-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathonnx_dynamo.html
1646 lines (1439 loc) · 151 KB
/
onnx_dynamo.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="viewport" content="width=device-width, initial-scale=1.0">
<title>TorchDynamo-based ONNX Exporter — PyTorch 2.4 documentation</title>
<link rel="canonical" href="https://pytorch.org/docs/stable/onnx_dynamo.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="FXE0007:fx-graph-to-onnx" href="generated/onnx_dynamo_diagnostics_rules/FXE0007%3Afx-graph-to-onnx.html" />
<link rel="prev" title="torch.onnx" href="onnx.html" />
<!-- Google Tag Manager -->
<script>(function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':
new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],
j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src=
'https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);
})(window,document,'script','dataLayer','GTM-T8XT4PS');</script>
<!-- End Google Tag Manager -->
<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 class="main-menu-item">
<div id="resourcesDropdownButton" data-toggle="resources-dropdown" class="resources-dropdown">
<a class="with-down-arrow">
Learn
</a>
<div class="resources-dropdown-menu">
<a class="nav-dropdown-item" href="https://pytorch.org/get-started">
<span class=dropdown-title>Get Started</span>
<p>Run PyTorch locally or get started quickly with one of the supported cloud platforms</p>
</a>
<a class="nav-dropdown-item" href="https://pytorch.org/tutorials">
<span class="dropdown-title">Tutorials</span>
<p>Whats new in PyTorch tutorials</p>
</a>
<a class="nav-dropdown-item" href="https://pytorch.org/tutorials/beginner/basics/intro.html">
<span class="dropdown-title">Learn the Basics</span>
<p>Familiarize yourself with PyTorch concepts and modules</p>
</a>
<a class="nav-dropdown-item" href="https://pytorch.org/tutorials/recipes/recipes_index.html">
<span class="dropdown-title">PyTorch Recipes</span>
<p>Bite-size, ready-to-deploy PyTorch code examples</p>
</a>
<a class="nav-dropdown-item" href="https://pytorch.org/tutorials/beginner/introyt.html">
<span class="dropdown-title">Intro to PyTorch - YouTube Series</span>
<p>Master PyTorch basics with our engaging YouTube tutorial series</p>
</a>
</div>
</div>
</li>
<li>
<div id="resourcesDropdownButton" data-toggle="resources-dropdown" class="resources-dropdown">
<a class="with-down-arrow">
Ecosystem
</a>
<div class="resources-dropdown-menu">
<a class="nav-dropdown-item" href="https://pytorch.org/ecosystem">
<span class="dropdown-title">Tools</span>
<p>Learn about the tools and frameworks in the PyTorch Ecosystem</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://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/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/ecosystem/contributor-awards-2023">
<span class="dropdown-title">Contributor Awards - 2023</span>
<p>Award winners announced at this year's PyTorch Conference</p>
</a>
</div>
</div>
</li>
<li>
<div id="resourcesDropdownButton" data-toggle="resources-dropdown" class="resources-dropdown">
<a class="with-down-arrow">
Edge
</a>
<div class="resources-dropdown-menu">
<a class="nav-dropdown-item" href="https://pytorch.org/edge">
<span class="dropdown-title">About PyTorch Edge</span>
<p>Build innovative and privacy-aware AI experiences for edge devices</p>
</a>
<a class="nav-dropdown-item" href="https://pytorch.org/executorch-overview">
<span class="dropdown-title">ExecuTorch</span>
<p>End-to-end solution for enabling on-device inference capabilities across mobile and edge devices</p>
</a>
</div>
</div>
</li>
<li class="main-menu-item">
<div id="resourcesDropdownButton" data-toggle="resources-dropdown" class="resources-dropdown">
<a class="with-down-arrow">
Docs
</a>
<div class="resources-dropdown-menu">
<a class="nav-dropdown-item" href="https://pytorch.org/docs/stable/index.html">
<span class="dropdown-title">PyTorch</span>
<p>Explore the documentation for comprehensive guidance on how to use PyTorch</p>
</a>
<a class="nav-dropdown-item" href="https://pytorch.org/pytorch-domains">
<span class="dropdown-title">PyTorch Domains</span>
<p>Read the PyTorch Domains documentation to learn more about domain-specific libraries</p>
</a>
</div>
</div>
</li>
<li>
<div id="resourcesDropdownButton" data-toggle="resources-dropdown" class="resources-dropdown">
<a class="with-down-arrow">
Blogs & News
</a>
<div class="resources-dropdown-menu">
<a class="nav-dropdown-item" href="https://pytorch.org/blog/">
<span class="dropdown-title">PyTorch Blog</span>
<p>Catch up on the latest technical news and happenings</p>
</a>
<a class="nav-dropdown-item" href="https://pytorch.org/community-blog">
<span class="dropdown-title">Community Blog</span>
<p>Stories from the PyTorch ecosystem</p>
</a>
<a class="nav-dropdown-item" href="https://pytorch.org/videos">
<span class="dropdown-title">Videos</span>
<p>Learn about the latest PyTorch tutorials, new, and more </p>
<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/events">
<span class="dropdown-title">Events</span>
<p>Find events, webinars, and podcasts</p>
</a>
</div>
</li>
<li>
<div id="resourcesDropdownButton" data-toggle="resources-dropdown" class="resources-dropdown">
<a class="with-down-arrow">
About
</a>
<div class="resources-dropdown-menu">
<a class="nav-dropdown-item" href="https://pytorch.org/foundation">
<span class="dropdown-title">PyTorch Foundation</span>
<p>Learn more about the PyTorch Foundation</p>
</a>
<a class="nav-dropdown-item" href="https://pytorch.org/governing-board">
<span class="dropdown-title">Governing Board</span>
<p></p>
</a>
</div>
</div>
</li>
<li class="main-menu-item">
<div class="no-dropdown">
<a href="https://pytorch.org/join" data-cta="join">
Become a Member
</a>
</div>
</li>
<li>
<div class="main-menu-item">
<a href="https://github.com/pytorch/pytorch" class="github-icon">
</a>
</div>
</li>
<!--- TODO: This block adds the search icon to the nav bar. We will enable it later.
<li>
<div class="main-menu-item">
<a href="https://github.com/pytorch/pytorch" class="search-icon">
</a>
</div>
</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'>2.4 ▼</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">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/custom_operators.html">PyTorch Custom Operators Landing Page</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/fsdp.html">FSDP Notes</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/get_start_xpu.html">Pytorch 2.4: Getting Started on Intel GPU</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">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="cpu.html">torch.cpu</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="torch_cuda_memory.html">Understanding CUDA Memory Usage</a></li>
<li class="toctree-l1"><a class="reference internal" href="torch_cuda_memory.html#generating-a-snapshot">Generating a Snapshot</a></li>
<li class="toctree-l1"><a class="reference internal" href="torch_cuda_memory.html#using-the-visualizer">Using the visualizer</a></li>
<li class="toctree-l1"><a class="reference internal" href="torch_cuda_memory.html#snapshot-api-reference">Snapshot API Reference</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="xpu.html">torch.xpu</a></li>
<li class="toctree-l1"><a class="reference internal" href="mtia.html">torch.mtia</a></li>
<li class="toctree-l1"><a class="reference internal" href="meta.html">Meta device</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="export.html">torch.export</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.pipelining.html">torch.distributed.pipelining</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="torch.compiler.html">torch.compiler</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="fx.experimental.html">torch.fx.experimental</a></li>
<li class="toctree-l1"><a class="reference internal" href="hub.html">torch.hub</a></li>
<li class="toctree-l1"><a class="reference internal" href="jit.html">torch.jit</a></li>
<li class="toctree-l1"><a class="reference internal" href="linalg.html">torch.linalg</a></li>
<li class="toctree-l1"><a class="reference internal" href="monitor.html">torch.monitor</a></li>
<li class="toctree-l1"><a class="reference internal" href="signal.html">torch.signal</a></li>
<li class="toctree-l1"><a class="reference internal" href="special.html">torch.special</a></li>
<li class="toctree-l1"><a class="reference internal" href="torch.overrides.html">torch.overrides</a></li>
<li class="toctree-l1"><a class="reference internal" href="package.html">torch.package</a></li>
<li class="toctree-l1"><a class="reference internal" href="profiler.html">torch.profiler</a></li>
<li class="toctree-l1"><a class="reference internal" href="nn.init.html">torch.nn.init</a></li>
<li class="toctree-l1"><a class="reference internal" href="nn.attention.html">torch.nn.attention</a></li>
<li class="toctree-l1 current"><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="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="size.html">torch.Size</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="utils.html">torch.utils</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="deterministic.html">torch.utils.deterministic</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="module_tracker.html">torch.utils.module_tracker</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>
<li class="toctree-l1"><a class="reference internal" href="future_mod.html">torch.__future__</a></li>
<li class="toctree-l1"><a class="reference internal" href="logging.html">torch._logging</a></li>
<li class="toctree-l1"><a class="reference internal" href="torch_environment_variables.html">Torch Environment Variables</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="onnx.html">torch.onnx</a> ></li>
<li>TorchDynamo-based ONNX Exporter</li>
<li class="pytorch-breadcrumbs-aside">
<a href="_sources/onnx_dynamo.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">
<!-- Google Tag Manager (noscript) -->
<noscript><iframe src="https://www.googletagmanager.com/ns.html?id=GTM-T8XT4PS"
height="0" width="0" style="display:none;visibility:hidden"></iframe></noscript>
<!-- End Google Tag Manager (noscript) -->
<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">
<div class="section" id="torchdynamo-based-onnx-exporter">
<h1>TorchDynamo-based ONNX Exporter<a class="headerlink" href="#torchdynamo-based-onnx-exporter" title="Permalink to this heading">¶</a></h1>
<div class="contents local topic" id="contents">
<ul class="simple">
<li><p><a class="reference internal" href="#overview" id="id1">Overview</a></p></li>
<li><p><a class="reference internal" href="#dependencies" id="id2">Dependencies</a></p></li>
<li><p><a class="reference internal" href="#a-simple-example" id="id3">A simple example</a></p></li>
<li><p><a class="reference internal" href="#inspecting-the-onnx-model-using-gui" id="id4">Inspecting the ONNX model using GUI</a></p></li>
<li><p><a class="reference internal" href="#diagnosing-issues-with-sarif" id="id5">Diagnosing issues with SARIF</a></p></li>
<li><p><a class="reference internal" href="#api-reference" id="id6">API Reference</a></p></li>
</ul>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>The ONNX exporter for TorchDynamo is a rapidly evolving beta technology.</p>
</div>
<div class="section" id="overview">
<h2><a class="toc-backref" href="#id1">Overview</a><a class="headerlink" href="#overview" title="Permalink to this heading">¶</a></h2>
<p>The ONNX exporter leverages TorchDynamo engine to hook into Python’s frame evaluation API
and dynamically rewrite its bytecode into an FX Graph.
The resulting FX Graph is then polished before it is finally translated into an ONNX graph.</p>
<p>The main advantage of this approach is that the <a class="reference external" href="https://pytorch.org/docs/stable/fx.html">FX graph</a> is captured using
bytecode analysis that preserves the dynamic nature of the model instead of using traditional static tracing techniques.</p>
<p>The exporter is designed to be modular and extensible. It is composed of the following components:</p>
<blockquote>
<div><ul class="simple">
<li><p><strong>ONNX Exporter</strong>: <code class="xref py py-class docutils literal notranslate"><span class="pre">Exporter</span></code> main class that orchestrates the export process.</p></li>
<li><p><strong>ONNX Export Options</strong>: <a class="reference internal" href="#torch.onnx.ExportOptions" title="torch.onnx.ExportOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">ExportOptions</span></code></a> has a set of options that control the export process.</p></li>
<li><p><strong>ONNX Registry</strong>: <a class="reference internal" href="#torch.onnx.OnnxRegistry" title="torch.onnx.OnnxRegistry"><code class="xref py py-class docutils literal notranslate"><span class="pre">OnnxRegistry</span></code></a> is the registry of ONNX operators and functions.</p></li>
<li><p><strong>FX Graph Extractor</strong>: <code class="xref py py-class docutils literal notranslate"><span class="pre">FXGraphExtractor</span></code> extracts the FX graph from the PyTorch model.</p></li>
<li><p><strong>Fake Mode</strong>: <code class="xref py py-class docutils literal notranslate"><span class="pre">ONNXFakeContext</span></code> is a context manager that enables fake mode for large scale models.</p></li>
<li><p><strong>ONNX Program</strong>: <a class="reference internal" href="#torch.onnx.ONNXProgram" title="torch.onnx.ONNXProgram"><code class="xref py py-class docutils literal notranslate"><span class="pre">ONNXProgram</span></code></a> is the output of the exporter that contains the exported ONNX graph and diagnostics.</p></li>
<li><p><strong>ONNX Program Serializer</strong>: <a class="reference internal" href="#torch.onnx.ONNXProgramSerializer" title="torch.onnx.ONNXProgramSerializer"><code class="xref py py-class docutils literal notranslate"><span class="pre">ONNXProgramSerializer</span></code></a> serializes the exported model to a file.</p></li>
<li><p><strong>ONNX Diagnostic Options</strong>: <a class="reference internal" href="#torch.onnx.DiagnosticOptions" title="torch.onnx.DiagnosticOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">DiagnosticOptions</span></code></a> has a set of options that control the diagnostics emitted by the exporter.</p></li>
</ul>
</div></blockquote>
</div>
<div class="section" id="dependencies">
<h2><a class="toc-backref" href="#id2">Dependencies</a><a class="headerlink" href="#dependencies" title="Permalink to this heading">¶</a></h2>
<p>The ONNX exporter depends on extra Python packages:</p>
<blockquote>
<div><ul class="simple">
<li><p><a class="reference external" href="https://onnx.ai">ONNX</a></p></li>
<li><p><a class="reference external" href="https://onnxscript.ai">ONNX Script</a></p></li>
</ul>
</div></blockquote>
<p>They can be installed through <a class="reference external" href="https://pypi.org/project/pip/">pip</a>:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>--upgrade<span class="w"> </span>onnx<span class="w"> </span>onnxscript
</pre></div>
</div>
</div>
<div class="section" id="a-simple-example">
<h2><a class="toc-backref" href="#id3">A simple example</a><a class="headerlink" href="#a-simple-example" title="Permalink to this heading">¶</a></h2>
<p>See below a demonstration of exporter API in action with a simple Multilayer Perceptron (MLP) as example:</p>
<div class="highlight-python 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="k">class</span> <span class="nc">MLPModel</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="bp">self</span><span class="o">.</span><span class="n">fc0</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fc1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fc2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fc3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">True</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">tensor_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="n">tensor_x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc0</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">)</span>
<span class="n">tensor_x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">)</span>
<span class="n">tensor_x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc1</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">)</span>
<span class="n">tensor_x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">)</span>
<span class="n">tensor_x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc2</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">)</span>
<span class="n">tensor_x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">)</span>
<span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc3</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">)</span>
<span class="k">return</span> <span class="n">output</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">MLPModel</span><span class="p">()</span>
<span class="n">tensor_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">97</span><span class="p">,</span> <span class="mi">8</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">float32</span><span class="p">)</span>
<span class="n">onnx_program</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">dynamo_export</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">tensor_x</span><span class="p">)</span>
</pre></div>
</div>
<p>As the code above shows, all you need is to provide <a class="reference internal" href="#torch.onnx.dynamo_export" title="torch.onnx.dynamo_export"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.onnx.dynamo_export()</span></code></a> with an instance of the model and its input.
The exporter will then return an instance of <a class="reference internal" href="#torch.onnx.ONNXProgram" title="torch.onnx.ONNXProgram"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.onnx.ONNXProgram</span></code></a> that contains the exported ONNX graph along with extra information.</p>
<p>The in-memory model available through <code class="docutils literal notranslate"><span class="pre">onnx_program.model_proto</span></code> is an <code class="docutils literal notranslate"><span class="pre">onnx.ModelProto</span></code> object in compliance with the <a class="reference external" href="https://github.com/onnx/onnx/blob/main/docs/IR.md">ONNX IR spec</a>.
The ONNX model may then be serialized into a <a class="reference external" href="https://protobuf.dev/">Protobuf file</a> using the <a class="reference internal" href="#torch.onnx.ONNXProgram.save" title="torch.onnx.ONNXProgram.save"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.onnx.ONNXProgram.save()</span></code></a> API.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">onnx_program</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">"mlp.onnx"</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="inspecting-the-onnx-model-using-gui">
<h2><a class="toc-backref" href="#id4">Inspecting the ONNX model using GUI</a><a class="headerlink" href="#inspecting-the-onnx-model-using-gui" title="Permalink to this heading">¶</a></h2>
<p>You can view the exported model using <a class="reference external" href="https://netron.app/">Netron</a>.</p>
<a class="reference internal image-reference" href="_images/onnx_dynamo_mlp_model.png"><img alt="MLP model as viewed using Netron" src="_images/onnx_dynamo_mlp_model.png" style="width: 40%;" /></a>
<p>Note that each layer is represented in a rectangular box with a <em>f</em> icon in the top right corner.</p>
<a class="reference internal image-reference" href="_images/onnx_dynamo_mlp_model_function_highlight.png"><img alt="ONNX function highlighted on MLP model" src="_images/onnx_dynamo_mlp_model_function_highlight.png" style="width: 40%;" /></a>
<p>By expanding it, the function body is shown.</p>
<a class="reference internal image-reference" href="_images/onnx_dynamo_mlp_model_function_body.png"><img alt="ONNX function body" src="_images/onnx_dynamo_mlp_model_function_body.png" style="width: 50%;" /></a>
<p>The function body is a sequence of ONNX operators or other functions.</p>
</div>
<div class="section" id="diagnosing-issues-with-sarif">
<h2><a class="toc-backref" href="#id5">Diagnosing issues with SARIF</a><a class="headerlink" href="#diagnosing-issues-with-sarif" title="Permalink to this heading">¶</a></h2>
<p>ONNX diagnostics goes beyond regular logs through the adoption of
<a class="reference external" href="https://docs.oasis-open.org/sarif/sarif/v2.1.0/sarif-v2.1.0.html">Static Analysis Results Interchange Format (aka SARIF)</a>
to help users debug and improve their model using a GUI, such as
Visual Studio Code’s <a class="reference external" href="https://marketplace.visualstudio.com/items?itemName=MS-SarifVSCode.sarif-viewer">SARIF Viewer</a>.</p>
<p>The main advantages are:</p>
<blockquote>
<div><ul class="simple">
<li><p>The diagnostics are emitted in machine parseable <a class="reference external" href="https://docs.oasis-open.org/sarif/sarif/v2.1.0/sarif-v2.1.0.html">Static Analysis Results Interchange Format (SARIF)</a>.</p></li>
<li><p>A new clearer, structured way to add new and keep track of diagnostic rules.</p></li>
<li><p>Serve as foundation for more future improvements consuming the diagnostics.</p></li>
</ul>
</div></blockquote>
<div class="toctree-wrapper compound">
<p class="caption" role="heading"><span class="caption-text">ONNX Diagnostic SARIF Rules</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="generated/onnx_dynamo_diagnostics_rules/FXE0007%3Afx-graph-to-onnx.html">FXE0007:fx-graph-to-onnx</a></li>
<li class="toctree-l1"><a class="reference internal" href="generated/onnx_dynamo_diagnostics_rules/FXE0008%3Afx-node-to-onnx.html">FXE0008:fx-node-to-onnx</a></li>
<li class="toctree-l1"><a class="reference internal" href="generated/onnx_dynamo_diagnostics_rules/FXE0010%3Afx-pass.html">FXE0010:fx-pass</a></li>
<li class="toctree-l1"><a class="reference internal" href="generated/onnx_dynamo_diagnostics_rules/FXE0011%3Ano-symbolic-function-for-call-function.html">FXE0011:no-symbolic-function-for-call-function</a></li>
<li class="toctree-l1"><a class="reference internal" href="generated/onnx_dynamo_diagnostics_rules/FXE0012%3Aunsupported-fx-node-analysis.html">FXE0012:unsupported-fx-node-analysis</a></li>
<li class="toctree-l1"><a class="reference internal" href="generated/onnx_dynamo_diagnostics_rules/FXE0013%3Aop-level-debugging.html">FXE0013:op-level-debugging</a></li>
<li class="toctree-l1"><a class="reference internal" href="generated/onnx_dynamo_diagnostics_rules/FXE0014%3Afind-opschema-matched-symbolic-function.html">FXE0014:find-opschema-matched-symbolic-function</a></li>
<li class="toctree-l1"><a class="reference internal" href="generated/onnx_dynamo_diagnostics_rules/FXE0015%3Afx-node-insert-type-promotion.html">FXE0015:fx-node-insert-type-promotion</a></li>
<li class="toctree-l1"><a class="reference internal" href="generated/onnx_dynamo_diagnostics_rules/FXE0016%3Afind-operator-overloads-in-onnx-registry.html">FXE0016:find-operator-overloads-in-onnx-registry</a></li>
</ul>
</div>
</div>
<div class="section" id="api-reference">
<h2><a class="toc-backref" href="#id6">API Reference</a><a class="headerlink" href="#api-reference" title="Permalink to this heading">¶</a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="torch.onnx.dynamo_export">
<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">dynamo_export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">model_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">model_kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#torch.onnx.dynamo_export" title="Permalink to this definition">¶</a></dt>
<dd><p>Export a torch.nn.Module to an ONNX graph.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><em>Union</em></a><em>[</em><a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.modules.module.Module"><em>Module</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.12)"><em>Callable</em></a><em>, </em><a class="reference internal" href="export.html#torch.export.ExportedProgram" title="torch.export.exported_program.ExportedProgram"><em>ExportedProgram</em></a><em>]</em>) – The PyTorch model to be exported to ONNX.</p></li>
<li><p><strong>model_args</strong> – Positional inputs to <code class="docutils literal notranslate"><span class="pre">model</span></code>.</p></li>
<li><p><strong>model_kwargs</strong> – Keyword inputs to <code class="docutils literal notranslate"><span class="pre">model</span></code>.</p></li>
<li><p><strong>export_options</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.12)"><em>Optional</em></a><em>[</em><a class="reference internal" href="#torch.onnx.ExportOptions" title="torch.onnx.ExportOptions"><em>ExportOptions</em></a><em>]</em>) – Options to influence the export to ONNX.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>An in-memory representation of the exported ONNX model.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="#torch.onnx.ONNXProgram" title="torch.onnx.ONNXProgram"><em>ONNXProgram</em></a></p>
</dd>
</dl>
<p><strong>Example 1 - Simplest export</strong></p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MyModel</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="o">-></span> <span class="kc">None</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="bp">self</span><span class="o">.</span><span class="n">linear</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">Linear</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</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">bias</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">out</span> <span class="o">+</span> <span class="n">bias</span>
<span class="k">return</span> <span class="n">out</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">MyModel</span><span class="p">()</span>
<span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"bias"</span><span class="p">:</span> <span class="mf">3.</span><span class="p">}</span>
<span class="n">args</span> <span class="o">=</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),)</span>
<span class="n">onnx_program</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">dynamo_export</span><span class="p">(</span>
<span class="n">model</span><span class="p">,</span>
<span class="o">*</span><span class="n">args</span><span class="p">,</span>
<span class="o">**</span><span class="n">kwargs</span><span class="p">)</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">"my_simple_model.onnx"</span><span class="p">)</span>
</pre></div>
</div>
<p><strong>Example 2 - Exporting with dynamic shapes</strong></p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># The previous model can be exported with dynamic shapes</span>
<span class="n">export_options</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">ExportOptions</span><span class="p">(</span><span class="n">dynamic_shapes</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">onnx_program</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">dynamo_export</span><span class="p">(</span>
<span class="n">model</span><span class="p">,</span>
<span class="o">*</span><span class="n">args</span><span class="p">,</span>
<span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
<span class="n">export_options</span><span class="o">=</span><span class="n">export_options</span><span class="p">)</span>
<span class="n">onnx_program</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">"my_dynamic_model.onnx"</span><span class="p">)</span>
</pre></div>
</div>
<p>By printing input dynamic dimensions we can see the input shape is no longer (2,2,2)</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">input</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="go">name: "arg0"</span>
<span class="go">type {</span>
<span class="go"> tensor_type {</span>
<span class="go"> elem_type: 1</span>
<span class="go"> shape {</span>
<span class="go"> dim {</span>
<span class="go"> dim_param: "arg0_dim_0"</span>
<span class="go"> }</span>
<span class="go"> dim {</span>
<span class="go"> dim_param: "arg0_dim_1"</span>
<span class="go"> }</span>
<span class="go"> dim {</span>
<span class="go"> dim_param: "arg0_dim_2"</span>
<span class="go"> }</span>
<span class="go"> }</span>
<span class="go"> }</span>
<span class="go">}</span>
</pre></div>
</div>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.onnx.ExportOptions">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">ExportOptions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op_level_debug</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fake_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">onnx_registry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diagnostic_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#torch.onnx.ExportOptions" title="Permalink to this definition">¶</a></dt>
<dd><p>Options to influence the TorchDynamo ONNX exporter.</p>
<dl class="field-list simple">
<dt class="field-odd">Variables</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>dynamic_shapes</strong> (<em>Optional</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>]</em>) – Shape information hint for input/output tensors.
When <code class="docutils literal notranslate"><span class="pre">None</span></code>, the exporter determines the most compatible setting.
When <code class="docutils literal notranslate"><span class="pre">True</span></code>, all input shapes are considered dynamic.
When <code class="docutils literal notranslate"><span class="pre">False</span></code>, all input shapes are considered static.</p></li>
<li><p><strong>op_level_debug</strong> (<em>Optional</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>]</em>) – Whether to export the model with op-level debug information</p></li>
<li><p><strong>diagnostic_options</strong> (<a class="reference internal" href="#torch.onnx.DiagnosticOptions" title="torch.onnx.DiagnosticOptions"><em>DiagnosticOptions</em></a>) – The diagnostic options for the exporter.</p></li>
<li><p><strong>fake_context</strong> (<em>Optional</em><em>[</em><em>ONNXFakeContext</em><em>]</em>) – The fake context used for symbolic tracing.</p></li>
<li><p><strong>onnx_registry</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="#torch.onnx.OnnxRegistry" title="torch.onnx.OnnxRegistry"><em>OnnxRegistry</em></a><em>]</em>) – The ONNX registry used to register ATen operators to ONNX functions.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.onnx.enable_fake_mode">
<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">enable_fake_mode</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.onnx.enable_fake_mode" title="Permalink to this definition">¶</a></dt>
<dd><p>Enable fake mode for the duration of the context.</p>
<p>Internally it instantiates a <code class="xref py py-class docutils literal notranslate"><span class="pre">torch._subclasses.fake_tensor.FakeTensorMode</span></code> context manager
that converts user input and model parameters into <code class="xref py py-class docutils literal notranslate"><span class="pre">torch._subclasses.fake_tensor.FakeTensor</span></code>.</p>
<p>A <code class="xref py py-class docutils literal notranslate"><span class="pre">torch._subclasses.fake_tensor.FakeTensor</span></code>
is a <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> with the ability to run PyTorch code without having to
actually do computation through tensors allocated on a <code class="docutils literal notranslate"><span class="pre">meta</span></code> device. Because
there is no actual data being allocated on the device, this API allows for
exporting large models without the actual memory footprint needed for executing it.</p>
<p>It is highly recommended to enable fake mode when exporting models that
are too large to fit into memory.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>A <code class="xref py py-class docutils literal notranslate"><span class="pre">ONNXFakeContext</span></code> object that must be passed to <a class="reference internal" href="#torch.onnx.dynamo_export" title="torch.onnx.dynamo_export"><code class="xref py py-func docutils literal notranslate"><span class="pre">dynamo_export()</span></code></a>
through the <code class="xref py py-attr docutils literal notranslate"><span class="pre">ExportOptions.fake_context</span></code> argument.</p>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># xdoctest: +REQUIRES(env:TORCH_DOCTEST_ONNX)</span>
<span class="o">>>></span> <span class="kn">import</span> <span class="nn">torch</span>
<span class="o">>>></span> <span class="kn">import</span> <span class="nn">torch.onnx</span>
<span class="o">>>></span> <span class="k">class</span> <span class="nc">MyModel</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="c1"># Dummy model</span>
<span class="o">...</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="o">-></span> <span class="kc">None</span><span class="p">:</span>
<span class="o">...</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="o">...</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear</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">Linear</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="o">...</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="o">...</span> <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="o">...</span> <span class="k">return</span> <span class="n">out</span>
<span class="o">>>></span> <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">enable_fake_mode</span><span class="p">()</span> <span class="k">as</span> <span class="n">fake_context</span><span class="p">:</span>
<span class="o">...</span> <span class="n">my_nn_module</span> <span class="o">=</span> <span class="n">MyModel</span><span class="p">()</span>
<span class="o">...</span> <span class="n">arg1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="c1"># positional input 1</span>
<span class="o">>>></span> <span class="n">export_options</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">ExportOptions</span><span class="p">(</span><span class="n">fake_context</span><span class="o">=</span><span class="n">fake_context</span><span class="p">)</span>
<span class="o">>>></span> <span class="n">onnx_program</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">dynamo_export</span><span class="p">(</span>
<span class="o">...</span> <span class="n">my_nn_module</span><span class="p">,</span>
<span class="o">...</span> <span class="n">arg1</span><span class="p">,</span>
<span class="o">...</span> <span class="n">export_options</span><span class="o">=</span><span class="n">export_options</span>
<span class="o">...</span> <span class="p">)</span>
<span class="o">>>></span> <span class="c1"># Saving model WITHOUT initializers</span>
<span class="o">>>></span> <span class="n">onnx_program</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">"my_model_without_initializers.onnx"</span><span class="p">)</span>
<span class="o">>>></span> <span class="c1"># Saving model WITH initializers</span>
<span class="o">>>></span> <span class="n">onnx_program</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">"my_model_with_initializers.onnx"</span><span class="p">,</span> <span class="n">model_state</span><span class="o">=</span><span class="n">MyModel</span><span class="p">()</span><span class="o">.</span><span class="n">state_dict</span><span class="p">())</span>
</pre></div>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This API is experimental and is <em>NOT</em> backward-compatible.</p>
</div>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.onnx.ONNXProgram">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">ONNXProgram</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_proto</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_adapter</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_adapter</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diagnostic_context</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fake_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_exception</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_signature</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_torch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#torch.onnx.ONNXProgram" title="Permalink to this definition">¶</a></dt>
<dd><p>An in-memory representation of a PyTorch model that has been exported to ONNX.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model_proto</strong> (<em>onnx.ModelProto</em>) – The exported ONNX model as an <code class="xref py py-obj docutils literal notranslate"><span class="pre">onnx.ModelProto</span></code>.</p></li>
<li><p><strong>input_adapter</strong> (<em>io_adapter.InputAdapter</em>) – The input adapter used to convert PyTorch inputs into ONNX inputs.</p></li>
<li><p><strong>output_adapter</strong> (<em>io_adapter.OutputAdapter</em>) – The output adapter used to convert PyTorch outputs into ONNX outputs.</p></li>
<li><p><strong>diagnostic_context</strong> (<em>diagnostics.DiagnosticContext</em>) – Context object for the SARIF diagnostic system responsible for logging errors and metadata.</p></li>
<li><p><strong>fake_context</strong> (<em>Optional</em><em>[</em><em>ONNXFakeContext</em><em>]</em>) – The fake context used for symbolic tracing.</p></li>
<li><p><strong>export_exception</strong> (<em>Optional</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#Exception" title="(in Python v3.12)"><em>Exception</em></a><em>]</em>) – The exception that occurred during export, if any.</p></li>
<li><p><strong>model_signature</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="export.html#torch.export.ExportGraphSignature" title="torch.export.ExportGraphSignature"><em>torch.export.ExportGraphSignature</em></a><em>]</em>) – The model signature for the exported ONNX graph.</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.onnx.ONNXProgram.adapt_torch_inputs_to_onnx">
<span class="sig-name descname"><span class="pre">adapt_torch_inputs_to_onnx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">model_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_with_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">model_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/exporter.html#ONNXProgram.adapt_torch_inputs_to_onnx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.adapt_torch_inputs_to_onnx" title="Permalink to this definition">¶</a></dt>
<dd><p>Converts the PyTorch model inputs to exported ONNX model inputs format.</p>
<p>Due to design differences, input/output format between PyTorch model and exported
ONNX model are often not the same. E.g., None is allowed for PyTorch model, but are
not supported by ONNX. Nested constructs of tensors are allowed for PyTorch model,
but only flattened tensors are supported by ONNX, etc.</p>
<p>The actual adapting steps are associated with each individual export. It
depends on the PyTorch model, the particular set of model_args and model_kwargs
used for the export, and export options.</p>
<p>This method replays the adapting steps recorded during export.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model_args</strong> – The PyTorch model inputs.</p></li>
<li><p><strong>model_with_state_dict</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.12)"><em>Optional</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><em>Union</em></a><em>[</em><a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.modules.module.Module"><em>Module</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.12)"><em>Callable</em></a><em>, </em><a class="reference internal" href="export.html#torch.export.ExportedProgram" title="torch.export.exported_program.ExportedProgram"><em>ExportedProgram</em></a><em>]</em><em>]</em>) – The PyTorch model to get extra state from.
If not specified, the model used during export is used.
Required when <a class="reference internal" href="#torch.onnx.enable_fake_mode" title="torch.onnx.enable_fake_mode"><code class="xref py py-func docutils literal notranslate"><span class="pre">enable_fake_mode()</span></code></a> is used to extract real initializers as needed by the ONNX graph.</p></li>
<li><p><strong>model_kwargs</strong> – The PyTorch model keyword inputs.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A sequence of tensors converted from PyTorch model inputs.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Sequence" title="(in Python v3.12)"><em>Sequence</em></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><em>Union</em></a>[<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>, <a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a>, <a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a>, <a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a>, <a class="reference internal" href="tensor_attributes.html#torch.dtype" title="torch.dtype"><em>dtype</em></a>]]</p>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># xdoctest: +REQUIRES(env:TORCH_DOCTEST_ONNX)</span>
<span class="o">>>></span> <span class="kn">import</span> <span class="nn">torch</span>
<span class="o">>>></span> <span class="kn">import</span> <span class="nn">torch.onnx</span>
<span class="o">>>></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">Tuple</span>
<span class="o">>>></span> <span class="k">def</span> <span class="nf">func_nested_input</span><span class="p">(</span>
<span class="o">...</span> <span class="n">x_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="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span>
<span class="o">...</span> <span class="n">y_tuple</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">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="p">):</span>
<span class="o">...</span> <span class="k">if</span> <span class="s2">"a"</span> <span class="ow">in</span> <span class="n">x_dict</span><span class="p">:</span>
<span class="o">...</span> <span class="n">x</span> <span class="o">=</span> <span class="n">x_dict</span><span class="p">[</span><span class="s2">"a"</span><span class="p">]</span>
<span class="o">...</span> <span class="k">elif</span> <span class="s2">"b"</span> <span class="ow">in</span> <span class="n">x_dict</span><span class="p">:</span>
<span class="o">...</span> <span class="n">x</span> <span class="o">=</span> <span class="n">x_dict</span><span class="p">[</span><span class="s2">"b"</span><span class="p">]</span>
<span class="o">...</span> <span class="k">else</span><span class="p">:</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">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="o">...</span>
<span class="o">...</span> <span class="n">y1</span><span class="p">,</span> <span class="p">(</span><span class="n">y2</span><span class="p">,</span> <span class="n">y3</span><span class="p">)</span> <span class="o">=</span> <span class="n">y_tuple</span>
<span class="o">...</span>
<span class="o">...</span> <span class="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y1</span> <span class="o">+</span> <span class="n">y2</span> <span class="o">+</span> <span class="n">y3</span>
<span class="o">>>></span> <span class="n">x_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"a"</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="mf">1.</span><span class="p">)}</span>
<span class="o">>>></span> <span class="n">y_tuple</span> <span class="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="mf">2.</span><span class="p">),</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="mf">3.</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="mf">4.</span><span class="p">)))</span>
<span class="o">>>></span> <span class="n">onnx_program</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">dynamo_export</span><span class="p">(</span><span class="n">func_nested_input</span><span class="p">,</span> <span class="n">x_dict</span><span class="p">,</span> <span class="n">y_tuple</span><span class="p">)</span>
<span class="o">>>></span> <span class="nb">print</span><span class="p">(</span><span class="n">x_dict</span><span class="p">,</span> <span class="n">y_tuple</span><span class="p">)</span>
<span class="p">{</span><span class="s1">'a'</span><span class="p">:</span> <span class="n">tensor</span><span class="p">(</span><span class="mf">1.</span><span class="p">)}</span> <span class="p">(</span><span class="n">tensor</span><span class="p">(</span><span class="mf">2.</span><span class="p">),</span> <span class="p">(</span><span class="n">tensor</span><span class="p">(</span><span class="mf">3.</span><span class="p">),</span> <span class="n">tensor</span><span class="p">(</span><span class="mf">4.</span><span class="p">)))</span>
<span class="o">>>></span> <span class="nb">print</span><span class="p">(</span><span class="n">onnx_program</span><span class="o">.</span><span class="n">adapt_torch_inputs_to_onnx</span><span class="p">(</span><span class="n">x_dict</span><span class="p">,</span> <span class="n">y_tuple</span><span class="p">,</span> <span class="n">model_with_state_dict</span><span class="o">=</span><span class="n">func_nested_input</span><span class="p">))</span>
<span class="p">(</span><span class="n">tensor</span><span class="p">(</span><span class="mf">1.</span><span class="p">),</span> <span class="n">tensor</span><span class="p">(</span><span class="mf">2.</span><span class="p">),</span> <span class="n">tensor</span><span class="p">(</span><span class="mf">3.</span><span class="p">),</span> <span class="n">tensor</span><span class="p">(</span><span class="mf">4.</span><span class="p">))</span>
</pre></div>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This API is experimental and is <em>NOT</em> backward-compatible.</p>
</div>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.onnx.ONNXProgram.adapt_torch_outputs_to_onnx">
<span class="sig-name descname"><span class="pre">adapt_torch_outputs_to_onnx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_outputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_with_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/exporter.html#ONNXProgram.adapt_torch_outputs_to_onnx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.adapt_torch_outputs_to_onnx" title="Permalink to this definition">¶</a></dt>
<dd><p>Converts the PyTorch model outputs to exported ONNX model outputs format.</p>
<p>Due to design differences, input/output format between PyTorch model and exported
ONNX model are often not the same. E.g., None is allowed for PyTorch model, but are
not supported by ONNX. Nested constructs of tensors are allowed for PyTorch model,
but only flattened tensors are supported by ONNX, etc.</p>
<p>The actual adapting steps are associated with each individual export. It
depends on the PyTorch model, the particular set of model_args and model_kwargs
used for the export, and export options.</p>
<p>This method replays the adapting steps recorded during export.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><em>Any</em></a>) – The PyTorch model outputs.</p></li>
<li><p><strong>model_with_state_dict</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.12)"><em>Optional</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><em>Union</em></a><em>[</em><a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.modules.module.Module"><em>Module</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.12)"><em>Callable</em></a><em>, </em><a class="reference internal" href="export.html#torch.export.ExportedProgram" title="torch.export.exported_program.ExportedProgram"><em>ExportedProgram</em></a><em>]</em><em>]</em>) – The PyTorch model to get extra state from.
If not specified, the model used during export is used.
Required when <a class="reference internal" href="#torch.onnx.enable_fake_mode" title="torch.onnx.enable_fake_mode"><code class="xref py py-func docutils literal notranslate"><span class="pre">enable_fake_mode()</span></code></a> is used to extract real initializers as needed by the ONNX graph.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>PyTorch model outputs in exported ONNX model outputs format.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Sequence" title="(in Python v3.12)"><em>Sequence</em></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><em>Union</em></a>[<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>, <a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a>, <a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a>, <a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a>]]</p>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># xdoctest: +REQUIRES(env:TORCH_DOCTEST_ONNX)</span>
<span class="o">>>></span> <span class="kn">import</span> <span class="nn">torch</span>
<span class="o">>>></span> <span class="kn">import</span> <span class="nn">torch.onnx</span>
<span class="o">>>></span> <span class="k">def</span> <span class="nf">func_returning_tuples</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="o">...</span> <span class="n">x</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
<span class="o">...</span> <span class="n">y</span> <span class="o">=</span> <span class="n">y</span> <span class="o">+</span> <span class="n">z</span>
<span class="o">...</span> <span class="n">z</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
<span class="o">...</span> <span class="k">return</span> <span class="p">(</span><span class="n">x</span><span class="p">,</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="o">>>></span> <span class="n">x</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">1.</span><span class="p">)</span>
<span class="o">>>></span> <span class="n">y</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">2.</span><span class="p">)</span>
<span class="o">>>></span> <span class="n">z</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">3.</span><span class="p">)</span>
<span class="o">>>></span> <span class="n">onnx_program</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">dynamo_export</span><span class="p">(</span><span class="n">func_returning_tuples</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="o">>>></span> <span class="n">pt_output</span> <span class="o">=</span> <span class="n">func_returning_tuples</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="o">>>></span> <span class="nb">print</span><span class="p">(</span><span class="n">pt_output</span><span class="p">)</span>
<span class="p">(</span><span class="n">tensor</span><span class="p">(</span><span class="mf">3.</span><span class="p">),</span> <span class="p">(</span><span class="n">tensor</span><span class="p">(</span><span class="mf">5.</span><span class="p">),</span> <span class="n">tensor</span><span class="p">(</span><span class="mf">8.</span><span class="p">)))</span>
<span class="o">>>></span> <span class="nb">print</span><span class="p">(</span><span class="n">onnx_program</span><span class="o">.</span><span class="n">adapt_torch_outputs_to_onnx</span><span class="p">(</span><span class="n">pt_output</span><span class="p">,</span> <span class="n">model_with_state_dict</span><span class="o">=</span><span class="n">func_returning_tuples</span><span class="p">))</span>
<span class="p">[</span><span class="n">tensor</span><span class="p">(</span><span class="mf">3.</span><span class="p">),</span> <span class="n">tensor</span><span class="p">(</span><span class="mf">5.</span><span class="p">),</span> <span class="n">tensor</span><span class="p">(</span><span class="mf">8.</span><span class="p">)]</span>
</pre></div>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This API is experimental and is <em>NOT</em> backward-compatible.</p>
</div>
</dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="torch.onnx.ONNXProgram.diagnostic_context">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">diagnostic_context</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">diagnostics.DiagnosticContext</span></em><a class="headerlink" href="#torch.onnx.ONNXProgram.diagnostic_context" title="Permalink to this definition">¶</a></dt>
<dd><p>The diagnostic context associated with the export.</p>
</dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="torch.onnx.ONNXProgram.fake_context">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fake_context</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.12)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><span class="pre">ONNXFakeContext</span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#torch.onnx.ONNXProgram.fake_context" title="Permalink to this definition">¶</a></dt>
<dd><p>The fake context associated with the export.</p>
</dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="torch.onnx.ONNXProgram.model_proto">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">model_proto</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">onnx.ModelProto</span></em><a class="headerlink" href="#torch.onnx.ONNXProgram.model_proto" title="Permalink to this definition">¶</a></dt>
<dd><p>The exported ONNX model as an <code class="xref py py-obj docutils literal notranslate"><span class="pre">onnx.ModelProto</span></code>.</p>
</dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="torch.onnx.ONNXProgram.model_signature">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">model_signature</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.12)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="export.html#torch.export.graph_signature.ExportGraphSignature" title="torch.export.graph_signature.ExportGraphSignature"><span class="pre">ExportGraphSignature</span></a><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#torch.onnx.ONNXProgram.model_signature" title="Permalink to this definition">¶</a></dt>
<dd><p>The model signature for the exported ONNX graph.</p>
<p>This information is relevant because ONNX specification often differs from PyTorch’s, resulting
in a ONNX graph with input and output schema different from the actual PyTorch model implementation.
By using the model signature, the users can understand the inputs and outputs differences
and properly execute the model in ONNX Runtime.</p>
<p>NOTE: Model signature is only available when the ONNX graph was exported from a
<a class="reference internal" href="export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.export.ExportedProgram</span></code></a> object.</p>
<p>NOTE: Any transformation done to the model that changes the model signature must be accompanied
by updates to this model signature as well through <code class="xref py py-class docutils literal notranslate"><span class="pre">InputAdaptStep</span></code> and/or <code class="xref py py-class docutils literal notranslate"><span class="pre">OutputAdaptStep</span></code>.</p>
<p class="rubric">Example</p>
<p>The following model produces different sets of inputs and outputs.
The first 4 inputs are model parameters (namely conv1.weight, conv2.weight, fc1.weight, fc2.weight),
and the next 2 inputs are registered buffers (namely my_buffer2, my_buffer1) and finally
the last 2 inputs are user inputs (namely x and b).
The first output is a buffer mutation (namely my_buffer2) and the last output is the actual model output.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pprint</span>
<span class="gp">>>> </span><span class="k">class</span> <span class="nc">CustomModule</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="gp">... </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="gp">... </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="gp">... </span> <span class="bp">self</span><span class="o">.</span><span class="n">my_parameter</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">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mf">2.0</span><span class="p">))</span>
<span class="gp">... </span> <span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="s2">"my_buffer1"</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="mf">3.0</span><span class="p">))</span>
<span class="gp">... </span> <span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="s2">"my_buffer2"</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="mf">4.0</span><span class="p">))</span>
<span class="gp">... </span> <span class="bp">self</span><span class="o">.</span><span class="n">conv1</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">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="gp">... </span> <span class="bp">self</span><span class="o">.</span><span class="n">conv2</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">Conv2d</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="gp">... </span> <span class="bp">self</span><span class="o">.</span><span class="n">fc1</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">Linear</span><span class="p">(</span><span class="mi">9216</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="gp">... </span> <span class="bp">self</span><span class="o">.</span><span class="n">fc2</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">Linear</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="gp">... </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">b</span><span class="p">):</span>
<span class="gp">... </span> <span class="n">tensor_x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="gp">... </span> <span class="n">tensor_x</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">functional</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">)</span>
<span class="gp">... </span> <span class="n">tensor_x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">)</span>
<span class="gp">... </span> <span class="n">tensor_x</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">functional</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">)</span>
<span class="gp">... </span> <span class="n">tensor_x</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">functional</span><span class="o">.</span><span class="n">max_pool2d</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="gp">... </span> <span class="n">tensor_x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="gp">... </span> <span class="n">tensor_x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc1</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">)</span>
<span class="gp">... </span> <span class="n">tensor_x</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">functional</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">)</span>
<span class="gp">... </span> <span class="n">tensor_x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc2</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">)</span>
<span class="gp">... </span> <span class="n">output</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">functional</span><span class="o">.</span><span class="n">log_softmax</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="gp">... </span> <span class="p">(</span>
<span class="gp">... </span> <span class="bp">self</span><span class="o">.</span><span class="n">my_buffer2</span><span class="o">.</span><span class="n">add_</span><span class="p">(</span><span class="mf">1.0</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">my_buffer1</span>
<span class="gp">... </span> <span class="p">)</span> <span class="c1"># Mutate buffer through in-place addition</span>
<span class="gp">... </span> <span class="k">return</span> <span class="n">output</span>
<span class="gp">>>> </span><span class="n">inputs</span> <span class="o">=</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">64</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">28</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">float32</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">exported_program</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">export</span><span class="o">.</span><span class="n">export</span><span class="p">(</span><span class="n">CustomModule</span><span class="p">(),</span> <span class="n">args</span><span class="o">=</span><span class="n">inputs</span><span class="p">)</span><span class="o">.</span><span class="n">run_decompositions</span><span class="p">({})</span>
<span class="gp">>>> </span><span class="n">onnx_program</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">dynamo_export</span><span class="p">(</span><span class="n">exported_program</span><span class="p">,</span> <span class="o">*</span><span class="n">inputs</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">pprint</span><span class="o">.</span><span class="n">pprint</span><span class="p">(</span><span class="n">onnx_program</span><span class="o">.</span><span class="n">model_signature</span><span class="p">)</span>
<span class="go">ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.PARAMETER: 2>,</span>
<span class="go"> arg=TensorArgument(name='p_conv1_weight'),</span>
<span class="go"> target='conv1.weight',</span>
<span class="go"> persistent=None),</span>
<span class="go"> InputSpec(kind=<InputKind.PARAMETER: 2>,</span>
<span class="go"> arg=TensorArgument(name='p_conv2_weight'),</span>
<span class="go"> target='conv2.weight',</span>
<span class="go"> persistent=None),</span>
<span class="go"> InputSpec(kind=<InputKind.PARAMETER: 2>,</span>
<span class="go"> arg=TensorArgument(name='p_fc1_weight'),</span>
<span class="go"> target='fc1.weight',</span>
<span class="go"> persistent=None),</span>
<span class="go"> InputSpec(kind=<InputKind.PARAMETER: 2>,</span>