torch.nn.functional</a></li>
<li class="toctree-l1"><a class="reference internal" href="tensors.html">torch.Tensor</a></li>
<li class="toctree-l1"><a class="reference internal" href="tensor_attributes.html">Tensor Attributes</a></li>
<li class="toctree-l1"><a class="reference internal" href="tensor_view.html">Tensor Views</a></li>
<li class="toctree-l1"><a class="reference internal" href="amp.html">torch.amp</a></li>
<li class="toctree-l1"><a class="reference internal" href="autograd.html">torch.autograd</a></li>
<li class="toctree-l1"><a class="reference internal" href="library.html">torch.library</a></li>
<li class="toctree-l1"><a class="reference internal" href="cuda.html">torch.cuda</a></li>
<li class="toctree-l1"><a class="reference internal" href="mps.html">torch.mps</a></li>
<li class="toctree-l1"><a class="reference internal" href="backends.html">torch.backends</a></li>
<li class="toctree-l1"><a class="reference internal" href="distributed.html">torch.distributed</a></li>
<li class="toctree-l1"><a class="reference internal" href="distributed.algorithms.join.html">torch.distributed.algorithms.join</a></li>
<li class="toctree-l1"><a class="reference internal" href="distributed.elastic.html">torch.distributed.elastic</a></li>
<li class="toctree-l1"><a class="reference internal" href="fsdp.html">torch.distributed.fsdp</a></li>
<li class="toctree-l1"><a class="reference internal" href="distributed.optim.html">torch.distributed.optim</a></li>
<li class="toctree-l1"><a class="reference internal" href="distributed.tensor.parallel.html">torch.distributed.tensor.parallel</a></li>
<li class="toctree-l1"><a class="reference internal" href="distributed.checkpoint.html">torch.distributed.checkpoint</a></li>
<li class="toctree-l1"><a class="reference internal" href="distributions.html">torch.distributions</a></li>
<li class="toctree-l1"><a class="reference internal" href="_dynamo.html">torch._dynamo</a></li>
<li class="toctree-l1"><a class="reference internal" href="fft.html">torch.fft</a></li>
<li class="toctree-l1 current"><a class="reference internal" href="func.html">torch.func</a></li>
<li class="toctree-l1"><a class="reference internal" href="futures.html">torch.futures</a></li>
<li class="toctree-l1"><a class="reference internal" href="fx.html">torch.fx</a></li>
<li class="toctree-l1"><a class="reference internal" href="hub.html">torch.hub</a></li>
<li class="toctree-l1"><a class="reference internal" href="jit.html">torch.jit</a></li>
<li class="toctree-l1"><a class="reference internal" href="linalg.html">torch.linalg</a></li>
<li class="toctree-l1"><a class="reference internal" href="monitor.html">torch.monitor</a></li>
<li class="toctree-l1"><a class="reference internal" href="signal.html">torch.signal</a></li>
<li class="toctree-l1"><a class="reference internal" href="special.html">torch.special</a></li>
<li class="toctree-l1"><a class="reference internal" href="torch.overrides.html">torch.overrides</a></li>
<li class="toctree-l1"><a class="reference internal" href="package.html">torch.package</a></li>
<li class="toctree-l1"><a class="reference internal" href="profiler.html">torch.profiler</a></li>
<li class="toctree-l1"><a class="reference internal" href="nn.init.html">torch.nn.init</a></li>
<li class="toctree-l1"><a class="reference internal" href="onnx.html">torch.onnx</a></li>
<li class="toctree-l1"><a class="reference internal" href="onnx_diagnostics.html">torch.onnx diagnostics</a></li>
<li class="toctree-l1"><a class="reference internal" href="optim.html">torch.optim</a></li>
<li class="toctree-l1"><a class="reference internal" href="complex_numbers.html">Complex Numbers</a></li>
<li class="toctree-l1"><a class="reference internal" href="ddp_comm_hooks.html">DDP Communication Hooks</a></li>
<li class="toctree-l1"><a class="reference internal" href="pipeline.html">Pipeline Parallelism</a></li>
<li class="toctree-l1"><a class="reference internal" href="quantization.html">Quantization</a></li>
<li class="toctree-l1"><a class="reference internal" href="rpc.html">Distributed RPC Framework</a></li>
<li class="toctree-l1"><a class="reference internal" href="random.html">torch.random</a></li>
<li class="toctree-l1"><a class="reference internal" href="masked.html">torch.masked</a></li>
<li class="toctree-l1"><a class="reference internal" href="nested.html">torch.nested</a></li>
<li class="toctree-l1"><a class="reference internal" href="sparse.html">torch.sparse</a></li>
<li class="toctree-l1"><a class="reference internal" href="storage.html">torch.Storage</a></li>
<li class="toctree-l1"><a class="reference internal" href="testing.html">torch.testing</a></li>
<li class="toctree-l1"><a class="reference internal" href="benchmark_utils.html">torch.utils.benchmark</a></li>
<li class="toctree-l1"><a class="reference internal" href="bottleneck.html">torch.utils.bottleneck</a></li>
<li class="toctree-l1"><a class="reference internal" href="checkpoint.html">torch.utils.checkpoint</a></li>
<li class="toctree-l1"><a class="reference internal" href="cpp_extension.html">torch.utils.cpp_extension</a></li>
<li class="toctree-l1"><a class="reference internal" href="data.html">torch.utils.data</a></li>
<li class="toctree-l1"><a class="reference internal" href="jit_utils.html">torch.utils.jit</a></li>
<li class="toctree-l1"><a class="reference internal" href="dlpack.html">torch.utils.dlpack</a></li>
<li class="toctree-l1"><a class="reference internal" href="mobile_optimizer.html">torch.utils.mobile_optimizer</a></li>
<li class="toctree-l1"><a class="reference internal" href="model_zoo.html">torch.utils.model_zoo</a></li>
<li class="toctree-l1"><a class="reference internal" href="tensorboard.html">torch.utils.tensorboard</a></li>
<li class="toctree-l1"><a class="reference internal" href="type_info.html">Type Info</a></li>
<li class="toctree-l1"><a class="reference internal" href="named_tensor.html">Named Tensors</a></li>
<li class="toctree-l1"><a class="reference internal" href="name_inference.html">Named Tensors operator coverage</a></li>
<li class="toctree-l1"><a class="reference internal" href="config_mod.html">torch.__config__</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Libraries</span></p>
<ul>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/audio/stable">torchaudio</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/data">TorchData</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/torchrec">TorchRec</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/serve">TorchServe</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/text/stable">torchtext</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/vision/stable">torchvision</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/xla/">PyTorch on XLA Devices</a></li>
</ul>
</div>
</div>
</nav>
<div class="pytorch-container">
<div class="pytorch-page-level-bar" id="pytorch-page-level-bar">
<div class="pytorch-breadcrumbs-wrapper">
<div role="navigation" aria-label="breadcrumbs navigation">
<ul class="pytorch-breadcrumbs">
<li>
<a href="index.html">
Docs
</a> >
</li>
<li><a href="func.html">torch.func</a> ></li>
<li>UX Limitations</li>
<li class="pytorch-breadcrumbs-aside">
<a href="_sources/func.ux_limitations.rst.txt" rel="nofollow"><img src="_static/images/view-page-source-icon.svg"></a>
</li>
</ul>
</div>
</div>
<div class="pytorch-shortcuts-wrapper" id="pytorch-shortcuts-wrapper">
Shortcuts
</div>
</div>
<section data-toggle="wy-nav-shift" id="pytorch-content-wrap" class="pytorch-content-wrap">
<div class="pytorch-content-left">
<div class="rst-content">
<div role="main" class="main-content" itemscope="itemscope" itemtype="http://schema.org/Article">
<article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
<section id="ux-limitations">
<span id="id1"></span><h1>UX Limitations<a class="headerlink" href="#ux-limitations" title="Permalink to this heading">¶</a></h1>
<p>torch.func, like <a class="reference external" href="https://github.com/google/jax">JAX</a>, has restrictions around
what can be transformed. In general, JAX’s limitations are that transforms
only work with pure functions: that is, functions where the output is completely
determined by the input and that do not involve side effects (like mutation).</p>
<p>We have a similar guarantee: our transforms work well with pure functions.
However, we do support certain in-place operations. On one hand, writing code
compatible with function transforms may involve changing how you write PyTorch
code, on the other hand, you may find that our transforms let you express things
that were previously difficult to express in PyTorch.</p>
<section id="general-limitations">
<h2>General limitations<a class="headerlink" href="#general-limitations" title="Permalink to this heading">¶</a></h2>
<p>All torch.func transforms share a limitation in that a function should not
assign to global variables. Instead, all outputs to a function must be returned
from the function. This restriction comes from how torch.func is implemented:
each transform wraps Tensor inputs in special torch.func Tensor subclasses
that facilitate the transform.</p>
<p>So, instead of the following:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch.func</span> <span class="kn">import</span> <span class="n">grad</span>
<span class="c1"># Don't do this</span>
<span class="n">intermediate</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">global</span> <span class="n">intermediate</span>
<span class="n">intermediate</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">sin</span><span class="p">()</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">intermediate</span><span class="o">.</span><span class="n">sin</span><span class="p">()</span>
<span class="k">return</span> <span class="n">z</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">([])</span>
<span class="n">grad_x</span> <span class="o">=</span> <span class="n">grad</span><span class="p">(</span><span class="n">f</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
<p>Please rewrite <code class="docutils literal notranslate"><span class="pre">f</span></code> to return <code class="docutils literal notranslate"><span class="pre">intermediate</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="n">intermediate</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">sin</span><span class="p">()</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">intermediate</span><span class="o">.</span><span class="n">sin</span><span class="p">()</span>
<span class="k">return</span> <span class="n">z</span><span class="p">,</span> <span class="n">intermediate</span>
<span class="n">grad_x</span><span class="p">,</span> <span class="n">intermediate</span> <span class="o">=</span> <span class="n">grad</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">has_aux</span><span class="o">=</span><span class="kc">True</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="torch-autograd-apis">
<h2>torch.autograd APIs<a class="headerlink" href="#torch-autograd-apis" title="Permalink to this heading">¶</a></h2>
<p>If you are trying to use a <code class="docutils literal notranslate"><span class="pre">torch.autograd</span></code> API like <code class="docutils literal notranslate"><span class="pre">torch.autograd.grad</span></code>
or <code class="docutils literal notranslate"><span class="pre">torch.autograd.backward</span></code> inside of a function being transformed by
<a class="reference internal" href="generated/torch.func.vmap.html#torch.func.vmap" title="torch.func.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">vmap()</span></code></a> or one of torch.func’s AD transforms (<a class="reference internal" href="generated/torch.func.vjp.html#torch.func.vjp" title="torch.func.vjp"><code class="xref py py-func docutils literal notranslate"><span class="pre">vjp()</span></code></a>, <a class="reference internal" href="generated/torch.func.jvp.html#torch.func.jvp" title="torch.func.jvp"><code class="xref py py-func docutils literal notranslate"><span class="pre">jvp()</span></code></a>,
<a class="reference internal" href="generated/torch.func.jacrev.html#torch.func.jacrev" title="torch.func.jacrev"><code class="xref py py-func docutils literal notranslate"><span class="pre">jacrev()</span></code></a>, <a class="reference internal" href="generated/torch.func.jacfwd.html#torch.func.jacfwd" title="torch.func.jacfwd"><code class="xref py py-func docutils literal notranslate"><span class="pre">jacfwd()</span></code></a>), the transform may not be able to transform over it.
If it is unable to do so, you’ll receive an error message.</p>
<p>This is a fundamental design limitation in how PyTorch’s AD support is implemented
and the reason why we designed the torch.func library. Please instead use the torch.func
equivalents of the <code class="docutils literal notranslate"><span class="pre">torch.autograd</span></code> APIs:
- <code class="docutils literal notranslate"><span class="pre">torch.autograd.grad</span></code>, <code class="docutils literal notranslate"><span class="pre">Tensor.backward</span></code> -> <code class="docutils literal notranslate"><span class="pre">torch.func.vjp</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.func.grad</span></code>
- <code class="docutils literal notranslate"><span class="pre">torch.autograd.functional.jvp</span></code> -> <code class="docutils literal notranslate"><span class="pre">torch.func.jvp</span></code>
- <code class="docutils literal notranslate"><span class="pre">torch.autograd.functional.jacobian</span></code> -> <code class="docutils literal notranslate"><span class="pre">torch.func.jacrev</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.func.jacfwd</span></code>
- <code class="docutils literal notranslate"><span class="pre">torch.autograd.functional.hessian</span></code> -> <code class="docutils literal notranslate"><span class="pre">torch.func.hessian</span></code></p>
</section>
<section id="vmap-limitations">
<h2>vmap limitations<a class="headerlink" href="#vmap-limitations" title="Permalink to this heading">¶</a></h2>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><a class="reference internal" href="generated/torch.func.vmap.html#torch.func.vmap" title="torch.func.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">vmap()</span></code></a> is our most restrictive transform.
The grad-related transforms (<a class="reference internal" href="generated/torch.func.grad.html#torch.func.grad" title="torch.func.grad"><code class="xref py py-func docutils literal notranslate"><span class="pre">grad()</span></code></a>, <a class="reference internal" href="generated/torch.func.vjp.html#torch.func.vjp" title="torch.func.vjp"><code class="xref py py-func docutils literal notranslate"><span class="pre">vjp()</span></code></a>, <a class="reference internal" href="generated/torch.func.jvp.html#torch.func.jvp" title="torch.func.jvp"><code class="xref py py-func docutils literal notranslate"><span class="pre">jvp()</span></code></a>) do not
have these limitations. <a class="reference internal" href="generated/torch.func.jacfwd.html#torch.func.jacfwd" title="torch.func.jacfwd"><code class="xref py py-func docutils literal notranslate"><span class="pre">jacfwd()</span></code></a> (and <a class="reference internal" href="generated/torch.func.hessian.html#torch.func.hessian" title="torch.func.hessian"><code class="xref py py-func docutils literal notranslate"><span class="pre">hessian()</span></code></a>, which is
implemented with <a class="reference internal" href="generated/torch.func.jacfwd.html#torch.func.jacfwd" title="torch.func.jacfwd"><code class="xref py py-func docutils literal notranslate"><span class="pre">jacfwd()</span></code></a>) is a composition of <a class="reference internal" href="generated/torch.func.vmap.html#torch.func.vmap" title="torch.func.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">vmap()</span></code></a> and
<a class="reference internal" href="generated/torch.func.jvp.html#torch.func.jvp" title="torch.func.jvp"><code class="xref py py-func docutils literal notranslate"><span class="pre">jvp()</span></code></a> so it also has these limitations.</p>
</div>
<p><code class="docutils literal notranslate"><span class="pre">vmap(func)</span></code> is a transform that returns a function that maps <code class="docutils literal notranslate"><span class="pre">func</span></code> over
some new dimension of each input Tensor. The mental model for vmap is that it is
like running a for-loop: for pure functions (i.e. in the absence of side
effects), <code class="docutils literal notranslate"><span class="pre">vmap(f)(x)</span></code> is equivalent to:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">([</span><span class="n">f</span><span class="p">(</span><span class="n">x_i</span><span class="p">)</span> <span class="k">for</span> <span class="n">x_i</span> <span class="ow">in</span> <span class="n">x</span><span class="o">.</span><span class="n">unbind</span><span class="p">(</span><span class="mi">0</span><span class="p">)])</span>
</pre></div>
</div>
<section id="mutation-arbitrary-mutation-of-python-data-structures">
<h3>Mutation: Arbitrary mutation of Python data structures<a class="headerlink" href="#mutation-arbitrary-mutation-of-python-data-structures" title="Permalink to this heading">¶</a></h3>
<p>In the presence of side effects, <a class="reference internal" href="generated/torch.func.vmap.html#torch.func.vmap" title="torch.func.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">vmap()</span></code></a> no longer acts like it is running
a for-loop. For example, the following function:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
<span class="nb">list</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"hello!"</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">lst</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</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="n">result</span> <span class="o">=</span> <span class="n">vmap</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">in_dims</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="kc">None</span><span class="p">))(</span><span class="n">x</span><span class="p">,</span> <span class="n">lst</span><span class="p">)</span>
</pre></div>
</div>
<p>will print “hello!” once and pop only one element from <code class="docutils literal notranslate"><span class="pre">lst</span></code>.</p>
<p><a class="reference internal" href="generated/torch.func.vmap.html#torch.func.vmap" title="torch.func.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">vmap()</span></code></a> executes <code class="docutils literal notranslate"><span class="pre">f</span></code> a single time, so all side effects only happen once.</p>
<p>This is a consequence of how vmap is implemented. torch.func has a special,
internal BatchedTensor class. <code class="docutils literal notranslate"><span class="pre">vmap(f)(*inputs)</span></code> takes all Tensor inputs,
turns them into BatchedTensors, and calls <code class="docutils literal notranslate"><span class="pre">f(*batched_tensor_inputs)</span></code>.
BatchedTensor overrides the PyTorch API to produce batched (i.e. vectorized)
behavior for each PyTorch operator.</p>
</section>
<section id="mutation-in-place-pytorch-operations">
<h3>Mutation: in-place PyTorch Operations<a class="headerlink" href="#mutation-in-place-pytorch-operations" title="Permalink to this heading">¶</a></h3>
<p>You might be here due to receiving an error about vmap-incompatible in-place
operations. <a class="reference internal" href="generated/torch.func.vmap.html#torch.func.vmap" title="torch.func.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">vmap()</span></code></a> will raise an error if it encounters an unsupported PyTorch
in-place operation and it will succeed otherwise. Unsupported operations
are those that would cause a Tensor with more elements to be written to a
Tensor with fewer elements. Here’s an example of how this can occur:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">x</span><span class="o">.</span><span class="n">add_</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="c1"># When vmapped over, looks like it has shape [1]</span>
<span class="c1"># Raises an error because `x` has fewer elements than `y`.</span>
<span class="n">vmap</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">in_dims</span><span class="o">=</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="mi">0</span><span class="p">))(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">x</span></code> is a Tensor with one element, <code class="docutils literal notranslate"><span class="pre">y</span></code> is a Tensor with three elements.
<code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">+</span> <span class="pre">y</span></code> has three elements (due to broadcasting), but attempting to write
three elements back into <code class="docutils literal notranslate"><span class="pre">x</span></code>, which only has one element, raises an error
due to attempting to write three elements into a Tensor with a single element.</p>
<p>There is no problem if the Tensor being written to is batched under
<a class="reference internal" href="generated/torch.vmap.html#torch.vmap" title="torch.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">vmap()</span></code></a> (i.e. it is being vmapped over).</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">x</span><span class="o">.</span><span class="n">add_</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">expected</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
<span class="c1"># Does not raise an error because x is being vmapped over.</span>
<span class="n">vmap</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">in_dims</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">))(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">expected</span><span class="p">)</span>
</pre></div>
</div>
<p>One common fix for this is to replace calls to factory functions with
their “new_*” equivalent. For example:</p>
<ul class="simple">
<li><p>Replace <a class="reference internal" href="generated/torch.zeros.html#torch.zeros" title="torch.zeros"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.zeros()</span></code></a> with <code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.new_zeros()</span></code></p></li>
<li><p>Replace <a class="reference internal" href="generated/torch.empty.html#torch.empty" title="torch.empty"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.empty()</span></code></a> with <code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.new_empty()</span></code></p></li>
</ul>
<p>To see why this helps, consider the following.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">diag_embed</span><span class="p">(</span><span class="n">vec</span><span class="p">):</span>
<span class="k">assert</span> <span class="n">vec</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">==</span> <span class="mi">1</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">vec</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">vec</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">result</span><span class="o">.</span><span class="n">diagonal</span><span class="p">()</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">vec</span><span class="p">)</span>
<span class="k">return</span> <span class="n">result</span>
<span class="n">vecs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([[</span><span class="mf">0.</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="p">[</span><span class="mf">3.</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">]])</span>
<span class="c1"># RuntimeError: vmap: inplace arithmetic(self, *extra_args) is not possible ...</span>
<span class="n">vmap</span><span class="p">(</span><span class="n">diag_embed</span><span class="p">)(</span><span class="n">vecs</span><span class="p">)</span>
</pre></div>
</div>
<p>Inside of <a class="reference internal" href="generated/torch.vmap.html#torch.vmap" title="torch.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">vmap()</span></code></a>, <code class="docutils literal notranslate"><span class="pre">result</span></code> is a Tensor of shape [3, 3].
However, although <code class="docutils literal notranslate"><span class="pre">vec</span></code> looks like it has shape [3], <code class="docutils literal notranslate"><span class="pre">vec</span></code> actually has
underlying shape [2, 3].
It is not possible to copy <code class="docutils literal notranslate"><span class="pre">vec</span></code> into <code class="docutils literal notranslate"><span class="pre">result.diagonal()</span></code>, which has
shape [3], because it has too many elements.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">diag_embed</span><span class="p">(</span><span class="n">vec</span><span class="p">):</span>
<span class="k">assert</span> <span class="n">vec</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">==</span> <span class="mi">1</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">vec</span><span class="o">.</span><span class="n">new_zeros</span><span class="p">(</span><span class="n">vec</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">vec</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">result</span><span class="o">.</span><span class="n">diagonal</span><span class="p">()</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">vec</span><span class="p">)</span>
<span class="k">return</span> <span class="n">result</span>
<span class="n">vecs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([[</span><span class="mf">0.</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="p">[</span><span class="mf">3.</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">]])</span>
<span class="n">vmap</span><span class="p">(</span><span class="n">diag_embed</span><span class="p">)(</span><span class="n">vecs</span><span class="p">)</span>
</pre></div>
</div>
<p>Replacing <a class="reference internal" href="generated/torch.zeros.html#torch.zeros" title="torch.zeros"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.zeros()</span></code></a> with <code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.new_zeros()</span></code> makes it so that
<code class="docutils literal notranslate"><span class="pre">result</span></code> has an underlying Tensor of shape [2, 3, 3], so it is now possible
to copy <code class="docutils literal notranslate"><span class="pre">vec</span></code>, which has underlying shape [2, 3], into <code class="docutils literal notranslate"><span class="pre">result.diagonal()</span></code>.</p>
</section>
<section id="mutation-out-pytorch-operations">
<h3>Mutation: out= PyTorch Operations<a class="headerlink" href="#mutation-out-pytorch-operations" title="Permalink to this heading">¶</a></h3>
<p><a class="reference internal" href="generated/torch.func.vmap.html#torch.func.vmap" title="torch.func.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">vmap()</span></code></a> doesn’t support the <code class="docutils literal notranslate"><span class="pre">out=</span></code> keyword argument in PyTorch operations.
It will error out gracefully if it encounters that in your code.</p>
<p>This is not a fundamental limitation; we could theoretically support this in the
future but we have chosen not to for now.</p>
</section>
<section id="data-dependent-python-control-flow">
<h3>Data-dependent Python control flow<a class="headerlink" href="#data-dependent-python-control-flow" title="Permalink to this heading">¶</a></h3>
<p>We don’t yet support <code class="docutils literal notranslate"><span class="pre">vmap</span></code> over data-dependent control flow. Data-dependent
control flow is when the condition of an if-statement, while-loop, or
for-loop is a Tensor that is being <code class="docutils literal notranslate"><span class="pre">vmap</span></code>’ed over. For example, the
following will raise an error message:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">relu</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">if</span> <span class="n">x</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="n">x</span>
<span class="k">return</span> <span class="mi">0</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="n">vmap</span><span class="p">(</span><span class="n">relu</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
<p>However, any control flow that is not dependent on the values in <code class="docutils literal notranslate"><span class="pre">vmap</span></code>’ed
tensors will work:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">custom_dot</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="n">vmap</span><span class="p">(</span><span class="n">custom_dot</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
<p>JAX supports transforming over
<a class="reference external" href="https://jax.readthedocs.io/en/latest/jax.lax.html#control-flow-operators">data-dependent control flow</a>
using special control flow operators (e.g. <code class="docutils literal notranslate"><span class="pre">jax.lax.cond</span></code>, <code class="docutils literal notranslate"><span class="pre">jax.lax.while_loop</span></code>).
We’re investigating adding equivalents of those to PyTorch.</p>
</section>
<section id="data-dependent-operations-item">
<h3>Data-dependent operations (.item())<a class="headerlink" href="#data-dependent-operations-item" title="Permalink to this heading">¶</a></h3>
<p>We do not (and will not) support vmap over a user-defined function that calls
<code class="docutils literal notranslate"><span class="pre">.item()</span></code> on a Tensor. For example, the following will raise an error message:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="n">vmap</span><span class="p">(</span><span class="n">f</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
<p>Please try to rewrite your code to not use <code class="docutils literal notranslate"><span class="pre">.item()</span></code> calls.</p>
<p>You may also encounter an error message about using <code class="docutils literal notranslate"><span class="pre">.item()</span></code> but you might
not have used it. In those cases, it is possible that PyTorch internally is
calling <code class="docutils literal notranslate"><span class="pre">.item()</span></code> – please file an issue on GitHub and we’ll fix
PyTorch internals.</p>
</section>
<section id="dynamic-shape-operations-nonzero-and-friends">
<h3>Dynamic shape operations (nonzero and friends)<a class="headerlink" href="#dynamic-shape-operations-nonzero-and-friends" title="Permalink to this heading">¶</a></h3>
<p><code class="docutils literal notranslate"><span class="pre">vmap(f)</span></code> requires that <code class="docutils literal notranslate"><span class="pre">f</span></code> applied to every “example” in your input
returns a Tensor with the same shape. Operations such as <code class="docutils literal notranslate"><span class="pre">torch.nonzero</span></code>,
<code class="docutils literal notranslate"><span class="pre">torch.is_nonzero</span></code> are not supported and will error as a result.</p>
<p>To see why, consider the following example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">xs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([[</span><span class="mi">0</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="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">]])</span>
<span class="n">vmap</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nonzero</span><span class="p">)(</span><span class="n">xs</span><span class="p">)</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">torch.nonzero(xs[0])</span></code> returns a Tensor of shape 2;
but <code class="docutils literal notranslate"><span class="pre">torch.nonzero(xs[1])</span></code> returns a Tensor of shape 1.
We are unable to construct a single Tensor as an output;
the output would need to be a ragged Tensor (and PyTorch does not yet have
the concept of a ragged Tensor).</p>
</section>
</section>
<section id="randomness">
<h2>Randomness<a class="headerlink" href="#randomness" title="Permalink to this heading">¶</a></h2>
<p>The user’s intention when calling a random operation can be unclear. Specifically, some users may want
the random behavior to be the same across batches while others may want it to differ across batches.
To address this, <code class="docutils literal notranslate"><span class="pre">vmap</span></code> takes a randomness flag.</p>
<p>The flag can only be passed to vmap and can take on 3 values, “error,” “different,” or “same,” defaulting
to error. Under “error” mode, any call to a random function will produce an error asking the user to use
one of the other two flags based on their use case.</p>
<p>Under “different” randomness, elements in a batch produce different random values. For instance,</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">add_noise</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(())</span> <span class="c1"># y will be different across the batch</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">vmap</span><span class="p">(</span><span class="n">add_noise</span><span class="p">,</span> <span class="n">randomness</span><span class="o">=</span><span class="s2">"different"</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span> <span class="c1"># we get 3 different values</span>
</pre></div>
</div>
<p>Under “same” randomness, elements in a batch produce same random values. For instance,</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">add_noise</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(())</span> <span class="c1"># y will be the same across the batch</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">vmap</span><span class="p">(</span><span class="n">add_noise</span><span class="p">,</span> <span class="n">randomness</span><span class="o">=</span><span class="s2">"same"</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span> <span class="c1"># we get the same value, repeated 3 times</span>
</pre></div>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Our system only determine the randomness behavior of PyTorch operators and cannot control the
behavior of other libraries, like numpy. This is similar to JAX’s limitations with their solutions</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Multiple vmap calls using either type of supported randomness will not produce
the same results. Like with standard PyTorch, a user can get randomness reproducibility through
either using <code class="docutils literal notranslate"><span class="pre">torch.manual_seed()</span></code> outside of vmap or by using generators.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Finally, our randomness differs from JAX because we aren’t using a stateless PRNG, in part because PyTorch
doesn’t have full support for a stateless PRNG. Instead, we’ve introduced a flag system to allow for the
most common forms of randomness that we see. If your use case does not fit these forms of randomness, please
file an issue.</p>
</div>
</section>
</section>
</article>
</div>
<footer>
<div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
<a href="func.migrating.html" class="btn btn-neutral float-right" title="Migrating from functorch to torch.func" accesskey="n" rel="next">Next <img src="_static/images/chevron-right-orange.svg" class="next-page"></a>
<a href="func.batch_norm.html" class="btn btn-neutral" title="Patching Batch Norm" accesskey="p" rel="prev"><img src="_static/images/chevron-right-orange.svg" class="previous-page"> Previous</a>
</div>
<hr>
<div role="contentinfo">
<p>
© Copyright 2023, PyTorch Contributors.
</p>
</div>
<div>
Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
</div>
</footer>
</div>
<script>
var match = window.location.href.match(/\/_[a-zA-Z0-9_]*.html|_dynamo/gi);
var url = window.location.href.lastIndexOf(match[match.length-1]);
if (url)
{
var div = '<div class="admonition note"><p class="admonition-title">Note</p><p><i class="fa fa-exclamation-circle" aria-hidden="true"> </i> This page describes an internal API which is not intended to be used outside of the PyTorch codebase and can be modified or removed without notice.</p></div>'
document.getElementById("pytorch-article").insertAdjacentHTML('afterBegin', div)
}
</script>
</div>
<div class="pytorch-content-right" id="pytorch-content-right">
<div class="pytorch-right-menu" id="pytorch-right-menu">
<div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
<ul>
<li><a class="reference internal" href="#">UX Limitations</a><ul>
<li><a class="reference internal" href="#general-limitations">General limitations</a></li>
<li><a class="reference internal" href="#torch-autograd-apis">torch.autograd APIs</a></li>
<li><a class="reference internal" href="#vmap-limitations">vmap limitations</a><ul>
<li><a class="reference internal" href="#mutation-arbitrary-mutation-of-python-data-structures">Mutation: Arbitrary mutation of Python data structures</a></li>
<li><a class="reference internal" href="#mutation-in-place-pytorch-operations">Mutation: in-place PyTorch Operations</a></li>
<li><a class="reference internal" href="#mutation-out-pytorch-operations">Mutation: out= PyTorch Operations</a></li>
<li><a class="reference internal" href="#data-dependent-python-control-flow">Data-dependent Python control flow</a></li>
<li><a class="reference internal" href="#data-dependent-operations-item">Data-dependent operations (.item())</a></li>
<li><a class="reference internal" href="#dynamic-shape-operations-nonzero-and-friends">Dynamic shape operations (nonzero and friends)</a></li>
</ul>
</li>
<li><a class="reference internal" href="#randomness">Randomness</a></li>
</ul>
</li>
</ul>
</div>
</div>
</div>
</section>
</div>
<script type="text/javascript" id="documentation_options" data-url_root="./" src="_static/documentation_options.js"></script>
<script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
<script src="_static/jquery.js"></script>
<script src="_static/underscore.js"></script>
<script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
<script src="_static/doctools.js"></script>
<script src="_static/clipboard.min.js"></script>
<script src="_static/copybutton.js"></script>
<script type="text/javascript" src="_static/js/vendor/popper.min.js"></script>
<script type="text/javascript" src="_static/js/vendor/bootstrap.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/list.js/1.5.0/list.min.js"></script>
<script type="text/javascript" src="_static/js/theme.js"></script>
<script type="text/javascript">
jQuery(function () {
SphinxRtdTheme.Navigation.enable(true);
});
</script>
<script script type="text/javascript">
var collapsedSections = ['Developer Notes', 'Language Bindings', 'Libraries', 'Community'];
</script>
<img height="1" width="1" style="border-style:none;" alt="" src="https://www.googleadservices.com/pagead/conversion/795629140/?label=txkmCPmdtosBENSssfsC&guid=ON&script=0"/>
<!-- Begin Footer -->
<div class="container-fluid docs-tutorials-resources" id="docs-tutorials-resources">
<div class="container">
<div class="row">
<div class="col-md-4 text-center">
<h2>Docs</h2>
<p>Access comprehensive developer documentation for PyTorch</p>
<a class="with-right-arrow" href="https://pytorch.org/docs/stable/index.html">View Docs</a>
</div>
<div class="col-md-4 text-center">
<h2>Tutorials</h2>
<p>Get in-depth tutorials for beginners and advanced developers</p>
<a class="with-right-arrow" href="https://pytorch.org/tutorials">View Tutorials</a>
</div>
<div class="col-md-4 text-center">
<h2>Resources</h2>
<p>Find development resources and get your questions answered</p>
<a class="with-right-arrow" href="https://pytorch.org/resources">View Resources</a>
</div>
</div>
</div>
</div>
<footer class="site-footer">
<div class="container footer-container">
<div class="footer-logo-wrapper">
<a href="https://pytorch.org/" class="footer-logo"></a>
</div>
<div class="footer-links-wrapper">
<div class="footer-links-col">
<ul>
<li class="list-title"><a href="https://pytorch.org/">PyTorch</a></li>
<li><a href="https://pytorch.org/get-started">Get Started</a></li>
<li><a href="https://pytorch.org/features">Features</a></li>
<li><a href="https://pytorch.org/ecosystem">Ecosystem</a></li>
<li><a href="https://pytorch.org/blog/">Blog</a></li>
<li><a href="https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md">Contributing</a></li>
</ul>
</div>
<div class="footer-links-col">
<ul>
<li class="list-title"><a href="https://pytorch.org/resources">Resources</a></li>
<li><a href="https://pytorch.org/tutorials">Tutorials</a></li>
<li><a href="https://pytorch.org/docs/stable/index.html">Docs</a></li>
<li><a href="https://discuss.pytorch.org" target="_blank">Discuss</a></li>
<li><a href="https://github.com/pytorch/pytorch/issues" target="_blank">Github Issues</a></li>
<li><a href="https://pytorch.org/assets/brand-guidelines/PyTorch-Brand-Guidelines.pdf" target="_blank">Brand Guidelines</a></li>
</ul>
</div>
<div class="footer-links-col">
<ul>
<li class="list-title">Stay up to date</li>
<li><a href="https://www.facebook.com/pytorch" target="_blank">Facebook</a></li>
<li><a href="https://twitter.com/pytorch" target="_blank">Twitter</a></li>
<li><a href="https://www.youtube.com/pytorch" target="_blank">YouTube</a></li>
<li><a href="https://www.linkedin.com/company/pytorch" target="_blank">LinkedIn</a></li>
</ul>
</div>
<div class="footer-links-col">
<ul>
<li class="list-title">PyTorch Podcasts</li>
<li><a href="https://open.spotify.com/show/6UzHKeiy368jKfQMKKvJY5" target="_blank">Spotify</a></li>
<li><a href="https://podcasts.apple.com/us/podcast/pytorch-developer-podcast/id1566080008" target="_blank">Apple</a></li>
<li><a href="https://www.google.com/podcasts?feed=aHR0cHM6Ly9mZWVkcy5zaW1wbGVjYXN0LmNvbS9PQjVGa0lsOA%3D%3D" target="_blank">Google</a></li>
<li><a href="https://music.amazon.com/podcasts/7a4e6f0e-26c2-49e9-a478-41bd244197d0/PyTorch-Developer-Podcast?" target="_blank">Amazon</a></li>
</ul>
</div>
</div>
<div class="privacy-policy">
<ul>
<li class="privacy-policy-links"><a href="https://www.linuxfoundation.org/terms/" target="_blank">Terms</a></li>
<li class="privacy-policy-links">|</li>
<li class="privacy-policy-links"><a href="https://www.linuxfoundation.org/privacy-policy/" target="_blank">Privacy</a></li>
</ul>
</div>
<div class="copyright">
<p>© Copyright The Linux Foundation. The PyTorch Foundation is a project of The Linux Foundation.
For web site terms of use, trademark policy and other policies applicable to The PyTorch Foundation please see
<a href="www.linuxfoundation.org/policies/">www.linuxfoundation.org/policies/</a>. The PyTorch Foundation supports the PyTorch open source
project, which has been established as PyTorch Project a Series of LF Projects, LLC. For policies applicable to the PyTorch Project a Series of LF Projects, LLC,
please see <a href="www.lfprojects.org/policies/">www.lfprojects.org/policies/</a>.</p>
</div>
</div>
</footer>
<div class="cookie-banner-wrapper">
<div class="container">
<p class="gdpr-notice">To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: <a href="https://www.facebook.com/policies/cookies/">Cookies Policy</a>.</p>
<img class="close-button" src="_static/images/pytorch-x.svg">
</div>
</div>
<!-- End Footer -->
<!-- Begin Mobile Menu -->
<div class="mobile-main-menu">
<div class="container-fluid">
<div class="container">
<div class="mobile-main-menu-header-container">
<a class="header-logo" href="https://pytorch.org/" aria-label="PyTorch"></a>
<a class="main-menu-close-button" href="#" data-behavior="close-mobile-menu"></a>
</div>
</div>
</div>
<div class="mobile-main-menu-links-container">
<div class="main-menu">
<ul>
<li>
<a href="https://pytorch.org/get-started">Get Started</a>
</li>
<li>
<a href="https://pytorch.org/ecosystem">Ecosystem</a>
</li>
<li>
<a href="https://pytorch.org/mobile">Mobile</a>
</li>
<li>
<a href="https://pytorch.org/blog/">Blog</a>
</li>