-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathbenchmark_utils.html
1381 lines (1174 loc) · 95.1 KB
/
benchmark_utils.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>Benchmark Utils - torch.utils.benchmark — PyTorch 2.4 documentation</title>
<link rel="canonical" href="https://pytorch.org/docs/stable/benchmark_utils.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="torch.utils.bottleneck" href="bottleneck.html" />
<link rel="prev" title="torch.utils.swap_tensors" href="generated/torch.utils.swap_tensors.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"><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 current"><a class="current reference internal" href="#">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>Benchmark Utils - torch.utils.benchmark</li>
<li class="pytorch-breadcrumbs-aside">
<a href="_sources/benchmark_utils.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="module-torch.utils.benchmark">
<span id="benchmark-utils-torch-utils-benchmark"></span><h1>Benchmark Utils - torch.utils.benchmark<a class="headerlink" href="#module-torch.utils.benchmark" title="Permalink to this heading">¶</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="torch.utils.benchmark.Timer">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.benchmark.</span></span><span class="sig-name descname"><span class="pre">Timer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stmt='pass'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">setup='pass'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_setup=''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timer=<built-in</span> <span class="pre">function</span> <span class="pre">perf_counter></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">globals=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sub_label=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">env=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_threads=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">language=Language.PYTHON</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/timer.html#Timer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Timer" title="Permalink to this definition">¶</a></dt>
<dd><p>Helper class for measuring execution time of PyTorch statements.</p>
<p>For a full tutorial on how to use this class, see:
<a class="reference external" href="https://pytorch.org/tutorials/recipes/recipes/benchmark.html">https://pytorch.org/tutorials/recipes/recipes/benchmark.html</a></p>
<p>The PyTorch Timer is based on <cite>timeit.Timer</cite> (and in fact uses
<cite>timeit.Timer</cite> internally), but with several key differences:</p>
<ol class="arabic simple">
<li><dl class="simple">
<dt>Runtime aware:</dt><dd><p>Timer will perform warmups (important as some elements of PyTorch are
lazily initialized), set threadpool size so that comparisons are
apples-to-apples, and synchronize asynchronous CUDA functions when
necessary.</p>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt>Focus on replicates:</dt><dd><p>When measuring code, and particularly complex kernels / models,
run-to-run variation is a significant confounding factor. It is
expected that all measurements should include replicates to quantify
noise and allow median computation, which is more robust than mean.
To that effect, this class deviates from the <cite>timeit</cite> API by
conceptually merging <cite>timeit.Timer.repeat</cite> and <cite>timeit.Timer.autorange</cite>.
(Exact algorithms are discussed in method docstrings.) The <cite>timeit</cite>
method is replicated for cases where an adaptive strategy is not
desired.</p>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt>Optional metadata:</dt><dd><p>When defining a Timer, one can optionally specify <cite>label</cite>, <cite>sub_label</cite>,
<cite>description</cite>, and <cite>env</cite>. (Defined later) These fields are included in
the representation of result object and by the <cite>Compare</cite> class to group
and display results for comparison.</p>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt>Instruction counts</dt><dd><p>In addition to wall times, Timer can run a statement under Callgrind
and report instructions executed.</p>
</dd>
</dl>
</li>
</ol>
<p>Directly analogous to <cite>timeit.Timer</cite> constructor arguments:</p>
<blockquote>
<div><p><cite>stmt</cite>, <cite>setup</cite>, <cite>timer</cite>, <cite>globals</cite></p>
</div></blockquote>
<p>PyTorch Timer specific constructor arguments:</p>
<blockquote>
<div><p><cite>label</cite>, <cite>sub_label</cite>, <cite>description</cite>, <cite>env</cite>, <cite>num_threads</cite></p>
</div></blockquote>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>stmt</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Code snippet to be run in a loop and timed.</p></li>
<li><p><strong>setup</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Optional setup code. Used to define variables used in <cite>stmt</cite></p></li>
<li><p><strong>global_setup</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – (C++ only)
Code which is placed at the top level of the file for things like
<cite>#include</cite> statements.</p></li>
<li><p><strong>timer</strong> (<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><em>[</em><em>]</em><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>]</em>) – Callable which returns the current time. If PyTorch was built
without CUDA or there is no GPU present, this defaults to
<cite>timeit.default_timer</cite>; otherwise it will synchronize CUDA before
measuring the time.</p></li>
<li><p><strong>globals</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.Dict" title="(in Python v3.12)"><em>Dict</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><em>Any</em></a><em>]</em><em>]</em>) – A dict which defines the global variables when <cite>stmt</cite> is being
executed. This is the other method for providing variables which
<cite>stmt</cite> needs.</p></li>
<li><p><strong>label</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/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>]</em>) – String which summarizes <cite>stmt</cite>. For instance, if <cite>stmt</cite> is
“torch.nn.functional.relu(torch.add(x, 1, out=out))”
one might set label to “ReLU(x + 1)” to improve readability.</p></li>
<li><p><strong>sub_label</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/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>]</em>) – <p>Provide supplemental information to disambiguate measurements
with identical stmt or label. For instance, in our example
above sub_label might be “float” or “int”, so that it is easy
to differentiate:
“ReLU(x + 1): (float)”</p>
<p>”ReLU(x + 1): (int)”
when printing Measurements or summarizing using <cite>Compare</cite>.</p>
</p></li>
<li><p><strong>description</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/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>]</em>) – <p>String to distinguish measurements with identical label and
sub_label. The principal use of <cite>description</cite> is to signal to
<cite>Compare</cite> the columns of data. For instance one might set it
based on the input size to create a table of the form:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="o">|</span> <span class="n">n</span><span class="o">=</span><span class="mi">1</span> <span class="o">|</span> <span class="n">n</span><span class="o">=</span><span class="mi">4</span> <span class="o">|</span> <span class="o">...</span>
<span class="o">-------------</span> <span class="o">...</span>
<span class="n">ReLU</span><span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span> <span class="p">(</span><span class="nb">float</span><span class="p">)</span> <span class="o">|</span> <span class="o">...</span> <span class="o">|</span> <span class="o">...</span> <span class="o">|</span> <span class="o">...</span>
<span class="n">ReLU</span><span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span> <span class="p">(</span><span class="nb">int</span><span class="p">)</span> <span class="o">|</span> <span class="o">...</span> <span class="o">|</span> <span class="o">...</span> <span class="o">|</span> <span class="o">...</span>
</pre></div>
</div>
<p>using <cite>Compare</cite>. It is also included when printing a Measurement.</p>
</p></li>
<li><p><strong>env</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/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>]</em>) – This tag indicates that otherwise identical tasks were run in
different environments, and are therefore not equivalent, for
instance when A/B testing a change to a kernel. <cite>Compare</cite> will
treat Measurements with different <cite>env</cite> specification as distinct
when merging replicate runs.</p></li>
<li><p><strong>num_threads</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – The size of the PyTorch threadpool when executing <cite>stmt</cite>. Single
threaded performance is important as both a key inference workload
and a good indicator of intrinsic algorithmic efficiency, so the
default is set to one. This is in contrast to the default PyTorch
threadpool size which tries to utilize all cores.</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.benchmark.Timer.adaptive_autorange">
<span class="sig-name descname"><span class="pre">adaptive_autorange</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</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">min_run_time</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_run_time</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callback</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/utils/benchmark/utils/timer.html#Timer.adaptive_autorange"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Timer.adaptive_autorange" title="Permalink to this definition">¶</a></dt>
<dd><p>Similar to <cite>blocked_autorange</cite> but also checks for variablility in measurements
and repeats until iqr/median is smaller than <cite>threshold</cite> or <cite>max_run_time</cite> is reached.</p>
<p>At a high level, adaptive_autorange executes the following pseudo-code:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>`setup`
times = []
while times.sum < max_run_time
start = timer()
for _ in range(block_size):
`stmt`
times.append(timer() - start)
enough_data = len(times)>3 and times.sum > min_run_time
small_iqr=times.iqr/times.mean<threshold
if enough_data and small_iqr:
break
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>threshold</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – value of iqr/median threshold for stopping</p></li>
<li><p><strong>min_run_time</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – total runtime needed before checking <cite>threshold</cite></p></li>
<li><p><strong>max_run_time</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – total runtime for all measurements regardless of <cite>threshold</cite></p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A <cite>Measurement</cite> object that contains measured runtimes and
repetition counts, and can be used to compute statistics.
(mean, median, etc.)</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="#torch.utils.benchmark.Measurement" title="torch.utils.benchmark.utils.common.Measurement"><em>Measurement</em></a></p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.benchmark.Timer.blocked_autorange">
<span class="sig-name descname"><span class="pre">blocked_autorange</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">callback</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">min_run_time</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/timer.html#Timer.blocked_autorange"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Timer.blocked_autorange" title="Permalink to this definition">¶</a></dt>
<dd><p>Measure many replicates while keeping timer overhead to a minimum.</p>
<p>At a high level, blocked_autorange executes the following pseudo-code:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>`setup`
total_time = 0
while total_time < min_run_time
start = timer()
for _ in range(block_size):
`stmt`
total_time += (timer() - start)
</pre></div>
</div>
<p>Note the variable <cite>block_size</cite> in the inner loop. The choice of block
size is important to measurement quality, and must balance two
competing objectives:</p>
<blockquote>
<div><ol class="arabic simple">
<li><p>A small block size results in more replicates and generally
better statistics.</p></li>
<li><p>A large block size better amortizes the cost of <cite>timer</cite>
invocation, and results in a less biased measurement. This is
important because CUDA synchronization time is non-trivial
(order single to low double digit microseconds) and would
otherwise bias the measurement.</p></li>
</ol>
</div></blockquote>
<p>blocked_autorange sets block_size by running a warmup period,
increasing block size until timer overhead is less than 0.1% of
the overall computation. This value is then used for the main
measurement loop.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>A <cite>Measurement</cite> object that contains measured runtimes and
repetition counts, and can be used to compute statistics.
(mean, median, etc.)</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference internal" href="#torch.utils.benchmark.Measurement" title="torch.utils.benchmark.utils.common.Measurement"><em>Measurement</em></a></p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.benchmark.Timer.collect_callgrind">
<span class="sig-name descname"><span class="pre">collect_callgrind</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">number</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></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">repeats</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">collect_baseline</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">retain_out_file</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#torch.utils.benchmark.CallgrindStats" title="torch.utils.benchmark.utils.valgrind_wrapper.timer_interface.CallgrindStats"><span class="pre">CallgrindStats</span></a></span></span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/timer.html#Timer.collect_callgrind"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Timer.collect_callgrind" title="Permalink to this definition">¶</a></dt>
<dt class="sig sig-object py">
<span class="sig-name descname"><span class="pre">collect_callgrind</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">number</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></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">repeats</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">collect_baseline</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">retain_out_file</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#torch.utils.benchmark.CallgrindStats" title="torch.utils.benchmark.utils.valgrind_wrapper.timer_interface.CallgrindStats"><span class="pre">CallgrindStats</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span></span></span></dt>
<dd><p>Collect instruction counts using Callgrind.</p>
<p>Unlike wall times, instruction counts are deterministic
(modulo non-determinism in the program itself and small amounts of
jitter from the Python interpreter.) This makes them ideal for detailed
performance analysis. This method runs <cite>stmt</cite> in a separate process
so that Valgrind can instrument the program. Performance is severely
degraded due to the instrumentation, however this is ameliorated by
the fact that a small number of iterations is generally sufficient to
obtain good measurements.</p>
<p>In order to to use this method <cite>valgrind</cite>, <cite>callgrind_control</cite>, and
<cite>callgrind_annotate</cite> must be installed.</p>
<p>Because there is a process boundary between the caller (this process)
and the <cite>stmt</cite> execution, <cite>globals</cite> cannot contain arbitrary in-memory
data structures. (Unlike timing methods) Instead, globals are
restricted to builtins, <cite>nn.Modules</cite>’s, and TorchScripted functions/modules
to reduce the surprise factor from serialization and subsequent
deserialization. The <cite>GlobalsBridge</cite> class provides more detail on this
subject. Take particular care with nn.Modules: they rely on pickle and
you may need to add an import to <cite>setup</cite> for them to transfer properly.</p>
<p>By default, a profile for an empty statement will be collected and
cached to indicate how many instructions are from the Python loop which
drives <cite>stmt</cite>.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>A <cite>CallgrindStats</cite> object which provides instruction counts and
some basic facilities for analyzing and manipulating results.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.benchmark.Timer.timeit">
<span class="sig-name descname"><span class="pre">timeit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">number</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000000</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/timer.html#Timer.timeit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Timer.timeit" title="Permalink to this definition">¶</a></dt>
<dd><p>Mirrors the semantics of timeit.Timer.timeit().</p>
<p>Execute the main statement (<cite>stmt</cite>) <cite>number</cite> times.
<a class="reference external" href="https://docs.python.org/3/library/timeit.html#timeit.Timer.timeit">https://docs.python.org/3/library/timeit.html#timeit.Timer.timeit</a></p>
<dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="#torch.utils.benchmark.Measurement" title="torch.utils.benchmark.utils.common.Measurement"><em>Measurement</em></a></p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.utils.benchmark.Measurement">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.benchmark.</span></span><span class="sig-name descname"><span class="pre">Measurement</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">number_per_run</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raw_times</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">task_spec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata</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/utils/benchmark/utils/common.html#Measurement"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Measurement" title="Permalink to this definition">¶</a></dt>
<dd><p>The result of a Timer measurement.</p>
<p>This class stores one or more measurements of a given statement. It is
serializable and provides several convenience methods
(including a detailed __repr__) for downstream consumers.</p>
<dl class="field-list simple">
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.benchmark.Measurement.merge">
<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">merge</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">measurements</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/common.html#Measurement.merge"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Measurement.merge" title="Permalink to this definition">¶</a></dt>
<dd><p>Convenience method for merging replicates.</p>
<p>Merge will extrapolate times to <cite>number_per_run=1</cite> and will not
transfer any metadata. (Since it might differ between replicates)</p>
<dl class="field-list simple">
<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.List" title="(in Python v3.12)"><em>List</em></a>[<a class="reference internal" href="#torch.utils.benchmark.Measurement" title="torch.utils.benchmark.utils.common.Measurement"><em>Measurement</em></a>]</p>
</dd>
</dl>
</dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="torch.utils.benchmark.Measurement.significant_figures">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">significant_figures</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/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></em><a class="headerlink" href="#torch.utils.benchmark.Measurement.significant_figures" title="Permalink to this definition">¶</a></dt>
<dd><p>Approximate significant figure estimate.</p>
<p>This property is intended to give a convenient way to estimate the
precision of a measurement. It only uses the interquartile region to
estimate statistics to try to mitigate skew from the tails, and
uses a static z value of 1.645 since it is not expected to be used
for small values of <cite>n</cite>, so z can approximate <cite>t</cite>.</p>
<p>The significant figure estimation used in conjunction with the
<cite>trim_sigfig</cite> method to provide a more human interpretable data
summary. __repr__ does not use this method; it simply displays raw
values. Significant figure estimation is intended for <cite>Compare</cite>.</p>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.utils.benchmark.CallgrindStats">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.benchmark.</span></span><span class="sig-name descname"><span class="pre">CallgrindStats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">task_spec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">number_per_run</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">built_with_debug_symbols</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">baseline_inclusive_stats</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">baseline_exclusive_stats</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stmt_inclusive_stats</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stmt_exclusive_stats</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stmt_callgrind_out</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#CallgrindStats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.CallgrindStats" title="Permalink to this definition">¶</a></dt>
<dd><p>Top level container for Callgrind results collected by Timer.</p>
<p>Manipulation is generally done using the FunctionCounts class, which is
obtained by calling <cite>CallgrindStats.stats(…)</cite>. Several convenience
methods are provided as well; the most significant is
<cite>CallgrindStats.as_standardized()</cite>.</p>
<dl class="field-list simple">
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.benchmark.CallgrindStats.as_standardized">
<span class="sig-name descname"><span class="pre">as_standardized</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#CallgrindStats.as_standardized"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.CallgrindStats.as_standardized" title="Permalink to this definition">¶</a></dt>
<dd><p>Strip library names and some prefixes from function strings.</p>
<p>When comparing two different sets of instruction counts, on stumbling
block can be path prefixes. Callgrind includes the full filepath
when reporting a function (as it should). However, this can cause
issues when diffing profiles. If a key component such as Python
or PyTorch was built in separate locations in the two profiles, which
can result in something resembling:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">23234231</span> <span class="o">/</span><span class="n">tmp</span><span class="o">/</span><span class="n">first_build_dir</span><span class="o">/</span><span class="n">thing</span><span class="o">.</span><span class="n">c</span><span class="p">:</span><span class="n">foo</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="mi">9823794</span> <span class="o">/</span><span class="n">tmp</span><span class="o">/</span><span class="n">first_build_dir</span><span class="o">/</span><span class="n">thing</span><span class="o">.</span><span class="n">c</span><span class="p">:</span><span class="n">bar</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="o">...</span>
<span class="mi">53453</span> <span class="o">.../</span><span class="n">aten</span><span class="o">/</span><span class="n">src</span><span class="o">/</span><span class="n">Aten</span><span class="o">/...</span><span class="p">:</span><span class="n">function_that_actually_changed</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="o">...</span>
<span class="o">-</span><span class="mi">9823794</span> <span class="o">/</span><span class="n">tmp</span><span class="o">/</span><span class="n">second_build_dir</span><span class="o">/</span><span class="n">thing</span><span class="o">.</span><span class="n">c</span><span class="p">:</span><span class="n">bar</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="o">-</span><span class="mi">23234231</span> <span class="o">/</span><span class="n">tmp</span><span class="o">/</span><span class="n">second_build_dir</span><span class="o">/</span><span class="n">thing</span><span class="o">.</span><span class="n">c</span><span class="p">:</span><span class="n">foo</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
</pre></div>
</div>
<p>Stripping prefixes can ameliorate this issue by regularizing the
strings and causing better cancellation of equivalent call sites
when diffing.</p>
<dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="#torch.utils.benchmark.CallgrindStats" title="torch.utils.benchmark.utils.valgrind_wrapper.timer_interface.CallgrindStats"><em>CallgrindStats</em></a></p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.benchmark.CallgrindStats.counts">
<span class="sig-name descname"><span class="pre">counts</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">denoise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#CallgrindStats.counts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.CallgrindStats.counts" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the total number of instructions executed.</p>
<p>See <cite>FunctionCounts.denoise()</cite> for an explanation of the <cite>denoise</cite> arg.</p>
<dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.benchmark.CallgrindStats.delta">
<span class="sig-name descname"><span class="pre">delta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inclusive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#CallgrindStats.delta"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.CallgrindStats.delta" title="Permalink to this definition">¶</a></dt>
<dd><p>Diff two sets of counts.</p>
<p>One common reason to collect instruction counts is to determine the
the effect that a particular change will have on the number of instructions
needed to perform some unit of work. If a change increases that number, the
next logical question is “why”. This generally involves looking at what part
if the code increased in instruction count. This function automates that
process so that one can easily diff counts on both an inclusive and
exclusive basis.</p>
<dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="#torch.utils.benchmark.FunctionCounts" title="torch.utils.benchmark.utils.valgrind_wrapper.timer_interface.FunctionCounts"><em>FunctionCounts</em></a></p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.benchmark.CallgrindStats.stats">
<span class="sig-name descname"><span class="pre">stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inclusive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#CallgrindStats.stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.CallgrindStats.stats" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns detailed function counts.</p>
<p>Conceptually, the FunctionCounts returned can be thought of as a tuple
of (count, path_and_function_name) tuples.</p>
<p><cite>inclusive</cite> matches the semantics of callgrind. If True, the counts
include instructions executed by children. <cite>inclusive=True</cite> is useful
for identifying hot spots in code; <cite>inclusive=False</cite> is useful for
reducing noise when diffing counts from two different runs. (See
CallgrindStats.delta(…) for more details)</p>
<dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="#torch.utils.benchmark.FunctionCounts" title="torch.utils.benchmark.utils.valgrind_wrapper.timer_interface.FunctionCounts"><em>FunctionCounts</em></a></p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.utils.benchmark.FunctionCounts">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.benchmark.</span></span><span class="sig-name descname"><span class="pre">FunctionCounts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inclusive</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_rows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_linewidth</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/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#FunctionCounts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.FunctionCounts" title="Permalink to this definition">¶</a></dt>
<dd><p>Container for manipulating Callgrind results.</p>
<dl class="simple">
<dt>It supports:</dt><dd><ol class="arabic simple">
<li><p>Addition and subtraction to combine or diff results.</p></li>
<li><p>Tuple-like indexing.</p></li>
<li><p>A <cite>denoise</cite> function which strips CPython calls which are known to
be non-deterministic and quite noisy.</p></li>
<li><p>Two higher order methods (<cite>filter</cite> and <cite>transform</cite>) for custom
manipulation.</p></li>
</ol>
</dd>
</dl>
<dl class="field-list simple">
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.benchmark.FunctionCounts.denoise">
<span class="sig-name descname"><span class="pre">denoise</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#FunctionCounts.denoise"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.FunctionCounts.denoise" title="Permalink to this definition">¶</a></dt>
<dd><p>Remove known noisy instructions.</p>
<p>Several instructions in the CPython interpreter are rather noisy. These
instructions involve unicode to dictionary lookups which Python uses to
map variable names. FunctionCounts is generally a content agnostic
container, however this is sufficiently important for obtaining
reliable results to warrant an exception.</p>
<dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="#torch.utils.benchmark.FunctionCounts" title="torch.utils.benchmark.utils.valgrind_wrapper.timer_interface.FunctionCounts"><em>FunctionCounts</em></a></p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.benchmark.FunctionCounts.filter">
<span class="sig-name descname"><span class="pre">filter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filter_fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#FunctionCounts.filter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.FunctionCounts.filter" title="Permalink to this definition">¶</a></dt>
<dd><p>Keep only the elements where <cite>filter_fn</cite> applied to function name returns True.</p>
<dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="#torch.utils.benchmark.FunctionCounts" title="torch.utils.benchmark.utils.valgrind_wrapper.timer_interface.FunctionCounts"><em>FunctionCounts</em></a></p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.benchmark.FunctionCounts.transform">
<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">map_fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#FunctionCounts.transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.FunctionCounts.transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Apply <cite>map_fn</cite> to all of the function names.</p>
<p>This can be used to regularize function names (e.g. stripping irrelevant
parts of the file path), coalesce entries by mapping multiple functions
to the same name (in which case the counts are added together), etc.</p>
<dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="#torch.utils.benchmark.FunctionCounts" title="torch.utils.benchmark.utils.valgrind_wrapper.timer_interface.FunctionCounts"><em>FunctionCounts</em></a></p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.utils.benchmark.Compare">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.benchmark.</span></span><span class="sig-name descname"><span class="pre">Compare</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">results</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/compare.html#Compare"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Compare" title="Permalink to this definition">¶</a></dt>
<dd><p>Helper class for displaying the results of many measurements in a
formatted table.</p>
<p>The table format is based on the information fields provided in
<a class="reference internal" href="#torch.utils.benchmark.Timer" title="torch.utils.benchmark.Timer"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.utils.benchmark.Timer</span></code></a> (<cite>description</cite>, <cite>label</cite>, <cite>sub_label</cite>,
<cite>num_threads</cite>, etc).</p>
<p>The table can be directly printed using <a class="reference internal" href="#torch.utils.benchmark.Compare.print" title="torch.utils.benchmark.Compare.print"><code class="xref py py-meth docutils literal notranslate"><span class="pre">print()</span></code></a> or casted as a <cite>str</cite>.</p>
<p>For a full tutorial on how to use this class, see:
<a class="reference external" href="https://pytorch.org/tutorials/recipes/recipes/benchmark.html">https://pytorch.org/tutorials/recipes/recipes/benchmark.html</a></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>results</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><em>List</em></a><em>[</em><a class="reference internal" href="#torch.utils.benchmark.Measurement" title="torch.utils.benchmark.utils.common.Measurement"><em>Measurement</em></a><em>]</em>) – List of Measurment to display.</p>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.benchmark.Compare.colorize">
<span class="sig-name descname"><span class="pre">colorize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rowwise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/compare.html#Compare.colorize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Compare.colorize" title="Permalink to this definition">¶</a></dt>
<dd><p>Colorize formatted table.</p>
<p>Colorize columnwise by default.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.benchmark.Compare.extend_results">
<span class="sig-name descname"><span class="pre">extend_results</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">results</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/compare.html#Compare.extend_results"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Compare.extend_results" title="Permalink to this definition">¶</a></dt>
<dd><p>Append results to already stored ones.</p>
<p>All added results must be instances of <code class="docutils literal notranslate"><span class="pre">Measurement</span></code>.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.benchmark.Compare.highlight_warnings">
<span class="sig-name descname"><span class="pre">highlight_warnings</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/compare.html#Compare.highlight_warnings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Compare.highlight_warnings" title="Permalink to this definition">¶</a></dt>
<dd><p>Enables warning highlighting when building formatted table.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.benchmark.Compare.print">
<span class="sig-name descname"><span class="pre">print</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/compare.html#Compare.print"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Compare.print" title="Permalink to this definition">¶</a></dt>
<dd><p>Print formatted table</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.benchmark.Compare.trim_significant_figures">
<span class="sig-name descname"><span class="pre">trim_significant_figures</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/compare.html#Compare.trim_significant_figures"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Compare.trim_significant_figures" title="Permalink to this definition">¶</a></dt>
<dd><p>Enables trimming of significant figures when building the formatted table.</p>
</dd></dl>
</dd></dl>
<span class="target" id="module-torch.utils.benchmark.examples"></span><span class="target" id="module-torch.utils.benchmark.op_fuzzers"></span><span class="target" id="module-torch.utils.benchmark.utils"></span><span class="target" id="module-torch.utils.benchmark.utils.valgrind_wrapper"></span></div>
</article>
</div>
<footer>