forked from pytorch/pytorch.github.io
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtorch.html
1655 lines (1305 loc) · 133 KB
/
torch.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 name="robots" content="noindex">
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>torch — PyTorch 1.12 documentation</title>
<link rel="canonical" href="https://pytorch.org/docs/stable/_modules/torch.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/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/panels-main.c949a650a448cc0ae9fd3441c0e17fb0.css" type="text/css" />
<link rel="stylesheet" href="../_static/panels-variables.06eb56fa6e07937060861dad626602ad.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" />
<!-- 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/torchrec">
<span class="dropdown-title">TorchRec</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/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/#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/resources">
<span class="dropdown-title">Developer Resources</span>
<p>Find resources and get questions answered</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'>1.12 ▼</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"><span class="caption-text">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/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"><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"><span class="caption-text">Python API</span></p>
<ul>
<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="../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="../distributions.html">torch.distributions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../fft.html">torch.fft</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="../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="../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="../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="../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"><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="http://pytorch.org/xla/">PyTorch on XLA Devices</a></li>
</ul>
<p class="caption"><span class="caption-text">Community</span></p>
<ul>
<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/governance.html">PyTorch Governance</a></li>
<li class="toctree-l1"><a class="reference internal" href="../community/persons_of_interest.html">PyTorch Governance | Persons of Interest</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="index.html">Module code</a> ></li>
<li>torch</li>
<li class="pytorch-breadcrumbs-aside">
</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">
<h1>Source code for torch</h1><div class="highlight"><pre>
<span></span>
<span class="sa">r</span><span class="sd">"""</span>
<span class="sd">The torch package contains data structures for multi-dimensional</span>
<span class="sd">tensors and defines mathematical operations over these tensors.</span>
<span class="sd">Additionally, it provides many utilities for efficient serializing of</span>
<span class="sd">Tensors and arbitrary types, and other useful utilities.</span>
<span class="sd">It has a CUDA counterpart, that enables you to run your tensor computations</span>
<span class="sd">on an NVIDIA GPU with compute capability >= 3.0.</span>
<span class="sd">"""</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">platform</span>
<span class="kn">import</span> <span class="nn">textwrap</span>
<span class="kn">import</span> <span class="nn">ctypes</span>
<span class="kn">import</span> <span class="nn">warnings</span>
<span class="kn">import</span> <span class="nn">inspect</span>
<span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">version_info</span> <span class="o"><</span> <span class="p">(</span><span class="mi">3</span><span class="p">,):</span>
<span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">"Python 2 has reached end-of-life and is no longer supported by PyTorch."</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">._utils</span> <span class="kn">import</span> <span class="n">_import_dotted_name</span><span class="p">,</span> <span class="n">classproperty</span>
<span class="kn">from</span> <span class="nn">._utils_internal</span> <span class="kn">import</span> <span class="n">get_file_path</span><span class="p">,</span> <span class="n">prepare_multiprocessing_environment</span><span class="p">,</span> \
<span class="n">USE_RTLD_GLOBAL_WITH_LIBTORCH</span><span class="p">,</span> <span class="n">USE_GLOBAL_DEPS</span>
<span class="c1"># TODO(torch_deploy) figure out how to freeze version.py in fbcode build</span>
<span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">executable</span> <span class="o">==</span> <span class="s1">'torch_deploy'</span><span class="p">:</span>
<span class="n">__version__</span> <span class="o">=</span> <span class="s2">"torch-deploy-1.8"</span>
<span class="k">else</span><span class="p">:</span>
<span class="kn">from</span> <span class="nn">.torch_version</span> <span class="kn">import</span> <span class="n">__version__</span> <span class="k">as</span> <span class="n">__version__</span>
<span class="kn">from</span> <span class="nn">._six</span> <span class="kn">import</span> <span class="n">string_classes</span> <span class="k">as</span> <span class="n">_string_classes</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Set</span><span class="p">,</span> <span class="n">Type</span><span class="p">,</span> <span class="n">TYPE_CHECKING</span><span class="p">,</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Callable</span>
<span class="kn">import</span> <span class="nn">builtins</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
<span class="s1">'typename'</span><span class="p">,</span> <span class="s1">'is_tensor'</span><span class="p">,</span> <span class="s1">'is_storage'</span><span class="p">,</span> <span class="s1">'set_default_tensor_type'</span><span class="p">,</span>
<span class="s1">'set_rng_state'</span><span class="p">,</span> <span class="s1">'get_rng_state'</span><span class="p">,</span> <span class="s1">'manual_seed'</span><span class="p">,</span> <span class="s1">'initial_seed'</span><span class="p">,</span> <span class="s1">'seed'</span><span class="p">,</span>
<span class="s1">'save'</span><span class="p">,</span> <span class="s1">'load'</span><span class="p">,</span> <span class="s1">'set_printoptions'</span><span class="p">,</span> <span class="s1">'chunk'</span><span class="p">,</span> <span class="s1">'split'</span><span class="p">,</span> <span class="s1">'stack'</span><span class="p">,</span> <span class="s1">'matmul'</span><span class="p">,</span>
<span class="s1">'no_grad'</span><span class="p">,</span> <span class="s1">'enable_grad'</span><span class="p">,</span> <span class="s1">'rand'</span><span class="p">,</span> <span class="s1">'randn'</span><span class="p">,</span> <span class="s1">'inference_mode'</span><span class="p">,</span>
<span class="s1">'DoubleStorage'</span><span class="p">,</span> <span class="s1">'FloatStorage'</span><span class="p">,</span> <span class="s1">'LongStorage'</span><span class="p">,</span> <span class="s1">'IntStorage'</span><span class="p">,</span>
<span class="s1">'ShortStorage'</span><span class="p">,</span> <span class="s1">'CharStorage'</span><span class="p">,</span> <span class="s1">'ByteStorage'</span><span class="p">,</span> <span class="s1">'BoolStorage'</span><span class="p">,</span>
<span class="s1">'_TypedStorage'</span><span class="p">,</span>
<span class="s1">'DoubleTensor'</span><span class="p">,</span> <span class="s1">'FloatTensor'</span><span class="p">,</span> <span class="s1">'LongTensor'</span><span class="p">,</span> <span class="s1">'IntTensor'</span><span class="p">,</span>
<span class="s1">'ShortTensor'</span><span class="p">,</span> <span class="s1">'CharTensor'</span><span class="p">,</span> <span class="s1">'ByteTensor'</span><span class="p">,</span> <span class="s1">'BoolTensor'</span><span class="p">,</span> <span class="s1">'Tensor'</span><span class="p">,</span>
<span class="s1">'lobpcg'</span><span class="p">,</span> <span class="s1">'use_deterministic_algorithms'</span><span class="p">,</span>
<span class="s1">'are_deterministic_algorithms_enabled'</span><span class="p">,</span>
<span class="s1">'is_deterministic_algorithms_warn_only_enabled'</span><span class="p">,</span>
<span class="s1">'set_deterministic_debug_mode'</span><span class="p">,</span> <span class="s1">'get_deterministic_debug_mode'</span><span class="p">,</span>
<span class="s1">'set_float32_matmul_precision'</span><span class="p">,</span> <span class="s1">'get_float32_matmul_precision'</span><span class="p">,</span>
<span class="s1">'set_warn_always'</span><span class="p">,</span> <span class="s1">'is_warn_always_enabled'</span><span class="p">,</span>
<span class="p">]</span>
<span class="c1">################################################################################</span>
<span class="c1"># Load the extension module</span>
<span class="c1">################################################################################</span>
<span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">platform</span> <span class="o">==</span> <span class="s1">'win32'</span><span class="p">:</span>
<span class="n">pfiles_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s1">'ProgramFiles'</span><span class="p">,</span> <span class="s1">'C:</span><span class="se">\\</span><span class="s1">Program Files'</span><span class="p">)</span>
<span class="n">py_dll_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">exec_prefix</span><span class="p">,</span> <span class="s1">'Library'</span><span class="p">,</span> <span class="s1">'bin'</span><span class="p">)</span>
<span class="n">th_dll_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="vm">__file__</span><span class="p">),</span> <span class="s1">'lib'</span><span class="p">)</span>
<span class="c1"># When users create a virtualenv that inherits the base environment,</span>
<span class="c1"># we will need to add the corresponding library directory into</span>
<span class="c1"># DLL search directories. Otherwise, it will rely on `PATH` which</span>
<span class="c1"># is dependent on user settings.</span>
<span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">exec_prefix</span> <span class="o">!=</span> <span class="n">sys</span><span class="o">.</span><span class="n">base_exec_prefix</span><span class="p">:</span>
<span class="n">base_py_dll_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">base_exec_prefix</span><span class="p">,</span> <span class="s1">'Library'</span><span class="p">,</span> <span class="s1">'bin'</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">base_py_dll_path</span> <span class="o">=</span> <span class="s1">''</span>
<span class="n">dll_paths</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">,</span> <span class="p">[</span><span class="n">th_dll_path</span><span class="p">,</span> <span class="n">py_dll_path</span><span class="p">,</span> <span class="n">base_py_dll_path</span><span class="p">]))</span>
<span class="k">if</span> <span class="nb">all</span><span class="p">([</span><span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="s1">'nvToolsExt64_1.dll'</span><span class="p">))</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">dll_paths</span><span class="p">]):</span>
<span class="n">nvtoolsext_dll_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
<span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s1">'NVTOOLSEXT_PATH'</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">pfiles_path</span><span class="p">,</span> <span class="s1">'NVIDIA Corporation'</span><span class="p">,</span> <span class="s1">'NvToolsExt'</span><span class="p">)),</span> <span class="s1">'bin'</span><span class="p">,</span> <span class="s1">'x64'</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">nvtoolsext_dll_path</span> <span class="o">=</span> <span class="s1">''</span>
<span class="kn">from</span> <span class="nn">.version</span> <span class="kn">import</span> <span class="n">cuda</span> <span class="k">as</span> <span class="n">cuda_version</span>
<span class="kn">import</span> <span class="nn">glob</span>
<span class="k">if</span> <span class="n">cuda_version</span> <span class="ow">and</span> <span class="nb">all</span><span class="p">([</span><span class="ow">not</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="s1">'cudart64*.dll'</span><span class="p">))</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">dll_paths</span><span class="p">]):</span>
<span class="n">cuda_version_1</span> <span class="o">=</span> <span class="n">cuda_version</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">'.'</span><span class="p">,</span> <span class="s1">'_'</span><span class="p">)</span>
<span class="n">cuda_path_var</span> <span class="o">=</span> <span class="s1">'CUDA_PATH_V'</span> <span class="o">+</span> <span class="n">cuda_version_1</span>
<span class="n">default_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">pfiles_path</span><span class="p">,</span> <span class="s1">'NVIDIA GPU Computing Toolkit'</span><span class="p">,</span> <span class="s1">'CUDA'</span><span class="p">,</span> <span class="s1">'v'</span> <span class="o">+</span> <span class="n">cuda_version</span><span class="p">)</span>
<span class="n">cuda_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="n">cuda_path_var</span><span class="p">,</span> <span class="n">default_path</span><span class="p">),</span> <span class="s1">'bin'</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">cuda_path</span> <span class="o">=</span> <span class="s1">''</span>
<span class="n">dll_paths</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">,</span> <span class="p">[</span><span class="n">nvtoolsext_dll_path</span><span class="p">,</span> <span class="n">cuda_path</span><span class="p">]))</span>
<span class="n">kernel32</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">WinDLL</span><span class="p">(</span><span class="s1">'kernel32.dll'</span><span class="p">,</span> <span class="n">use_last_error</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">with_load_library_flags</span> <span class="o">=</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">kernel32</span><span class="p">,</span> <span class="s1">'AddDllDirectory'</span><span class="p">)</span>
<span class="n">prev_error_mode</span> <span class="o">=</span> <span class="n">kernel32</span><span class="o">.</span><span class="n">SetErrorMode</span><span class="p">(</span><span class="mh">0x0001</span><span class="p">)</span>
<span class="n">kernel32</span><span class="o">.</span><span class="n">LoadLibraryW</span><span class="o">.</span><span class="n">restype</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_void_p</span>
<span class="k">if</span> <span class="n">with_load_library_flags</span><span class="p">:</span>
<span class="n">kernel32</span><span class="o">.</span><span class="n">AddDllDirectory</span><span class="o">.</span><span class="n">restype</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_void_p</span>
<span class="n">kernel32</span><span class="o">.</span><span class="n">LoadLibraryExW</span><span class="o">.</span><span class="n">restype</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_void_p</span>
<span class="k">for</span> <span class="n">dll_path</span> <span class="ow">in</span> <span class="n">dll_paths</span><span class="p">:</span>
<span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">version_info</span> <span class="o">>=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">8</span><span class="p">):</span>
<span class="n">os</span><span class="o">.</span><span class="n">add_dll_directory</span><span class="p">(</span><span class="n">dll_path</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">with_load_library_flags</span><span class="p">:</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">kernel32</span><span class="o">.</span><span class="n">AddDllDirectory</span><span class="p">(</span><span class="n">dll_path</span><span class="p">)</span>
<span class="k">if</span> <span class="n">res</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">err</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">WinError</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">get_last_error</span><span class="p">())</span>
<span class="n">err</span><span class="o">.</span><span class="n">strerror</span> <span class="o">+=</span> <span class="sa">f</span><span class="s1">' Error adding "</span><span class="si">{</span><span class="n">dll_path</span><span class="si">}</span><span class="s1">" to the DLL directories.'</span>
<span class="k">raise</span> <span class="n">err</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">ctypes</span><span class="o">.</span><span class="n">CDLL</span><span class="p">(</span><span class="s1">'vcruntime140.dll'</span><span class="p">)</span>
<span class="n">ctypes</span><span class="o">.</span><span class="n">CDLL</span><span class="p">(</span><span class="s1">'msvcp140.dll'</span><span class="p">)</span>
<span class="n">ctypes</span><span class="o">.</span><span class="n">CDLL</span><span class="p">(</span><span class="s1">'vcruntime140_1.dll'</span><span class="p">)</span>
<span class="k">except</span> <span class="ne">OSError</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'''Microsoft Visual C++ Redistributable is not installed, this may lead to the DLL load failure.</span>
<span class="s1"> It can be downloaded at https://aka.ms/vs/16/release/vc_redist.x64.exe'''</span><span class="p">)</span>
<span class="n">dlls</span> <span class="o">=</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">th_dll_path</span><span class="p">,</span> <span class="s1">'*.dll'</span><span class="p">))</span>
<span class="n">path_patched</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">for</span> <span class="n">dll</span> <span class="ow">in</span> <span class="n">dlls</span><span class="p">:</span>
<span class="n">is_loaded</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">if</span> <span class="n">with_load_library_flags</span><span class="p">:</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">kernel32</span><span class="o">.</span><span class="n">LoadLibraryExW</span><span class="p">(</span><span class="n">dll</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mh">0x00001100</span><span class="p">)</span>
<span class="n">last_error</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">get_last_error</span><span class="p">()</span>
<span class="k">if</span> <span class="n">res</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">last_error</span> <span class="o">!=</span> <span class="mi">126</span><span class="p">:</span>
<span class="n">err</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">WinError</span><span class="p">(</span><span class="n">last_error</span><span class="p">)</span>
<span class="n">err</span><span class="o">.</span><span class="n">strerror</span> <span class="o">+=</span> <span class="sa">f</span><span class="s1">' Error loading "</span><span class="si">{</span><span class="n">dll</span><span class="si">}</span><span class="s1">" or one of its dependencies.'</span>
<span class="k">raise</span> <span class="n">err</span>
<span class="k">elif</span> <span class="n">res</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">is_loaded</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">is_loaded</span><span class="p">:</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">path_patched</span><span class="p">:</span>
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'PATH'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">';'</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">dll_paths</span> <span class="o">+</span> <span class="p">[</span><span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'PATH'</span><span class="p">]])</span>
<span class="n">path_patched</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">kernel32</span><span class="o">.</span><span class="n">LoadLibraryW</span><span class="p">(</span><span class="n">dll</span><span class="p">)</span>
<span class="k">if</span> <span class="n">res</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">err</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">WinError</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">get_last_error</span><span class="p">())</span>
<span class="n">err</span><span class="o">.</span><span class="n">strerror</span> <span class="o">+=</span> <span class="sa">f</span><span class="s1">' Error loading "</span><span class="si">{</span><span class="n">dll</span><span class="si">}</span><span class="s1">" or one of its dependencies.'</span>
<span class="k">raise</span> <span class="n">err</span>
<span class="n">kernel32</span><span class="o">.</span><span class="n">SetErrorMode</span><span class="p">(</span><span class="n">prev_error_mode</span><span class="p">)</span>
<span class="c1"># See Note [Global dependencies]</span>
<span class="k">def</span> <span class="nf">_load_global_deps</span><span class="p">():</span>
<span class="k">if</span> <span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span> <span class="o">==</span> <span class="s1">'Windows'</span> <span class="ow">or</span> <span class="n">sys</span><span class="o">.</span><span class="n">executable</span> <span class="o">==</span> <span class="s1">'torch_deploy'</span><span class="p">:</span>
<span class="k">return</span>
<span class="n">lib_name</span> <span class="o">=</span> <span class="s1">'libtorch_global_deps'</span> <span class="o">+</span> <span class="p">(</span><span class="s1">'.dylib'</span> <span class="k">if</span> <span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span> <span class="o">==</span> <span class="s1">'Darwin'</span> <span class="k">else</span> <span class="s1">'.so'</span><span class="p">)</span>
<span class="n">here</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="vm">__file__</span><span class="p">)</span>
<span class="n">lib_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="n">here</span><span class="p">),</span> <span class="s1">'lib'</span><span class="p">,</span> <span class="n">lib_name</span><span class="p">)</span>
<span class="n">ctypes</span><span class="o">.</span><span class="n">CDLL</span><span class="p">(</span><span class="n">lib_path</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="n">ctypes</span><span class="o">.</span><span class="n">RTLD_GLOBAL</span><span class="p">)</span>
<span class="k">if</span> <span class="p">(</span><span class="n">USE_RTLD_GLOBAL_WITH_LIBTORCH</span> <span class="ow">or</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s1">'TORCH_USE_RTLD_GLOBAL'</span><span class="p">))</span> <span class="ow">and</span> \
<span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span> <span class="o">!=</span> <span class="s1">'Windows'</span><span class="p">:</span>
<span class="c1"># Do it the hard way. You might want to load libtorch with RTLD_GLOBAL in a</span>
<span class="c1"># few circumstances:</span>
<span class="c1">#</span>
<span class="c1"># 1. You're in a build environment (e.g., fbcode) where</span>
<span class="c1"># libtorch_global_deps is not available, but you still need</span>
<span class="c1"># to get mkl to link in with RTLD_GLOBAL or it will just</span>
<span class="c1"># not work.</span>
<span class="c1">#</span>
<span class="c1"># 2. You're trying to run PyTorch under UBSAN and you need</span>
<span class="c1"># to ensure that only one copy of libtorch is loaded, so</span>
<span class="c1"># vptr checks work properly</span>
<span class="c1">#</span>
<span class="c1"># If you're using this setting, you must verify that all the libraries</span>
<span class="c1"># you load consistently use the same libstdc++, or you may have</span>
<span class="c1"># mysterious segfaults.</span>
<span class="c1">#</span>
<span class="kn">import</span> <span class="nn">os</span> <span class="k">as</span> <span class="nn">_dl_flags</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">_dl_flags</span><span class="p">,</span> <span class="s1">'RTLD_GLOBAL'</span><span class="p">)</span> <span class="ow">or</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">_dl_flags</span><span class="p">,</span> <span class="s1">'RTLD_LAZY'</span><span class="p">):</span>
<span class="k">try</span><span class="p">:</span>
<span class="c1"># next try if DLFCN exists</span>
<span class="kn">import</span> <span class="nn">DLFCN</span> <span class="k">as</span> <span class="nn">_dl_flags</span> <span class="c1"># type: ignore[import, no-redef]</span>
<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
<span class="c1"># as a last attempt, use compile-time constants</span>
<span class="kn">import</span> <span class="nn">torch._dl</span> <span class="k">as</span> <span class="nn">_dl_flags</span> <span class="c1"># type: ignore[import, no-redef]</span>
<span class="n">old_flags</span> <span class="o">=</span> <span class="n">sys</span><span class="o">.</span><span class="n">getdlopenflags</span><span class="p">()</span>
<span class="n">sys</span><span class="o">.</span><span class="n">setdlopenflags</span><span class="p">(</span><span class="n">_dl_flags</span><span class="o">.</span><span class="n">RTLD_GLOBAL</span> <span class="o">|</span> <span class="n">_dl_flags</span><span class="o">.</span><span class="n">RTLD_LAZY</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">torch._C</span> <span class="kn">import</span> <span class="o">*</span> <span class="c1"># noqa: F403</span>
<span class="n">sys</span><span class="o">.</span><span class="n">setdlopenflags</span><span class="p">(</span><span class="n">old_flags</span><span class="p">)</span>
<span class="k">del</span> <span class="n">old_flags</span>
<span class="k">del</span> <span class="n">_dl_flags</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># Easy way. You want this most of the time, because it will prevent</span>
<span class="c1"># C++ symbols from libtorch clobbering C++ symbols from other</span>
<span class="c1"># libraries, leading to mysterious segfaults.</span>
<span class="c1">#</span>
<span class="c1"># If building in an environment where libtorch_global_deps isn't available</span>
<span class="c1"># like parts of fbsource, but where RTLD_GLOBAL causes segfaults, you will</span>
<span class="c1"># want USE_RTLD_GLOBAL_WITH_LIBTORCH = False and USE_GLOBAL_DEPS = False</span>
<span class="c1">#</span>
<span class="c1"># See Note [Global dependencies]</span>
<span class="k">if</span> <span class="n">USE_GLOBAL_DEPS</span><span class="p">:</span>
<span class="n">_load_global_deps</span><span class="p">()</span>
<span class="kn">from</span> <span class="nn">torch._C</span> <span class="kn">import</span> <span class="o">*</span> <span class="c1"># noqa: F403</span>
<span class="c1"># Appease the type checker; ordinarily this binding is inserted by the</span>
<span class="c1"># torch._C module initialization code in C</span>
<span class="k">if</span> <span class="n">TYPE_CHECKING</span><span class="p">:</span>
<span class="kn">import</span> <span class="nn">torch._C</span> <span class="k">as</span> <span class="nn">_C</span>
<span class="c1"># Check to see if we can load C extensions, and if not provide some guidance</span>
<span class="c1"># on what the problem might be.</span>
<span class="k">try</span><span class="p">:</span>
<span class="c1"># _initExtension is chosen (arbitrarily) as a sentinel.</span>
<span class="kn">from</span> <span class="nn">torch._C</span> <span class="kn">import</span> <span class="n">_initExtension</span>
<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
<span class="kn">import</span> <span class="nn">torch._C</span> <span class="k">as</span> <span class="nn">_C_for_compiled_check</span>
<span class="c1"># The __file__ check only works for Python 3.7 and above.</span>
<span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">version_info</span> <span class="o">>=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">7</span><span class="p">)</span> <span class="ow">and</span> <span class="n">_C_for_compiled_check</span><span class="o">.</span><span class="vm">__file__</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span><span class="n">textwrap</span><span class="o">.</span><span class="n">dedent</span><span class="p">(</span><span class="s1">'''</span>
<span class="s1"> Failed to load PyTorch C extensions:</span>
<span class="s1"> It appears that PyTorch has loaded the `torch/_C` folder</span>
<span class="s1"> of the PyTorch repository rather than the C extensions which</span>
<span class="s1"> are expected in the `torch._C` namespace. This can occur when</span>
<span class="s1"> using the `install` workflow. e.g.</span>
<span class="s1"> $ python setup.py install && python -c "import torch"</span>
<span class="s1"> This error can generally be solved using the `develop` workflow</span>
<span class="s1"> $ python setup.py develop && python -c "import torch" # This should succeed</span>
<span class="s1"> or by running Python from a different directory.</span>
<span class="s1"> '''</span><span class="p">)</span><span class="o">.</span><span class="n">strip</span><span class="p">())</span> <span class="kn">from</span> <span class="bp">None</span>
<span class="k">raise</span> <span class="c1"># If __file__ is not None the cause is unknown, so just re-raise.</span>
<span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="nb">dir</span><span class="p">(</span><span class="n">_C</span><span class="p">):</span>
<span class="k">if</span> <span class="n">name</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">'_'</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">name</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">'Base'</span><span class="p">):</span>
<span class="n">__all__</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
<span class="n">obj</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">_C</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span>
<span class="k">if</span> <span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">Callable</span><span class="p">)</span> <span class="ow">or</span> <span class="n">inspect</span><span class="o">.</span><span class="n">isclass</span><span class="p">(</span><span class="n">obj</span><span class="p">)):</span> <span class="c1"># type: ignore[arg-type]</span>
<span class="k">if</span> <span class="p">(</span><span class="n">obj</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">!=</span> <span class="s1">'torch'</span><span class="p">):</span>
<span class="c1"># TODO: fix their module from C++ side</span>
<span class="k">if</span> <span class="n">name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'DisableTorchFunction'</span><span class="p">,</span> <span class="s1">'Generator'</span><span class="p">]:</span>
<span class="n">obj</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">=</span> <span class="s1">'torch'</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">TYPE_CHECKING</span><span class="p">:</span>
<span class="c1"># issue 38137 and python issue 43367. Submodules of a C extension are</span>
<span class="c1"># non-standard, and attributes of those submodules cannot be pickled since</span>
<span class="c1"># pickle expect to be able to import them as "from _C.sub import attr"</span>
<span class="c1"># which fails with "_C is not a package</span>
<span class="k">for</span> <span class="n">attr</span> <span class="ow">in</span> <span class="nb">dir</span><span class="p">(</span><span class="n">_C</span><span class="p">):</span>
<span class="n">candidate</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">_C</span><span class="p">,</span> <span class="n">attr</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">candidate</span><span class="p">)</span> <span class="ow">is</span> <span class="nb">type</span><span class="p">(</span><span class="n">_C</span><span class="p">):</span>
<span class="c1"># submodule</span>
<span class="k">if</span> <span class="sa">f</span><span class="s1">'torch._C.</span><span class="si">{</span><span class="n">attr</span><span class="si">}</span><span class="s1">'</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sys</span><span class="o">.</span><span class="n">modules</span><span class="p">:</span>
<span class="n">sys</span><span class="o">.</span><span class="n">modules</span><span class="p">[</span><span class="sa">f</span><span class="s1">'torch._C.</span><span class="si">{</span><span class="n">attr</span><span class="si">}</span><span class="s1">'</span><span class="p">]</span> <span class="o">=</span> <span class="n">candidate</span>
<span class="c1">################################################################################</span>
<span class="c1"># Define basic utilities</span>
<span class="c1">################################################################################</span>
<span class="k">def</span> <span class="nf">typename</span><span class="p">(</span><span class="n">o</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
<span class="k">return</span> <span class="n">o</span><span class="o">.</span><span class="n">type</span><span class="p">()</span>
<span class="n">module</span> <span class="o">=</span> <span class="s1">''</span>
<span class="n">class_name</span> <span class="o">=</span> <span class="s1">''</span>
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="s1">'__module__'</span><span class="p">)</span> <span class="ow">and</span> <span class="n">o</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">!=</span> <span class="s1">'builtins'</span> \
<span class="ow">and</span> <span class="n">o</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">!=</span> <span class="s1">'__builtin__'</span> <span class="ow">and</span> <span class="n">o</span><span class="o">.</span><span class="vm">__module__</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">module</span> <span class="o">=</span> <span class="n">o</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">+</span> <span class="s1">'.'</span>
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="s1">'__qualname__'</span><span class="p">):</span>
<span class="n">class_name</span> <span class="o">=</span> <span class="n">o</span><span class="o">.</span><span class="vm">__qualname__</span>
<span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="s1">'__name__'</span><span class="p">):</span>
<span class="n">class_name</span> <span class="o">=</span> <span class="n">o</span><span class="o">.</span><span class="vm">__name__</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">class_name</span> <span class="o">=</span> <span class="n">o</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span>
<span class="k">return</span> <span class="n">module</span> <span class="o">+</span> <span class="n">class_name</span>
<span class="k">def</span> <span class="nf">is_tensor</span><span class="p">(</span><span class="n">obj</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">"""Returns True if `obj` is a PyTorch tensor.</span>
<span class="sd"> Note that this function is simply doing ``isinstance(obj, Tensor)``.</span>
<span class="sd"> Using that ``isinstance`` check is better for typechecking with mypy,</span>
<span class="sd"> and more explicit - so it's recommended to use that instead of</span>
<span class="sd"> ``is_tensor``.</span>
<span class="sd"> Args:</span>
<span class="sd"> obj (Object): Object to test</span>
<span class="sd"> Example::</span>
<span class="sd"> >>> x=torch.tensor([1,2,3])</span>
<span class="sd"> >>> torch.is_tensor(x)</span>
<span class="sd"> True</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">is_storage</span><span class="p">(</span><span class="n">obj</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">"""Returns True if `obj` is a PyTorch storage object.</span>
<span class="sd"> Args:</span>
<span class="sd"> obj (Object): Object to test</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="n">obj</span><span class="p">)</span> <span class="ow">in</span> <span class="n">_storage_classes</span>
<span class="k">def</span> <span class="nf">set_default_tensor_type</span><span class="p">(</span><span class="n">t</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">"""Sets the default ``torch.Tensor`` type to floating point tensor type</span>
<span class="sd"> ``t``. This type will also be used as default floating point type for</span>
<span class="sd"> type inference in :func:`torch.tensor`.</span>
<span class="sd"> The default floating point tensor type is initially ``torch.FloatTensor``.</span>
<span class="sd"> Args:</span>
<span class="sd"> t (type or string): the floating point tensor type or its name</span>
<span class="sd"> Example::</span>
<span class="sd"> >>> torch.tensor([1.2, 3]).dtype # initial default for floating point is torch.float32</span>
<span class="sd"> torch.float32</span>
<span class="sd"> >>> torch.set_default_tensor_type(torch.DoubleTensor)</span>
<span class="sd"> >>> torch.tensor([1.2, 3]).dtype # a new floating point tensor</span>
<span class="sd"> torch.float64</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">_string_classes</span><span class="p">):</span>
<span class="n">t</span> <span class="o">=</span> <span class="n">_import_dotted_name</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
<span class="n">_C</span><span class="o">.</span><span class="n">_set_default_tensor_type</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">set_default_dtype</span><span class="p">(</span><span class="n">d</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">"""</span>
<span class="sd"> Sets the default floating point dtype to :attr:`d`. Supports torch.float32</span>
<span class="sd"> and torch.float64 as inputs. Other dtypes may be accepted without complaint</span>
<span class="sd"> but are not supported and are unlikely to work as expected.</span>
<span class="sd"> When PyTorch is initialized its default floating point dtype is torch.float32,</span>
<span class="sd"> and the intent of set_default_dtype(torch.float64) is to facilitate NumPy-like</span>
<span class="sd"> type inference. The default floating point dtype is used to:</span>
<span class="sd"> 1. Implicitly determine the default complex dtype. When the default floating point</span>
<span class="sd"> type is float32 the default complex dtype is complex64, and when the default</span>
<span class="sd"> floating point type is float64 the default complex type is complex128.</span>
<span class="sd"> 2. Infer the dtype for tensors constructed using Python floats or complex Python</span>
<span class="sd"> numbers. See examples below.</span>
<span class="sd"> 3. Determine the result of type promotion between bool and integer tensors and</span>
<span class="sd"> Python floats and complex Python numbers.</span>
<span class="sd"> Args:</span>
<span class="sd"> d (:class:`torch.dtype`): the floating point dtype to make the default.</span>
<span class="sd"> Either torch.float32 or torch.float64.</span>
<span class="sd"> Example:</span>
<span class="sd"> >>> # initial default for floating point is torch.float32</span>
<span class="sd"> >>> # Python floats are interpreted as float32</span>
<span class="sd"> >>> torch.tensor([1.2, 3]).dtype</span>
<span class="sd"> torch.float32</span>
<span class="sd"> >>> # initial default for floating point is torch.complex64</span>
<span class="sd"> >>> # Complex Python numbers are interpreted as complex64</span>
<span class="sd"> >>> torch.tensor([1.2, 3j]).dtype</span>
<span class="sd"> torch.complex64</span>
<span class="sd"> >>> torch.set_default_dtype(torch.float64)</span>
<span class="sd"> >>> # Python floats are now interpreted as float64</span>
<span class="sd"> >>> torch.tensor([1.2, 3]).dtype # a new floating point tensor</span>
<span class="sd"> torch.float64</span>
<span class="sd"> >>> # Complex Python numbers are now interpreted as complex128</span>
<span class="sd"> >>> torch.tensor([1.2, 3j]).dtype # a new complex tensor</span>
<span class="sd"> torch.complex128</span>
<span class="sd"> """</span>
<span class="n">_C</span><span class="o">.</span><span class="n">_set_default_dtype</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">use_deterministic_algorithms</span><span class="p">(</span><span class="n">mode</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">warn_only</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">""" Sets whether PyTorch operations must use "deterministic"</span>
<span class="sd"> algorithms. That is, algorithms which, given the same input, and when</span>
<span class="sd"> run on the same software and hardware, always produce the same output.</span>
<span class="sd"> When enabled, operations will use deterministic algorithms when available,</span>
<span class="sd"> and if only nondeterministic algorithms are available they will throw a</span>
<span class="sd"> :class:`RuntimeError` when called.</span>
<span class="sd"> .. note:: :func:`torch.set_deterministic_debug_mode` offers an alternative</span>
<span class="sd"> interface for this feature.</span>
<span class="sd"> The following normally-nondeterministic operations will act</span>
<span class="sd"> deterministically when ``mode=True``:</span>
<span class="sd"> * :class:`torch.nn.Conv1d` when called on CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.Conv2d` when called on CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.Conv3d` when called on CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.ConvTranspose1d` when called on CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.ConvTranspose2d` when called on CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.ConvTranspose3d` when called on CUDA tensor</span>
<span class="sd"> * :func:`torch.bmm` when called on sparse-dense CUDA tensors</span>
<span class="sd"> * :func:`torch.Tensor.__getitem__` when attempting to differentiate a CPU tensor</span>
<span class="sd"> and the index is a list of tensors</span>
<span class="sd"> * :func:`torch.Tensor.index_put` with ``accumulate=False``</span>
<span class="sd"> * :func:`torch.Tensor.index_put` with ``accumulate=True`` when called on a CPU</span>
<span class="sd"> tensor</span>
<span class="sd"> * :func:`torch.Tensor.put_` with ``accumulate=True`` when called on a CPU</span>
<span class="sd"> tensor</span>
<span class="sd"> * :func:`torch.Tensor.scatter_add_` when ``input`` dimension is one and called</span>
<span class="sd"> on a CUDA tensor</span>
<span class="sd"> * :func:`torch.gather` when ``input`` dimension is one and called</span>
<span class="sd"> on a CUDA tensor that requires grad</span>
<span class="sd"> * :func:`torch.index_add` when called on CUDA tensor</span>
<span class="sd"> * :func:`torch.index_select` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :func:`torch.repeat_interleave` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :func:`torch.Tensor.index_copy` when called on a CPU or CUDA tensor</span>
<span class="sd"> The following normally-nondeterministic operations will throw a</span>
<span class="sd"> :class:`RuntimeError` when ``mode=True``:</span>
<span class="sd"> * :class:`torch.nn.AvgPool3d` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.AdaptiveAvgPool2d` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.AdaptiveAvgPool3d` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.MaxPool3d` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.AdaptiveMaxPool2d` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.FractionalMaxPool2d` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.FractionalMaxPool3d` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :func:`torch.nn.functional.interpolate` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> and one of the following modes is used:</span>
<span class="sd"> - ``linear``</span>
<span class="sd"> - ``bilinear``</span>
<span class="sd"> - ``bicubic``</span>
<span class="sd"> - ``trilinear``</span>
<span class="sd"> * :class:`torch.nn.ReflectionPad1d` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.ReflectionPad2d` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.ReflectionPad3d` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.ReplicationPad1d` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.ReplicationPad2d` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.ReplicationPad3d` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.NLLLoss` when called on a CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.CTCLoss` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> * :class:`torch.nn.EmbeddingBag` when attempting to differentiate a CUDA tensor when</span>
<span class="sd"> ``mode='max'``</span>
<span class="sd"> * :func:`torch.Tensor.scatter_add_` when ``input`` dimension is larger than one</span>
<span class="sd"> and called on a CUDA tensor</span>
<span class="sd"> * :func:`torch.gather` when ``input`` dimension is larger than one</span>
<span class="sd"> and called on a CUDA tensor that requires grad</span>
<span class="sd"> * :func:`torch.Tensor.put_` when ``accumulate=False``</span>
<span class="sd"> * :func:`torch.Tensor.put_` when ``accumulate=True`` and called on a CUDA tensor</span>
<span class="sd"> * :func:`torch.histc` when called on a CUDA tensor</span>
<span class="sd"> * :func:`torch.bincount` when called on a CUDA tensor</span>
<span class="sd"> * :func:`torch.kthvalue` with called on a CUDA tensor</span>
<span class="sd"> * :func:`torch.median` with indices output when called on a CUDA tensor</span>
<span class="sd"> * :func:`torch.nn.functional.grid_sample` when attempting to differentiate a CUDA tensor</span>
<span class="sd"> A handful of CUDA operations are nondeterministic if the CUDA version is</span>
<span class="sd"> 10.2 or greater, unless the environment variable ``CUBLAS_WORKSPACE_CONFIG=:4096:8``</span>
<span class="sd"> or ``CUBLAS_WORKSPACE_CONFIG=:16:8`` is set. See the CUDA documentation for more</span>
<span class="sd"> details: `<https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility>`_</span>
<span class="sd"> If one of these environment variable configurations is not set, a :class:`RuntimeError`</span>
<span class="sd"> will be raised from these operations when called with CUDA tensors:</span>
<span class="sd"> * :func:`torch.mm`</span>
<span class="sd"> * :func:`torch.mv`</span>
<span class="sd"> * :func:`torch.bmm`</span>
<span class="sd"> Note that deterministic operations tend to have worse performance than</span>
<span class="sd"> nondeterministic operations.</span>
<span class="sd"> .. note::</span>
<span class="sd"> This flag does not detect or prevent nondeterministic behavior caused</span>
<span class="sd"> by calling an inplace operation on a tensor with an internal memory</span>
<span class="sd"> overlap or by giving such a tensor as the :attr:`out` argument for an</span>
<span class="sd"> operation. In these cases, multiple writes of different data may target</span>
<span class="sd"> a single memory location, and the order of writes is not guaranteed.</span>
<span class="sd"> Args:</span>
<span class="sd"> mode (:class:`bool`): If True, makes potentially nondeterministic</span>
<span class="sd"> operations switch to a deterministic algorithm or throw a runtime</span>
<span class="sd"> error. If False, allows nondeterministic operations.</span>
<span class="sd"> Keyword args:</span>
<span class="sd"> warn_only (:class:`bool`, optional): If True, operations that do not</span>
<span class="sd"> have a deterministic implementation will throw a warning instead of</span>
<span class="sd"> an error. Default: ``False``</span>
<span class="sd"> Example::</span>
<span class="sd"> >>> torch.use_deterministic_algorithms(True)</span>
<span class="sd"> # Forward mode nondeterministic error</span>
<span class="sd"> >>> torch.randn(10).index_copy(0, torch.tensor([0]), torch.randn(1))</span>
<span class="sd"> ...</span>
<span class="sd"> RuntimeError: index_copy does not have a deterministic implementation...</span>
<span class="sd"> # Backward mode nondeterministic error</span>
<span class="sd"> >>> torch.randn(10, requires_grad=True, device='cuda').index_select(0, torch.tensor([0], device='cuda')).backward()</span>
<span class="sd"> ...</span>
<span class="sd"> RuntimeError: index_add_cuda_ does not have a deterministic implementation...</span>
<span class="sd"> """</span>
<span class="n">_C</span><span class="o">.</span><span class="n">_set_deterministic_algorithms</span><span class="p">(</span><span class="n">mode</span><span class="p">,</span> <span class="n">warn_only</span><span class="o">=</span><span class="n">warn_only</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">are_deterministic_algorithms_enabled</span><span class="p">():</span>
<span class="sa">r</span><span class="sd">"""Returns True if the global deterministic flag is turned on. Refer to</span>
<span class="sd"> :func:`torch.use_deterministic_algorithms` documentation for more details.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">_C</span><span class="o">.</span><span class="n">_get_deterministic_algorithms</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">is_deterministic_algorithms_warn_only_enabled</span><span class="p">():</span>
<span class="sa">r</span><span class="sd">"""Returns True if the global deterministic flag is set to warn only.</span>
<span class="sd"> Refer to :func:`torch.use_deterministic_algorithms` documentation for more</span>
<span class="sd"> details.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">_C</span><span class="o">.</span><span class="n">_get_deterministic_algorithms_warn_only</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">set_deterministic_debug_mode</span><span class="p">(</span><span class="n">debug_mode</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">builtins</span><span class="o">.</span><span class="n">int</span><span class="p">,</span> <span class="nb">str</span><span class="p">])</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
<span class="sa">r</span><span class="sd">"""Sets the debug mode for deterministic operations.</span>
<span class="sd"> .. note:: This is an alternative interface for</span>
<span class="sd"> :func:`torch.use_deterministic_algorithms`. Refer to that function's</span>
<span class="sd"> documentation for details about affected operations.</span>
<span class="sd"> Args:</span>
<span class="sd"> debug_mode(str or int): If "default" or 0, don't error or warn on</span>
<span class="sd"> nondeterministic operations. If "warn" or 1, warn on</span>
<span class="sd"> nondeterministic operations. If "error" or 2, error on</span>
<span class="sd"> nondeterministic operations.</span>
<span class="sd"> """</span>
<span class="c1"># NOTE: builtins.int is used here because int in this scope resolves</span>
<span class="c1"># to torch.int</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">debug_mode</span><span class="p">,</span> <span class="p">(</span><span class="n">builtins</span><span class="o">.</span><span class="n">int</span><span class="p">,</span> <span class="nb">str</span><span class="p">)):</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'debug_mode must be str or int, but got </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">debug_mode</span><span class="p">)</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">debug_mode</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
<span class="k">if</span> <span class="n">debug_mode</span> <span class="o">==</span> <span class="s1">'default'</span><span class="p">:</span>
<span class="n">debug_mode</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">elif</span> <span class="n">debug_mode</span> <span class="o">==</span> <span class="s1">'warn'</span><span class="p">:</span>
<span class="n">debug_mode</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">elif</span> <span class="n">debug_mode</span> <span class="o">==</span> <span class="s1">'error'</span><span class="p">:</span>
<span class="n">debug_mode</span> <span class="o">=</span> <span class="mi">2</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
<span class="s1">'invalid value of debug_mode, expected one of `default`, '</span>
<span class="sa">f</span><span class="s1">'`warn`, `error`, but got </span><span class="si">{</span><span class="n">debug_mode</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="k">if</span> <span class="n">debug_mode</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">_C</span><span class="o">.</span><span class="n">_set_deterministic_algorithms</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">debug_mode</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">_C</span><span class="o">.</span><span class="n">_set_deterministic_algorithms</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">warn_only</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">debug_mode</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">_C</span><span class="o">.</span><span class="n">_set_deterministic_algorithms</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
<span class="s1">'invalid value of debug_mode, expected 0, 1, or 2, '</span>
<span class="sa">f</span><span class="s1">'but got </span><span class="si">{</span><span class="n">debug_mode</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">get_deterministic_debug_mode</span><span class="p">()</span> <span class="o">-></span> <span class="n">builtins</span><span class="o">.</span><span class="n">int</span><span class="p">:</span>
<span class="sa">r</span><span class="sd">"""Returns the current value of the debug mode for deterministic</span>
<span class="sd"> operations. Refer to :func:`torch.set_deterministic_debug_mode`</span>
<span class="sd"> documentation for more details.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">_C</span><span class="o">.</span><span class="n">_get_deterministic_algorithms</span><span class="p">():</span>
<span class="k">if</span> <span class="n">_C</span><span class="o">.</span><span class="n">_get_deterministic_algorithms_warn_only</span><span class="p">():</span>
<span class="k">return</span> <span class="mi">1</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="mi">2</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="mi">0</span>
<span class="k">def</span> <span class="nf">get_float32_matmul_precision</span><span class="p">()</span> <span class="o">-></span> <span class="n">builtins</span><span class="o">.</span><span class="n">str</span><span class="p">:</span>
<span class="sa">r</span><span class="sd">"""Returns the current value of float32 matrix multiplication precision. Refer to</span>
<span class="sd"> :func:`torch.set_float32_matmul_precision` documentation for more details.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">_C</span><span class="o">.</span><span class="n">_get_float32_matmul_precision</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">set_float32_matmul_precision</span><span class="p">(</span><span class="n">precision</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">"""Sets the internal precision of float32 matrix multiplications.</span>
<span class="sd"> Running float32 matrix multiplications in lower precision may significantly increase</span>
<span class="sd"> performance, and in some programs the loss of precision has a negligible impact.</span>
<span class="sd"> Supports three settings:</span>
<span class="sd"> * "highest", float32 matrix multiplications use the float32 datatype for</span>
<span class="sd"> internal computations.</span>
<span class="sd"> * "high", float32 matrix multiplications use the TensorFloat32 or bfloat16_3x</span>