-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathjit_language_reference.html
1750 lines (1496 loc) · 122 KB
/
jit_language_reference.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>TorchScript Language Reference — PyTorch 2.0 documentation</title>
<link rel="canonical" href="https://pytorch.org/docs/stable/jit_language_reference.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="TorchScript Language Reference" href="jit_language_reference_v2.html" />
<link rel="prev" title="TorchScript Builtins" href="jit_builtin_functions.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"><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 current"><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="jit.html">TorchScript</a> ></li>
<li>TorchScript Language Reference</li>
<li class="pytorch-breadcrumbs-aside">
<a href="_sources/jit_language_reference.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">
<nav class="contents local" id="contents" role="doc-toc">
<ul class="simple">
<li><p><a class="reference internal" href="#torchscript-language-reference" id="id7">TorchScript Language Reference</a></p>
<ul>
<li><p><a class="reference internal" href="#supported-type" id="id8">Types</a></p></li>
<li><p><a class="reference internal" href="#expressions" id="id9">Expressions</a></p></li>
<li><p><a class="reference internal" href="#statements" id="id10">Statements</a></p></li>
<li><p><a class="reference internal" href="#variable-resolution" id="id11">Variable Resolution</a></p></li>
<li><p><a class="reference internal" href="#use-of-python-values" id="id12">Use of Python Values</a></p></li>
</ul>
</li>
</ul>
</nav>
<section id="torchscript-language-reference">
<span id="language-reference"></span><h1><a class="toc-backref" href="#id7" role="doc-backlink">TorchScript Language Reference</a><a class="headerlink" href="#torchscript-language-reference" title="Permalink to this heading">¶</a></h1>
<p>TorchScript is a statically typed subset of Python that can either be written directly (using
the <a class="reference internal" href="generated/torch.jit.script.html#torch.jit.script" title="torch.jit.script"><code class="xref py py-func docutils literal notranslate"><span class="pre">@torch.jit.script</span></code></a> decorator) or generated automatically from Python code via
tracing. When using tracing, code is automatically converted into this subset of
Python by recording only the actual operators on tensors and simply executing and
discarding the other surrounding Python code.</p>
<p>When writing TorchScript directly using <code class="docutils literal notranslate"><span class="pre">@torch.jit.script</span></code> decorator, the programmer must
only use the subset of Python supported in TorchScript. This section documents
what is supported in TorchScript as if it were a language reference for a stand
alone language. Any features of Python not mentioned in this reference are not
part of TorchScript. See <cite>Builtin Functions</cite> for a complete reference of available
PyTorch tensor methods, modules, and functions.</p>
<p>As a subset of Python, any valid TorchScript function is also a valid Python
function. This makes it possible to <cite>disable TorchScript</cite> and debug the
function using standard Python tools like <code class="docutils literal notranslate"><span class="pre">pdb</span></code>. The reverse is not true: there
are many valid Python programs that are not valid TorchScript programs.
Instead, TorchScript focuses specifically on the features of Python that are
needed to represent neural network models in PyTorch.</p>
<section id="supported-type">
<span id="types"></span><span id="id1"></span><h2><a class="toc-backref" href="#id8" role="doc-backlink">Types</a><a class="headerlink" href="#supported-type" title="Permalink to this heading">¶</a></h2>
<p>The largest difference between TorchScript and the full Python language is that
TorchScript only supports a small set of types that are needed to express neural
net models. In particular, TorchScript supports:</p>
<table class="docutils align-default">
<thead>
<tr class="row-odd"><th class="head"><p>Type</p></th>
<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">Tensor</span></code></p></td>
<td><p>A PyTorch tensor of any dtype, dimension, or backend</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">Tuple[T0,</span> <span class="pre">T1,</span> <span class="pre">...,</span> <span class="pre">TN]</span></code></p></td>
<td><p>A tuple containing subtypes <code class="docutils literal notranslate"><span class="pre">T0</span></code>, <code class="docutils literal notranslate"><span class="pre">T1</span></code>, etc. (e.g. <code class="docutils literal notranslate"><span class="pre">Tuple[Tensor,</span> <span class="pre">Tensor]</span></code>)</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">bool</span></code></p></td>
<td><p>A boolean value</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">int</span></code></p></td>
<td><p>A scalar integer</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">float</span></code></p></td>
<td><p>A scalar floating point number</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">str</span></code></p></td>
<td><p>A string</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">List[T]</span></code></p></td>
<td><p>A list of which all members are type <code class="docutils literal notranslate"><span class="pre">T</span></code></p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">Optional[T]</span></code></p></td>
<td><p>A value which is either None or type <code class="docutils literal notranslate"><span class="pre">T</span></code></p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">Dict[K,</span> <span class="pre">V]</span></code></p></td>
<td><p>A dict with key type <code class="docutils literal notranslate"><span class="pre">K</span></code> and value type <code class="docutils literal notranslate"><span class="pre">V</span></code>. Only <code class="docutils literal notranslate"><span class="pre">str</span></code>, <code class="docutils literal notranslate"><span class="pre">int</span></code>, and <code class="docutils literal notranslate"><span class="pre">float</span></code> are allowed as key types.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">T</span></code></p></td>
<td><p>A <a class="reference internal" href="#torchscript-class">TorchScript Class</a></p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">E</span></code></p></td>
<td><p>A <a class="reference internal" href="#torchscript-enum">TorchScript Enum</a></p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">NamedTuple[T0,</span> <span class="pre">T1,</span> <span class="pre">...]</span></code></p></td>
<td><p>A <a class="reference external" href="https://docs.python.org/3/library/collections.html#collections.namedtuple" title="(in Python v3.11)"><code class="xref py py-func docutils literal notranslate"><span class="pre">collections.namedtuple</span></code></a> tuple type</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">Union[T0,</span> <span class="pre">T1,</span> <span class="pre">...]</span></code></p></td>
<td><p>One of the subtypes <code class="docutils literal notranslate"><span class="pre">T0</span></code>, <code class="docutils literal notranslate"><span class="pre">T1</span></code>, etc.</p></td>
</tr>
</tbody>
</table>
<p>Unlike Python, each variable in TorchScript function must have a single static type.
This makes it easier to optimize TorchScript functions.</p>
<p>Example (a type mismatch)</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
<span class="k">def</span> <span class="nf">an_error</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="p">:</span>
<span class="n">r</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">r</span> <span class="o">=</span> <span class="mi">4</span>
<span class="k">return</span> <span class="n">r</span>
</pre></div>
</div>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>Traceback (most recent call last):
...
RuntimeError: ...
Type mismatch: r is set to type Tensor in the true branch and type int in the false branch:
@torch.jit.script
def an_error(x):
if x:
~~~~~
r = torch.rand(1)
~~~~~~~~~~~~~~~~~
else:
~~~~~
r = 4
~~~~~ <--- HERE
return r
and was used here:
else:
r = 4
return r
~ <--- HERE...
</pre></div>
</div>
<section id="unsupported-typing-constructs">
<h3>Unsupported Typing Constructs<a class="headerlink" href="#unsupported-typing-constructs" title="Permalink to this heading">¶</a></h3>
<p>TorchScript does not support all features and types of the <a class="reference external" href="https://docs.python.org/3/library/typing.html#module-typing" title="(in Python v3.11)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">typing</span></code></a> module. Some of these
are more fundamental things that are unlikely to be added in the future while others
may be added if there is enough user demand to make it a priority.</p>
<p>These types and features from the <a class="reference external" href="https://docs.python.org/3/library/typing.html#module-typing" title="(in Python v3.11)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">typing</span></code></a> module are unavailable in TorchScript.</p>
<table class="docutils align-default">
<thead>
<tr class="row-odd"><th class="head"><p>Item</p></th>
<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><code class="xref any docutils literal notranslate"><span class="pre">typing.Any</span></code></a></p></td>
<td><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><code class="xref any docutils literal notranslate"><span class="pre">typing.Any</span></code></a> is currently in development but not yet released</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.NoReturn" title="(in Python v3.11)"><code class="xref any docutils literal notranslate"><span class="pre">typing.NoReturn</span></code></a></p></td>
<td><p>Not implemented</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Sequence" title="(in Python v3.11)"><code class="xref any docutils literal notranslate"><span class="pre">typing.Sequence</span></code></a></p></td>
<td><p>Not implemented</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.11)"><code class="xref any docutils literal notranslate"><span class="pre">typing.Callable</span></code></a></p></td>
<td><p>Not implemented</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Literal" title="(in Python v3.11)"><code class="xref any docutils literal notranslate"><span class="pre">typing.Literal</span></code></a></p></td>
<td><p>Not implemented</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.ClassVar" title="(in Python v3.11)"><code class="xref any docutils literal notranslate"><span class="pre">typing.ClassVar</span></code></a></p></td>
<td><p>Not implemented</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Final" title="(in Python v3.11)"><code class="xref any docutils literal notranslate"><span class="pre">typing.Final</span></code></a></p></td>
<td><p>This is supported for <a class="reference internal" href="#module-attributes"><span class="std std-ref">module attributes</span></a> class attribute annotations but not for functions</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.AnyStr" title="(in Python v3.11)"><code class="xref any docutils literal notranslate"><span class="pre">typing.AnyStr</span></code></a></p></td>
<td><p>TorchScript does not support <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bytes" title="(in Python v3.11)"><code class="xref any docutils literal notranslate"><span class="pre">bytes</span></code></a> so this type is not used</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.overload" title="(in Python v3.11)"><code class="xref any docutils literal notranslate"><span class="pre">typing.overload</span></code></a></p></td>
<td><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.overload" title="(in Python v3.11)"><code class="xref any docutils literal notranslate"><span class="pre">typing.overload</span></code></a> is currently in development but not yet released</p></td>
</tr>
<tr class="row-odd"><td><p>Type aliases</p></td>
<td><p>Not implemented</p></td>
</tr>
<tr class="row-even"><td><p>Nominal vs structural subtyping</p></td>
<td><p>Nominal typing is in development, but structural typing is not</p></td>
</tr>
<tr class="row-odd"><td><p>NewType</p></td>
<td><p>Unlikely to be implemented</p></td>
</tr>
<tr class="row-even"><td><p>Generics</p></td>
<td><p>Unlikely to be implemented</p></td>
</tr>
</tbody>
</table>
<p>Any other functionality from the <a class="reference external" href="https://docs.python.org/3/library/typing.html#module-typing" title="(in Python v3.11)"><code class="xref any docutils literal notranslate"><span class="pre">typing</span></code></a> module not explicitly listed in this documentation is unsupported.</p>
</section>
<section id="default-types">
<h3>Default Types<a class="headerlink" href="#default-types" title="Permalink to this heading">¶</a></h3>
<p>By default, all parameters to a TorchScript function are assumed to be Tensor.
To specify that an argument to a TorchScript function is another type, it is possible to use
MyPy-style type annotations using the types listed above.</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
<span class="k">def</span> <span class="nf">foo</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">tup</span><span class="p">):</span>
<span class="c1"># type: (int, Tuple[Tensor, Tensor]) -> Tensor</span>
<span class="n">t0</span><span class="p">,</span> <span class="n">t1</span> <span class="o">=</span> <span class="n">tup</span>
<span class="k">return</span> <span class="n">t0</span> <span class="o">+</span> <span class="n">t1</span> <span class="o">+</span> <span class="n">x</span>
<span class="nb">print</span><span class="p">(</span><span class="n">foo</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">3</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">3</span><span class="p">))))</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>It is also possible to annotate types with Python 3 type hints from the
<code class="docutils literal notranslate"><span class="pre">typing</span></code> module.</p>
<div class="highlight-python3 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">typing</span> <span class="kn">import</span> <span class="n">Tuple</span>
<span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
<span class="k">def</span> <span class="nf">foo</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">tup</span><span class="p">:</span> <span class="n">Tuple</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="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">])</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="n">t0</span><span class="p">,</span> <span class="n">t1</span> <span class="o">=</span> <span class="n">tup</span>
<span class="k">return</span> <span class="n">t0</span> <span class="o">+</span> <span class="n">t1</span> <span class="o">+</span> <span class="n">x</span>
<span class="nb">print</span><span class="p">(</span><span class="n">foo</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">3</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">3</span><span class="p">))))</span>
</pre></div>
</div>
</div>
<p>An empty list is assumed to be <code class="docutils literal notranslate"><span class="pre">List[Tensor]</span></code> and empty dicts
<code class="docutils literal notranslate"><span class="pre">Dict[str,</span> <span class="pre">Tensor]</span></code>. To instantiate an empty list or dict of other types,
use <cite>Python 3 type hints</cite>.</p>
<p>Example (type annotations for Python 3):</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Tuple</span>
<span class="k">class</span> <span class="nc">EmptyDataStructures</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</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="o">-></span> <span class="n">Tuple</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]],</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">int</span><span class="p">]]:</span>
<span class="c1"># This annotates the list to be a `List[Tuple[int, float]]`</span>
<span class="n">my_list</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
<span class="n">my_list</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">item</span><span class="p">()))</span>
<span class="n">my_dict</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">return</span> <span class="n">my_list</span><span class="p">,</span> <span class="n">my_dict</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">EmptyDataStructures</span><span class="p">())</span>
</pre></div>
</div>
</section>
<section id="optional-type-refinement">
<h3>Optional Type Refinement<a class="headerlink" href="#optional-type-refinement" title="Permalink to this heading">¶</a></h3>
<p>TorchScript will refine the type of a variable of type <code class="docutils literal notranslate"><span class="pre">Optional[T]</span></code> when
a comparison to <code class="docutils literal notranslate"><span class="pre">None</span></code> is made inside the conditional of an if-statement or checked in an <code class="docutils literal notranslate"><span class="pre">assert</span></code>.
The compiler can reason about multiple <code class="docutils literal notranslate"><span class="pre">None</span></code> checks that are combined with
<code class="docutils literal notranslate"><span class="pre">and</span></code>, <code class="docutils literal notranslate"><span class="pre">or</span></code>, and <code class="docutils literal notranslate"><span class="pre">not</span></code>. Refinement will also occur for else blocks of if-statements
that are not explicitly written.</p>
<p>The <code class="docutils literal notranslate"><span class="pre">None</span></code> check must be within the if-statement’s condition; assigning
a <code class="docutils literal notranslate"><span class="pre">None</span></code> check to a variable and using it in the if-statement’s condition will
not refine the types of variables in the check.
Only local variables will be refined, an attribute like <code class="docutils literal notranslate"><span class="pre">self.x</span></code> will not and must assigned to
a local variable to be refined.</p>
<p>Example (refining types on parameters and locals):</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span>
<span class="k">class</span> <span class="nc">M</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="n">z</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">z</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="c1"># If `z` is None, its type cannot be inferred, so it must</span>
<span class="c1"># be specified (above)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">z</span> <span class="o">=</span> <span class="n">z</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</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">z</span><span class="p">):</span>
<span class="c1"># type: (Optional[int], Optional[int], Optional[int]) -> int</span>
<span class="k">if</span> <span class="n">x</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">x</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="mi">1</span>
<span class="c1"># Refinement for an attribute by assigning it to a local</span>
<span class="n">z</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">z</span>
<span class="k">if</span> <span class="n">y</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">z</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">y</span> <span class="o">+</span> <span class="n">z</span>
<span class="c1"># Refinement via an `assert`</span>
<span class="k">assert</span> <span class="n">z</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="n">x</span> <span class="o">+=</span> <span class="n">z</span>
<span class="k">return</span> <span class="n">x</span>
<span class="n">module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">M</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span>
<span class="n">module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">M</span><span class="p">(</span><span class="kc">None</span><span class="p">))</span>
</pre></div>
</div>
</section>
<section id="id2">
<span id="torchscript-classes"></span><span id="torchscript-class"></span><span id="id3"></span><h3>TorchScript Classes<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h3>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>TorchScript class support is experimental. Currently it is best suited
for simple record-like types (think a <code class="docutils literal notranslate"><span class="pre">NamedTuple</span></code> with methods
attached).</p>
</div>
<p>Python classes can be used in TorchScript if they are annotated with <a class="reference internal" href="generated/torch.jit.script.html#torch.jit.script" title="torch.jit.script"><code class="xref py py-func docutils literal notranslate"><span class="pre">@torch.jit.script</span></code></a>,
similar to how you would declare a TorchScript function:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
<span class="k">class</span> <span class="nc">Foo</span><span class="p">:</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</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="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="n">x</span>
<span class="k">def</span> <span class="nf">aug_add_x</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inc</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">+=</span> <span class="n">inc</span>
</pre></div>
</div>
<p>This subset is restricted:</p>
<ul>
<li><p>All functions must be valid TorchScript functions (including <code class="docutils literal notranslate"><span class="pre">__init__()</span></code>).</p></li>
<li><p>Classes must be new-style classes, as we use <code class="docutils literal notranslate"><span class="pre">__new__()</span></code> to construct them with pybind11.</p></li>
<li><p>TorchScript classes are statically typed. Members can only be declared by assigning to
self in the <code class="docutils literal notranslate"><span class="pre">__init__()</span></code> method.</p>
<blockquote>
<div><p>For example, assigning to <code class="docutils literal notranslate"><span class="pre">self</span></code> outside of the <code class="docutils literal notranslate"><span class="pre">__init__()</span></code> method:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
<span class="k">class</span> <span class="nc">Foo</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">assign_x</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
</pre></div>
</div>
<p>Will result in:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>RuntimeError:
Tried to set nonexistent attribute: x. Did you forget to initialize it in __init__()?:
def assign_x(self):
self.x = torch.rand(2, 3)
~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
</pre></div>
</div>
</div></blockquote>
</li>
<li><p>No expressions except method definitions are allowed in the body of the class.</p></li>
<li><p>No support for inheritance or any other polymorphism strategy, except for inheriting
from <code class="docutils literal notranslate"><span class="pre">object</span></code> to specify a new-style class.</p></li>
</ul>
<p>After a class is defined, it can be used in both TorchScript and Python interchangeably
like any other TorchScript type:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Declare a TorchScript class</span>
<span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
<span class="k">class</span> <span class="nc">Pair</span><span class="p">:</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">first</span><span class="p">,</span> <span class="n">second</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">first</span> <span class="o">=</span> <span class="n">first</span>
<span class="bp">self</span><span class="o">.</span><span class="n">second</span> <span class="o">=</span> <span class="n">second</span>
<span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
<span class="k">def</span> <span class="nf">sum_pair</span><span class="p">(</span><span class="n">p</span><span class="p">):</span>
<span class="c1"># type: (Pair) -> Tensor</span>
<span class="k">return</span> <span class="n">p</span><span class="o">.</span><span class="n">first</span> <span class="o">+</span> <span class="n">p</span><span class="o">.</span><span class="n">second</span>
<span class="n">p</span> <span class="o">=</span> <span class="n">Pair</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">rand</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">torch</span><span class="o">.</span><span class="n">rand</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="nb">print</span><span class="p">(</span><span class="n">sum_pair</span><span class="p">(</span><span class="n">p</span><span class="p">))</span>
</pre></div>
</div>
</section>
<section id="id4">
<span id="torchscript-enums"></span><span id="torchscript-enum"></span><span id="id5"></span><h3>TorchScript Enums<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h3>
<p>Python enums can be used in TorchScript without any extra annotation or code:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">enum</span> <span class="kn">import</span> <span class="n">Enum</span>
<span class="k">class</span> <span class="nc">Color</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
<span class="n">RED</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">GREEN</span> <span class="o">=</span> <span class="mi">2</span>
<span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
<span class="k">def</span> <span class="nf">enum_fn</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">Color</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">Color</span><span class="p">)</span> <span class="o">-></span> <span class="nb">bool</span><span class="p">:</span>
<span class="k">if</span> <span class="n">x</span> <span class="o">==</span> <span class="n">Color</span><span class="o">.</span><span class="n">RED</span><span class="p">:</span>
<span class="k">return</span> <span class="kc">True</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">==</span> <span class="n">y</span>
</pre></div>
</div>
<p>After an enum is defined, it can be used in both TorchScript and Python interchangeably
like any other TorchScript type. The type of the values of an enum must be <code class="docutils literal notranslate"><span class="pre">int</span></code>,
<code class="docutils literal notranslate"><span class="pre">float</span></code>, or <code class="docutils literal notranslate"><span class="pre">str</span></code>. All values must be of the same type; heterogenous types for enum
values are not supported.</p>
</section>
<section id="named-tuples">
<h3>Named Tuples<a class="headerlink" href="#named-tuples" title="Permalink to this heading">¶</a></h3>
<p>Types produced by <a class="reference external" href="https://docs.python.org/3/library/collections.html#collections.namedtuple" title="(in Python v3.11)"><code class="xref py py-func docutils literal notranslate"><span class="pre">collections.namedtuple</span></code></a> can be used in TorchScript.</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">collections</span>
<span class="n">Point</span> <span class="o">=</span> <span class="n">collections</span><span class="o">.</span><span class="n">namedtuple</span><span class="p">(</span><span class="s1">'Point'</span><span class="p">,</span> <span class="p">[</span><span class="s1">'x'</span><span class="p">,</span> <span class="s1">'y'</span><span class="p">])</span>
<span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
<span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="n">point</span><span class="p">):</span>
<span class="c1"># type: (Point) -> Tensor</span>
<span class="k">return</span> <span class="n">point</span><span class="o">.</span><span class="n">x</span> <span class="o">+</span> <span class="n">point</span><span class="o">.</span><span class="n">y</span>
<span class="n">p</span> <span class="o">=</span> <span class="n">Point</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">rand</span><span class="p">(</span><span class="mi">3</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">rand</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">total</span><span class="p">(</span><span class="n">p</span><span class="p">))</span>
</pre></div>
</div>
</section>
<section id="iterables">
<span id="jit-iterables"></span><h3>Iterables<a class="headerlink" href="#iterables" title="Permalink to this heading">¶</a></h3>
<p>Some functions (for example, <a class="reference external" href="https://docs.python.org/3/library/functions.html#zip" title="(in Python v3.11)"><code class="xref any docutils literal notranslate"><span class="pre">zip</span></code></a> and <a class="reference external" href="https://docs.python.org/3/library/functions.html#enumerate" title="(in Python v3.11)"><code class="xref any docutils literal notranslate"><span class="pre">enumerate</span></code></a>) can only operate on iterable types.
Iterable types in TorchScript include <code class="docutils literal notranslate"><span class="pre">Tensor</span></code>s, lists, tuples, dictionaries, strings,
<a class="reference internal" href="generated/torch.nn.ModuleList.html#torch.nn.ModuleList" title="torch.nn.ModuleList"><code class="xref any py py-class docutils literal notranslate"><span class="pre">torch.nn.ModuleList</span></code></a> and <a class="reference internal" href="generated/torch.nn.ModuleDict.html#torch.nn.ModuleDict" title="torch.nn.ModuleDict"><code class="xref any py py-class docutils literal notranslate"><span class="pre">torch.nn.ModuleDict</span></code></a>.</p>
</section>
</section>
<section id="expressions">
<h2><a class="toc-backref" href="#id9" role="doc-backlink">Expressions</a><a class="headerlink" href="#expressions" title="Permalink to this heading">¶</a></h2>
<p>The following Python Expressions are supported.</p>
<section id="literals">
<h3>Literals<a class="headerlink" href="#literals" title="Permalink to this heading">¶</a></h3>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kc">True</span>
<span class="kc">False</span>
<span class="kc">None</span>
<span class="s1">'string literals'</span>
<span class="s2">"string literals"</span>
<span class="mi">3</span> <span class="c1"># interpreted as int</span>
<span class="mf">3.4</span> <span class="c1"># interpreted as a float</span>
</pre></div>
</div>
<section id="list-construction">
<h4>List Construction<a class="headerlink" href="#list-construction" title="Permalink to this heading">¶</a></h4>
<p>An empty list is assumed have type <code class="docutils literal notranslate"><span class="pre">List[Tensor]</span></code>.
The types of other list literals are derived from the type of the members.
See <a class="reference internal" href="#default-types">Default Types</a> for more details.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]</span>
<span class="p">[]</span>
<span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">3</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">4</span><span class="p">)]</span>
</pre></div>
</div>
</section>
<section id="tuple-construction">
<h4>Tuple Construction<a class="headerlink" href="#tuple-construction" title="Permalink to this heading">¶</a></h4>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="p">(</span><span class="mi">3</span><span class="p">,)</span>
</pre></div>
</div>
</section>
<section id="dict-construction">
<h4>Dict Construction<a class="headerlink" href="#dict-construction" title="Permalink to this heading">¶</a></h4>
<p>An empty dict is assumed have type <code class="docutils literal notranslate"><span class="pre">Dict[str,</span> <span class="pre">Tensor]</span></code>.
The types of other dict literals are derived from the type of the members.
See <a class="reference internal" href="#default-types">Default Types</a> for more details.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">{</span><span class="s1">'hello'</span><span class="p">:</span> <span class="mi">3</span><span class="p">}</span>
<span class="p">{}</span>
<span class="p">{</span><span class="s1">'a'</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">3</span><span class="p">),</span> <span class="s1">'b'</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">4</span><span class="p">)}</span>
</pre></div>
</div>
</section>
</section>
<section id="variables">
<h3>Variables<a class="headerlink" href="#variables" title="Permalink to this heading">¶</a></h3>
<p>See <a class="reference internal" href="#variable-resolution">Variable Resolution</a> for how variables are resolved.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">my_variable_name</span>
</pre></div>
</div>
</section>
<section id="arithmetic-operators">
<h3>Arithmetic Operators<a class="headerlink" href="#arithmetic-operators" title="Permalink to this heading">¶</a></h3>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="n">a</span> <span class="o">-</span> <span class="n">b</span>
<span class="n">a</span> <span class="o">*</span> <span class="n">b</span>
<span class="n">a</span> <span class="o">/</span> <span class="n">b</span>
<span class="n">a</span> <span class="o">^</span> <span class="n">b</span>
<span class="n">a</span> <span class="o">@</span> <span class="n">b</span>
</pre></div>
</div>
</section>
<section id="comparison-operators">
<h3>Comparison Operators<a class="headerlink" href="#comparison-operators" title="Permalink to this heading">¶</a></h3>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">==</span> <span class="n">b</span>
<span class="n">a</span> <span class="o">!=</span> <span class="n">b</span>
<span class="n">a</span> <span class="o"><</span> <span class="n">b</span>
<span class="n">a</span> <span class="o">></span> <span class="n">b</span>
<span class="n">a</span> <span class="o"><=</span> <span class="n">b</span>
<span class="n">a</span> <span class="o">>=</span> <span class="n">b</span>
</pre></div>
</div>
</section>
<section id="logical-operators">
<h3>Logical Operators<a class="headerlink" href="#logical-operators" title="Permalink to this heading">¶</a></h3>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="ow">and</span> <span class="n">b</span>
<span class="n">a</span> <span class="ow">or</span> <span class="n">b</span>
<span class="ow">not</span> <span class="n">b</span>
</pre></div>
</div>
</section>
<section id="subscripts-and-slicing">
<h3>Subscripts and Slicing<a class="headerlink" href="#subscripts-and-slicing" title="Permalink to this heading">¶</a></h3>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">t</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">t</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">t</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">2</span><span class="p">]</span>
<span class="n">t</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
<span class="n">t</span><span class="p">[:</span><span class="mi">1</span><span class="p">]</span>
<span class="n">t</span><span class="p">[:]</span>
<span class="n">t</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="n">t</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="n">t</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:</span><span class="mi">1</span><span class="p">]</span>
<span class="n">t</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">:,</span> <span class="mi">0</span><span class="p">]</span>
<span class="n">t</span><span class="p">[</span><span class="mi">1</span><span class="p">:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
<span class="n">t</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span>
</pre></div>
</div>
</section>
<section id="function-calls">
<h3>Function Calls<a class="headerlink" href="#function-calls" title="Permalink to this heading">¶</a></h3>
<p>Calls to <cite>builtin functions</cite></p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int</span><span class="p">)</span>
</pre></div>
</div>
<p>Calls to other script functions:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
<span class="k">def</span> <span class="nf">foo</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="mi">1</span>
<span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
<span class="k">def</span> <span class="nf">bar</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">foo</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="method-calls">
<h3>Method Calls<a class="headerlink" href="#method-calls" title="Permalink to this heading">¶</a></h3>
<p>Calls to methods of builtin types like tensor: <code class="docutils literal notranslate"><span class="pre">x.mm(y)</span></code></p>
<p>On modules, methods must be compiled before they can be called. The TorchScript
compiler recursively compiles methods it sees when compiling other methods. By default,
compilation starts on the <code class="docutils literal notranslate"><span class="pre">forward</span></code> method. Any methods called by <code class="docutils literal notranslate"><span class="pre">forward</span></code> will
be compiled, and any methods called by those methods, and so on. To start compilation at
a method other than <code class="docutils literal notranslate"><span class="pre">forward</span></code>, use the <a class="reference internal" href="jit.html#torch.jit.export" title="torch.jit.export"><code class="xref py py-func docutils literal notranslate"><span class="pre">@torch.jit.export</span></code></a> decorator
(<code class="docutils literal notranslate"><span class="pre">forward</span></code> implicitly is marked <code class="docutils literal notranslate"><span class="pre">@torch.jit.export</span></code>).</p>
<p>Calling a submodule directly (e.g. <code class="docutils literal notranslate"><span class="pre">self.resnet(input)</span></code>) is equivalent to
calling its <code class="docutils literal notranslate"><span class="pre">forward</span></code> method (e.g. <code class="docutils literal notranslate"><span class="pre">self.resnet.forward(input)</span></code>).</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">import</span> <span class="nn">torchvision</span>
<span class="k">class</span> <span class="nc">MyModule</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="n">means</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">103.939</span><span class="p">,</span> <span class="mf">116.779</span><span class="p">,</span> <span class="mf">123.68</span><span class="p">])</span>
<span class="bp">self</span><span class="o">.</span><span class="n">means</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">means</span><span class="o">.</span><span class="n">resize_</span><span class="p">(</span><span class="mi">1</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="mi">1</span><span class="p">))</span>
<span class="n">resnet</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">resnet18</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">resnet</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">resnet</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">helper</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">resnet</span><span class="p">(</span><span class="nb">input</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">means</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">helper</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>